Sample records for satellite-derived high-resolution land
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Analysis of high resolution satellite digital data for land use studies ...
African Journals Online (AJOL)
High-resolution satellite data can give vital information about land cover, which can lead to better interpretation and classification of land resources. This study examined the relationship between Systeme Probatoire d'Observation de la Terre (SPOT) digital data and land use types in the derived savanna ecosystem of ...
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Hester, David Barry
The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the
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Directory of Open Access Journals (Sweden)
Bo Gao
2014-09-01
Full Text Available Land surface albedo is an essential parameter for monitoring global/regional climate and land surface energy balance. Although many studies have been conducted on global or regional land surface albedo using various remote sensing data over the past few decades, land surface albedo product with a high spatio–temporal resolution is currently very scarce. This paper proposes a method for deriving land surface albedo with a high spatio–temporal resolution (space: 30 m and time: 2–4 days. The proposed method works by combining the land surface reflectance data at 30 m spatial resolution obtained from the charge-coupled devices in the Huanjing-1A and -1B (HJ-1A/B satellites with the Moderate Resolution Imaging Spectroradiometer (MODIS land surface bidirectional reflectance distribution function (BRDF parameters product (MCD43A1, which is at a spatial resolution of 500 m. First, the land surface BRDF parameters for HJ-1A/B land surface reflectance with a spatial–temporal resolutions of 30 m and 2–4 day are calculated on the basis of the prior knowledge from the MODIS BRDF product; then, the calculated high resolution BRDF parameters are integrated over the illuminating/viewing hemisphere to produce the white- and black-sky albedos at 30 m resolution. These results form the basis for the final land surface albedo derivation by accounting for the proportion of direct and diffuse solar radiation arriving at the ground. The albedo retrieved by this novel method is compared with MODIS land surface albedo products, as well as with ground measurements. The results show that the derived land surface albedo during the growing season of 2012 generally achieved a mean absolute accuracy of ±0.044, and a root mean square error of 0.039, confirming the effectiveness of the newly proposed method.
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International Nuclear Information System (INIS)
Bauer, T.B.
2001-08-01
The availability of the new very high resolution satellite imagery will offer a wide range of new applications in the field of remote sensing. Information about actual land use is an important task for the management and planning in urban areas. High resolution satellite data will be an alternative to aerial photographs for updating and maintaining cartographic and geographic databases at reduced costs. The aim of the research is to formalize the visual interpretation procedure in order to automate the whole process. The assumption underlying this approach is that the land use functions can be distinguished on the basis of the differences in spatial distribution and pattern of land cover forms. Therefore a two-stage classification procedure is applied. In a first stage a land cover map is produced. In a second stage the morphological properties and spatial patterns of the land cover objects are analyzed with the structural analyzing and mapping system leading to a characterization and description of distinct urban land use categories. This information is then used for building a rule system that is implemented in a new commercial software tool called eCognition. An object-oriented classifier applies the rules to the land cover objects resulting in the required land use map. The potential of this method is demonstrated in a case study using IKONOS data covering a part of the metropolitan area of Vienna. (author)
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Directory of Open Access Journals (Sweden)
E. Fundisi
2017-09-01
Full Text Available Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.
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Fundisi, E.; Musakwa, W.
2017-09-01
Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.
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Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images
Directory of Open Access Journals (Sweden)
Biao Wang
2017-08-01
Full Text Available Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.
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Landslide detection using very high-resolution satellite imageries
Suga, Yuzo; Konishi, Tomohisa
2012-10-01
The heavy rain induced by the 12th typhoon caused landslide disaster at Kii Peninsula in the middle part of Japan. We propose a quick response method for landslide disaster mapping using very high resolution (VHR) satellite imageries. Especially, Synthetic Aperture Radar (SAR) is effective because it has the capability of all weather and day/night observation. In this study, multi-temporal COSMO-SkyMed imageries were used to detect the landslide areas. It was difficult to detect the landslide areas using only backscatter change pattern derived from pre- and post-disaster COSMOSkyMed imageries. Thus, the authors adopted a correlation analysis which the moving window was selected for the correlation coefficient calculation. Low value of the correlation coefficient reflects land cover change between pre- and post-disaster imageries. This analysis is effective for the detection of landslides using SAR data. The detected landslide areas were compared with the area detected by EROS-B high resolution optical image. In addition, we have developed 3D viewing system for geospatial visualizing of the damaged area using these satellite image data with digital elevation model. The 3D viewing system has the performance of geographic measurement with respect to elevation height, area and volume calculation, and cross section drawing including landscape viewing and image layer construction using a mobile personal computer with interactive operation. As the result, it was verified that a quick response for the detection of landslide disaster at the initial stage could be effectively performed using optical and SAR very high resolution satellite data by means of 3D viewing system.
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Boyle, Sarah A; Kennedy, Christina M; Torres, Julio; Colman, Karen; Pérez-Estigarribia, Pastor E; de la Sancha, Noé U
2014-01-01
Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.
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Kotsuki, S.; Tanaka, K.
2015-01-01
To date, many studies have performed numerical estimations of food production and agricultural water demand to understand the present and future supply-demand relationship. A crop calendar (CC) is an essential input datum to estimate food production and agricultural water demand accurately with the numerical estimations. CC defines the date or month when farmers plant and harvest in cropland. This study aims to develop a new global data set of a satellite-derived crop calendar for agricultural simulations (SACRA) and reveal advantages and disadvantages of the satellite-derived CC compared to other global products. We estimate global CC at a spatial resolution of 5 min (≈10 km) using the satellite-sensed NDVI data, which corresponds well to vegetation growth and death on the land surface. We first demonstrate that SACRA shows similar spatial pattern in planting date compared to a census-based product. Moreover, SACRA reflects a variety of CC in the same administrative unit, since it uses high-resolution satellite data. However, a disadvantage is that the mixture of several crops in a grid is not considered in SACRA. We also address that the cultivation period of SACRA clearly corresponds to the time series of NDVI. Therefore, accuracy of SACRA depends on the accuracy of NDVI used for the CC estimation. Although SACRA shows different CC from a census-based product in some regions, multiple usages of the two products are useful to take into consideration the uncertainty of the CC. An advantage of SACRA compared to the census-based products is that SACRA provides not only planting/harvesting dates but also a peak date from the time series of NDVI data.
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NCAR High-resolution Land Data Assimilation System and Its Recent Applications
Chen, F.; Manning, K.; Barlage, M.; Gochis, D.; Tewari, M.
2008-05-01
A High-Resolution Land Data Assimilation System (HRLDAS) has been developed at NCAR to meet the need for high-resolution initial conditions of land state (soil moisture and temperature) by today's numerical weather prediction models coupled to a land surface model such as the WRF/Noah coupled modeling system. Intended for conterminous US application, HRLDAS uses observed hourly 4-km national precipitation analysis and satellite-derived surface-solar-downward radiation to drive, in uncoupled mode, the Noah land surface model to simulate long-term evolution of soil state. The advantage of HRLDAS is its use of 1-km resolution land-use and soil texture maps and 4-km rainfall data. As a result, it is able to capture fine-scale heterogeneity at the surface and in the soil. The ultimate goal of HRLDAS development is to characterize soil moisture/temperature and vegetation variability at small scales (~4km) over large areas to provide improved initial land and vegetation conditions for the WRF/Noah coupled model. Hence, HRLDAS is configured after the WRF/Noah coupled model configuration to ensure the consistency in model resolution, physical configuration (e.g., terrain height), soil model, and parameters between the uncoupled soil initialization system and its coupled forecast counterpart. We will discuss various characteristics of HRLDAS, including its spin-up and sensitivity to errors in forcing data. We will describe recent enhancement in terms of hydrological modeling and the use of remote sensing data. We will discuss recent applications of HRLDAS for flood forecast, agriculture, and arctic land system.
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Fang, Li
The Geostationary Operational Environmental Satellites (GOES) have been continuously monitoring the earth surface since 1970, providing valuable and intensive data from a very broad range of wavelengths, day and night. The National Oceanic and Atmospheric Administration's (NOAA's) National Environmental Satellite, Data, and Information Service (NESDIS) is currently operating GOES-15 and GOES-13. The design of the GOES series is now heading to the 4 th generation. GOES-R, as a representative of the new generation of the GOES series, is scheduled to be launched in 2015 with higher spatial and temporal resolution images and full-time soundings. These frequent observations provided by GOES Image make them attractive for deriving information on the diurnal land surface temperature (LST) cycle and diurnal temperature range (DTR). These parameters are of great value for research on the Earth's diurnal variability and climate change. Accurate derivation of satellite-based LSTs from thermal infrared data has long been an interesting and challenging research area. To better support the research on climate change, the generation of consistent GOES LST products for both GOES-East and GOES-West from operational dataset as well as historical archive is in great demand. The derivation of GOES LST products and the evaluation of proposed retrieval methods are two major objectives of this study. Literature relevant to satellite-based LST retrieval techniques was reviewed. Specifically, the evolution of two LST algorithm families and LST retrieval methods for geostationary satellites were summarized in this dissertation. Literature relevant to the evaluation of satellite-based LSTs was also reviewed. All the existing methods are a valuable reference to develop the GOES LST product. The primary objective of this dissertation is the development of models for deriving consistent GOES LSTs with high spatial and high temporal coverage. Proper LST retrieval algorithms were studied
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Directory of Open Access Journals (Sweden)
N. Ghilain
2012-08-01
Full Text Available Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I, showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI and Fractional Vegetation Cover (FVC products for evapotranspiration monitoring with a land surface model at 3–5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land
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GRANULOMETRIC MAPS FROM HIGH RESOLUTION SATELLITE IMAGES
Directory of Open Access Journals (Sweden)
Catherine Mering
2011-05-01
Full Text Available A new method of land cover mapping from satellite images using granulometric analysis is presented here. Discontinuous landscapes such as steppian bushes of semi arid regions and recently growing urban settlements are especially concerned by this study. Spatial organisations of the land cover are quantified by means of the size distribution analysis of the land cover units extracted from high resolution remotely sensed images. A granulometric map is built by automatic classification of every pixel of the image according to the granulometric density inside a sliding neighbourhood. Granulometric mapping brings some advantages over traditional thematic mapping by remote sensing by focusing on fine spatial events and small changes in one peculiar category of the landscape.
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Gao, Bo; Jia, Li; Wang, Tianxing
2014-01-01
Land surface albedo is an essential parameter for monitoring global/regional climate and land surface energy balance. Although many studies have been conducted on global or regional land surface albedo using various remote sensing data over the past few decades, land surface albedo product with a high spatio-temporal resolution is currently very scarce. This paper proposes a method for deriving land surface albedo with a high spatio-temporal resolution (space: 30 m and time: 2-4 days). The pr...
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International Nuclear Information System (INIS)
Schroeder, R; Rawlins, M A; McDonald, K C; Podest, E; Zimmermann, R; Kueppers, M
2010-01-01
Wetlands are not only primary producers of atmospheric greenhouse gases but also possess unique features that are favourable for application of satellite microwave remote sensing to monitoring their status and trend. In this study we apply combined passive and active microwave remote sensing data sets from the NASA sensors AMSR-E and QuikSCAT to map surface water dynamics over Northern Eurasia. We demonstrate our method on the evolution of large wetland complexes for two consecutive years from January 2006 to December 2007. We apply river discharge measurements from the Ob River along with land surface runoff simulations derived from the Pan-Arctic Water Balance Model during and after snowmelt in 2006 and 2007 to interpret the abundance of widespread flooding along the River Ob in early summer of 2007 observed in the remote sensing products. The coarse-resolution, 25 km, surface water product is compared to a high-resolution, 30 m, inundation map derived from ALOS PALSAR (Advanced Land Observation Satellite phased array L-band synthetic aperture radar) imagery acquired for 11 July 2006, and extending along a transect in the central Western Siberian Plain. We found that the surface water fraction derived from the combined AMSR-E/QuikSCAT data sets closely tracks the inundation mapped using higher-resolution ALOS PALSAR data.
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Directory of Open Access Journals (Sweden)
Annett Bartsch
2016-11-01
Full Text Available Most applications of land cover maps that have been derived from satellite data over the Arctic require higher thematic detail than available in current global maps. A range of application studies has been reviewed, including up-scaling of carbon fluxes and pools, permafrost feature mapping and transition monitoring. Early land cover mapping studies were driven by the demand to characterize wildlife habitats. Later, in the 1990s, up-scaling of in situ measurements became central to the discipline of land cover mapping on local to regional scales at several sites across the Arctic. This includes the Kuparuk basin in Alaska, the Usa basin and the Lena Delta in Russia. All of these multi-purpose land cover maps have been derived from Landsat data. High resolution maps (from optical satellite data serve frequently as input for the characterization of periglacial features and also flux tower footprints in recent studies. The most used map to address circumpolar issues is the CAVM (Circum Arctic Vegetation Map based on AVHRR (1 km and has been manually derived. It provides the required thematic detail for many applications, but is confined to areas north of the treeline, and it is limited in spatial detail. A higher spatial resolution circumpolar land cover map with sufficient thematic content would be beneficial for a range of applications. Such a land cover classification should be compatible with existing global maps and applicable for multiple purposes. The thematic content of existing global maps has been assessed by comparison to the CAVM and regional maps. None of the maps provides the required thematic detail. Spatial resolution has been compared to used classes for local to regional applications. The required thematic detail increases with spatial resolution since coarser datasets are usually applied over larger areas covering more relevant landscape units. This is especially of concern when the entire Arctic is addressed. A spatial
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Byun, Daewon W; Kim, Soontae; Czader, Beata; Nowak, David; Stetson, Stephen; Estes, Mark
2005-06-01
The Houston-Galveston Area (HGA) is one of the most severe ozone non-attainment regions in the US. To study the effectiveness of controlling anthropogenic emissions to mitigate regional ozone nonattainment problems, it is necessary to utilize adequate datasets describing the environmental conditions that influence the photochemical reactivity of the ambient atmosphere. Compared to the anthropogenic emissions from point and mobile sources, there are large uncertainties in the locations and amounts of biogenic emissions. For regional air quality modeling applications, biogenic emissions are not directly measured but are usually estimated with meteorological data such as photo-synthetically active solar radiation, surface temperature, land type, and vegetation database. In this paper, we characterize these meteorological input parameters and two different land use land cover datasets available for HGA: the conventional biogenic vegetation/land use data and satellite-derived high-resolution land cover data. We describe the procedures used for the estimation of biogenic emissions with the satellite derived land cover data and leaf mass density information. Air quality model simulations were performed using both the original and the new biogenic emissions estimates. The results showed that there were considerable uncertainties in biogenic emissions inputs. Subsequently, ozone predictions were affected up to 10 ppb, but the magnitudes and locations of peak ozone varied each day depending on the upwind or downwind positions of the biogenic emission sources relative to the anthropogenic NOx and VOC sources. Although the assessment had limitations such as heterogeneity in the spatial resolutions, the study highlighted the significance of biogenic emissions uncertainty on air quality predictions. However, the study did not allow extrapolation of the directional changes in air quality corresponding to the changes in LULC because the two datasets were based on vastly different
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Pengra, Bruce; Long, Jordan; Dahal, Devendra; Stehman, Stephen V.; Loveland, Thomas R.
2015-01-01
The methodology for selection, creation, and application of a global remote sensing validation dataset using high resolution commercial satellite data is presented. High resolution data are obtained for a stratified random sample of 500 primary sampling units (5 km × 5 km sample blocks), where the stratification based on Köppen climate classes is used to distribute the sample globally among biomes. The high resolution data are classified to categorical land cover maps using an analyst mediated classification workflow. Our initial application of these data is to evaluate a global 30 m Landsat-derived, continuous field tree cover product. For this application, the categorical reference classification produced at 2 m resolution is converted to percent tree cover per 30 m pixel (secondary sampling unit)for comparison to Landsat-derived estimates of tree cover. We provide example results (based on a subsample of 25 sample blocks in South America) illustrating basic analyses of agreement that can be produced from these reference data. Commercial high resolution data availability and data quality are shown to provide a viable means of validating continuous field tree cover. When completed, the reference classifications for the full sample of 500 blocks will be released for public use.
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Jeffrey T. Morisette; Jaime E. Nickeson; Paul Davis; Yujie Wang; Yuhong Tian; Curtis E. Woodcock; Nikolay Shabanov; Matthew Hansen; Warren B. Cohen; Doug R. Oetter; Robert E. Kennedy
2003-01-01
Phase 1I of the Scientific Data Purchase (SDP) has provided NASA investigators access to data from four different satellite and airborne data sources. The Moderate Resolution Imaging Spectrometer (MODIS) land discipline team (MODLAND) sought to utilize these data in support of land product validation activities with a lbcus on tile EOS Land Validation Core Sites. These...
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TRANSFER OF TECHNOLOGY FOR CADASTRAL MAPPING IN TAJIKISTAN USING HIGH RESOLUTION SATELLITE DATA
Directory of Open Access Journals (Sweden)
R. Kaczynski
2012-07-01
Full Text Available European Commission funded project entitled: "Support to the mapping and certification capacity of the Agency of Land Management, Geodesy and Cartography" in Tajikistan was run by FINNMAP FM-International and Human Dynamics from Nov. 2006 to June 2011. The Agency of Land Management, Geodesy and Cartography is the state agency responsible for development, implementation, monitoring and evaluation of state policies on land tenure and land management, including the on-going land reform and registration of land use rights. The specific objective was to support and strengthen the professional capacity of the "Fazo" Institute in the field of satellite geodesy, digital photogrammetry, advanced digital satellite image processing of high resolution satellite data and digital cartography. Lectures and on-the-job trainings for the personnel of "Fazo" and Agency in satellite geodesy, digital photogrammetry, cartography and the use of high resolution satellite data for cadastral mapping have been organized. Standards and Quality control system for all data and products have been elaborated and implemented in the production line. Technical expertise and trainings in geodesy, photogrammetry and satellite image processing to the World Bank project "Land Registration and Cadastre System for Sustainable Agriculture" has also been completed in Tajikistan. The new map projection was chosen and the new unclassified geodetic network has been established for all of the country in which all agricultural parcel boundaries are being mapped. IKONOS, QuickBird and WorldView1 panchromatic data have been used for orthophoto generation. Average accuracy of space triangulation of non-standard (long up to 90km satellite images of QuickBird Pan and IKONOS Pan on ICPs: RMSEx = 0.5m and RMSEy = 0.5m have been achieved. Accuracy of digital orthophoto map is RMSExy = 1.0m. More then two and half thousands of digital orthophoto map sheets in the scale of 1:5000 with pixel size 0.5m
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A Land Product Characterization System for Comparative Analysis of Satellite Data and Products
Directory of Open Access Journals (Sweden)
Kevin Gallo
2017-12-01
Full Text Available A Land Product Characterization System (LPCS has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the application of global multi-satellite and in situ data for characterization and validation of higher-level, satellite-derived, land surface products (e.g., surface reflectance, normalized difference vegetation index, and land surface temperature. The LPCS includes data search, inventory, access, and analysis functions that will permit data to be easily identified, retrieved, co-registered, and compared statistically through a single interface. The system currently includes data and products available from Landsat 4 through 8, Moderate Resolution Imaging Spectroradiometer (MODIS Terra and Aqua, Suomi National Polar-Orbiting Partnership (S-NPP/Joint Polar Satellite System (JPSS Visible Infrared Imaging Radiometer Suite (VIIRS, and simulated data for the Geostationary Operational Environmental Satellite (GOES-16 Advanced Baseline Imager (ABI. In addition to the future inclusion of in situ data, higher-level land products from the European Space Agency (ESA Sentinel-2 and -3 series of satellites, and other high and medium resolution spatial sensors, will be included as available. When fully implemented, any of the sensor data or products included in the LPCS would be available for comparative analysis.
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Automated Generation of the Alaska Coastline Using High-Resolution Satellite Imagery
Roth, G.; Porter, C. C.; Cloutier, M. D.; Clementz, M. E.; Reim, C.; Morin, P. J.
2015-12-01
Previous campaigns to map Alaska's coast at high resolution have relied on airborne, marine, or ground-based surveying and manual digitization. The coarse temporal resolution, inability to scale geographically, and high cost of field data acquisition in these campaigns is inadequate for the scale and speed of recent coastal change in Alaska. Here, we leverage the Polar Geospatial Center (PGC) archive of DigitalGlobe, Inc. satellite imagery to produce a state-wide coastline at 2 meter resolution. We first select multispectral imagery based on time and quality criteria. We then extract the near-infrared (NIR) band from each processed image, and classify each pixel as water or land with a pre-determined NIR threshold value. Processing continues with vectorizing the water-land boundary, removing extraneous data, and attaching metadata. Final coastline raster and vector products maintain the original accuracy of the orthorectified satellite data, which is often within the local tidal range. The repeat frequency of coastline production can range from 1 month to 3 years, depending on factors such as satellite capacity, cloud cover, and floating ice. Shadows from trees or structures complicate the output and merit further data cleaning. The PGC's imagery archive, unique expertise, and computing resources enabled us to map the Alaskan coastline in a few months. The DigitalGlobe archive allows us to update this coastline as new imagery is acquired, and facilitates baseline data for studies of coastal change and improvement of topographic datasets. Our results are not simply a one-time coastline, but rather a system for producing multi-temporal, automated coastlines. Workflows and tools produced with this project can be freely distributed and utilized globally. Researchers and government agencies must now consider how they can incorporate and quality-control this high-frequency, high-resolution data to meet their mapping standards and research objectives.
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High-resolution Monthly Satellite Precipitation Product over the Conterminous United States
Hashemi, H.; Fayne, J.; Knight, R. J.; Lakshmi, V.
2017-12-01
We present a data set that enhanced the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) monthly product 3B43 in its accuracy and spatial resolution. For this, we developed a correction function to improve the accuracy of TRMM 3B43, spatial resolution of 25 km, by estimating and removing the bias in the satellite data using a ground-based precipitation data set. We observed a strong relationship between the bias and land surface elevation; TRMM 3B43 tends to underestimate the ground-based product at elevations above 1500 m above mean sea level (m.amsl) over the conterminous United States. A relationship was developed between satellite bias and elevation. We then resampled TRMM 3B43 to the Digital Elevation Model (DEM) data set at a spatial resolution of 30 arc second ( 1 km on the ground). The produced high-resolution satellite-based data set was corrected using the developed correction function based on the bias-elevation relationship. Assuming that each rain gauge represents an area of 1 km2, we verified our product against 9,200 rain gauges across the conterminous United States. The new product was compared with the gauges, which have 50, 60, 70, 80, 90, and 100% temporal coverage within the TRMM period of 1998 to 2015. Comparisons between the high-resolution corrected satellite-based data and gauges showed an excellent agreement. The new product captured more detail in the changes in precipitation over the mountainous region than the original TRMM 3B43.
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C-CAP Santa Cruz 2001 era High Resolution Land Cover Metadata
National Oceanic and Atmospheric Administration, Department of Commerce — This dataset represents land cover for the San Lorenzo River basin in Santa Cruz County, California derived from high resolution imagery. The land cover features in...
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Parida, P. K.; Sanabada, M. K.; Tripathi, S.
2014-11-01
Advancements in satellite sensor technology enabling capturing of geometrically accurate images of earth's surface coupled with DGPS/ETS and GIS technology holds the capability of large scale mapping of land resources at cadastral level. High Resolution Satellite Images depict field bunds distinctly. Thus plot parcels are to be delineated from cloud free ortho-images and obscured/difficult areas are to be surveyed using DGPS and ETS. The vector datasets thus derived through RS/DGPS/ETS survey are to be integrated in GIS environment to generate the base cadastral vector datasets for further settlement/title confirmation activities. The objective of this paper is to illustrate the efficacy of a hybrid methodology employed in Pitambarpur Sasana village under Digapahandi Tahasil of Ganjam district, as a pilot project, particularly in Odisha scenario where the land parcel size is very small. One of the significant observations of the study is matching of Cadastral map area i.e. 315.454 Acres, the image map area i.e. 314.887 Acres and RoR area i.e. 313.815 Acre. It was revealed that 79 % of plots derived by high-tech survey method show acceptable level of accuracy despite the fact that the mode of area measurement by ground and automated method has significant variability. The variations are more in case of Government lands, Temple/Trust lands, Common Property Resources and plots near to river/nalas etc. The study indicates that the adopted technology can be extended to other districts and cadastral resurvey and updating work can be done for larger areas of the country using this methodology.
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Directory of Open Access Journals (Sweden)
Ning Zeng
2013-10-01
Full Text Available Leaf Area Index (LAI represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN from the latest version (third generation of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees.
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A method for generating high resolution satellite image time series
Guo, Tao
2014-10-01
There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation
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The fusion of satellite and UAV data: simulation of high spatial resolution band
Jenerowicz, Agnieszka; Siok, Katarzyna; Woroszkiewicz, Malgorzata; Orych, Agata
2017-10-01
Remote sensing techniques used in the precision agriculture and farming that apply imagery data obtained with sensors mounted on UAV platforms became more popular in the last few years due to the availability of low- cost UAV platforms and low- cost sensors. Data obtained from low altitudes with low- cost sensors can be characterised by high spatial and radiometric resolution but quite low spectral resolution, therefore the application of imagery data obtained with such technology is quite limited and can be used only for the basic land cover classification. To enrich the spectral resolution of imagery data acquired with low- cost sensors from low altitudes, the authors proposed the fusion of RGB data obtained with UAV platform with multispectral satellite imagery. The fusion is based on the pansharpening process, that aims to integrate the spatial details of the high-resolution panchromatic image with the spectral information of lower resolution multispectral or hyperspectral imagery to obtain multispectral or hyperspectral images with high spatial resolution. The key of pansharpening is to properly estimate the missing spatial details of multispectral images while preserving their spectral properties. In the research, the authors presented the fusion of RGB images (with high spatial resolution) obtained with sensors mounted on low- cost UAV platforms and multispectral satellite imagery with satellite sensors, i.e. Landsat 8 OLI. To perform the fusion of UAV data with satellite imagery, the simulation of the panchromatic bands from RGB data based on the spectral channels linear combination, was conducted. Next, for simulated bands and multispectral satellite images, the Gram-Schmidt pansharpening method was applied. As a result of the fusion, the authors obtained several multispectral images with very high spatial resolution and then analysed the spatial and spectral accuracies of processed images.
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Effects of satellite image spatial aggregation and resolution on estimates of forest land area
M.D. Nelson; R.E. McRoberts; G.R. Holden; M.E. Bauer
2009-01-01
Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We...
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Directory of Open Access Journals (Sweden)
Johannes Stoffels
2015-06-01
Full Text Available A key factor for operational forest management and forest monitoring is the availability of up-to-date spatial information on the state of forest resources. Earth observation can provide valuable contributions to these information needs. The German federal state of Rhineland-Palatinate transferred its inherited forest information system to a new architecture that is better able to serve the needs of centralized inventory and planning services, down to the level of forest districts. During this process, a spatially adaptive classification approach was developed to derive high-resolution forest information layers (e.g., forest type, tree species distribution, development stages based on multi-temporal satellite data. This study covers the application of the developed approach to a regional scale (federal state level and the further adaptation of the design to meet the information needs of the state forest service. The results confirm that the operational requirements for mapping accuracy can, in principle, be fulfilled. However, the state-wide mapping experiment also revealed that the ability to meet the required level of accuracy is largely dependent on the availability of satellite observations within the optimum phenological time-windows.
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Egypt satellite images for land surface characterization
DEFF Research Database (Denmark)
Hasager, Charlotte Bay
images used for mapping the vegetation cover types and other land cover types in Egypt. The mapping ranges from 1 km resolution to 30 m resolution. The aim is to provide satellite image mapping with land surface characteristics relevant for roughness mapping.......Satellite images provide information on the land surface properties. From optical remote sensing images in the blue, green, red and near-infrared part of the electromagnetic spectrum it is possible to identify a large number of surface features. The report briefly describes different satellite...
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Xueke Li; Taixia Wu; Kai Liu; Yao Li; Lifu Zhang
2016-01-01
The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, ...
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Monitoring Snow and Land Ice Using Satellite data in the GMES Project CryoLand
Bippus, Gabriele; Nagler, Thomas
2013-04-01
The main objectives of the project "CryoLand - GMES Service Snow and Land Ice" are to develop, implement and validate services for snow, glaciers and lake and river ice products as a Downstream Service within the Global Monitoring for Environment and Security (GMES) program of the European Commission. CryoLand exploits Earth Observation data from current optical and microwave sensors and of the upcoming GMES Sentinel satellite family. The project prepares also the basis for the cryospheric component of the GMES Land Monitoring services. The CryoLand project team consists of 10 partner organisations from Austria, Finland, Norway, Sweden, Switzerland and Romania and is funded by the 7th Framework Program of the European Commission. The CryoLand baseline products for snow include fractional snow extent from optical satellite data, the extent of melting snow from SAR data, and coarse resolution snow water equivalent maps from passive microwave data. Experimental products include maps of snow surface wetness and temperature. The products range from large scale coverage at medium resolution to regional products with high resolution, in order to address a wide user community. Medium resolution optical data (e.g. MODIS, in the near future Sentinel-3) and SAR (ENVISAT ASAR, in the near future Sentinel-1) are the main sources of EO data for generating large scale products in near real time. For generation of regional products high resolution satellite data are used. Glacier products are based on high resolution optical (e.g. SPOT-5, in the near future Sentinel-2) and SAR (TerraSAR-X, in the near future Sentinel-1) data and include glacier outlines, mapping of glacier facies, glacier lakes and ice velocity. The glacier products are generated on users demand. Current test areas are located in the Alps, Norway, Greenland and the Himalayan Mountains. The lake and river ice products include ice extent and its temporal changes and snow extent on ice. The algorithms for these
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C-CAP Kahoolawe 2005 era High Resolution Land Cover Metadata
National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land derived from high resolution imagery and was analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine...
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Wang, N.; Yang, R.
2018-04-01
Chinese high -resolution (HR) remote sensing satellites have made huge leap in the past decade. Commercial satellite datasets, such as GF-1, GF-2 and ZY-3 images, the panchromatic images (PAN) resolution of them are 2 m, 1 m and 2.1 m and the multispectral images (MS) resolution are 8 m, 4 m, 5.8 m respectively have been emerged in recent years. Chinese HR satellite imagery has been free downloaded for public welfare purposes using. Local government began to employ more professional technician to improve traditional land management technology. This paper focused on analysing the actual requirements of the applications in government land law enforcement in Guangxi Autonomous Region. 66 counties in Guangxi Autonomous Region were selected for illegal land utilization spot extraction with fusion Chinese HR images. The procedure contains: A. Defines illegal land utilization spot type. B. Data collection, GF-1, GF-2, and ZY-3 datasets were acquired in the first half year of 2016 and other auxiliary data were collected in 2015. C. Batch process, HR images were collected for batch preprocessing through ENVI/IDL tool. D. Illegal land utilization spot extraction by visual interpretation. E. Obtaining attribute data with ArcGIS Geoprocessor (GP) model. F. Thematic mapping and surveying. Through analysing 42 counties results, law enforcement officials found 1092 illegal land using spots and 16 suspicious illegal mining spots. The results show that Chinese HR satellite images have great potential for feature information extraction and the processing procedure appears robust.
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Role of light satellites in the high-resolution Earth observation domain
Fishman, Moshe
1999-12-01
Current 'classic' applications using and exploring space based earth imagery are exclusive, narrow niche tailored, expensive and hardly accessible. On the other side new, inexpensive and widely used 'consumable' applications will be only developed concurrently to the availability of appropriate imagery allowing that process. A part of these applications can be imagined today, like WWW based 'virtual tourism' or news media, but the history of technological, cultural and entertainment evolution teaches us that most of future applications are unpredictable -- they emerge together with the platforms enabling their appearance. The only thing, which can be ultimately stated, is that the definitive condition for such applications is the availability of the proper imagery platform providing low cost, high resolution, large area, quick response, simple accessibility and quick dissemination of the raw picture. This platform is a constellation of Earth Observation satellites. Up to 1995 the Space Based High Resolution Earth Observation Domain was dominated by heavy, super-expensive and very inflexible birds. The launch of Israeli OFEQ-3 Satellite by MBT Division of Israel Aircraft Industries (IAI) marked the entrance to new era of light, smart and cheap Low Earth Orbited Imaging satellites. The Earth Resource Observation System (EROS) initiated by West Indian Space, is based on OFEQ class Satellites design and it is capable to gather visual data of Earth Surface both at high resolution and large image capacity. The main attributes, derived from its compact design, low weight and sophisticated logic and which convert the EROS Satellite to valuable and productive system, are discussed. The major advantages of Light Satellites in High Resolution Earth Observation Domain are presented and WIS guidelines featuring the next generation of LEO Imaging Systems are included.
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Framework of Jitter Detection and Compensation for High Resolution Satellites
Directory of Open Access Journals (Sweden)
Xiaohua Tong
2014-05-01
Full Text Available Attitude jitter is a common phenomenon in the application of high resolution satellites, which may result in large errors of geo-positioning and mapping accuracy. Therefore, it is critical to detect and compensate attitude jitter to explore the full geometric potential of high resolution satellites. In this paper, a framework of jitter detection and compensation for high resolution satellites is proposed and some preliminary investigation is performed. Three methods for jitter detection are presented as follows. (1 The first one is based on multispectral images using parallax between two different bands in the image; (2 The second is based on stereo images using rational polynomial coefficients (RPCs; (3 The third is based on panchromatic images employing orthorectification processing. Based on the calculated parallax maps, the frequency and amplitude of the detected jitter are obtained. Subsequently, two approaches for jitter compensation are conducted. (1 The first one is to conduct the compensation on image, which uses the derived parallax observations for resampling; (2 The second is to conduct the compensation on attitude data, which treats the influence of jitter on attitude as correction of charge-coupled device (CCD viewing angles. Experiments with images from several satellites, such as ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiaometer, LRO (Lunar Reconnaissance Orbiter and ZY-3 (ZiYuan-3 demonstrate the promising performance and feasibility of the proposed framework.
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Directory of Open Access Journals (Sweden)
Xueke Li
2016-05-01
Full Text Available The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1 opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m to EO-1 Hyperion (with a spatial resolution of 30 m. The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC, pixel-based support vector machine (PSVM, hybrid maximum likelihood classifier (HMLC, and hybrid support vector machine (HSVM were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the
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Comparing land surface phenology derived from satellite and GPS network microwave remote sensing.
Jones, Matthew O; Kimball, John S; Small, Eric E; Larson, Kristine M
2014-08-01
The land surface phenology (LSP) start of season (SOS) metric signals the seasonal onset of vegetation activity, including canopy growth and associated increases in land-atmosphere water, energy and carbon (CO2) exchanges influencing weather and climate variability. The vegetation optical depth (VOD) parameter determined from satellite passive microwave remote sensing provides for global LSP monitoring that is sensitive to changes in vegetation canopy water content and biomass, and insensitive to atmosphere and solar illumination constraints. Direct field measures of canopy water content and biomass changes desired for LSP validation are generally lacking due to the prohibitive costs of maintaining regional monitoring networks. Alternatively, a normalized microwave reflectance index (NMRI) derived from GPS base station measurements is sensitive to daily vegetation water content changes and may provide for effective microwave LSP validation. We compared multiyear (2007-2011) NMRI and satellite VOD records at over 300 GPS sites in North America, and their derived SOS metrics for a subset of 24 homogenous land cover sites to investigate VOD and NMRI correspondence, and potential NMRI utility for LSP validation. Significant correlations (P<0.05) were found at 276 of 305 sites (90.5 %), with generally favorable correspondence in the resulting SOS metrics (r (2)=0.73, P<0.001, RMSE=36.8 days). This study is the first attempt to compare satellite microwave LSP metrics to a GPS network derived reflectance index and highlights both the utility and limitations of the NMRI data for LSP validation, including spatial scale discrepancies between local NMRI measurements and relatively coarse satellite VOD retrievals.
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Husak, G. J.; Marshall, M. T.; Michaelsen, J.; Pedreros, D.; Funk, C.; Galu, G.
2008-07-01
Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.
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High resolution satellite imagery : from spies to pipeline management
Energy Technology Data Exchange (ETDEWEB)
Adam, S. [Canadian Geomatic Solutions Ltd., Calgary, AB (Canada); Farrell, M. [TransCanada Transmission, Calgary, AB (Canada)
2000-07-01
The launch of Space Imaging's IKONOS satellite in September 1999 has opened the door for corridor applications. The technology has been successfully implemented by TransCanada PipeLines in mapping over 1500 km of their mainline. IKONOS is the world's first commercial high resolution satellite which collects data at 1-meter black/white and 4-meter multi-spectral. Its use is regulated by the U.S. government. It is the best source of high resolution satellite image data. Other sources include the Indian Space Agency's IRS-1 C/D satellite and the Russian SPIN-2 which provides less reliable coverage. In addition, two more high resolution satellites may be launched this year to provide imagery every day of the year. IKONOS scenes as narrow as 5 km can be purchased. TransCanada conducted a pilot study to determine if high resolution satellite imagery is as effective as ortho-photos for identifying population structures within a buffer of TransCanada's east line right-of-way. The study examined three unique segments where residential, commercial, industrial and public features were compared. It was determined that IKONOS imagery is as good as digital ortho-photos for updating structures from low to very high density areas. The satellite imagery was also logistically easier than ortho-photos to acquire. This will be even more evident when the IKONOS image archives begins to grow. 4 tabs., 3 figs.
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Strengthening IAEA safeguards using high-resolution commercial satellite imagery
International Nuclear Information System (INIS)
Zhang Hui
2001-01-01
Full text: In May 1997, the IAEA Board of Governors adopted the Additional Safeguards Protocol to improve its ability to detect the undeclared production of fissile material. This new strengthened safeguards system has opened the door for the IAEA to use of all types of information, including the potential use of commercial satellite imagery. We have therefore been investigating the feasibility of strengthening IAEA safeguards using commercial satellite imagery. Based on our analysis on a number of one-meter resolution IKONOS satellite images of military nuclear production facilities at nuclear states including Russia, China, India, Pakistan and Israel, we found that the new high-resolution commercial satellite imagery would play a new and valuable role in strengthening IAEA safeguards. Since 1999, images with a resolution of one meter have been available commercially from Space Imaging's IKONOS satellite. One-meter images from other companies are expected to enter the market soon. Although still an order of magnitude less capable than military imaging satellites, the capabilities of these new high-resolution commercial satellites are good enough to detect and identify the major visible characteristics of nuclear production facilities and sites. Unlike the classified spy satellite photos limited to few countries, the commercial satellite imagery is commercially available to anyone who wants to purchase it. Therefore, the new commercial satellite open a new chance that each state, international organizations, and non-governmental groups could use the commercial images to play a more proactive role in monitoring the nuclear activities in related countries and verifying the compliance of non-proliferation agreements. This could help galvanize support for intensified efforts to slow the pace of nuclear proliferation. To produce fissile materials (plutonium and highly enriched uranium) for weapons, a country would operate dedicated plutonium-production reactors and the
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Fensholt, R.; Anyamba, A.; Huber, S.; Proud, S. R.; Tucker, C. J.; Small, J.; Pak, E.; Rasmussen, M. O.; Sandholt, I.; Shisanya, C.
2011-01-01
Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board MSG with an imaging capability every 15 minutes which is substantially greater than any temporal resolution that can be obtained from existing polar operational environmental satellites (POES) systems currently in use for environmental monitoring. Different areas of the African continent were affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher temporal resolution cloud-free (less than 5 days) measurements of the environment as compared to existing POES systems. SEVIRI MSG 5-day continental scale composites will enable rapid assessment of environmental conditions and improved early warning of disasters for the African continent such as flooding or droughts. The high temporal resolution geostationary data will complement existing higher spatial resolution polar-orbiting satellite data for various dynamic environmental and natural resource applications of terrestrial ecosystems.
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Estimation of Chinese surface NO2 concentrations combining satellite data and Land Use Regression
Anand, J.; Monks, P.
2016-12-01
Monitoring surface-level air quality is often limited by in-situ instrument placement and issues arising from harmonisation over long timescales. Satellite instruments can offer a synoptic view of regional pollution sources, but in many cases only a total or tropospheric column can be measured. In this work a new technique of estimating surface NO2 combining both satellite and in-situ data is presented, in which a Land Use Regression (LUR) model is used to create high resolution pollution maps based on known predictor variables such as population density, road networks, and land cover. By employing a mixed effects approach, it is possible to take advantage of the spatiotemporal variability in the satellite-derived column densities to account for daily and regional variations in surface NO2 caused by factors such as temperature, elevation, and wind advection. In this work, surface NO2 maps are modelled over the North China Plain and Pearl River Delta during high-pollution episodes by combining in-situ measurements and tropospheric columns from the Ozone Monitoring Instrument (OMI). The modelled concentrations show good agreement with in-situ data and surface NO2 concentrations derived from the MACC-II global reanalysis.
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National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...
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Liu, Mingliang; Tian, Hanqin
2010-09-01
One of the major limitations in assessing the impacts of human activities on global biogeochemical cycles and climate is a shortage of reliable data on historical land cover and land use change (LCLUC). China had extreme discrepancies in estimating contemporary and historical patterns of LCLUC over the last 3 centuries because of its geographical complexity, long history of land use, and limited national surveys. This study aims to characterize the spatial and temporal patterns of China's LCLUC during 1700-2005 by reconstructing historical gridded data sets from high-resolution satellite data and long-term historical survey data. During this 300 year period, the major characteristics of LCLUC in China have been shrinking forest (decreased by 22%) and expanding cropland (increased by 42%) and urban areas (including urban and rural settlements, factories, quarries, mining, and other built-up land). New cropland areas have come almost equally from both forested and nonforested land. This study also revealed that substantial conversion between forest and woodland can be attributed to forest harvest, forest regeneration, and land degradation. During 1980-2005, LCLUC was characterized by shrinking cropland, expanding urban and forest areas, and large decadal variations on a national level. LCLUC in China showed significant spatial variations during different time periods, which were caused by spatial heterogeneity in vegetation, soils, and climate and regional imbalance in economy development. During 1700-2005, forests shrunk rapidly while croplands expanded in the northeast and southwest of China. During 1980-2005, we found a serious loss of cropland and urban sprawl in the eastern plain, north, and southeast regions of China and a large increase in forested area in the southeast and southwest regions. The reconstructed LCLUC data sets from this study could be used to assess the impacts of land use change on biogeochemical cycles, the water cycle, and the regional
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Zhang, Wen-Yan; Lin, Chao-Yuan
2017-04-01
The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.
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Classification of high resolution satellite images
Karlsson, Anders
2003-01-01
In this thesis the Support Vector Machine (SVM)is applied on classification of high resolution satellite images. Sveral different measures for classification, including texture mesasures, 1st order statistics, and simple contextual information were evaluated. Additionnally, the image was segmented, using an enhanced watershed method, in order to improve the classification accuracy.
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COMPARATIVE ASSESSMENT OF VERY HIGH RESOLUTION SATELLITE AND AERIAL ORTHOIMAGERY
Directory of Open Access Journals (Sweden)
P. Agrafiotis
2015-03-01
Full Text Available This paper aims to assess the accuracy and radiometric quality of orthorectified high resolution satellite imagery from Pleiades-1B satellites through a comparative evaluation of their quantitative and qualitative properties. A Pleiades-B1 stereopair of high resolution images taken in 2013, two adjacent GeoEye-1 stereopairs from 2011 and aerial orthomosaic (LSO provided by NCMA S.A (Hellenic Cadastre from 2007 have been used for the comparison tests. As control dataset orthomosaic from aerial imagery provided also by NCMA S.A (0.25m GSD from 2012 was selected. The process for DSM and orthoimage production was performed using commercial digital photogrammetric workstations. The two resulting orthoimages and the aerial orthomosaic (LSO were relatively and absolutely evaluated for their quantitative and qualitative properties. Test measurements were performed using the same check points in order to establish their accuracy both as far as the single point coordinates as well as their distances are concerned. Check points were distributed according to JRC Guidelines for Best Practice and Quality Checking of Ortho Imagery and NSSDA standards while areas with different terrain relief and land cover were also included. The tests performed were based also on JRC and NSSDA accuracy standards. Finally, tests were carried out in order to assess the radiometric quality of the orthoimagery. The results are presented with a statistical analysis and they are evaluated in order to present the merits and demerits of the imaging sensors involved for orthoimage production. The results also serve for a critical approach for the usability and cost efficiency of satellite imagery for the production of Large Scale Orthophotos.
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Sandborn, A.; Engstrom, R.; Yu, Q.
2014-12-01
Mapping urban areas via satellite imagery is an important task for detecting and anticipating land cover and land use change at multiple scales. As developing countries experience substantial urban growth and expansion, remotely sensed based estimates of population and quality of life indicators can provide timely and spatially explicit information to researchers and planners working to determine how cities are changing. In this study, we use commercial high spatial resolution satellite imagery in combination with fine resolution census data to determine the ability of using remotely sensed data to reveal the spatial patterns of quality of life in Accra, Ghana. Traditionally, spectral characteristics are used on a per-pixel basis to determine land cover; however, in this study, we test a new methodology that quantifies spatial characteristics using a variety of spatial features observed in the imagery to determine the properties of an urban area. The spatial characteristics used in this study include histograms of oriented gradients, PanTex, Fourier transform, and line support regions. These spatial features focus on extracting structural and textural patterns of built-up areas, such as homogeneous building orientations and straight line indices. Information derived from aggregating the descriptive statistics of the spatial features at both the fine-resolution census unit and the larger neighborhood level are then compared to census derived quality of life indicators including information about housing, education, and population estimates. Preliminary results indicate that there are correlations between straight line indices and census data including available electricity and literacy rates. Results from this study will be used to determine if this methodology provides a new and improved way to measure a city structure in developing cities and differentiate between residential and commercial land use zones, as well as formal versus informal housing areas.
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Vanhellemont, Q.
2016-02-01
Since the launch of Landsat-8 (L8) in 2013, a joint NASA/USGS programme, new applications of high resolution imagery for coastal and inland waters have become apparent. The optical imaging instrument on L8, the Operational Land Imager (OLI), is much improved compared to its predecessors on L5 and L7, especially with regards to SNR and digitization, and is therefore well suited for retrieving water reflectances and derived parameters such as turbidity and suspended sediment concentration. In June 2015, the European Space Agency (ESA) successfully launched a similar instrument, the MultiSpectral Imager (MSI), on board of Sentinel-2A (S2A). Imagery from both L8 and S2A are free of charge and publicly available (S2A starting at the end of 2015). Atmospheric correction schemes and processing software is under development in the EC-FP7 HIGHROC project. The spatial resolution of these instruments (10-60 m) is a great improvement over typical moderate resolution ocean colour sensors such as MODIS and MERIS (0.25 - 1 km). At higher resolution, many more lakes, rivers, ports and estuaries are spatially resolved, and can thus now be studied using satellite data, unlocking potential for mandatory monitoring e.g. under European Directives such as the Marine Strategy Framework Directive and the Water Framework Directive. We present new applications of these high resolution data, such as monitoring of offshore constructions, wind farms, sediment transport, dredging and dumping, shipping and fishing activities. The spatial variability at sub moderate resolution (0.25 - 1 km) scales can be assessed, as well as the impact of sub grid scale variability (including ships and platforms used for validation) on the moderate pixel retrieval. While the daily revisit time of the moderate resolution sensors is vastly superior to those of the high resolution satellites, at the equator respectively 16 and 10 days for L8 and S2A, the low revisit times can be partially mitigated by combining data
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Ouma, Yashon O.
2016-01-01
Technologies for imaging the surface of the Earth, through satellite based Earth observations (EO) have enormously evolved over the past 50 years. The trends are likely to evolve further as the user community increases and their awareness and demands for EO data also increases. In this review paper, a development trend on EO imaging systems is presented with the objective of deriving the evolving patterns for the EO user community. From the review and analysis of medium-to-high resolution EO-based land-surface sensor missions, it is observed that there is a predictive pattern in the EO evolution trends such that every 10-15 years, more sophisticated EO imaging systems with application specific capabilities are seen to emerge. Such new systems, as determined in this review, are likely to comprise of agile and small payload-mass EO land surface imaging satellites with the ability for high velocity data transmission and huge volumes of spatial, spectral, temporal and radiometric resolution data. This availability of data will magnify the phenomenon of ;Big Data; in Earth observation. Because of the ;Big Data; issue, new computing and processing platforms such as telegeoprocessing and grid-computing are expected to be incorporated in EO data processing and distribution networks. In general, it is observed that the demand for EO is growing exponentially as the application and cost-benefits are being recognized in support of resource management.
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Land use change detection based on multi-date imagery from different satellite sensor systems
Stow, Douglas A.; Collins, Doretta; Mckinsey, David
1990-01-01
An empirical study is conducted to assess the accuracy of land use change detection using satellite image data acquired ten years apart by sensors with differing spatial resolutions. The primary goals of the investigation were to (1) compare standard change detection methods applied to image data of varying spatial resolution, (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice versa in the registration process, (3) determine if Landsat/Thermatic Mapper or SPOT/High Resolution Visible multispectral data provide more accurate detection of land use changes when registered to historical Landsat/MSS data. It is concluded that image ratioing of multisensor, multidate satellite data produced higher change detection accuracies than did principal components analysis, and that it is useful as a land use change enhancement method.
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A high-resolution and observationally constrained OMI NO2 satellite retrieval
International Nuclear Information System (INIS)
Goldberg, Daniel L.; Lamsal, Lok N.; Loughner, Christopher P.
2017-01-01
Here, this work presents a new high-resolution NO 2 dataset derived from the NASA Ozone Monitoring Instrument (OMI) NO 2 version 3.0 retrieval that can be used to estimate surface-level concentrations. The standard NASA product uses NO 2 vertical profile shape factors from a 1.25° × 1° (~110 km × 110 km) resolution Global Model Initiative (GMI) model simulation to calculate air mass factors, a critical value used to determine observed tropospheric NO 2 vertical columns. To better estimate vertical profile shape factors, we use a high-resolution (1.33 km × 1.33 km) Community Multi-scale Air Quality (CMAQ) model simulation constrained by in situ aircraft observations to recalculate tropospheric air mass factors and tropospheric NO 2 vertical columns during summertime in the eastern US. In this new product, OMI NO 2 tropospheric columns increase by up to 160% in city centers and decrease by 20–50 % in the rural areas outside of urban areas when compared to the operational NASA product. Our new product shows much better agreement with the Pandora NO 2 and Airborne Compact Atmospheric Mapper (ACAM) NO 2 spectrometer measurements acquired during the DISCOVER-AQ Maryland field campaign. Furthermore, the correlation between our satellite product and EPA NO 2 monitors in urban areas has improved dramatically: r 2 = 0.60 in the new product vs. r 2 = 0.39 in the operational product, signifying that this new product is a better indicator of surface concentrations than the operational product. Our work emphasizes the need to use both high-resolution and high-fidelity models in order to recalculate satellite data in areas with large spatial heterogeneities in NO x emissions. Although the current work is focused on the eastern US, the methodology developed in this work can be applied to other world regions to produce high-quality region-specific NO 2 satellite retrievals.
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Satellite-derived land surface parameters for mesoscale modelling of the Mexico City basin
Directory of Open Access Journals (Sweden)
B. de Foy
2006-01-01
Full Text Available Mesoscale meteorological modelling is an important tool to help understand air pollution and heat island effects in urban areas. Accurate wind simulations are difficult to obtain in areas of weak synoptic forcing. Local factors have a dominant role in the circulation and include land surface parameters and their interaction with the atmosphere. This paper examines an episode during the MCMA-2003 field campaign held in the Mexico City Metropolitan Area (MCMA in April of 2003. Because the episode has weak synoptic forcing, there is the potential for the surface heat budget to influence the local meteorology. High resolution satellite observations are used to specify the land use, vegetation fraction, albedo and surface temperature in the MM5 model. Making use of these readily available data leads to improved meteorological simulations in the MCMA, both for the wind circulation patterns and the urban heat island. Replacing values previously obtained from land-use tables with actual measurements removes the number of unknowns in the model and increases the accuracy of the energy budget. In addition to improving the understanding of local meteorology, this sets the stage for the use of advanced urban modules.
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National Satellite Land Remote Sensing Data Archive
Faundeen, John L.; Kelly, Francis P.; Holm, Thomas M.; Nolt, Jenna E.
2013-01-01
The National Satellite Land Remote Sensing Data Archive (NSLRSDA) resides at the U.S. Geological Survey's (USGS) Earth Resources Observation and Science (EROS) Center. Through the Land Remote Sensing Policy Act of 1992, the U.S. Congress directed the Department of the Interior (DOI) to establish a permanent Government archive containing satellite remote sensing data of the Earth's land surface and to make this data easily accessible and readily available. This unique DOI/USGS archive provides a comprehensive, permanent, and impartial observational record of the planet's land surface obtained throughout more than five decades of satellite remote sensing. Satellite-derived data and information products are primary sources used to detect and understand changes such as deforestation, desertification, agricultural crop vigor, water quality, invasive plant species, and certain natural hazards such as flood extent and wildfire scars.
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Directory of Open Access Journals (Sweden)
F. Xu
2017-08-01
Full Text Available The determination of area-averaged evapotranspiration (ET at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC sites and four groups of large-aperture scintillometers (LASs, were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this
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Xu, Feinan; Wang, Weizhen; Wang, Jiemin; Xu, Ziwei; Qi, Yuan; Wu, Yueru
2017-08-01
The determination of area-averaged evapotranspiration (ET) at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC) sites and four groups of large-aperture scintillometers (LASs), were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this work will be
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LandSat-Based Land Use-Land Cover (Vector)
Minnesota Department of Natural Resources — Vector-based land cover data set derived from classified 30 meter resolution Thematic Mapper satellite imagery. Classification is divided into 16 classes with source...
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LandSat-Based Land Use-Land Cover (Raster)
Minnesota Department of Natural Resources — Raster-based land cover data set derived from 30 meter resolution Thematic Mapper satellite imagery. Classification is divided into 16 classes with source imagery...
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Barrios, José Miguel; Ghilain, Nicolas; Arboleda, Alirio; Gellens-Meulenberghs, Françoise
2014-05-01
Evapotranspiration (ET) is the water flux going from the surface into the atmosphere as result of soil and surface water evaporation and plant transpiration. It constitutes a key component of the water cycle and its quantification is of crucial importance for a number of applications like water management, climatic modelling, agriculture monitoring and planning, etc. Estimating ET is not an easy task; specially if large areas are envisaged and various spatio-temporal patterns of ET are present as result of heterogeneity in land cover, land use and climatic conditions. In this respect, spaceborne remote sensing (RS) provides the only alternative to continuously measure surface parameters related to ET over large areas. The Royal Meteorological Institute (RMI) of Belgium, in the framework of EUMETSAT's "Land Surface Analysis-Satellite Application Facility" (LSA-SAF), has developed a model for the estimation of ET. The model is forced by RS data, numerical weather predictions and land cover information. The RS forcing is derived from measurements by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. This ET model is operational and delivers ET estimations over the whole field of view of the MSG satellite (Europe, Africa and Eastern South America) (http://landsaf.meteo.pt) every 30 minutes. The spatial resolution of MSG is 3 x 3 km at subsatellite point and about 4 x 5 km in continental Europe. The spatial resolution of this product may constrain its full exploitation as the interest of potential users (farmers and natural resources scientists) may lie on smaller spatial units. This study aimed at testing methodological alternatives to combine RS imagery (geostationary and polar orbit satellites) for the estimation of ET such that the spatial resolution of the final product is improved. In particular, the study consisted in the implementation of two approaches for combining the current ET estimations with
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Quantifying the clear-sky bias of satellite-derived infrared LST
Ermida, S. L.; Trigo, I. F.; DaCamara, C.
2017-12-01
Land surface temperature (LST) is one of the most relevant parameters when addressing the physical processes that take place at the surface of the Earth. Satellite data are particularly appropriate for measuring LST over the globe with high temporal resolution. Remote-sensed LST estimation from space-borne sensors has been systematically performed over the Globe for nearly 3 decades and geostationary LST climate data records are now available. The retrieval of LST from satellite observations generally relies on measurements in the thermal infrared (IR) window. Although there is a large number of IR sensors on-board geostationary satellites and polar orbiters suitable for LST retrievals with different temporal and spatial resolutions, the use of IR observations limits LST estimates to clear sky conditions. As a consequence, climate studies based on IR LST are likely to be affected by the restriction of LST data to cloudless conditions. However, such "clear sky bias" has never been quantified and, therefore, the actual impact of relying only on clear sky data is still to be determined. On the other hand, an "all-weather" global LST database may be set up based on passive microwave (MW) measurements which are much less affected by clouds. An 8-year record of all-weather MW LST is here used to quantify the clear-sky bias of IR LST at global scale based on MW observations performed by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) onboard NASA's Aqua satellite. Selection of clear-sky and cloudy pixels is based on information derived from measurements performed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the same satellite.
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Qi, K.; Qingfeng, G.
2017-12-01
With the popular use of High-Resolution Satellite (HRS) images, more and more research efforts have been placed on land-use scene classification. However, it makes the task difficult with HRS images for the complex background and multiple land-cover classes or objects. This article presents a multiscale deeply described correlaton model for land-use scene classification. Specifically, the convolutional neural network is introduced to learn and characterize the local features at different scales. Then, learnt multiscale deep features are explored to generate visual words. The spatial arrangement of visual words is achieved through the introduction of adaptive vector quantized correlograms at different scales. Experiments on two publicly available land-use scene datasets demonstrate that the proposed model is compact and yet discriminative for efficient representation of land-use scene images, and achieves competitive classification results with the state-of-art methods.
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Mapping Impervious Surfaces Globally at 30m Resolution Using Landsat Global Land Survey Data
Brown de Colstoun, E.; Huang, C.; Wolfe, R. E.; Tan, B.; Tilton, J.; Smith, S.; Phillips, J.; Wang, P.; Ling, P.; Zhan, J.; Xu, X.; Taylor, M. P.
2013-12-01
Impervious surfaces, mainly artificial structures and roads, cover less than 1% of the world's land surface (1.3% over USA). Regardless of the relatively small coverage, impervious surfaces have a significant impact on the environment. They are the main source of the urban heat island effect, and affect not only the energy balance, but also hydrology and carbon cycling, and both land and aquatic ecosystem services. In the last several decades, the pace of converting natural land surface to impervious surfaces has increased. Quantitatively monitoring the growth of impervious surface expansion and associated urbanization has become a priority topic across both the physical and social sciences. The recent availability of consistent, global scale data sets at 30m resolution such as the Global Land Survey from the Landsat satellites provides an unprecedented opportunity to map global impervious cover and urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such a buildings, roads and parking lots. With long term GLS data now available for the 1975, 1990, 2000, 2005 and 2010 time periods, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. In the Global Land Survey - Imperviousness Mapping Project (GLS-IMP), we are producing the first global 30 m spatial resolution impervious cover data set. We have processed the GLS 2010 data set to surface reflectance (8500+ TM and ETM+ scenes) and are using a supervised classification method using a regression tree to produce continental scale impervious cover data sets. A very large set of accurate training samples is the key to the supervised classifications and is being derived through the interpretation of high spatial resolution (~2 m or less) commercial satellite data (Quickbird and Worldview2) available to us through the unclassified
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Enwright, Nicholas M.; Jones, William R.; Garber, Adrienne L.; Keller, Matthew J.
2014-01-01
Long-term monitoring efforts often use remote sensing to track trends in habitat or landscape conditions over time. To most appropriately compare observations over time, long-term monitoring efforts strive for consistency in methods. Thus, advances and changes in technology over time can present a challenge. For instance, modern camera technology has led to an increasing availability of very high-resolution imagery (i.e. submetre and metre) and a shift from analogue to digital photography. While numerous studies have shown that image resolution can impact the accuracy of classifications, most of these studies have focused on the impacts of comparing spatial resolution changes greater than 2 m. Thus, a knowledge gap exists on the impacts of minor changes in spatial resolution (i.e. submetre to about 1.5 m) in very high-resolution aerial imagery (i.e. 2 m resolution or less). This study compared the impact of spatial resolution on land/water classifications of an area dominated by coastal marsh vegetation in Louisiana, USA, using 1:12,000 scale colour-infrared analogue aerial photography (AAP) scanned at four different dot-per-inch resolutions simulating ground sample distances (GSDs) of 0.33, 0.54, 1, and 2 m. Analysis of the impact of spatial resolution on land/water classifications was conducted by exploring various spatial aspects of the classifications including density of waterbodies and frequency distributions in waterbody sizes. This study found that a small-magnitude change (1–1.5 m) in spatial resolution had little to no impact on the amount of water classified (i.e. percentage mapped was less than 1.5%), but had a significant impact on the mapping of very small waterbodies (i.e. waterbodies ≤ 250 m2). These findings should interest those using temporal image classifications derived from very high-resolution aerial photography as a component of long-term monitoring programs.
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High resolution satellite image indexing and retrieval using SURF features and bag of visual words
Bouteldja, Samia; Kourgli, Assia
2017-03-01
In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.
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A high accuracy land use/cover retrieval system
Directory of Open Access Journals (Sweden)
Alaa Hefnawy
2012-03-01
Full Text Available The effects of spatial resolution on the accuracy of mapping land use/cover types have received increasing attention as a large number of multi-scale earth observation data become available. Although many methods of semi automated image classification of remotely sensed data have been established for improving the accuracy of land use/cover classification during the past 40 years, most of them were employed in single-resolution image classification, which led to unsatisfactory results. In this paper, we propose a multi-resolution fast adaptive content-based retrieval system of satellite images. Through our proposed system, we apply a Super Resolution technique for the Landsat-TM images to have a high resolution dataset. The human–computer interactive system is based on modified radial basis function for retrieval of satellite database images. We apply the backpropagation supervised artificial neural network classifier for both the multi and single resolution datasets. The results show significant improved land use/cover classification accuracy for the multi-resolution approach compared with those from single-resolution approach.
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Quantifying scaling effects on satellite-derived forest area estimates for the conterminous USA
Daolan Zheng; L.S. Heath; M.J. Ducey; J.E. Smith
2009-01-01
We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30m satellite-derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non-forest); and (2) coarser resolutions (1km and 10 km). Standard errors of the model estimates were 2.3%...
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International Nuclear Information System (INIS)
Saeed, U.; Daud, A.; Ashraf, S.; Mahmood, A.
2006-01-01
Mangrove forest is an integral part of inter-tidal zone of the coastal environment extending throughout the tropics and subtropics of the world. In Pakistan, for the last thirty years, remote-sensing data has significantly been used for area estimation of mangrove forests. In the previous studies medium resolution satellite data have been used for the area estimation of mangrove forests that revealed some of the discrepancies in terms of recognition of the subtle variations of landcover features in the satellite imagery. Current study aims at the classification techniques employed for the area estimation using high and medium resolution satellite imageries. To study the effects of spatial resolution on classification results, three different satellite data were used, including Quickbird, TERRA and Landsat satellites. Thematic map derived from Quickbird data was comprised of maximum number of land cover classes with a definite zone of mangroves that extends from regeneration to mature canopies. Total estimated mangroves extent was 370 ha with 57.45, 125.9, 180.89, and 5.35 ha of tall, medium, small, and new recruitment mangrove plants respectively. While mangrove area estimations from thematic maps derived using TERRA and Landsat satellite data, showed a gradual increase in the mangrove extent from 390.95 ha to 417.92 ha. This increase in area is an indicative of the fact that some of the landcover classes may have been miss-classified and hence added to the area under mangrove forests. This study also showed that high-resolution satellite data could be used for identifying different height zones of mangrove forests, along with an accurate delineation of classes like salt bushes and algae, which could not be classified otherwise. (author)
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Land use/land cover mapping using multi-scale texture processing of high resolution data
Wong, S. N.; Sarker, M. L. R.
2014-02-01
Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road.
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Land use/land cover mapping using multi-scale texture processing of high resolution data
International Nuclear Information System (INIS)
Wong, S N; Sarker, M L R
2014-01-01
Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road
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SCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountains
Rodríguez-Jeangros, Nicolás
2017-10-02
Land cover (LC) products, derived primarily from satellite spectral imagery, are essential inputs for environmental studies because LC is a critical driver of processes involved in hydrology, ecology, and climatology, among others. However, existing LC products each have different temporal and spatial resolutions and different LC classes that rarely provide the detail required by these studies. Using multiple existing LC products, we implement our Spatiotemporal Categorical Map Fusion (SCaMF) methodology over a large region of the Rocky Mountains (RM), encompassing sections of six states, to create a new LC product, SCaMF–RM. To do this, we must adapt SCaMF to address the prediction of LC in large space–time regions that present nonstationarities, and we add more flexibility in the LC classifications of the predicted product. SCaMF–RM is produced at two high spatial resolutions, 30 and 50 m, and a yearly frequency for the 30-year period 1983–2012. When multiple products are available in time, we illustrate how SCaMF–RM captures relevant information from the different LC products and improves upon flaws observed in other products. Future work needed includes an exhaustive validation not only of SCaMF–RM but also of all input LC products.
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Parinussa, R.M.; Meesters, A.G.C.A.; Liu, Y.Y.; Dorigo, W.; Wagner, W.; de Jeu, R.A.M.
2011-01-01
A time-efficient solution to estimate the error of satellite surface soil moisture from the land parameter retrieval model is presented. The errors are estimated using an analytical solution for soil moisture retrievals from this radiative-transfer-based model that derives soil moisture from
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Haque, Md. Enamul; Al-Ramadan, Baqer; Johnson, Brian A.
2016-07-01
Multiresolution segmentation and rule-based classification techniques are used to classify objects from very high-resolution satellite images of urban areas. Custom rules are developed using different spectral, geometric, and textural features with five scale parameters, which exploit varying classification accuracy. Principal component analysis is used to select the most important features out of a total of 207 different features. In particular, seven different object types are considered for classification. The overall classification accuracy achieved for the rule-based method is 95.55% and 98.95% for seven and five classes, respectively. Other classifiers that are not using rules perform at 84.17% and 97.3% accuracy for seven and five classes, respectively. The results exploit coarse segmentation for higher scale parameter and fine segmentation for lower scale parameter. The major contribution of this research is the development of rule sets and the identification of major features for satellite image classification where the rule sets are transferable and the parameters are tunable for different types of imagery. Additionally, the individual objectwise classification and principal component analysis help to identify the required object from an arbitrary number of objects within images given ground truth data for the training.
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National Oceanic and Atmospheric Administration, Department of Commerce — Detailed, shallow-water coral reef ecosystem maps were generated by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations...
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Sentinel-2: next generation satellites for optical land observation from space
Lautenschläger, G.; Gessner, R.; Gockel, W.; Haas, C.; Schweickert, G.; Bursch, S.; Welsch, M.; Sontag, H.
2013-10-01
The first Sentinel-2 satellites, which constitute the next generation of operational Earth observation satellites for optical land monitoring from space, are undergoing completion in the facilities at Astrium ready for launch end 2014. Sentinel-2 will feature a major breakthrough in the area of optical land observation since it will for the first time enable continuous and systematic acquisition of all land surfaces world-wide with the Multi-Spectral Instrument (MSI), thus providing the basis for a truly operational service. Flying in the same orbital plane and spaced at 180°, the constellation of two satellites, designed for an in-orbit nominal operational lifetime of 7 years each, will acquire all land surfaces in only 5 days at the equator. In order to support emergency operations, the satellites can further be operated in an extended observation mode allowing to image any point on Earth even on a daily basis. MSI acquires images in 13 spectral channels from Visible-to-Near Infrared (VNIR) to Short Wave Infrared (SWIR) with a swath of almost 300 km on ground and a spatial resolution up to 10 m. The data ensure continuity to the existing data sets produced by the series of Landsat and SPOT satellites, and will further provide detailed spectral information to enable derivation of biophysical or geophysical products. Excellent geometric image quality performances are achieved with geolocation better than 16 m, thanks to an innovative instrument design in conjunction with a high-performance satellite AOCS subsystem centered around a 2-band GPS receiver, high-performance star trackers and a fiberoptic gyro. To cope with the high data volume on-board, data are compressed using a state-of-the-art wavelet compression scheme. Thanks to a powerful mission data handling system built around a newly developed very large solid-state mass memory based on flash technology, on-board compression losses will be kept to a minimum. The Sentinel-2 satellite design features a highly
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Global High Resolution Sea Surface Flux Parameters From Multiple Satellites
Zhang, H.; Reynolds, R. W.; Shi, L.; Bates, J. J.
2007-05-01
Advances in understanding the coupled air-sea system and modeling of the ocean and atmosphere demand increasingly higher resolution data, such as air-sea fluxes of up to 3 hourly and every 50 km. These observational requirements can only be met by utilizing multiple satellite observations. Generation of such high resolution products from multiple-satellite and in-situ observations on an operational basis has been started at the U.S. National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center. Here we describe a few products that are directly related to the computation of turbulent air-sea fluxes. Sea surface wind speed has been observed from in-situ instruments and multiple satellites, with long-term observations ranging from one satellite in the mid 1987 to six or more satellites since mid 2002. A blended product with a global 0.25° grid and four snapshots per day has been produced for July 1987 to present, using a near Gaussian 3-D (x, y, t) interpolation to minimize aliases. Wind direction has been observed from fewer satellites, thus for the blended high resolution vector winds and wind stresses, the directions are taken from the NCEP Re-analysis 2 (operationally run near real time) for climate consistency. The widely used Reynolds Optimum Interpolation SST analysis has been improved with higher resolutions (daily and 0.25°). The improvements use both infrared and microwave satellite data that are bias-corrected by in- situ observations for the period 1985 to present. The new versions provide very significant improvements in terms of resolving ocean features such as the meandering of the Gulf Stream, the Aghulas Current, the equatorial jets and other fronts. The Ta and Qa retrievals are based on measurements from the AMSU sounder onboard the NOAA satellites. Ta retrieval uses AMSU-A data, while Qa retrieval uses both AMSU-A and AMSU-B observations. The retrieval algorithms are developed using the neural network approach. Training
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Zhong, L.; Ma, Y.
2017-12-01
Land-atmosphere energy transfer is of great importance in land-atmosphere interactions and atmospheric boundary layer processes over the Tibetan Plateau (TP). The energy fluxes have high temporal variability, especially in their diurnal cycle, which cannot be acquired by polar-orbiting satellites alone because of their low temporal resolution. Therefore, it's of great practical significance to retrieve land surface heat fluxes by a combination use of geostationary and polar orbiting satellites. In this study, a time series of the hourly LST was estimated from thermal infrared data acquired by the Chinese geostationary satellite FengYun 2C (FY-2C) over the TP. The split window algorithm (SWA) was optimized using a regression method based on the observations from the Enhanced Observing Period (CEOP) of the Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet) and Tibetan observation and research platform (TORP), the land surface emissivity (LSE) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the water vapor content from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) project. The 10-day composite hourly LST data were generated via the maximum value composite (MVC) method to reduce the cloud effects. The derived LST was validated by the field observations of CAMP/Tibet and TORP. The results show that the retrieved LST and in situ data have a very good correlation (with root mean square error (RMSE), mean bias (MB), mean absolute error (MAE) and correlation coefficient (R) values of 1.99 K, 0.83 K, 1.71 K, and 0.991, respectively). Together with other characteristic parameters derived from polar-orbiting satellites and meteorological forcing data, the energy balance budgets have been retrieved finally. The validation results showed there was a good consistency between estimation results and in-situ measurements over the TP, which prove the robustness of the proposed estimation
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Hurricane Satellite (HURSAT) from Advanced Very High Resolution Radiometer (AVHRR)
National Oceanic and Atmospheric Administration, Department of Commerce — Huricane Satellite (HURSAT)-Advanced Very High Resolution Radiometer (AVHRR) is used to extend the HURSAT data set such that appling the Objective Dvorak technique...
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Many societal applications of soil moisture data products require high spatial resolution and numerical accuracy. Current thermal geostationary satellite sensors (GOES Imager and GOES-R ABI) could produce 2-16km resolution soil moisture proxy data. Passive microwave satellite radiometers (e.g. AMSR...
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Accuracy Assessment of Satellite Derived Forest Cover Products in South and Southeast Asia
Gilani, H.; Xu, X.; Jain, A. K.
2017-12-01
South and Southeast Asia (SSEA) region occupies 16 % of worlds land area. It is home to over 50% of the world's population. The SSEA's countries are experiencing significant land-use and land-cover changes (LULCCs), primarily in agriculture, forest, and urban land. For this study, we compiled four existing global forest cover maps for year 2010 by Gong et al.(2015), Hansen et al. (2013), Sexton et al.(2013) and Shimada et al. (2014), which were all medium resolution (≤30 m) products based on Landsat and/or PALSAR satellite images. To evaluate the accuracy of these forest products, we used three types of information: (1) ground measurements, (2) high resolution satellite images and (3) forest cover maps produced at the national scale. The stratified random sampling technique was used to select a set of validation data points from the ground and high-resolution satellite images. Then the confusion matrix method was used to assess and rank the accuracy of the forest cover products for the entire SSEA region. We analyzed the spatial consistency of different forest cover maps, and further evaluated the consistency with terrain characteristics. Our study suggests that global forest cover mapping algorithms are trained and tested using limited ground measurement data. We found significant uncertainties in mountainous areas due to the topographical shadow effect and the dense tree canopies effects. The findings of this study will facilitate to improve our understanding of the forest cover dynamics and their impacts on the quantities and pathways of terrestrial carbon and nitrogen fluxes. Gong, P., et al. (2012). "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data." International Journal of Remote Sensing 34(7): 2607-2654. Hansen, M. C., et al. (2013). "High-Resolution Global Maps of 21st-Century Forest Cover Change." Science 342(6160): 850-853. Sexton, J. O., et al. (2013). "Global, 30-m resolution
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Bourrel, Luc; Brodu, Nicolas; Frappart, Frédéric
2016-04-01
Remotely sensed images allow a frequent monitoring of land cover variations at regional and global scale. Recently launched Sentinel-1 satellite offers a global cover of land areas at an unprecedented spatial (20 m) and temporal (6 days at the Equator). We propose here to compare the performances of commonly used supervised classification techniques (i.e., k-nearest neighbors, linear and Gaussian support vector machines, naive Bayes, linear and quadratic discriminant analyzes, adaptative boosting, loggit regression, ridge regression with one-vs-one voting, random forest, extremely randomized trees) for land cover applications in the Guayas Basin, the largest river basin of the Pacific coast of Ecuator (area ~32,000 km²). The reason of this choice is the importance of this region in Ecuatorian economy as its watershed represents 13% of the total area of Ecuador where 40% of the Ecuadorian population lives. It also corresponds to the most productive region of Ecuador for agriculture and aquaculture. Fifty percents of the country shrimp farming production comes from this watershed, and represents with agriculture the largest source of revenue of the country. Similar comparisons are also performed using ENVISAT ASAR images acquired in global mode (1 km of spatial resolution). Accuracy of the results will be achieved using land cover map derived from multi-spectral images.
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Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of derived wetland maps were limited or often unsatisfactory largely due to the relatively coarse spatial resolution of conventional satellite imagery. This re...
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Linear mixing model applied to coarse resolution satellite data
Holben, Brent N.; Shimabukuro, Yosio E.
1992-01-01
A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies.
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National Oceanic and Atmospheric Administration, Department of Commerce — Detailed, shallow-water coral reef ecosystem maps were generated by rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations...
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Jacqueminet, Christine; Kermadi, Saïda; Michel, Kristell; Jankowfsky, Sonja; Braud, Isabelle; Branger, Flora; Beal, David; Gagnage, Matthieu
2010-05-01
Keywords : land cover mapping, very high resolution, remote sensing processing techniques, object oriented approach, distributed hydrological model, peri-urban area Urbanization and other modifications of land use affect the hydrological cycle of suburban catchments. In order to quantify these impacts, the AVuPUR project (Assessing the Vulnerability of Peri-Urban Rivers) is currently developing a distributed hydrological model that includes anthropogenic features. The case study is the Yzeron catchment (150 km²), located close to Lyon city, France. This catchment experiences a growing of urbanization and a modification of traditional land use since the middle of the 20th century, resulting in an increase of flooding, water pollution and river banks erosion. This contribution discusses the potentials of automated data processing techniques on three different VHR images, in order to produce appropriate and detailed land cover data for the models. Of particular interest is the identification of impermeable surfaces (buildings, roads, and parking places) and permeable surfaces (forest areas, agricultural fields, gardens, trees…) within the catchment, because their infiltration capacity and their impact on runoff generation are different. Three aerial and spatial images were acquired: (1) BD Ortho IGN aerial images, 0.50 m resolution, visible bands, may 5th 2008; (2) QuickBird satellite image, 2.44 m resolution, visible and near-infrared bands, august 29th 2008; (3) Spot satellite image, 2.50 m resolution, visible and near-infrared bands, September 22nd 2008. From these images, we developed three image processing methods: (1) a pixel-based method associated to a segmentation using Matlab®, (2) a pixel-based method using ENVI®, (3) an object-based classification using Definiens®. We extracted six land cover types from the BD Ortho IGN (visible bands) and height classes from the satellite images (visible and near infrared bands). The three classified images are
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Directory of Open Access Journals (Sweden)
Andrew J Tatem
2007-12-01
Full Text Available Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas.We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps.We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km(2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk and are freely available.
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Tatem, Andrew J.; Noor, Abdisalan M.; von Hagen, Craig; Di Gregorio, Antonio; Hay, Simon I.
2007-01-01
Background Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas. Methodology/Principal Findings We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps. Conclusions We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available. PMID:18074022
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A high-resolution land-use map; Nogales, Sonora, Mexico
Norman, Laura M.; Villarreal, Miguel L.; Wallace, Cynthia S.A.; Gil Anaya, Claudia Z.; Diaz Arcos, Israel; Gray, Floyd
2010-01-01
The cities of Nogales, Sonora, and Nogales, Arizona, are located in the Ambos Nogales Watershed, a topographically irregular bowl-shaped area with a northward gradient. Throughout history, residents in both cities have been affected by flooding. Currently, the primary method for regulating this runoff is to build a series of detention basins in Nogales, Sonora. Additionally, the municipality also is considering land-use planning to help mitigate flooding. This paper describes the production of a 10-meter resolution land-use map, derived from 2008 aerial photos of the Nogales, Sonora Watershed for modeling impacts of the detention basin construction and in support of an ?Early Warning Hazard System? for the region.
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Adachi, Minaco; Ito, Akihiko; Yonemura, Seiichiro; Takeuchi, Wataru
2017-09-15
Soil respiration is one of the largest carbon fluxes from terrestrial ecosystems. Estimating global soil respiration is difficult because of its high spatiotemporal variability and sensitivity to land-use change. Satellite monitoring provides useful data for estimating the global carbon budget, but few studies have estimated global soil respiration using satellite data. We provide preliminary insights into the estimation of global soil respiration in 2001 and 2009 using empirically derived soil temperature equations for 17 ecosystems obtained by field studies, as well as MODIS climate data and land-use maps at a 4-km resolution. The daytime surface temperature from winter to early summer based on the MODIS data tended to be higher than the field-observed soil temperatures in subarctic and temperate ecosystems. The estimated global soil respiration was 94.8 and 93.8 Pg C yr -1 in 2001 and 2009, respectively. However, the MODIS land-use maps had insufficient spatial resolution to evaluate the effect of land-use change on soil respiration. The spatial variation of soil respiration (Q 10 ) values was higher but its spatial variation was lower in high-latitude areas than in other areas. However, Q 10 in tropical areas was more variable and was not accurately estimated (the values were >7.5 or soil respiration in tropical ecosystems. To solve these problems, it will be necessary to validate our results using a combination of remote sensing data at higher spatial resolution and field observations for many different ecosystems, and it will be necessary to account for the effects of more soil factors in the predictive equations. Copyright © 2017 Elsevier Ltd. All rights reserved.
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MODELING AND SIMULATION OF HIGH RESOLUTION OPTICAL REMOTE SENSING SATELLITE GEOMETRIC CHAIN
Directory of Open Access Journals (Sweden)
Z. Xia
2018-04-01
Full Text Available The high resolution satellite with the longer focal length and the larger aperture has been widely used in georeferencing of the observed scene in recent years. The consistent end to end model of high resolution remote sensing satellite geometric chain is presented, which consists of the scene, the three line array camera, the platform including attitude and position information, the time system and the processing algorithm. The integrated design of the camera and the star tracker is considered and the simulation method of the geolocation accuracy is put forward by introduce the new index of the angle between the camera and the star tracker. The model is validated by the geolocation accuracy simulation according to the test method of the ZY-3 satellite imagery rigorously. The simulation results show that the geolocation accuracy is within 25m, which is highly consistent with the test results. The geolocation accuracy can be improved about 7 m by the integrated design. The model combined with the simulation method is applicable to the geolocation accuracy estimate before the satellite launching.
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Design of the high resolution optical instrument for the Pleiades HR Earth observation satellites
Lamard, Jean-Luc; Gaudin-Delrieu, Catherine; Valentini, David; Renard, Christophe; Tournier, Thierry; Laherrere, Jean-Marc
2017-11-01
As part of its contribution to Earth observation from space, ALCATEL SPACE designed, built and tested the High Resolution cameras for the European intelligence satellites HELIOS I and II. Through these programmes, ALCATEL SPACE enjoys an international reputation. Its capability and experience in High Resolution instrumentation is recognised by the most customers. Coming after the SPOT program, it was decided to go ahead with the PLEIADES HR program. PLEIADES HR is the optical high resolution component of a larger optical and radar multi-sensors system : ORFEO, which is developed in cooperation between France and Italy for dual Civilian and Defense use. ALCATEL SPACE has been entrusted by CNES with the development of the high resolution camera of the Earth observation satellites PLEIADES HR. The first optical satellite of the PLEIADES HR constellation will be launched in mid-2008, the second will follow in 2009. To minimize the development costs, a mini satellite approach has been selected, leading to a compact concept for the camera design. The paper describes the design and performance budgets of this novel high resolution and large field of view optical instrument with emphasis on the technological features. This new generation of camera represents a breakthrough in comparison with the previous SPOT cameras owing to a significant step in on-ground resolution, which approaches the capabilities of aerial photography. Recent advances in detector technology, optical fabrication and electronics make it possible for the PLEIADES HR camera to achieve their image quality performance goals while staying within weight and size restrictions normally considered suitable only for much lower performance systems. This camera design delivers superior performance using an innovative low power, low mass, scalable architecture, which provides a versatile approach for a variety of imaging requirements and allows for a wide number of possibilities of accommodation with a mini-satellite
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A Method for Retrieving Daily Land Surface Albedo from Space at 30-m Resolution
Directory of Open Access Journals (Sweden)
Bo Gao
2015-08-01
Full Text Available Land surface albedo data with high spatio-temporal resolution are increasingly important for scientific studies addressing spatially and/or temporally small-scale phenomena, such as urban heat islands and urban land surface energy balance. Our previous study derived albedo data with 2–4-day and 30-m temporal and spatial resolution that have better spatio-temporal resolution than existing albedo data, but do not completely satisfy the requirements for monitoring high-frequency land surface changes at the small scale. Downscaling technology provides a chance to further improve the albedo data spatio-temporal resolution and accuracy. This paper introduces a method that combines downscaling technology for land surface reflectance with an empirical method of deriving land surface albedo. Firstly, downscaling daily MODIS land surface reflectance data (MOD09GA from 500 m to 30 m on the basis of HJ-1A/B BRDF data with 2–4-day and 30-m temporal and spatial resolution is performed: this is the key step in the improved method. Subsequently, the daily 30-m land surface albedo data are derived by an empirical method combining prior knowledge of the MODIS BRDF product and the downscaled daily 30-m reflectance. Validation of albedo data obtained using the proposed method shows that the new method has both improved spatio-temporal resolution and good accuracy (a total absolute accuracy of 0.022 and a total root mean squared error at six sites of 0.028.
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Mapping turbidity in the Charles River, Boston using a high-resolution satellite.
Hellweger, Ferdi L; Miller, Will; Oshodi, Kehinde Sarat
2007-09-01
The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).
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Kawano, N.; Varquez, A. C. G.; Dong, Y.; Kanda, M.
2016-12-01
Numerical model such as Weather Research and Forecasting model coupled with single-layer Urban Canopy Model (WRF-UCM) is one of the powerful tools to investigate urban heat island. Urban parameters such as average building height (Have), plain area index (λp) and frontal area index (λf), are necessary inputs for the model. In general, these parameters are uniformly assumed in WRF-UCM but this leads to unrealistic urban representation. Distributed urban parameters can also be incorporated into WRF-UCM to consider a detail urban effect. The problem is that distributed building information is not readily available for most megacities especially in developing countries. Furthermore, acquiring real building parameters often require huge amount of time and money. In this study, we investigated the potential of using globally available satellite-captured datasets for the estimation of the parameters, Have, λp, and λf. Global datasets comprised of high spatial resolution population dataset (LandScan by Oak Ridge National Laboratory), nighttime lights (NOAA), and vegetation fraction (NASA). True samples of Have, λp, and λf were acquired from actual building footprints from satellite images and 3D building database of Tokyo, New York, Paris, Melbourne, Istanbul, Jakarta and so on. Regression equations were then derived from the block-averaging of spatial pairs of real parameters and global datasets. Results show that two regression curves to estimate Have and λf from the combination of population and nightlight are necessary depending on the city's level of development. An index which can be used to decide which equation to use for a city is the Gross Domestic Product (GDP). On the other hand, λphas less dependence on GDP but indicated a negative relationship to vegetation fraction. Finally, a simplified but precise approximation of urban parameters through readily-available, high-resolution global datasets and our derived regressions can be utilized to estimate a
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Materne, A.; Bardoux, A.; Geoffray, H.; Tournier, T.; Kubik, P.; Morris, D.; Wallace, I.; Renard, C.
2017-11-01
The PLEIADES-HR Earth observing satellites, under CNES development, combine a 0.7m resolution panchromatic channel, and a multispectral channel allowing a 2.8 m resolution, in 4 spectral bands. The 2 satellites will be placed on a sun-synchronous orbit at an altitude of 695 km. The camera operates in push broom mode, providing images across a 20 km swath. This paper focuses on the specifications, design and performance of the TDI detectors developed by e2v technologies under CNES contract for the panchromatic channel. Design drivers, derived from the mission and satellite requirements, architecture of the sensor and measurement results for key performances of the first prototypes are presented.
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A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi
Smith, Kathryn E.L.; Nayegandhi, Amar; Brock, John C.
2010-01-01
INTRODUCTION Land-use and land-cover (LULC) data provide important information for environmental management. Data pertaining to land-cover and land-management activities are a common requirement for spatial analyses, such as watershed modeling, climate change, and hazard assessment. In coastal areas, land development, storms, and shoreline modification amplify the need for frequent and detailed land-cover datasets. The northern Gulf of Mexico coastal area is no exception. The impact of severe storms, increases in urban area, dramatic changes in land cover, and loss of coastal-wetland habitat all indicate a vital need for reliable and comparable land-cover data. Four main attributes define a land-cover dataset: the date/time of data collection, the spatial resolution, the type of classification, and the source data. The source data are the foundation dataset used to generate LULC classification and are typically remotely sensed data, such as aerial photography or satellite imagery. These source data have a large influence on the final LULC data product, so much so that one can classify LULC datasets into two general groups: LULC data derived from aerial photography and LULC data derived from satellite imagery. The final LULC data can be converted from one format to another (for instance, vector LULC data can be converted into raster data for analysis purposes, and vice versa), but each subsequent dataset maintains the imprint of the source medium within its spatial accuracy and data features. The source data will also influence the spatial and temporal resolution, as well as the type of classification. The intended application of the LULC data typically defines the type of source data and methodology, with satellite imagery being selected for large landscapes (state-wide, national data products) and repeatability (environmental monitoring and change analysis). The coarse spatial scale and lack of refined land-use categories are typical drawbacks to satellite
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Directory of Open Access Journals (Sweden)
Si-Bo Duan
2014-04-01
Full Text Available The diurnal cycle of land surface temperature (LST is an important element of the climate system. Geostationary satellites can provide the diurnal cycle of LST with low spatial resolution and incomplete global coverage, which limits its applications in some studies. In this study, we propose a method to estimate the diurnal cycle of LST at high temporal and spatial resolution from clear-sky MODIS data. This method was evaluated using the MSG-SEVIRI-derived LSTs. The results indicate that this method fits the diurnal cycle of LST well, with root mean square error (RMSE values less than 1 K for most pixels. Because MODIS provides at most four observations per day at a given location, this method was further evaluated using only four MSG-SEVIRI-derived LSTs corresponding to the MODIS overpass times (10:30, 13:30, 22:30, and 01:30 local solar time. The results show that the RMSE values using only four MSG-SEVIRI-derived LSTs are approximately two times larger than those using all LSTs. The spatial distribution of the modeled LSTs at the MODIS pixel scale is presented from 07:00 to 05:00 local solar time of the next day with an increment of 2 hours. The diurnal cycle of the modeled LSTs describes the temporal evolution of the LSTs at the MODIS pixel scale.
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A data mining approach for sharpening satellite thermal imagery over land
Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes which are at significant...
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Wang, Z.; Roman, M. O.; Pahlevan, N.; Stachura, M.; McCorkel, J.; Bland, G.; Schaaf, C.
2016-12-01
Albedo is a key climate forcing variable that governs the absorption of incoming solar radiation and its ultimate transfer to the atmosphere. Albedo contributes significant uncertainties in the simulation of climate changes; and as such, it is defined by the Global Climate Observing System (GCOS) as a terrestrial essential climate variable (ECV) required by global and regional climate and biogeochemical models. NASA's Goddard Space Flight Center's Multi AngLe Imaging Bidirectional Reflectance Distribution Function small-UAS (MALIBU) is part of a series of pathfinder missions to develop enhanced multi-angular remote sensing techniques using small Unmanned Aircraft Systems (sUAS). The MALIBU instrument package includes two multispectral imagers oriented at two different viewing geometries (i.e., port and starboard sides) capture vegetation optical properties and structural characteristics. This is achieved by analyzing the surface reflectance anisotropy signal (i.e., BRDF shape) obtained from the combination of surface reflectance from different view-illumination angles and spectral channels. Satellite measures of surface albedo from MODIS, VIIRS, and Landsat have been evaluated by comparison with spatially representative albedometer data from sparsely distributed flux towers at fixed heights. However, the mismatch between the footprint of ground measurements and the satellite footprint challenges efforts at validation, especially for heterogeneous landscapes. The BRDF (Bidirectional Reflectance Distribution Function) models of surface anisotropy have only been evaluated with airborne BRDF data over a very few locations. The MALIBU platform that acquires extremely high resolution sub-meter measures of surface anisotropy and surface albedo, can thus serve as an important source of reference data to enable global land product validation efforts, and resolve the errors and uncertainties in the various existing products generated by NASA and its national and
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2011-06-01
This report describes an accuracy assessment of extracted features derived from three : subsets of Quickbird pan-sharpened high resolution satellite image for the area of the : Port of Los Angeles, CA. Visual Learning Systems Feature Analyst and D...
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Solomos, S.; Amiridis, V.; Zanis, P.; Gerasopoulos, E.; Sofiou, F. I.; Herekakis, T.; Brioude, J.; Stohl, A.; Kahn, R. A.; Kontoes, C.
2015-01-01
A total number of 20,212 fire hot spots were recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument over Greece during the period 2002e2013. The Fire Radiative Power (FRP) of these events ranged from 10 up to 6000 MW at 1 km resolution, and many of these fire episodes resulted in long-range transport of smoke over distances up to several hundred kilometers. Three different smoke episodes over Greece are analyzed here using real time hot-spot observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) satellite instrument as well as from MODIS hot-spots. Simulations of smoke dispersion are performed with the FLEXPART-WRF model and particulate matter emissions are calculated directly from the observed FRP. The modeled smoke plumes are compared with smoke stereo-heights from the Multiangle Imaging Spectroradiometer (MISR) instrument and the sensitivities to atmospheric and modeling parameters are examined. Driving the simulations with high resolution meteorology (4 4 km) and using geostationary satellite data to identify the hot spots allows the description of local scale features that govern smoke dispersion. The long-range transport of smoke is found to be favored over the complex coastline environment of Greece due to the abrupt changes between land and marine planetary boundary layers (PBL) and the decoupling of smoke layers from the surface.
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Farrell, S. L.; Kurtz, N. T.; Richter-Menge, J.; Harbeck, J. P.; Onana, V.
2012-12-01
Satellite-derived estimates of ice thickness and observations of ice extent over the last decade point to a downward trend in the basin-scale ice volume of the Arctic Ocean. This loss has broad-ranging impacts on the regional climate and ecosystems, as well as implications for regional infrastructure, marine navigation, national security, and resource exploration. New observational datasets at small spatial and temporal scales are now required to improve our understanding of physical processes occurring within the ice pack and advance parameterizations in the next generation of numerical sea-ice models. High-resolution airborne and satellite observations of the sea ice are now available at meter-scale resolution or better that provide new details on the properties and morphology of the ice pack across basin scales. For example the NASA IceBridge airborne campaign routinely surveys the sea ice of the Arctic and Southern Oceans with an advanced sensor suite including laser and radar altimeters and digital cameras that together provide high-resolution measurements of sea ice freeboard, thickness, snow depth and lead distribution. Here we present statistical analyses of the ice pack primarily derived from the following IceBridge instruments: the Digital Mapping System (DMS), a nadir-looking, high-resolution digital camera; the Airborne Topographic Mapper, a scanning lidar; and the University of Kansas snow radar, a novel instrument designed to estimate snow depth on sea ice. Together these instruments provide data from which a wide range of sea ice properties may be derived. We provide statistics on lead distribution and spacing, lead width and area, floe size and distance between floes, as well as ridge height, frequency and distribution. The goals of this study are to (i) identify unique statistics that can be used to describe the characteristics of specific ice regions, for example first-year/multi-year ice, diffuse ice edge/consolidated ice pack, and convergent
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Kong, J.; Ryu, Y.
2017-12-01
Algorithms for fusing high temporal frequency and high spatial resolution satellite images are widely used to develop dense time-series land surface observations. While many studies have revealed that the synthesized frequent high spatial resolution images could be successfully applied in vegetation mapping and monitoring, validation and correction of fused images have not been focused than its importance. To evaluate the precision of fused image in pixel level, in-situ reflectance measurements which could account for the pixel-level heterogeneity are necessary. In this study, the synthetic images of land surface reflectance were predicted by the coarse high-frequency images acquired from MODIS and high spatial resolution images from Landsat-8 OLI using the Flexible Spatiotemporal Data Fusion (FSDAF). Ground-based reflectance was measured by JAZ Spectrometer (Ocean Optics, Dunedin, FL, USA) on rice paddy during five main growth stages in Cheorwon-gun, Republic of Korea, where the landscape heterogeneity changes through the growing season. After analyzing the spatial heterogeneity and seasonal variation of land surface reflectance based on the ground measurements, the uncertainties of the fused images were quantified at pixel level. Finally, this relationship was applied to correct the fused reflectance images and build the seasonal time series of rice paddy surface reflectance. This dataset could be significant for rice planting area extraction, phenological stages detection, and variables estimation.
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Rapid response flood detection using the MSG geostationary satellite
DEFF Research Database (Denmark)
Proud, Simon Richard; Fensholt, Rasmus; Rasmussen, Laura Vang
2011-01-01
A novel technique for the detection of flooded land using satellite data is presented. This new method takes advantage of the high temporal resolution of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) series of satellites to derive several p...... of data gathered during the 2009 flooding events in West Africa shows that the presented method can detect floods of comparable size to the SEVIRI pixel resolution on a short timescale, making it a valuable tool for large scale flood mapping....
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The influence of land surface parameters on energy flux densities derived from remote sensing data
Energy Technology Data Exchange (ETDEWEB)
Tittebrand, A.; Schwiebus, A. [Inst. for Hydrology und Meteorology, TU Dresden (Germany); Berger, F.H. [Observatory Lindenberg, German Weather Service, Lindenberg (Germany)
2005-04-01
Knowledge of the vegetation properties surface reflectance, normalised difference vegetation index (NDVI) and leaf area index (LAI) are essential for the determination of the heat and water fluxes between terrestrial ecosystems and the atmosphere. Remote sensing data can be used to derive spatial estimates of the required surface properties. The determination of land surface parameters and their influence on radiant and energy flux densities is investigated with data of different remote sensing systems. Sensitivity studies show the importance of correctly derived land surface properties to estimate the key quantity of the hydrological cycle, the evapotranspiration (L.E), most exactly. In addition to variable parameters like LAI or NDVI there are also parameters which are can not be inferred from satellite data but needed for the Penman-Monteith approach. Fixed values are assumed for these variables because they have little influence on L.E. Data of Landsat-7 ETM+ and NOAA-16 AVHRR are used to show results in different spatial resolution. The satellite derived results are compared with ground truth data provided by the Observatory Lindenberg of the German Weather Service. (orig.)
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Satellite-Based Sunshine Duration for Europe
Directory of Open Access Journals (Sweden)
Bodo Ahrens
2013-06-01
Full Text Available In this study, two different methods were applied to derive daily and monthly sunshine duration based on high-resolution satellite products provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT Satellite Application Facility on Climate Monitoring using data from Meteosat Second Generation (MSG SEVIRI (Spinning Enhanced Visible and Infrared Imager. The satellite products were either hourly cloud type or hourly surface incoming direct radiation. The satellite sunshine duration estimates were not found to be significantly different using the native 15-minute temporal resolution of SEVIRI. The satellite-based sunshine duration products give additional spatial information over the European continent compared with equivalent in situ-based products. An evaluation of the satellite sunshine duration by product intercomparison and against station measurements was carried out to determine their accuracy. The satellite data were found to be within ±1 h/day compared to high-quality Baseline Surface Radiation Network or surface synoptic observations (SYNOP station measurements. The satellite-based products differ more over the oceans than over land, mainly because of the treatment of fractional clouds in the cloud type-based sunshine duration product. This paper presents the methods used to derive the satellite sunshine duration products and the performance of the different retrievals. The main benefits and disadvantages compared to station-based products are also discussed.
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Ardalan, A.; Safari, A.; Grafarend, E.
2003-04-01
A new ellipsoidal gravimetric-satellite altimetry boundary value problem has been developed and successfully tested. This boundary value problem has been constructed for gravity observables of the type (i) gravity potential (ii) gravity intensity (iii) deflection of vertical and (iv) satellite altimetry data. The developed boundary value problem is enjoying the ellipsoidal nature and as such can take advantage of high precision GPS observations in the set-up of the problem. The highlights of the solution are as follows: begin{itemize} Application of ellipsoidal harmonic expansion up to degree/order and ellipsoidal centrifugal field for the reduction of global gravity and isostasy effects from the gravity observable at the surface of the Earth. Application of ellipsoidal Newton integral on the equal area map projection surface for the reduction of residual mass effects within a radius of 55 km around the computational point. Ellipsoidal harmonic downward continuation of the residual observables from the surface of the earth down to the surface of reference ellipsoid using the ellipsoidal height of the observation points derived from GPS. Restore of the removed effects at the application points on the surface of reference ellipsoid. Conversion of the satellite altimetry derived heights of the water bodies into potential. Combination of the downward continued gravity information with the potential equivalent of the satellite altimetry derived heights of the water bodies. Application of ellipsoidal Bruns formula for converting the potential values on the surface of the reference ellipsoid into the geoidal heights (i.e. ellipsoidal heights of the geoid) with respect to the reference ellipsoid. Computation of the high-resolution geoid of Iran has successfully tested this new methodology!
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RELATIVE ORIENTATION AND MODIFIED PIECEWISE EPIPOLAR RESAMPLING FOR HIGH RESOLUTION SATELLITE IMAGES
Directory of Open Access Journals (Sweden)
K. Gong
2017-05-01
Full Text Available High resolution, optical satellite sensors are boosted to a new era in the last few years, because satellite stereo images at half meter or even 30cm resolution are available. Nowadays, high resolution satellite image data have been commonly used for Digital Surface Model (DSM generation and 3D reconstruction. It is common that the Rational Polynomial Coefficients (RPCs provided by the vendors have rough precision and there is no ground control information available to refine the RPCs. Therefore, we present two relative orientation methods by using corresponding image points only: the first method will use quasi ground control information, which is generated from the corresponding points and rough RPCs, for the bias-compensation model; the second method will estimate the relative pointing errors on the matching image and remove this error by an affine model. Both methods do not need ground control information and are applied for the entire image. To get very dense point clouds, the Semi-Global Matching (SGM method is an efficient tool. However, before accomplishing the matching process the epipolar constraints are required. In most conditions, satellite images have very large dimensions, contrary to the epipolar geometry generation and image resampling, which is usually carried out in small tiles. This paper also presents a modified piecewise epipolar resampling method for the entire image without tiling. The quality of the proposed relative orientation and epipolar resampling method are evaluated, and finally sub-pixel accuracy has been achieved in our work.
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Burton, S. P.; Ferrare, R. A.; Hostetler, C. A.; Hair, J. W.; Kittaka, C.; Vaughn, M. A.; Remer, L. A.
2010-01-01
We derive aerosol extinction profiles from airborne and space-based lidar backscatter signals by constraining the retrieval with column aerosol optical thickness (AOT), with no need to rely on assumptions about aerosol type or lidar ratio. The backscatter data were acquired by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL) and by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The HSRL also simultaneously measures aerosol extinction coefficients independently using the high spectral resolution lidar technique, thereby providing an ideal data set for evaluating the retrieval. We retrieve aerosol extinction profiles from both HSRL and CALIOP attenuated backscatter data constrained with HSRL, Moderate-Resolution Imaging Spectroradiometer (MODIS), and Multiangle Imaging Spectroradiometer column AOT. The resulting profiles are compared with the aerosol extinction measured by HSRL. Retrievals are limited to cases where the column aerosol thickness is greater than 0.2 over land and 0.15 over water. In the case of large AOT, the results using the Aqua MODIS constraint over water are poorer than Aqua MODIS over land or Terra MODIS. The poorer results relate to an apparent bias in Aqua MODIS AOT over water observed in August 2007. This apparent bias is still under investigation. Finally, aerosol extinction coefficients are derived from CALIPSO backscatter data using AOT from Aqua MODIS for 28 profiles over land and 9 over water. They agree with coincident measurements by the airborne HSRL to within +/-0.016/km +/- 20% for at least two-thirds of land points and within +/-0.028/km +/- 20% for at least two-thirds of ocean points.
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Dixon, T. H.; A Karegar, M.; Uebbing, B.; Kusche, J.; Fenoglio-Marc, L.
2017-12-01
Coastal Louisiana is experiencing the highest rate of relative sea-level rise in North America due to the combination of sea-level rise and subsidence of the deltaic plain. The land subsidence in this region is studied using various techniques, with continuous GPS site providing high temporal resolution. Here, we use high resolution tide-gauge data and advanced processing of satellite altimetry to derive vertical displacements time series at NOAA tide-gauge stations along the coast (Figure 1). We apply state-of-the-art retracking techniques to process raw altimetry data, allowing high accuracy on range measurements close to the coast. Data from Jason-1, -2 and -3, Envisat, Saral and Cryosat-2 are used, corrected for solid Earth tide, pole tide and tidal ocean loading, using background models consistent with the GPS processing technique. We reprocess the available GPS data using precise point positioning and estimate the rate uncertainty accounting for correlated noise. The displacement time series are derived by directly subtracting tide-gauge data from the altimetry sea-level anomaly data. The quality of the derived displacement rates is evaluated in Grand Isle, Amerada Pass and Shell Beach where GPS data are available adjacent to the tide gauges. We use this technique to infer vertical displacement at tide gauges in New Orleans (New Canal Station) and Port Fourchon and Southwest Pass along the coastline.
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Directory of Open Access Journals (Sweden)
Manuel J. Mayr
2015-04-01
Full Text Available The Leaf Area Index (LAI is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS operationally produce global datasets of LAI. Due to their role as an input to large-scale modeling activities, evaluation and verification of such datasets are of high importance. In this context, savannas appear to be underrepresented with regards to their heterogeneous appearance (e.g., tree/grass-ratio, seasonality. Here, we aim to examine the LAI in a heterogeneous savanna ecosystem located in Namibia’s Owamboland during the dry season. Ground measurements of LAI are used to derive a high-resolution LAI model with RapidEye satellite data. This model is related to the corresponding MODIS LAI/FPAR (Fraction of Absorbed Photosynthetically Active Radiation scene (MOD15A2 in order to evaluate its performance at the intended annual minimum during the dry season. Based on a field survey we first assessed vegetation patterns from species composition and elevation for 109 sites. Secondly, we measured in situ LAI to quantitatively estimate the available vegetation (mean = 0.28. Green LAI samples were then empirically modeled (LAImodel with high resolution RapidEye imagery derived Difference Vegetation Index (DVI using a linear regression (R2 = 0.71. As indicated by several measures of model performance, the comparison with MOD15A2 revealed moderate consistency mostly due to overestimation by the aggregated LAImodel. Model constraints aside, this study may point to important issues for MOD15A2 in savannas concerning the underlying MODIS Land Cover product (MCD12Q1 and a potential adjustment by means of the MODIS Burned Area product (MCD45A1.
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Kim, J.; Lin, S.; Hong, J.; Park, D.; Yoon, S.; Kim, Y.
2010-12-01
High resolution satellite imagery acquired from orbiters are able to provide detailed topographic information and therefore are recognised as an important tool for investigating planetary and terrestrial topography. The heritage of in-orbit high resolution imaging technology is now implemented in a series of Martian Missions, such as HiRISE (High Resolution Imaging Science Experiment) and CTX (Context Camera) onboard the MRO (Mars Reconnaissance Orbiter). In order to fully utilise the data derived from image systems carried on various Mars orbiters, the generalised algorithms of image processing and photogrammetric Mars DTM extraction have been developed and implemented by Kim and Muller (2009), in which non-rigorous sensor model and hierarchical geomatics control were employed. Due to the successful “from medium to high” control strategy performed during processing, stable horizontal and vertical photogrammetric accuracy of resultant Mars DTM was achievable when compared with MOLA (Mars Obiter Laser Altimeter) DTM. Recently, the algorithms developed in Kim and Muller (2009) were further updated by employing advanced image matcher and improved sensor model. As the photogrammetric qualities of the updated topographic products are verified and the spatial solution can be up to sub-meter scale, they are of great value to be exploited for Martian rover landing site selection and rover route navigation. To this purpose, the DTMs and ortho-rectified imagery obtained from CTX and HiRISE covering potential future rovers and existing MER (Mars Exploration Rover) landing sites were firstly processed. For landing site selection, the engineering constraints such as slope and surface roughness were computed from DTMs. In addition, the combination of virtual topography and the estimated rover location was able to produce a sophisticated environment simulation of rover’s landing site. Regarding the rover navigation, the orbital DTMs and the images taken from cameras
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Digital Repository Service at National Institute of Oceanography (India)
Chatterjee, S.; Bhattacharyya, R.; Michael, L.; Krishna, K.S.; Majumdar, T.J.
Geoid and gravity anomalies derived from satellite altimetry are gradually gaining importance in marine geoscientific investigations. Keeping this in mind, we have validated ERS-1 (168 day repeat) altimeter data and very high-resolution free...
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FFT-enhanced IHS transform method for fusing high-resolution satellite images
Ling, Y.; Ehlers, M.; Usery, E.L.; Madden, M.
2007-01-01
Existing image fusion techniques such as the intensity-hue-saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information. ?? 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
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Precision Viticulture from Multitemporal, Multispectral Very High Resolution Satellite Data
Kandylakis, Z.; Karantzalos, K.
2016-06-01
In order to exploit efficiently very high resolution satellite multispectral data for precision agriculture applications, validated methodologies should be established which link the observed reflectance spectra with certain crop/plant/fruit biophysical and biochemical quality parameters. To this end, based on concurrent satellite and field campaigns during the veraison period, satellite and in-situ data were collected, along with several grape samples, at specific locations during the harvesting period. These data were collected for a period of three years in two viticultural areas in Northern Greece. After the required data pre-processing, canopy reflectance observations, through the combination of several vegetation indices were correlated with the quantitative results from the grape/must analysis of grape sampling. Results appear quite promising, indicating that certain key quality parameters (like brix levels, total phenolic content, brix to total acidity, anthocyanin levels) which describe the oenological potential, phenolic composition and chromatic characteristics can be efficiently estimated from the satellite data.
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Anand, Jasdeep S.; Monks, Paul S.
2017-07-01
Land use regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed-effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during the period 2005-2015. In situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A, and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in situ data shows that the mixed-effects LUR model using OMI data has a high predictive power (adj. R2 = 0. 84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0. 11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.
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Analysis of smear in high-resolution remote sensing satellites
Wahballah, Walid A.; Bazan, Taher M.; El-Tohamy, Fawzy; Fathy, Mahmoud
2016-10-01
High-resolution remote sensing satellites (HRRSS) that use time delay and integration (TDI) CCDs have the potential to introduce large amounts of image smear. Clocking and velocity mismatch smear are two of the key factors in inducing image smear. Clocking smear is caused by the discrete manner in which the charge is clocked in the TDI-CCDs. The relative motion between the HRRSS and the observed object obliges that the image motion velocity must be strictly synchronized with the velocity of the charge packet transfer (line rate) throughout the integration time. During imaging an object off-nadir, the image motion velocity changes resulting in asynchronization between the image velocity and the CCD's line rate. A Model for estimating the image motion velocity in HRRSS is derived. The influence of this velocity mismatch combined with clocking smear on the modulation transfer function (MTF) is investigated by using Matlab simulation. The analysis is performed for cross-track and along-track imaging with different satellite attitude angles and TDI steps. The results reveal that the velocity mismatch ratio and the number of TDI steps have a serious impact on the smear MTF; a velocity mismatch ratio of 2% degrades the MTFsmear by 32% at Nyquist frequency when the TDI steps change from 32 to 96. In addition, the results show that to achieve the requirement of MTFsmear >= 0.95 , for TDI steps of 16 and 64, the allowable roll angles are 13.7° and 6.85° and the permissible pitch angles are no more than 9.6° and 4.8°, respectively.
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Delineation of wetland areas from high resolution WorldView-2 data by object-based method
International Nuclear Information System (INIS)
Hassan, N; Hamid, J R A; Adnan, N A; Jaafar, M
2014-01-01
Various classification methods are available that can be used to delineate land cover types. Object-based is one of such methods for delineating the land cover from satellite imageries. This paper focuses on the digital image processing aspects of discriminating wetland areas via object-based method using high resolution satellite multispectral WorldView-2 image data taken over part of Penang Island region. This research is an attempt to improve the wetland area delineation in conjunction with a range of classification techniques which can be applied to satellite data with high spatial and spectral resolution such as World View 2. The intent is to determine a suitable approach to delineate and map these wetland areas more appropriately. There are common parameters to take into account that are pivotal in object-based method which are the spatial resolution and the range of spectral channels of the imaging sensor system. The preliminary results of the study showed object-based analysis is capable of delineating wetland region of interest with an accuracy that is acceptable to the required tolerance for land cover classification
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The high resolution optical instruments for the Pleiades HR Earth observation satellites
Gaudin-Delrieu, Catherine; Lamard, Jean-Luc; Cheroutre, Philippe; Bailly, Bruno; Dhuicq, Pierre; Puig, Olivier
2017-11-01
Coming after the SPOT satellites series, PLEIADESHR is a CNES optical high resolution satellite dedicated to Earth observation, part of a larger optical and radar multi-sensors system, ORFEO, which is developed in cooperation between France and Italy for dual Civilian and Defense use. The development of the two PLEIADES-HR cameras was entrusted by CNES to Thales Alenia Space. This new generation of instrument represents a breakthrough in comparison with the previous SPOT instruments owing to a significant step in on-ground resolution, which approaches the capabilities of aerial photography. The PLEIADES-HR instrument program benefits from Thales Alenia Space long and successful heritage in Earth observation from space. The proposed solution benefits from an extensive use of existing products, Cannes Space Optics Centre facilities, unique in Europe, dedicated to High Resolution instruments. The optical camera provides wide field panchromatic images supplemented by 4 multispectral channels with narrow spectral bands. The optical concept is based on a four mirrors Korsch telescope. Crucial improvements in detector technology, optical fabrication and electronics make it possible for the PLEIADES-HR instrument to achieve the image quality requirements while respecting the drastic limitations of mass and volume imposed by the satellite agility needs and small launchers compatibility. The two flight telescopes were integrated, aligned and tested. After the integration phase, the alignment, mainly based on interferometric measurements in vacuum chamber, was successfully achieved within high accuracy requirements. The wave front measurements show outstanding performances, confirmed, after the integration of the PFM Detection Unit, by MTF measurements on the Proto-Flight Model Instrument. Delivery of the proto flight model occurred mi-2008. The FM2 Instrument delivery is planned Q2-2009. The first optical satellite launch of the PLEIADES-HR constellation is foreseen
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The Study of Land Use Classification Based on SPOT6 High Resolution Data
Wu Song; Jiang Qigang
2016-01-01
A method is carried out to quick classification extract of the type of land use in agricultural areas, which is based on the spot6 high resolution remote sensing classification data and used of the good nonlinear classification ability of support vector machine. The results show that the spot6 high resolution remote sensing classification data can realize land classification efficiently, the overall classification accuracy reached 88.79% and Kappa factor is 0.8632 which means that the classif...
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DEFF Research Database (Denmark)
Rasmussen, Mads Olander; Goettsche, Frank-M.; Diop, Doudou
2011-01-01
A tree survey and an analysis of high resolution satellite data were performed to characterise the woody vegetation within a 10 x 10 km(2) area around a site located close to the town of Dahra in the semiarid northern part of Senegal. The surveyed parameters were tree species, height, tree crown...
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Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery
McCarthy, Matthew J.; Merton, Elizabeth J.; Muller-Karger, Frank E.
2015-08-01
Accurate wetland maps are a fundamental requirement for land use management and for wetland restoration planning. Several wetland map products are available today; most of them based on remote sensing images, but their different data sources and mapping methods lead to substantially different estimations of wetland location and extent. We used two very high-resolution (2 m) WorldView-2 satellite images and one (30 m) Landsat 8 Operational Land Imager (OLI) image to assess wetland coverage in two coastal areas of Tampa Bay (Florida): Fort De Soto State Park and Weedon Island Preserve. An initial unsupervised classification derived from WorldView-2 was more accurate at identifying wetlands based on ground truth data collected in the field than the classification derived from Landsat 8 OLI (82% vs. 46% accuracy). The WorldView-2 data was then used to define the parameters of a simple and efficient decision tree with four nodes for a more exacting classification. The criteria for the decision tree were derived by extracting radiance spectra at 1500 separate pixels from the WorldView-2 data within field-validated regions. Results for both study areas showed high accuracy in both wetland (82% at Fort De Soto State Park, and 94% at Weedon Island Preserve) and non-wetland vegetation classes (90% and 83%, respectively). Historical, published land-use maps overestimate wetland surface cover by factors of 2-10 in the study areas. The proposed methods improve speed and efficiency of wetland map production, allow semi-annual monitoring through repeat satellite passes, and improve the accuracy and precision with which wetlands are identified.
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Examining the utility of satellite-based wind sheltering estimates for lake hydrodynamic modeling
Van Den Hoek, Jamon; Read, Jordan S.; Winslow, Luke A.; Montesano, Paul; Markfort, Corey D.
2015-01-01
Satellite-based measurements of vegetation canopy structure have been in common use for the last decade but have never been used to estimate canopy's impact on wind sheltering of individual lakes. Wind sheltering is caused by slower winds in the wake of topography and shoreline obstacles (e.g. forest canopy) and influences heat loss and the flux of wind-driven mixing energy into lakes, which control lake temperatures and indirectly structure lake ecosystem processes, including carbon cycling and thermal habitat partitioning. Lakeshore wind sheltering has often been parameterized by lake surface area but such empirical relationships are only based on forested lakeshores and overlook the contributions of local land cover and terrain to wind sheltering. This study is the first to examine the utility of satellite imagery-derived broad-scale estimates of wind sheltering across a diversity of land covers. Using 30 m spatial resolution ASTER GDEM2 elevation data, the mean sheltering height, hs, being the combination of local topographic rise and canopy height above the lake surface, is calculated within 100 m-wide buffers surrounding 76,000 lakes in the U.S. state of Wisconsin. Uncertainty of GDEM2-derived hs was compared to SRTM-, high-resolution G-LiHT lidar-, and ICESat-derived estimates of hs, respective influences of land cover type and buffer width on hsare examined; and the effect of including satellite-based hs on the accuracy of a statewide lake hydrodynamic model was discussed. Though GDEM2 hs uncertainty was comparable to or better than other satellite-based measures of hs, its higher spatial resolution and broader spatial coverage allowed more lakes to be included in modeling efforts. GDEM2 was shown to offer superior utility for estimating hs compared to other satellite-derived data, but was limited by its consistent underestimation of hs, inability to detect within-buffer hs variability, and differing accuracy across land cover types. Nonetheless
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Pelta, Ran; Chudnovsky, Alexandra A
2017-02-01
Although meteorological monitoring stations provide accurate measurements of Air Temperature (AT), their spatial coverage within a given region is limited and thus is often insufficient for exposure and epidemiological studies. In many applications, satellite imagery measures energy flux, which is spatially continuous, and calculates Brightness Temperature (BT) that used as an input parameter. Although both quantities (AT-BT) are physically related, the correlation between them is not straightforward, and varies daily due to parameters such as meteorological conditions, surface moisture, land use, satellite-surface geometry and others. In this paper we first investigate the relationship between AT and BT as measured by 39 meteorological stations in Israel during 1984-2015. Thereafter, we apply mixed regression models with daily random slopes to calibrate Landsat BT data with monitored AT measurements for the period 1984-2015. Results show that AT can be predicted with high accuracy by using BT with high spatial resolution. The model shows relatively high accuracy estimation of AT (R 2 =0.92, RMSE=1.58°C, slope=0.90). Incorporating meteorological parameters into the model generates better accuracy (R 2 =0.935) than the AT-BT model (R 2 =0.92). Furthermore, based on the relatively high model accuracy, we investigated the spatial patterns of AT within the study domain. In the latter we focused on July-August, as these two months are characterized by relativity stable synoptic conditions in the study area. In addition, a temporal change in AT during the last 30years was estimated and verified using available meteorological stations and two additional remote sensing platforms. Finally, the impact of different land coverage on AT were estimated, as an example of future application of the presented approach. Copyright © 2016 Elsevier B.V. All rights reserved.
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Impact of high resolution land surface initialization in Indian summer ...
Indian Academy of Sciences (India)
The direct impact of high resolution land surface initialization on the forecast bias in a regional climate model in recent years ... surface initialization using a regional climate model. ...... ization of the snow field in a cloud model; J. Clim. Appl.
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Lakshmi, V.; Mladenova, I. E.; Narayan, U.
2009-12-01
Soil moisture is known to be an essential factor in controlling the partitioning of rainfall into surface runoff and infiltration and solar energy into latent and sensible heat fluxes. Remote sensing has long proven its capability to obtain soil moisture in near real-time. However, at the present time we have the Advanced Scanning Microwave Radiometer (AMSR-E) on board NASA’s AQUA platform is the only satellite sensor that supplies a soil moisture product. AMSR-E coarse spatial resolution (~ 50 km at 6.9 GHz) strongly limits its applicability for small scale studies. A very promising technique for spatial disaggregation by combining radar and radiometer observations has been demonstrated by the authors using a methodology is based on the assumption that any change in measured brightness temperature and backscatter from one to the next time step is due primarily to change in soil wetness. The approach uses radiometric estimates of soil moisture at a lower resolution to compute the sensitivity of radar to soil moisture at the lower resolution. This estimate of sensitivity is then disaggregated using vegetation water content, vegetation type and soil texture information, which are the variables on which determine the radar sensitivity to soil moisture and are generally available at a scale of radar observation. This change detection algorithm is applied to several locations. We have used aircraft observed active and passive data over Walnut Creek watershed in Central Iowa in 2002; the Little Washita Watershed in Oklahoma in 2003 and the Murrumbidgee Catchment in southeastern Australia for 2006. All of these locations have different soils and land cover conditions which leads to a rigorous test of the disaggregation algorithm. Furthermore, we compare the derived high spatial resolution soil moisture to in-situ sampling and ground observation networks
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Role of land state in a high resolution mesoscale model
Indian Academy of Sciences (India)
... Proceedings – Mathematical Sciences · Resonance – Journal of Science ... Land surface characteristics; high resolution mesoscale model; Uttarakhand ... to predict realistic location, timing, amount,intensity and distribution of rainfall ... region embedded within two low pressure centers over Arabian Seaand Bay of Bengal.
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All-weather Land Surface Temperature Estimation from Satellite Data
Zhou, J.; Zhang, X.
2017-12-01
Satellite remote sensing, including the thermal infrared (TIR) and passive microwave (MW), provides the possibility to observe LST at large scales. For better modeling the land surface processes with high temporal resolutions, all-weather LST from satellite data is desirable. However, estimation of all-weather LST faces great challenges. On the one hand, TIR remote sensing is limited to clear-sky situations; this drawback reduces its usefulness under cloudy conditions considerably, especially in regions with frequent and/or permanent clouds. On the other hand, MW remote sensing suffers from much greater thermal sampling depth (TSD) and coarser spatial resolution than TIR; thus, MW LST is generally lower than TIR LST, especially at daytime. Two case studies addressing the challenges mentioned previously are presented here. The first study is for the development of a novel thermal sampling depth correction method (TSDC) to estimate the MW LST over barren land; this second study is for the development of a feasible method to merge the TIR and MW LSTs by addressing the coarse resolution of the latter one. In the first study, the core of the TSDC method is a new formulation of the passive microwave radiation balance equation, which allows linking bulk MW radiation to the soil temperature at a specific depth, i.e. the representative temperature: this temperature is then converted to LST through an adapted soil heat conduction equation. The TSDC method is applied to the 6.9 GHz channel in vertical polarization of AMSR-E. Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation is based on in-situ LSTs measured at the Gobabeb site in western Namibia. The results demonstrate the high accuracy of the TSDC method: it yields a root-mean squared error (RMSE) of 2 K and ignorable systematic error over barren land. In the second study, the method consists of two core processes: (1) estimation of MW LST from MW brightness temperature and (2
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A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land
Directory of Open Access Journals (Sweden)
Feng Gao
2012-10-01
Full Text Available Thermal infrared (TIR imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to shortwave band pixel resolutions, which are often fine enough for field-scale applications. A classic thermal sharpening technique, TsHARP, uses a relationship between land surface temperature (LST and Normalized Difference Vegetation Index (NDVI developed empirically at the TIR pixel resolution and applied at the NDVI pixel resolution. However, recent studies show that unique relationships between temperature and NDVI may only exist for a limited class of landscapes, with mostly green vegetation and homogeneous air and soil conditions. To extend application of thermal sharpening to more complex conditions, a new data mining sharpener (DMS technique is developed. The DMS approach builds regression trees between TIR band brightness temperatures and shortwave spectral reflectances based on intrinsic sample characteristics. A comparison of sharpening techniques applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska indicates that the DMS outperformed TsHARP in all cases. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water bodies, thin clouds or terrain. While the DMS technique can provide fine resolution TIR imagery, there are limits to the sharpening ratios that can be reasonably implemented. Consequently, sharpening techniques cannot replace actual thermal band imagery at fine resolutions or missions that
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Andreas, Heri; Usriyah; Zainal Abidin, Hasanuddin; Anggreni Sarsito, Dina
2017-06-01
Tidal inundation (in Javanese they call it “Rob”) is now becoming a well known phenomenon along northern coast of Java Indonesia (Pantura). The occurrence of tidal inundation was recognized at least in the early 2000 and even earlier. In the recent years the tidal inundation comes not only at a high tide but even at the regular tide in some area across Pantura. In fact in location such as Pondok Bali, north of Blanakan, north of Pekalongan, north of Semarang and north west of Demak, seems those areas are sinking to the sea through times. Sea level rise and land subsidence are considered as main factors deriving the occurrence of this tidal inundation. We were using time series of high resolution satellite image data and insitu data measurements to mapping the tidal inundation along northern coast of Java. All available data from google data satellite archives (year 2000- recent years) and any available sources being analyze together with field surveys tagging and also from media information. As a result we can see the tidal inundation are taking place in Tanggerang, Jakarta, Bekasi, Cilamaya, Pondok Bali, Blanakan, Indramayu, Cirebon, Brebes, Tegal, Pemalang, Pekalongan, Kendal, Semarang, Demak, Gresik, Surabaya, Sidoarjo and Pasuruan.
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Evaluation of the Precision of Satellite-Derived Sea Surface Temperature Fields
Wu, F.; Cornillon, P. C.; Guan, L.
2016-02-01
A great deal of attention has been focused on the temporal accuracy of satellite-derived sea surface temperature (SST) fields with little attention being given to their spatial precision. Specifically, the primary measure of the quality of SST fields has been the bias and variance of selected values minus co-located (in space and time) in situ values. Contributing values, determined by the location of the in situ values and the necessity that the satellite-derived values be cloud free, are generally widely separated in space and time hence provide little information related to the pixel-to-pixel uncertainty in the retrievals. But the main contribution to the uncertainty in satellite-derived SST retrievals relates to atmospheric contamination and because the spatial scales of atmospheric features are, in general, large compared with the pixel separation of modern infra-red sensors, the pixel-to-pixel uncertainty is often smaller than the accuracy determined from in situ match-ups. This makes selection of satellite-derived datasets for the study of submesoscale processes, for which the spatial structure of the upper ocean is significant, problematic. In this presentation we present a methodology to characterize the spatial precision of satellite-derived SST fields. The method is based on an examination of the high wavenumber tail of the 2-D spectrum of SST fields in the Sargasso Sea, a low energy region of the ocean close to the track of the MV Oleander, a container ship making weekly roundtrips between New York and Bermuda, with engine intake temperatures sampled every 75 m along track. Important spectral characteristics are the point at which the satellite-derived spectra separate from the Oleander spectra and the spectral slope following separation. In this presentation a number of high resolution 375 m to 10 km SST datasets are evaluated based on this approach.
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High-resolution seismic reflection surveying with a land streamer
Cengiz Tapırdamaz, Mustafa; Cankurtaranlar, Ali; Ergintav, Semih; Kurt, Levent
2013-04-01
In this study, newly designed seismic reflection data acquisition array (land streamer) is utilized to image the shallow subsurface. Our acquisition system consist of 24 geophones screwed on iron plates with 2 m spacing, moving on the surface of the earth which are connected with fire hose. Completely original, 4.5 Kg weight iron plates provides satisfactory coupling. This land-streamer system enables rapid and cost effective acquisition of seismic reflection data due to its operational facilities. First test studies were performed using various seismic sources such as a mini-vibro truck, buffalo-gun and hammer. The final fieldwork was performed on a landslide area which was studied before. Data acquisition was carried out on the line that was previously measured by the seismic survey using 5 m geophone and shot spacing. This line was chosen in order to re-image known reflection patterns obtained from the previous field study. Taking penetration depth into consideration, a six-cartridge buffalo-gun was selected as a seismic source to achieve high vertical resolution. Each shot-point drilled 50 cm for gunshots to obtain high resolution source signature. In order to avoid surface waves, the offset distance between the source and the first channel was chosen to be 50 m and the shot spacing was 2 m. These acquisition parameters provided 12 folds at each CDP points. Spatial sampling interval was 1 m at the surface. The processing steps included standard stages such as gain recovery, editing, frequency filtering, CDP sorting, NMO correction, static correction and stacking. Furthermore, surface consistent residual static corrections were applied recursively to improve image quality. 2D F-K filter application was performed to suppress air and surface waves at relatively deep part of the seismic section. Results show that, this newly designed, high-resolution land seismic data acquisition equipment (land-streamer) can be successfully used to image subsurface. Likewise
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AlShamsi, Meera R.
2016-10-01
Over the past years, there has been various urban development all over the UAE. Dubai is one of the cities that experienced rapid growth in both development and population. That growth can have a negative effect on the surrounding environment. Hence, there has been a necessity to protect the environment from these fast pace changes. One of the major impacts this growth can have is on vegetation. As technology is evolving day by day, there is a possibility to monitor changes that are happening on different areas in the world using satellite imagery. The data from these imageries can be utilized to identify vegetation in different areas of an image through a process called vegetation detection. Being able to detect and monitor vegetation is very beneficial for municipal planning and management, and environment authorities. Through this, analysts can monitor vegetation growth in various areas and analyze these changes. By utilizing satellite imagery with the necessary data, different types of vegetation can be studied and analyzed, such as parks, farms, and artificial grass in sports fields. In this paper, vegetation features are detected and extracted through SAFIY system (i.e. the Smart Application for Feature extraction and 3D modeling using high resolution satellite ImagerY) by using high-resolution satellite imagery from DubaiSat-2 and DEIMOS-2 satellites, which provide panchromatic images of 1m resolution and spectral bands (red, green, blue and near infrared) of 4m resolution. SAFIY system is a joint collaboration between MBRSC and DEIMOS Space UK. It uses image-processing algorithms to extract different features (roads, water, vegetation, and buildings) to generate vector maps data. The process to extract green areas (vegetation) utilize spectral information (such as, the red and near infrared bands) from the satellite images. These detected vegetation features will be extracted as vector data in SAFIY system and can be updated and edited by end-users, such as
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Said, N. M.; Mahmud, M. R.; Hasan, R. C.
2017-10-01
Over the years, the acquisition technique of bathymetric data has evolved from a shipborne platform to airborne and presently, utilising space-borne acquisition. The extensive development of remote sensing technology has brought in the new revolution to the hydrographic surveying. Satellite-Derived Bathymetry (SDB), a space-borne acquisition technique which derives bathymetric data from high-resolution multispectral satellite imagery for various purposes recently considered as a new promising technology in the hydrographic surveying industry. Inspiring by this latest developments, a comprehensive study was initiated by National Hydrographic Centre (NHC) and Universiti Teknologi Malaysia (UTM) to analyse SDB as a means for shallow water area acquisition. By adopting additional adjustment in calibration stage, a marginal improvement discovered on the outcomes from both Stumpf and Lyzenga algorithms where the RMSE values for the derived (predicted) depths were 1.432 meters and 1.728 meters respectively. This paper would deliberate in detail the findings from the study especially on the accuracy level and practicality of SDB over the tropical environmental setting in Malaysia.
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Midekisa, Alemayehu; Holl, Felix; Savory, David J; Andrade-Pacheco, Ricardo; Gething, Peter W; Bennett, Adam; Sturrock, Hugh J W
2017-01-01
Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.
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Norouzi, H.; Bah, A.; Prakash, S.; Nouri, N.; Blake, R.
2017-12-01
A great percentage of the world's population reside in urban areas that are exposed to the threats of global and regional climate changes and associated extreme weather events. Among them, urban heat islands have significant health and economic impacts due to higher thermal gradients of impermeable surfaces in urban regions compared to their surrounding rural areas. Therefore, accurate characterization of the surface energy balance in urban regions are required to predict these extreme events. High spatial resolution Land surface temperature (LST) in the scale of street level in the cities can provide wealth of information to study surface energy balance and eventually providing a reliable heat index. In this study, we estimate high-resolution LST maps using combination of LandSat 8 and infrared based satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and newly launched Geostationary Operational Environmental Satellite-R Series (GOES-R). Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, MODIS and GOES-R have lower spatial resolution (1km and 4km respectively) with much higher temporal resolution. Several statistical downscaling methods were investigated to provide high spatiotemporal LST maps in urban regions. The results reveal that statistical methods such as Principal Component Analysis (PCA) can provide reliable estimations of LST downscaling with 2K accuracy. Other methods also were tried including aggregating (up-scaling) the high-resolution data to a coarse one to examine the limitations and to build the model. Additionally, we deployed flux towers over distinct materials such as concrete, asphalt, and rooftops in New York City to monitor the sensible and latent heat fluxes through eddy covariance method. To account for the incoming and outgoing radiation, a 4-component radiometer is used that can observe both incoming and outgoing longwave and shortwave radiation. This
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Hydrologic Simulation in Mediterranean flood prone Watersheds using high-resolution quality data
Eirini Vozinaki, Anthi; Alexakis, Dimitrios; Pappa, Polixeni; Tsanis, Ioannis
2015-04-01
Flooding is a significant threat causing lots of inconveniencies in several societies, worldwide. The fact that the climatic change is already happening, increases the flooding risk, which is no longer a substantial menace to several societies and their economies. The improvement of spatial-resolution and accuracy of the topography and land use data due to remote sensing techniques could provide integrated flood inundation simulations. In this work hydrological analysis of several historic flood events in Mediterranean flood prone watersheds (island of Crete/Greece) takes place. Satellite images of high resolution are elaborated. A very high resolution (VHR) digital elevation model (DEM) is produced from a GeoEye-1 0.5-m-resolution satellite stereo pair and is used for floodplain management and mapping applications such as watershed delineation and river cross-section extraction. Sophisticated classification algorithms are implemented for improving Land Use/ Land Cover maps accuracy. In addition, soil maps are updated with means of Radar satellite images. The above high-resolution data are innovatively used to simulate and validate several historical flood events in Mediterranean watersheds, which have experienced severe flooding in the past. The hydrologic/hydraulic models used for flood inundation simulation in this work are HEC-HMS and HEC-RAS. The Natural Resource Conservation Service (NRCS) curve number (CN) approach is implemented to account for the effect of LULC and soil on the hydrologic response of the catchment. The use of high resolution data provides detailed validation results and results of high precision, accordingly. Furthermore, the meteorological forecasting data, which are also combined to the simulation model results, manage the development of an integrated flood forecasting and early warning system tool, which is capable of confronting or even preventing this imminent risk. The research reported in this paper was fully supported by the
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Sreejith, K. M.; Chaubey, A. K.; Mishra, Akhil; Kumar, Shravan; Rajawat, A. S.
2016-12-01
Marine gravity data derived from satellite altimeters are effective tools in mapping fine-scale tectonic features of the ocean basins such as pseudofaults, fracture zones and seamounts, particularly when the ocean basins are carpeted with thick sediments. We use high-resolution satellite-generated gravity and seismic reflection data to map boundaries of pseudofaults and transferred crust related to the Paleocene spreading ridge propagation in the Arabian and its conjugate Eastern Somali basins. The study has provided refinement in the position of previously reported pseudofaults and their spatial extensions in the conjugate basins. It is observed that the transferred crustal block bounded by inner pseudofault and failed spreading ridge is characterized by a gravity low and rugged basement. The refined satellite gravity image of the Arabian Basin also revealed three seamounts in close proximity to the pseudofaults, which were not reported earlier. In the Eastern Somali Basin, seamounts are aligned along NE-SW direction forming ∼300 km long seamount chain. Admittance analysis and Flexural model studies indicated that the seamount chain is isostatically compensated locally with Effective Elastic Thickness (Te) of 3-4 km. Based on the present results and published plate tectonic models, we interpret that the seamounts in the Arabian Basin are formed by spreading ridge propagation and are associated with pseudofaults, whereas the seamount chain in the Eastern Somali Basin might have probably originated due to melting and upwelling of upper mantle heterogeneities in advance of migrating/propagating paleo Carlsberg Ridge.
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Derived crop management data for the LandCarbon Project
Schmidt, Gail; Liu, Shu-Guang; Oeding, Jennifer
2011-01-01
The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop
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Palaseanu, M.; Thatcher, C.; Danielson, J.; Gesch, D. B.; Poppenga, S.; Kottermair, M.; Jalandoni, A.; Carlson, E.
2016-12-01
Coastal topographic and bathymetric (topobathymetric) data with high spatial resolution (1-meter or better) and high vertical accuracy are needed to assess the vulnerability of Pacific Islands to climate change impacts, including sea level rise. According to the Intergovernmental Panel on Climate Change reports, low-lying atolls in the Pacific Ocean are extremely vulnerable to king tide events, storm surge, tsunamis, and sea-level rise. The lack of coastal topobathymetric data has been identified as a critical data gap for climate vulnerability and adaptation efforts in the Republic of the Marshall Islands (RMI). For Majuro Atoll, home to the largest city of RMI, the only elevation dataset currently available is the Shuttle Radar Topography Mission data which has a 30-meter spatial resolution and 16-meter vertical accuracy (expressed as linear error at 90%). To generate high-resolution digital elevation models (DEMs) in the RMI, elevation information and photographic imagery have been collected from field surveys using GNSS/total station and unmanned aerial vehicles for Structure-from-Motion (SfM) point cloud generation. Digital Globe WorldView II imagery was processed to create SfM point clouds to fill in gaps in the point cloud derived from the higher resolution UAS photos. The combined point cloud data is filtered and classified to bare-earth and georeferenced using the GNSS data acquired on roads and along survey transects perpendicular to the coast. A total station was used to collect elevation data under tree canopies where heavy vegetation cover blocked the view of GNSS satellites. A subset of the GPS / total station data was set aside for error assessment of the resulting DEM.
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Drzewiecki, Wojciech; Wężyk, Piotr; Pierzchalski, Marcin; Szafrańska, Beata
2014-06-01
In 2011 the Marshal Office of Małopolska Voivodeship decided to evaluate the vulnerability of soils to water erosion for the entire region. The quantitative and qualitative assessment of the erosion risk for the soils of the Małopolska region was done based on the USLE approach. The special work-flow of geoinformation technologies was used to fulfil this goal. A high-resolution soil map, together with rainfall data, a detailed digital elevation model and statistical information about areas sown with particular crops created the input information for erosion modelling in GIS environment. The satellite remote sensing technology and the object-based image analysis (OBIA) approach gave valuable support to this study. RapidEye satellite images were used to obtain the essential up-to-date data about land use and vegetation cover for the entire region (15,000 km2). The application of OBIA also led to defining the direction of field cultivation and the mapping of contour tillage areas. As a result, the spatially differentiated values of erosion control practice factor were used. Both, the potential and the actual soil erosion risk were assessed quantificatively and qualitatively. The results of the erosion assessment in the Małopolska Voivodeship reveal the fact that a majority of its agricultural lands is characterized by moderate or low erosion risk levels. However, high-resolution erosion risk maps show its substantial spatial diversity. According to our study, average or higher actual erosion intensity levels occur for 10.6 % of agricultural land, i.e. 3.6 % of the entire voivodeship area. In 20 % of the municipalities there is a very urgent demand for erosion control. In the next 23 % an urgent erosion control is needed. Our study showed that even a slight improvement of P-factor estimation may have an influence on modeling results. In our case, despite a marginal change of erosion assessment figures on a regional scale, the influence on the final prioritization of
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A High-Resolution Terrestrial Modeling System (TMS): A Demonstration in China
Duan, Q.; Dai, Y.; Zheng, X.; Ye, A.; Ji, D.; Chen, Z.
2013-12-01
This presentation describes a terrestrial modeling system (TMS) developed at Beijing Normal University. The TMS is designed to be driven by multi-sensor meteorological and land surface observations, including those from satellites and land based observing stations. The purposes of the TMS are (1) to provide a land surface parameterization scheme fully capable of being coupled with the Earth system models; (2) to provide a standalone platform for retrospective historical simulation and for forecasting of future land surface processes at different space and time scales; and (3) to provide a platform for studying human-Earth system interactions and for understanding climate change impacts. This system is built on capabilities among several groups at BNU, including the Common Land Model (CoLM) system, high-resolution atmospheric forcing data sets, high resolution land surface characteristics data sets, data assimilation and uncertainty analysis platforms, ensemble prediction platform, and high-performance computing facilities. This presentation intends to describe the system design and demonstrate the capabilities of TMS with results from a China-wide application.
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de Hoogh, Kees; Gulliver, John; Donkelaar, Aaron van; Martin, Randall V; Marshall, Julian D; Bechle, Matthew J; Cesaroni, Giulia; Pradas, Marta Cirach; Dedele, Audrius; Eeftens, Marloes|info:eu-repo/dai/nl/315028300; Forsberg, Bertil; Galassi, Claudia; Heinrich, Joachim; Hoffmann, Barbara; Jacquemin, Bénédicte; Katsouyanni, Klea; Korek, Michal; Künzli, Nino; Lindley, Sarah J; Lepeule, Johanna; Meleux, Frederik; de Nazelle, Audrey; Nieuwenhuijsen, Mark; Nystad, Wenche; Raaschou-Nielsen, Ole; Peters, Annette; Peuch, Vincent-Henri; Rouil, Laurence; Udvardy, Orsolya; Slama, Rémy; Stempfelet, Morgane; Stephanou, Euripides G; Tsai, Ming Y; Yli-Tuomi, Tarja; Weinmayr, Gudrun; Brunekreef, Bert|info:eu-repo/dai/nl/067548180; Vienneau, Danielle; Hoek, Gerard|info:eu-repo/dai/nl/069553475
2016-01-01
Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models,
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Shin, S.; Pokhrel, Y. N.
2016-12-01
Land surface models have been used to assess water resources sustainability under changing Earth environment and increasing human water needs. Overwhelming observational records indicate that human activities have ubiquitous and pertinent effects on the hydrologic cycle; however, they have been crudely represented in large scale land surface models. In this study, we enhance an integrated continental-scale land hydrology model named Leaf-Hydro-Flood to better represent land-water management. The model is implemented at high resolution (5km grids) over the continental US. Surface water and groundwater are withdrawn based on actual practices. Newly added irrigation, water diversion, and dam operation schemes allow better simulations of stream flows, evapotranspiration, and infiltration. Results of various hydrologic fluxes and stores from two sets of simulation (one with and the other without human activities) are compared over a range of river basin and aquifer scales. The improved simulations of land hydrology have potential to build consistent modeling framework for human-water-climate interactions.
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Land surface skin temperature climatology: benefitting from the strengths of satellite observations
International Nuclear Information System (INIS)
Jin Menglin; Dickinson, Robert E
2010-01-01
Surface skin temperature observations (T skin ), as obtained by satellite remote sensing, provide useful climatological information of high spatial resolution and global coverage that enhances the traditional ground observations of surface air temperature (T air ) and so, reveal new information about land surface characteristics. This letter analyzes nine years of moderate-resolution imaging spectroradiometer (MODIS) skin temperature observations to present monthly skin temperature diurnal, seasonal, and inter-annual variations at a 0.05 deg. latitude/longitude grid over the global land surface and combines these measurements with other MODIS-based variables in an effort to understand the physical mechanisms responsible for T skin variations. In particular, skin temperature variations are found to be closely related to vegetation cover, clouds, and water vapor, but to differ from 2 m surface T air in terms of both physical meaning and magnitude. Therefore, the two temperatures (T skin and T air ) are complementary in their contribution of valuable information to the study of climate change.
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Wessels, R. L.; Griswold, J. P.
2014-12-01
Satellite remote sensing provided timely observations of the volcanic unrest and several months-long eruption at Sinabung Volcano, Indonesia. Visible to thermal optical and synthetic aperture radar (SAR) systems provided frequent observations of Sinabung. High resolution image data with spatial resolutions from 0.5 to 1.5m offered detailed measurements of early summit deformation and subsequent lava dome and lava flow extrusion. The high resolution data were captured by commercial satellites such as WorldView-1 and -2 visible to near-infrared (VNIR) sensors and by CosmoSkyMed, Radarsat-2, and TerraSar-X SAR systems. Less frequent 90 to 100m spatial resolution night time thermal infrared (TIR) observations were provided by ASTER and Landsat-8. The combination of data from multiple sensors allowed us to construct a more complete timeline of volcanic activity than was available via only ground-based observations. This satellite observation timeline documents estimates of lava volume and effusion rates and major explosive and lava collapse events. Frequent, repeat volume estimates suggest at least three high effusion rate pulses of up to 20 m3/s occurred during the first three months of lava effusion with an average effusion rate of 6m3/s from January 2014 to August 2014. Many of these rates and events show some correlation to variations in the Real-time Seismic-Amplitude Measurement (RSAM) documented by the Indonesian Center for Volcanology and Geologic Hazard Mitigation (CVGHM).
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Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia
Directory of Open Access Journals (Sweden)
Hao Guo
2015-06-01
Full Text Available This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA, Climate Prediction Center morphing technique (CMORPH, Global Satellite Mapping of Precipitation (GSMaP and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN. All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB (128.17% while GSMaP_Gauge shows consistent high correlation coefficient (CC (>0.8 but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67. Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%, CSI (less than 45% and relatively high FAR (more than 35%.
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International Nuclear Information System (INIS)
Roupioz, L; Nerry, F; Jia, L; Menenti, M
2014-01-01
The Qinghai-Tibetan Plateau is characterised by a very strong relief which affects albedo retrieval from satellite data. The objective of this study is to highlight the effects of sub-pixel topography and to account for those effects when retrieving land surface albedo from geostationary satellite FengYun-2D (FY-2D) data with 1.25km spatial resolution using the high spatial resolution (30 m) data of the Digital Elevation Model (DEM) from ASTER. The methodology integrates the effects of sub-pixel topography on the estimation of the total irradiance received at the surface, allowing the computation of the topographically corrected surface reflectance. Furthermore, surface albedo is estimated by applying the parametric BRDF (Bidirectional Reflectance Distribution Function) model called RPV (Rahman-Pinty-Verstraete) to the terrain corrected surface reflectance. The results, evaluated against ground measurements collected over several experimental sites on the Qinghai-Tibetan Plateau, document the advantage of integrating the sub-pixel topography effects in the land surface reflectance at 1km resolution to estimate the land surface albedo. The results obtained after using sub-pixel topographic correction are compared with the ones obtained after using pixel level topographic correction. The preliminary results imply that, in highly rugged terrain, the sub-pixel topography correction method gives more accurate results. The pixel level correction tends to overestimate surface albedo
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Zhang, Pei-feng; Hu, Yuan-man; He, Hong-shi
2010-05-01
The demand for accurate and up-to-date spatial information of urban buildings is becoming more and more important for urban planning, environmental protection, and other vocations. Today's commercial high-resolution satellite imagery offers the potential to extract the three-dimensional information of urban buildings. This paper extracted the three-dimensional information of urban buildings from QuickBird imagery, and validated the precision of the extraction based on Barista software. It was shown that the extraction of three-dimensional information of the buildings from high-resolution satellite imagery based on Barista software had the advantages of low professional level demand, powerful universality, simple operation, and high precision. One pixel level of point positioning and height determination accuracy could be achieved if the digital elevation model (DEM) and sensor orientation model had higher precision and the off-Nadir View Angle was relatively perfect.
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Directory of Open Access Journals (Sweden)
A. Bartsch
2012-02-01
Full Text Available Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH4. The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the global scale, but the resolution is rather coarse (>25 km. The purpose of the present study is to assess the capability of active microwave sensors to derive inundation dynamics for use in land surface and climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR operating in C-band since, among microwave systems, it has comparably high spatial resolution and data availability, and long-term continuity is expected.
C-band data from ENVISAT ASAR (Advanced SAR operating in wide swath mode (150 m resolution were investigated and an automated detection procedure for deriving open water fraction has been developed. More than 4000 samples (single acquisitions tiled onto 0.5° grid cells have been analysed for July and August in 2007 and 2008 for a study region in Western Siberia. Simple classification algorithms were applied and found to be robust when the water surface was smooth. Modification of input parameters results in differences below 1 % open water fraction. The major issue to address was the frequent occurrence of waves due to wind and precipitation, which reduces the separability of the water class from other land cover classes. Statistical measures of the backscatter distribution were applied in order to retrieve suitable classification data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution was used. On average only 40 % of acquisitions allow a separation of the open water class. Although satellite data are available every 2–3 days over the Western Siberian -
Alexeeff, Stacey E; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A
2015-01-01
Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R(2) yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R(2) yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.
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Belward, Alan S.; Skøien, Jon O.
2015-05-01
This paper presents a compendium of satellites under civilian and/or commercial control with the potential to gather global land-cover observations. From this we show that a growing number of sovereign states are acquiring capacity for space based land-cover observations and show how geopolitical patterns of ownership are changing. We discuss how the number of satellites flying at any time has progressed as a function of increased launch rates and mission longevity, and how the spatial resolutions of the data they collect has evolved. The first such satellite was launched by the USA in 1972. Since then government and/or private entities in 33 other sovereign states and geopolitical groups have chosen to finance such missions and 197 individual satellites with a global land-cover observing capacity have been successfully launched. Of these 98 were still operating at the end of 2013. Since the 1970s the number of such missions failing within 3 years of launch has dropped from around 60% to less than 20%, the average operational life of a mission has almost tripled, increasing from 3.3 years in the 1970s to 8.6 years (and still lengthening), the average number of satellites launched per-year/per-decade has increased from 2 to 12 and spatial resolution increased from around 80 m to less than 1 m multispectral and less than half a meter for panchromatic; synthetic aperture radar resolution has also fallen, from 25 m in the 1970s to 1 m post 2007. More people in more countries have access to data from global land-cover observing spaceborne missions at a greater range of spatial resolutions than ever before. We provide a compendium of such missions, analyze the changes and shows how innovation, the need for secure data-supply, national pride, falling costs and technological advances may underpin the trends we document.
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Directory of Open Access Journals (Sweden)
TONG Xiaohua
2017-10-01
Full Text Available Satellite jitter is a common and complex phenomenon for the on-orbit high resolution satellites, which may affect the mapping accuracy and quality of imagery. A framework of jitter detection and compensation integrating data processing of multiple sensors is proposed in this paper. Jitter detection is performed based on multispectral imagery, three-line-array imagery, dense ground control and attitude measurement data, and jitter compensation is conducted both on image and on attitude with the sensor model. The platform jitter of ZY-3 satellite is processed and analyzed using the proposed technology, and the results demonstrate the feasibility and reliability of jitter detection and compensation. The variation law analysis of jitter indicates that the frequencies of jitter of ZY-3 satellite hold in the range between 0.6 and 0.7 Hz, while the amplitudes of jitter of ZY-3 satellite drop from 1 pixel in the early stage to below 0.4 pixels and tend to remain stable in the following stage.
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A global, 30-m resolution land-surface water body dataset for 2000
Feng, M.; Sexton, J. O.; Huang, C.; Song, D. X.; Song, X. P.; Channan, S.; Townshend, J. R.
2014-12-01
Inland surface water is essential to terrestrial ecosystems and human civilization. The distribution of surface water in space and its change over time are related to many agricultural, environmental and ecological issues, and are important factors that must be considered in human socioeconomic development. Accurate mapping of surface water is essential for both scientific research and policy-driven applications. Satellite-based remote sensing provides snapshots of Earth's surface and can be used as the main input for water mapping, especially in large areas. Global water areas have been mapped with coarse resolution remotely sensed data (e.g., the Moderate Resolution Imaging Spectroradiometer (MODIS)). However, most inland rivers and water bodies, as well as their changes, are too small to map at such coarse resolutions. Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) imagery has a 30m spatial resolution and provides decades of records (~40 years). Since 2008, the opening of the Landsat archive, coupled with relatively lower costs associated with computing and data storage, has made comprehensive study of the dynamic changes of surface water over large even global areas more feasible. Although Landsat images have been used for regional and even global water mapping, the method can hardly be automated due to the difficulties on distinguishing inland surface water with variant degrees of impurities and mixing of soil background with only Landsat data. The spectral similarities to other land cover types, e.g., shadow and glacier remnants, also cause misidentification. We have developed a probabilistic based automatic approach for mapping inland surface water bodies. Landsat surface reflectance in multiple bands, derived water indices, and data from other sources are integrated to maximize the ability of identifying water without human interference. The approach has been implemented with open-source libraries to facilitate processing large
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Zhang, W.; Yi, Y.; Yang, K.; Kimball, J. S.
2016-12-01
The Tibetan Plateau (TP) is underlain by the world's largest extent of alpine permafrost ( 2.5×106 km2), dominated by sporadic and discontinuous permafrost with strong sensitivity to climate warming. Detailed permafrost distributions and patterns in most of the TP region are still unknown due to extremely sparse in-situ observations in this region characterized by heterogeneous land cover and large temporal dynamics in surface soil moisture conditions. Therefore, satellite-based temperature and moisture observations are essential for high-resolution mapping of permafrost distribution and soil active layer changes in the TP region. In this study, we quantify the TP regional permafrost distribution at 1-km resolution using a detailed satellite data-driven soil thermal process model (GIPL2). The soil thermal model is calibrated and validated using in-situ soil temperature/moisture observations from the CAMP/Tibet field campaign (9 sites: 0-300 cm soil depth sampling from 1997-2007), a multi-scale soil moisture and temperature monitoring network in the central TP (CTP-SMTMN, 57 sites: 5-40 cm, 2010-2014) and across the whole plateau (China Meteorology Administration, 98 sites: 0-320 cm, 2000-2015). Our preliminary results using the CAMP/Tibet and CTP-SMTMN network observations indicate strong controls of surface thermal and soil moisture conditions on soil freeze/thaw dynamics, which vary greatly with underlying topography, soil texture and vegetation cover. For regional mapping of soil freeze/thaw and permafrost dynamics, we use the most recent soil moisture retrievals from the NASA SMAP (Soil Moisture Active Passive) sensor to account for the effects of temporal soil moisture dynamics on soil thermal heat transfer, with surface thermal conditions defined by MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature records. Our study provides the first 1-km map of spatial patterns and recent changes of permafrost conditions in the TP.
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Ambiguity resolution for satellite Doppler positioning systems
Argentiero, P.; Marini, J.
1979-01-01
The implementation of satellite-based Doppler positioning systems frequently requires the recovery of transmitter position from a single pass of Doppler data. The least-squares approach to the problem yields conjugate solutions on either side of the satellite subtrack. It is important to develop a procedure for choosing the proper solution which is correct in a high percentage of cases. A test for ambiguity resolution which is the most powerful in the sense that it maximizes the probability of a correct decision is derived. When systematic error sources are properly included in the least-squares reduction process to yield an optimal solution the test reduces to choosing the solution which provides the smaller valuation of the least-squares loss function. When systematic error sources are ignored in the least-squares reduction, the most powerful test is a quadratic form comparison with the weighting matrix of the quadratic form obtained by computing the pseudoinverse of a reduced-rank square matrix. A formula for computing the power of the most powerful test is provided. Numerical examples are included in which the power of the test is computed for situations that are relevant to the design of a satellite-aided search and rescue system.
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Ribeiro, F.; Roberts, D. A.; Davis, F. W.; Antunes Daldegan, G.; Nackoney, J.; Hess, L. L.
2016-12-01
The Brazilian savanna, Cerrado, is the second largest biome over South America and the most floristically diverse savanna in the world. This biome is considered a conservation hotspot in respect to its biodiversity importance and rapid transformation of its landscape. The Cerrado's natural vegetation has been severely transformed by agriculture and pasture activities. Currently it is the main agricultural frontier in Brazil and one of the most threatened Brazilian biomes. This scenario results in environmental impacts such as ecosystems fragmentation as well as losses in connectivity, biodiversity and gene flow, changes in the microclimate and energy, carbon and nutrients cycles, among others. The Priority Areas for Conservation is a governmental program from Brazil that identifies areas with high conservation priority. One of this program's recommendation is a natural vegetation map including their major ecosystem classes. This study aims to generate more precise information for the Cerrado's vegetation. The main objective of this study is to identify which ecosystems are being prioritized and/or threatened by land use, refining information for further protection. In order to test methods, the priority area for conservation Chapada da Contagem was selected as the study site. This area is ranked as "extremely high priority" by the government and is located in the Federal District and Goias State, Brazil. Satellites with finer spatial resolution may improve the classification of the Cerrado's vegetation. Remote sensing methods and two criteria were tested using RapidEye 3A imagery (5m spatial resolution) collected in 2014 in order to classify the Cerrado's major land cover types of this area, as well as its land use. One criterion considers the Cerrado's major terrestrial ecosystems, which are divided into forest, savanna and grassland. The other involves scaling it down to the major physiognomic groups of each ecosystem. Other sources of environmental dataset such
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Advancing land surface model development with satellite-based Earth observations
Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo
2017-04-01
The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628
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McCabe, M.; Rosas Aguilar, J.; Parkes, S. D.; Aragon, B.
2017-12-01
Observation of land surface temperature (LST) has many practical uses, from studying boundary layer dynamics and land-atmosphere coupling, to investigating surface properties such as soil moisture status, heat stress and surface heat fluxes. Typically, LST is observed via satellite based sensors such as LandSat or via point measurements using IR radiometers. These measurements provide either good spatial coverage and resolution or good temporal coverage. However, neither are able to provide the needed spatial and temporal resolution for many of the research applications described above. Technological developments in the use of Unmanned Aerial Vehicles (UAVs), together with small thermal frame cameras, has enabled a capacity to overcome this spatiotemporal constraint. Utilising UAV platforms to collect LST measurements across diurnal cycles provides an opportunity to study how meteorological and surface properties vary in both space and time. Here we describe the collection of LST data from a multi-rotor UAV across a study domain that is observed multiple times throughout the day. Flights over crops of Rhodes grass and alfalfa, along with a bare desert surface, were repeated with between 8 and 11 surveys covering the period from early morning to sunset. Analysis of the collected thermal imagery shows that the constructed LST maps illustrate a strong diurnal cycle consistent with expected trends, but with considerable spatial and temporal variability observed within and between the different domains. These results offer new insights into the dynamics of land surface behavior in both dry and wet soil conditions and at spatiotemporal scales that are unable to be replicated using traditional satellite platforms.
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A NEW HIGH-RESOLUTION ELEVATION MODEL OF GREENLAND DERIVED FROM TANDEM-X
Directory of Open Access Journals (Sweden)
B. Wessel
2016-06-01
Full Text Available In this paper we present for the first time the new digital elevation model (DEM for Greenland produced by the TanDEM-X (TerraSAR add-on for digital elevation measurement mission. The new, full coverage DEM of Greenland has a resolution of 0.4 arc seconds corresponding to 12 m. It is composed of more than 7.000 interferometric synthetic aperture radar (InSAR DEM scenes. X-Band SAR penetrates the snow and ice pack by several meters depending on the structures within the snow, the acquisition parameters, and the dielectricity constant of the medium. Hence, the resulting SAR measurements do not represent the surface but the elevation of the mean phase center of the backscattered signal. Special adaptations on the nominal TanDEM-X DEM generation are conducted to maintain these characteristics and not to raise or even deform the DEM to surface reference data. For the block adjustment, only on the outer coastal regions ICESat (Ice, Cloud, and land Elevation Satellite elevations as ground control points (GCPs are used where mostly rock and surface scattering predominates. Comparisons with ICESat data and snow facies are performed. In the inner ice and snow pack, the final X-Band InSAR DEM of Greenland lies up to 10 m below the ICESat measurements. At the outer coastal regions it corresponds well with the GCPs. The resulting DEM is outstanding due to its resolution, accuracy and full coverage. It provides a high resolution dataset as basis for research on climate change in the arctic.
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International Nuclear Information System (INIS)
Lin, D H; Johnson, D R; Tweedie, C E; Andresen, C
2012-01-01
Analysis of time series imagery from satellite and aircraft platforms is useful for detecting land cover change at plot to regional scales. In this study, we created multi-temporal high spatial resolution land cover maps for seven locations in the Beringian Arctic and assessed the change in land cover over time. Land cover classifications were site specific and mostly aligned with a soil moisture gradient. Time series varied between 60 and 21 years. Four of the five landscapes studied in Alaska underwent an expansion of drier land cover classes while the two landscapes studies in Chukotka, Russia showed an expansion of wetter land cover types. While a range of land cover types was present across the landscapes studied, the extent of shrubs (in Chukotka) and open water (in Alaska) increased in all landscapes where these land cover types were present. The results support trends documented for regional change in NDVI (a measure of vegetation greenness and productivity) as well as a host of other long term, experimental and modeling studies. Using historic change trends for each land cover type at each landscape, we use a simple probabilistic vegetation model to establish hypotheses of future change trajectories for different land cover types at each of the landscapes investigated. This study is a contribution to the International Polar Year Back to the Future project (IPY-BTF). (letter)
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Just, Allan C; Wright, Robert O; Schwartz, Joel; Coull, Brent A; Baccarelli, Andrea A; Tellez-Rojo, Martha María; Moody, Emily; Wang, Yujie; Lyapustin, Alexei; Kloog, Itai
2015-07-21
Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most U.S. and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004-2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross-validation R(2) of 0.724. Cross-validated root-mean-squared prediction error (RMSPE) of the model was 5.55 μg/m(3). This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City.
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Vahmani, P.; Hogue, T. S.
2014-12-01
In the current work we investigate the utility of remote-sensing-based surface parameters in the Noah UCM (urban canopy model) over a highly developed urban area. Landsat and fused Landsat-MODIS data are utilized to generate high-resolution (30 m) monthly spatial maps of green vegetation fraction (GVF), impervious surface area (ISA), albedo, leaf area index (LAI), and emissivity in the Los Angeles metropolitan area. The gridded remotely sensed parameter data sets are directly substituted for the land-use/lookup-table-based values in the Noah-UCM modeling framework. Model performance in reproducing ET (evapotranspiration) and LST (land surface temperature) fields is evaluated utilizing Landsat-based LST and ET estimates from CIMIS (California Irrigation Management Information System) stations as well as in situ measurements. Our assessment shows that the large deviations between the spatial distributions and seasonal fluctuations of the default and measured parameter sets lead to significant errors in the model predictions of monthly ET fields (RMSE = 22.06 mm month-1). Results indicate that implemented satellite-derived parameter maps, particularly GVF, enhance the capability of the Noah UCM to reproduce observed ET patterns over vegetated areas in the urban domains (RMSE = 11.77 mm month-1). GVF plays the most significant role in reproducing the observed ET fields, likely due to the interaction with other parameters in the model. Our analysis also shows that remotely sensed GVF and ISA improve the model's capability to predict the LST differences between fully vegetated pixels and highly developed areas.
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Sure, A.; Dikshit, O.
2017-12-01
Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.
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Directory of Open Access Journals (Sweden)
K. Leempoel
2013-08-01
Full Text Available Mangrove forests are declining across the globe, mainly because of human intervention, and therefore require an evaluation of their past and present status (e.g. areal extent, species-level distribution, etc. to implement better conservation and management strategies. In this paper, mangrove cover dynamics at Gaoqiao (P. R. China were assessed through time using 1967, 2000 and 2009 satellite imagery (sensors Corona KH-4B, Landsat ETM+, GeoEye-1 respectively. Firstly, multi-temporal analysis of satellite data was undertaken, and secondly biotic and abiotic differences were analysed between the different mangrove stands, assessed through a supervised classification of a high-resolution satellite image. A major decline in mangrove cover (−36% was observed between 1967 and 2009 due to rice cultivation and aquaculture practices. Moreover, dike construction has prevented mangroves from expanding landward. Although a small increase of mangrove area was observed between 2000 and 2009 (+24%, the ratio mangrove / aquaculture kept decreasing due to increased aquaculture at the expense of rice cultivation in the vicinity. From the land-use/cover map based on ground-truth data (5 × 5 m plot-based tree measurements (August–September, 2009 as well as spectral reflectance values (obtained from pansharpened GeoEye-1, both Bruguiera gymnorrhiza and small Aegiceras corniculatum are distinguishable at 73–100% accuracy, whereas tall A. corniculatum was correctly classified at only 53% due to its mixed vegetation stands with B. gymnorrhiza (overall classification accuracy: 85%. In the case of sediments, sand proportion was significantly different between the three mangrove classes. Overall, the advantage of very high resolution satellite images like GeoEye-1 (0.5 m for mangrove spatial heterogeneity assessment and/or species-level discrimination was well demonstrated, along with the complexity to provide a precise classification for non-dominant species (e
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Directory of Open Access Journals (Sweden)
CAO Yungang
2016-10-01
Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.
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Pilant, A. N.; Baynes, J.; Dannenberg, M.
2012-12-01
The US EPA National Atlas for Sustainability is a web-based, easy-to-use, mapping application that allows users to view and analyze multiple ecosystem services in a specific region. The Atlas provides users with a visual method for interpreting ecosystem services and understanding how they can be conserved and enhanced for a sustainable future. The Urban Atlas component of the National Atlas will provide fine-scale information linking human health and well-being to environmental conditions such as urban heat islands, near-road pollution, resource use, access to recreation, drinking water quality and other quality of life indicators. The National Land Cover Data (NLCD) derived from 30 m scale 2006 Landsat imagery provides the land cover base for the Atlas. However, urban features and phenomena occur at finer spatial scales, so higher spatial resolution and more current LC maps are required. We used 4 band USDA NAIP imagery (1 m pixel size) and various classification approaches to produce urban land cover maps with these classes: impervious surface, grass and herbaceous, trees and forest, soil and barren, and water. Here we present the remote sensing methods used and results from four pilot cities in this effort, highlighting the pros and cons of the approach, and the benefits to sustainability and ecosystem services analysis. Example of high resolution land cover map derived from USDA NAIP aerial photo. Compare 30 m and 1 m resolution land cover maps of downtown Durham, NC.
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DEFF Research Database (Denmark)
Fensholt, Rasmus; Anyamba, Assaf; Huber Gharib, Silvia
2011-01-01
Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth’s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often...... is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI...... affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher...
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Monitoring Termite-Mediated Ecosystem Processes Using Moderate and High Resolution Satellite Imagery
Lind, B. M.; Hanan, N. P.
2016-12-01
Termites are considered dominant decomposers and prominent ecosystem engineers in the global tropics and they build some of the largest and architecturally most complex non-human-made structures in the world. Termite mounds significantly alter soil texture, structure, and nutrients, and have major implications for local hydrological dynamics, vegetation characteristics, and biological diversity. An understanding of how these processes change across large scales has been limited by our ability to detect termite mounds at high spatial resolutions. Our research develops methods to detect large termite mounds in savannas across extensive geographic areas using moderate and high resolution satellite imagery. We also investigate the effect of termite mounds on vegetation productivity using Landsat-8 maximum composite NDVI data as a proxy for production. Large termite mounds in arid and semi-arid Senegal generate highly reflective `mound scars' with diameters ranging from 10 m at minimum to greater than 30 m. As Sentinel-2 has several bands with 10 m resolution and Landsat-8 has improved calibration, higher radiometric resolution, 15 m spatial resolution (pansharpened), and improved contrast between vegetated and bare surfaces compared to previous Landsat missions, we found that the largest and most influential mounds in the landscape can be detected. Because mounds as small as 4 m in diameter are easily detected in high resolution imagery we used these data to validate detection results and quantify omission errors for smaller mounds.
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Ganguly, Sangram; Kalia, Subodh; Li, Shuang; Michaelis, Andrew; Nemani, Ramakrishna R.; Saatchi, Sassan A
2017-01-01
Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.
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Ganguly, S.; Kalia, S.; Li, S.; Michaelis, A.; Nemani, R. R.; Saatchi, S.
2017-12-01
Uncertainties in input land cover estimates contribute to a significant bias in modeled above gound biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition/ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree/non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial/satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.
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Directory of Open Access Journals (Sweden)
Chunzeng Wang
2015-03-01
Full Text Available High-precision land-cover-land-use GIS mapping was performed in four major townships in Maine’s Aroostook River Valley, using on-screen digitization and direct interpretation of very high spatial resolution satellite multispectral imagery (15–60 cm and high spatial resolution LiDAR data (2 m and the field mapping method. The project not only provides the first-ever high-precision land-use maps for northern Maine, but it also yields accurate hectarage estimates of different land-use types, in particular grassland, defined as fallow land, pasture, and hay field. This enables analysis of potential land availability and suitability for grass biomass production and other sustainable land uses. The results show that the total area of fallow land in the four towns is 7594 hectares, which accounts for 25% of total open land, and that fallow plots equal to or over four hectares in size total 4870, or 16% of open land. Union overlay analysis, using the Natural Resources Conservation Service (NRCS soil data, indicates that only a very small percentage of grassland (4.9% is on “poorly-drained” or “very-poorly-drained” soils, and that most grassland (85% falls into the “farmland of state importance” or “prime farmland” categories, as determined by NRCS. It is concluded that Maine’s Aroostook River Valley has an ample base of suitable, underutilized land for producing grass biomass.
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Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992
Qi, Sharon L.; Konduris, Alexandria; Litke, David W.; Dupree, Jean
2002-01-01
Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land overlying the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a water-quality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated-land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres).
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Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
Directory of Open Access Journals (Sweden)
Oscar Rojas
2013-04-01
Full Text Available Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.
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Harmonizing estimates of forest land area from national-level forest inventory and satellite imagery
Bonnie Ruefenacht; Mark D. Nelson; Mark Finco
2009-01-01
Estimates of forest land area are derived both from national-level forest inventories and satellite image-based map products. These estimates can differ substantially within subregional extents (e.g., states or provinces) primarily due to differences in definitions of forest land between inventory- and image-based approaches. We present a geospatial modeling approach...
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Global land cover mapping at 30 m resolution: A POK-based operational approach
Chen, Jun; Chen, Jin; Liao, Anping; Cao, Xin; Chen, Lijun; Chen, Xuehong; He, Chaoying; Han, Gang; Peng, Shu; Lu, Miao; Zhang, Weiwei; Tong, Xiaohua; Mills, Jon
2015-05-01
Global Land Cover (GLC) information is fundamental for environmental change studies, land resource management, sustainable development, and many other societal benefits. Although GLC data exists at spatial resolutions of 300 m and 1000 m, a 30 m resolution mapping approach is now a feasible option for the next generation of GLC products. Since most significant human impacts on the land system can be captured at this scale, a number of researchers are focusing on such products. This paper reports the operational approach used in such a project, which aims to deliver reliable data products. Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed. To handle the classification process of 10 land cover types, a split-and-merge strategy was employed, i.e. firstly each class identified in a prioritized sequence and then results are merged together. For the identification of each class, a robust integration of pixel-and object-based classification was developed. To improve the quality of the classification results, a knowledge-based interactive verification procedure was developed with the support of web service technology. The performance of the POK-based approach was tested using eight selected areas with differing landscapes from five different continents. An overall classification accuracy of over 80% was achieved. This indicates that the developed POK-based approach is effective and feasible
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The Generalized Gamma-DBN for High-Resolution SAR Image Classification
Directory of Open Access Journals (Sweden)
Zhiqiang Zhao
2018-06-01
Full Text Available With the increase of resolution, effective characterization of synthetic aperture radar (SAR image becomes one of the most critical problems in many earth observation applications. Inspired by deep learning and probability mixture models, a generalized Gamma deep belief network (g Γ-DBN is proposed for SAR image statistical modeling and land-cover classification in this work. Specifically, a generalized Gamma-Bernoulli restricted Boltzmann machine (gΓB-RBM is proposed to capture high-order statistical characterizes from SAR images after introducing the generalized Gamma distribution. After stacking the g Γ B-RBM and several standard binary RBMs in a hierarchical manner, a gΓ-DBN is constructed to learn high-level representation of different SAR land-covers. Finally, a discriminative neural network is constructed by adding an additional predict layer for different land-covers over the constructed deep structure. Performance of the proposed approach is evaluated via several experiments on some high-resolution SAR image patch sets and two large-scale scenes which are captured by ALOS PALSAR-2 and COSMO-SkyMed satellites respectively.
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Land mobile satellite services in Europe
Bartholomé, P.; Berretta, G.; Rogard, R.
The demand for land-mobile communications on a Europe-wide basis is an important and pressing problem. The pan-European cellular network now in the planning stage will be slow in coming and it will have its limitations. A regional satellite system for Europe to complement the cellular network is the only practical way to satisfy a specialised market that encompasses a population of several hundred thousand mobiles, including road vehicles, merchant shipping, fishing boats, and trains. The deployment of a regional system would take place in a number of phases, the first being based on a simple payload embarked on a host satellite belonging to a European organisation. Further phases will involve the development of more advanced payloads on dedicated satellites. For the long-term future, the use of satellites in highly inclined orbits is being considered as a means of improving their visibility and hence the service quality.
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A fast and automatic mosaic method for high-resolution satellite images
Chen, Hongshun; He, Hui; Xiao, Hongyu; Huang, Jing
2015-12-01
We proposed a fast and fully automatic mosaic method for high-resolution satellite images. First, the overlapped rectangle is computed according to geographical locations of the reference and mosaic images and feature points on both the reference and mosaic images are extracted by a scale-invariant feature transform (SIFT) algorithm only from the overlapped region. Then, the RANSAC method is used to match feature points of both images. Finally, the two images are fused into a seamlessly panoramic image by the simple linear weighted fusion method or other method. The proposed method is implemented in C++ language based on OpenCV and GDAL, and tested by Worldview-2 multispectral images with a spatial resolution of 2 meters. Results show that the proposed method can detect feature points efficiently and mosaic images automatically.
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Coastal Change Analysis Program (C-CAP) High Resolution Land Cover and Change Data
National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized high resolution land cover and change products for the coastal regions of the U.S....
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Hall-Brown, Mary
The heterogeneity of Arctic vegetation can make land cover classification vey difficult when using medium to small resolution imagery (Schneider et al., 2009; Muller et al., 1999). Using high radiometric and spatial resolution imagery, such as the SPOT 5 and IKONOS satellites, have helped arctic land cover classification accuracies rise into the 80 and 90 percentiles (Allard, 2003; Stine et al., 2010; Muller et al., 1999). However, those increases usually come at a high price. High resolution imagery is very expensive and can often add tens of thousands of dollars onto the cost of the research. The EO-1 satellite launched in 2002 carries two sensors that have high specral and/or high spatial resolutions and can be an acceptable compromise between the resolution versus cost issues. The Hyperion is a hyperspectral sensor with the capability of collecting 242 spectral bands of information. The Advanced Land Imager (ALI) is an advanced multispectral sensor whose spatial resolution can be sharpened to 10 meters. This dissertation compares the accuracies of arctic land cover classifications produced by the Hyperion and ALI sensors to the classification accuracies produced by the Systeme Pour l' Observation de le Terre (SPOT), the Landsat Thematic Mapper (TM) and the Landsat Enhanced Thematic Mapper Plus (ETM+) sensors. Hyperion and ALI images from August 2004 were collected over the Upper Kuparuk River Basin, Alaska. Image processing included the stepwise discriminant analysis of pixels that were positively classified from coinciding ground control points, geometric and radiometric correction, and principle component analysis. Finally, stratified random sampling was used to perform accuracy assessments on satellite derived land cover classifications. Accuracy was estimated from an error matrix (confusion matrix) that provided the overall, producer's and user's accuracies. This research found that while the Hyperion sensor produced classfication accuracies that were
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Verdin, Andrew; Funk, Christopher C.; Rajagopalan, Balaji; Kleiber, William
2016-01-01
Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.
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Rosas, Jorge
2014-12-01
Satellite remote sensing provides data on land surface characteristics, useful for mapping land surface energy fluxes and evapotranspiration (ET). Land-surface temperature (LST) derived from thermal infrared (TIR) satellite data has been reliably used as a remote indicator of ET and surface moisture status. However, TIR imagery usually operates at a coarser resolution than that of shortwave sensors on the same satellite platform, making it sometimes unsuitable for monitoring of field-scale crop conditions. This study applies the data mining sharpener (DMS; Gao et al., 2012) technique to data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which sharpens the 1 km thermal data down to the resolution of the optical data (250-500 m) based on functional LST and reflectance relationships established using a flexible regression tree approach. The DMS approach adopted here has been enhanced/refined for application over irrigated farming areas located in harsh desert environments in Saudi Arabia. The sharpened LST data is input to an integrated modeling system that uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI) in conjunction with model reanalysis data and remotely sensed data from polar orbiting (MODIS) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate daily estimates of evapotranspiration. Results are evaluated against available flux tower observations over irrigated maize near Riyadh in Saudi Arabia. Successful monitoring of field-scale changes in surface fluxes are of importance towards an efficient water use in areas where fresh water resources are scarce and poorly monitored. Gao, F.; Kustas, W.P.; Anderson, M.C. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sens. 2012, 4, 3287-3319.
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Sicard, Pierre; Martin-lauzer, François-regis
2017-04-01
In the context of global climate change and adjustment/resilience policies' design and implementation, there is a need not only i. for environmental monitoring, e.g. through a range of Earth Observations (EO) land "products" but ii. for a precise assessment of uncertainties of the aforesaid information that feed environmental decision-making (to be introduced in the EO metadata) and also iii. for a perfect handing of the thresholds which help translate "environment tolerance limits" to match detected EO changes through ecosystem modelling. Uncertainties' insight means precision and accuracy's knowledge and subsequent ability of setting thresholds for change detection systems. Traditionally, the validation of satellite-derived products has taken the form of intensive field campaigns to sanction the introduction of data processors in Payload Data Ground Segments chains. It is marred by logistical challenges and cost issues, reason why it is complemented by specific surveys at ground-based monitoring sites which can provide near-continuous observations at a high temporal resolution (e.g. RadCalNet). Unfortunately, most of the ground-level monitoring sites, in the number of 100th or 1000th, which are part of wider observation networks (e.g. FLUXNET, NEON, IMAGINES) mainly monitor the state of the atmosphere and the radiation exchange at the surface, which are different to the products derived from EO data. In addition they are "point-based" compared to the EO cover to be obtained from Sentinel-2 or Sentinel-3. Yet, data from these networks, processed by spatial extrapolation models, are well-suited to the bottom-up approach and relevant to the validation of vegetation parameters' consistency (e.g. leaf area index, fraction of absorbed photosynthetically active radiation). Consistency means minimal errors on spatial and temporal gradients of EO products. Test of the procedure for land-cover products' consistency assessment with field measurements delivered by worldwide
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International Nuclear Information System (INIS)
Gillet, N.; Ocvirk, P.; Aubert, D.; Knebe, A.; Yepes, G.; Libeskind, N.; Gottlöber, S.; Hoffman, Y.
2015-01-01
We search for vast planes of satellites (VPoS) in a high-resolution simulation of the Local Group performed by the CLUES project, which improves significantly the resolution of previous similar studies. We use a simple method for detecting planar configurations of satellites, and validate it on the known plane of M31. We implement a range of prescriptions for modeling the satellite populations, roughly reproducing the variety of recipes used in the literature, and investigate the occurrence and properties of planar structures in these populations. The structure of the simulated satellite systems is strongly non-random and contains planes of satellites, predominantly co-rotating, with, in some cases, sizes comparable to the plane observed in M31 by Ibata et al. However, the latter is slightly richer in satellites, slightly thinner, and has stronger co-rotation, which makes it stand out as overall more exceptional than the simulated planes, when compared to a random population. Although the simulated planes we find are generally dominated by one real structure forming its backbone, they are also partly fortuitous and are thus not kinematically coherent structures as a whole. Provided that the simulated and observed planes of satellites are indeed of the same nature, our results suggest that the VPoS of M31 is not a coherent disk and that one-third to one-half of its satellites must have large proper motions perpendicular to the plane
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Eugenio, F.; Martin, J.; Marcello, J.; Fraile-Nuez, E.
2014-06-01
El Hierro Island, located at the Canary Islands Archipelago in the Atlantic coast of North Africa, has been rocked by thousands of tremors and earthquakes since July 2011. Finally, an underwater volcanic eruption started 300 m below sea level on October 10, 2011. Since then, regular multidisciplinary monitoring has been carried out in order to quantify the environmental impacts caused by the submarine eruption. Thanks to this natural tracer release, multisensorial satellite imagery obtained from MODIS and MERIS sensors have been processed to monitor the volcano activity and to provide information on the concentration of biological, chemical and physical marine parameters. Specifically, low resolution satellite estimations of optimal diffuse attenuation coefficient (Kd) and chlorophyll-a (Chl-a) concentration under these abnormal conditions have been assessed. These remote sensing data have played a fundamental role during field campaigns guiding the oceanographic vessel to the appropriate sampling areas. In addition, to analyze El Hierro submarine volcano area, WorldView-2 high resolution satellite spectral bands were atmospherically and deglinted processed prior to obtain a high-resolution optimal diffuse attenuation coefficient model. This novel algorithm was developed using a matchup data set with MERIS and MODIS data, in situ transmittances measurements and a seawater radiative transfer model. Multisensor and multitemporal imagery processed from satellite remote sensing sensors have demonstrated to be a powerful tool for monitoring the submarine volcanic activities, such as discolored seawater, floating material and volcanic plume, having shown the capabilities to improve the understanding of submarine volcanic processes.
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Chandi Witharana; Heather J. Lynch
2016-01-01
The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowle...
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Advancing land surface model development with satellite-based Earth observations
Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo
2017-05-01
The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts, we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability, and understanding of climate system feedbacks.
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Modeling directional effects in land surface temperature derived from geostationary satellite data
DEFF Research Database (Denmark)
Rasmussen, Mads Olander
This PhD-thesis investigates the directional effects in land surface temperature (LST) estimates from the SEVIRI sensor onboard the Meteosat Second Generation (MSG) satellites. The directional effects are caused by the land surface structure (i.e. tree size and shape) interacting with the changing...... sun-target-sensor geometry. The directional effects occur because the different surface components, e.g. tree canopies and bare soil surfaces, will in many cases have significantly different temperatures. Depending on the viewing angle, different fractions of each of the components will be viewed...... by the sensor. This is further complicated by temperature differences between the sunlit and shaded parts of each of the components, controlled by the exposure of the components to direct sunlight. As the SEVIRI sensor is onboard a geostationary platform, the viewing geometry is fixed (for each pixel), while...
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Lai, Jiameng; Zhan, Wenfeng; Huang, Fan; Quan, Jinling; Hu, Leiqiu; Gao, Lun; Ju, Weimin
2018-05-01
The temporally regular and spatially comprehensive monitoring of surface urban heat islands (SUHIs) have been extremely difficult, until the advent of satellite-based land surface temperature (LST) products. However, these LST products have relatively higher errors compared to in situ measurements. This has resulted in comparatively inaccurate estimations of SUHI indicators and, consequently, may have distorted interpretations of SUHIs. Although reports have shown that LST qualities are important for SUHI interpretations, systematic investigations of the response of SUHI indicators to LST qualities across cities with dissimilar bioclimates are rare. To address this issue, we chose eighty-six major cities across mainland China and analyzed SUHI intensity (SUHII) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The LST-based SUHII differences due to inclusion or exclusion of MODIS quality control (QC) flags (i.e., ΔSUHII) were evaluated. Our major findings included, but are not limited to, the following four aspects: (1) SUHIIs can be significantly impacted by MODIS QC flags, and the associated QC-induced ΔSUHIIs generally accounted for 24.3% (29.9%) of the total SUHII value during the day (night); (2) the ΔSUHIIs differed between seasons, with considerable differences between transitional (spring and autumn) and extreme (summer and winter) seasons; (3) significant discrepancies also appeared among cities located in northern and southern regions, with northern cities often possessing higher annual mean ΔSUHIIs. The internal variations of ΔSUHIIs within individual cities also showed high heterogeneity, with ΔSUHII variations that generally exceeded 5.0 K (3.0 K) in northern (southern) cities; (4) ΔSUHIIs were negatively related to SUHIIs and cloud cover percentages (mostly in transitional seasons). No significant relationship was found in the extreme seasons. Our findings highlight the need to be extremely cautious when using LST
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International Nuclear Information System (INIS)
Yang Xu; Liu Dechang; Zhang Jielin
2008-01-01
In this study, the signification and content of prospecting information of uranium deposit are expounded. Quickbird high spatial resolution satellite data are used to extract the prospecting information of uranium deposit in Bashibulake area in the north of Tarim Basin. By using the pertinent methods of image processing, the information of ore-bearing bed, ore-control structure and mineralized alteration have been extracted. The results show a high consistency with the field survey. The aim of this study is to explore practicability of high spatial resolution satellite data for prospecting minerals, and to broaden the thinking of prospectation at similar area. (authors)
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Vahmani, P.; Hogue, T. S.
2014-07-01
In the current work we investigate the utility of remote sensing based surface parameters in the Noah-UCM (urban canopy model) over a highly developed urban area. Landsat and fused Landsat-MODIS data are utilized to generate high resolution (30 m) monthly spatial maps of green vegetation fraction (GVF), impervious surface area (ISA), albedo, leaf area index (LAI), and emissivity in the Los Angeles metropolitan area. The gridded remotely sensed parameter datasets are directly substituted for the land-use/lookup-table values in the Noah-UCM modeling framework. Model performance in reproducing ET (evapotranspiration) and LST (land surface temperature) fields is evaluated utilizing Landsat-based LST and ET estimates from CIMIS (California Irrigation Management Information System) stations as well as in-situ measurements. Our assessment shows that the large deviations between the spatial distributions and seasonal fluctuations of the default and measured parameter sets lead to significant errors in the model predictions of monthly ET fields (RMSE = 22.06 mm month-1). Results indicate that implemented satellite derived parameter maps, particularly GVF, enhance the Noah-UCM capability to reproduce observed ET patterns over vegetated areas in the urban domains (RMSE = 11.77 mm month-1). GVF plays the most significant role in reproducing the observed ET fields, likely due to the interaction with other parameters in the model. Our analysis also shows that remotely sensed GVF and ISA improve the model capability to predict the LST differences between fully vegetated pixels and highly developed areas. However, the model still underestimates remotely sensed LST values over highly developed areas. We hypothesize that the LST underestimation is due to structural formulation in the UCM and cannot be immediately solved with available parameter choices.
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Leprince, S.; Lin, J.; Ayoub, F.; Herman, F.; Avouac, J.
2013-12-01
We present the latest capabilities added to the Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) software, which aim at analyzing time-series of stereoscopic imagery to document 3D variations of the ground surface. We review the processing chain and present the new and improved modules for satellite pushbroom imagery, in particular the N-image bundle block adjustment to jointly optimize the viewing geometry of multiple acquisitions, the improved multi-scale image matching based on Semi-Global Matching (SGM) to extract high resolution topography, and the triangulation of multi-temporal disparity maps to derive 3D ground motion. In particular, processes are optimized to run on a cluster computing environment. This new suite of algorithms is applied to the study of Worldview stereo imagery above the Franz Josef, Fox, and Tasman Glaciers, New Zealand, acquired on 01/30/2013, 02/09/2013, and 02/28/2013. We derive high resolution (1m post-spacing) maps of ice flow in three dimensions, where ice velocities of up to 4 m/day are recorded. Images were collected in early summer during a dry and sunny period, which followed two weeks of unsettled weather with several heavy rainfall events across the Southern Alps. The 3D tracking of ice flow highlights the surface response of the glaciers to changes in effective pressure at the ice-bedrock interface due to heavy rainfall, at an unprecedented spatial resolution.
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Directory of Open Access Journals (Sweden)
Joseph St. Peter
2018-03-01
Full Text Available Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1 m resolution across roughly 11.6 million hectares (28.8 million acres with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery.
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High resolution mapping of urban areas using SPOT-5 images and ancillary data
Directory of Open Access Journals (Sweden)
Elif Sertel
2015-08-01
Full Text Available This research aims to propose new rule sets to be used for object based classification of SPOT-5 images to accurately create detailed urban land cover/use maps. In addition to SPOT-5 satellite images, Normalized Difference Vegetation Index (NDVI and Normalized Difference Water Index (NDWI maps, cadastral maps, Openstreet maps, road maps and Land Cover maps, were also integrated into classification to increase the accuracy of resulting maps. Gaziantep city, one of the highly populated cities of Turkey with different landscape patterns was selected as the study area. Different rule sets involving spectral, spatial and geometric characteristics were developed to be used for object based classification of 2.5 m resolution Spot-5 satellite images to automatically create urban map of the region. Twenty different land cover/use classes obtained from European Urban Atlas project were applied and an automatic classification approach was suggested for high resolution urban map creation and updating. Integration of different types of data into the classification decision tree increased the performance and accuracy of the suggested approach. The accuracy assessment results illustrated that with the usage of newly proposed rule set algorithms in object-based classification, urban areas represented with seventeen different sub-classes could be mapped with 94 % or higher overall accuracy.
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Otto, M.; Scherer, D.; Richters, J.
2011-05-01
High Altitude Wetlands of the Andes (HAWA) belong to a unique type of wetland within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand, HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV) indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 12 800 km2 situated in the Northwest of Lake Titicaca. The multi-temporal classification method used Normalized Differenced Vegetation Index (NDVI) and Normalized Differenced Infrared Index (NDII) data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000) and at the end of austral summer (May 2001). The mapping result indicates an unexpected high abundance of HAWA covering about 800 km2 of the study region (6 %). Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS). Analyses on the relation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43) and MODIS Eight Day Maximum Snow Extent data (MOD10A2) from 2000 to 2010. We found HAWA subtype specific dependencies on precipitation conditions. A strong relation exists between perennial HAWA and snow fall (r2: 0.82) in dry austral winter months (June to August) and between temporal HAWA and precipitation (r2: 0.75) during austral summer (March to May). Annual changes in spatial extend of perennial HAWA
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Kimm, H.; Guan, K.; Luo, Y.; Peng, J.; Mascaro, J.; Peng, B.
2017-12-01
Monitoring crop growth conditions is of primary interest to crop yield forecasting, food production assessment, and risk management of individual farmers and agribusiness. Despite its importance, there are limited access to field level crop growth/condition information in the public domain. This scarcity of ground truth data also hampers the use of satellite remote sensing for crop monitoring due to the lack of validation. Here, we introduce a new camera network (CropInsight) to monitor crop phenology, growth, and conditions that are designed for the US Corn Belt landscape. Specifically, this network currently includes 40 sites (20 corn and 20 soybean fields) across southern half of the Champaign County, IL ( 800 km2). Its wide distribution and automatic operation enable the network to capture spatiotemporal variations of crop growth condition continuously at the regional scale. At each site, low-maintenance, and high-resolution RGB digital cameras are set up having a downward view from 4.5 m height to take continuous images. In this study, we will use these images and novel satellite data to construct daily LAI map of the Champaign County at 30 m spatial resolution. First, we will estimate LAI from the camera images and evaluate it using the LAI data collected from LAI-2200 (LI-COR, Lincoln, NE). Second, we will develop relationships between the camera-based LAI estimation and vegetation indices derived from a newly developed MODIS-Landsat fusion product (daily, 30 m resolution, RGB + NIR + SWIR bands) and the Planet Lab's high-resolution satellite data (daily, 5 meter, RGB). Finally, we will scale up the above relationships to generate high spatiotemporal resolution crop LAI map for the whole Champaign County. The proposed work has potentials to expand to other agro-ecosystems and to the broader US Corn Belt.
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Paech, S.J.; Mecikalski, J.R.; Sumner, D.M.; Pathak, C.S.; Wu, Q.; Islam, S.; Sangoyomi, T.
2009-01-01
Estimates of incoming solar radiation (insolation) from Geostationary Operational Environmental Satellite observations have been produced for the state of Florida over a 10-year period (1995-2004). These insolation estimates were developed into well-calibrated half-hourly and daily integrated solar insolation fields over the state at 2 km resolution, in addition to a 2-week running minimum surface albedo product. Model results of the daily integrated insolation were compared with ground-based pyranometers, and as a result, the entire dataset was calibrated. This calibration was accomplished through a three-step process: (1) comparison with ground-based pyranometer measurements on clear (noncloudy) reference days, (2) correcting for a bias related to cloudiness, and (3) deriving a monthly bias correction factor. Precalibration results indicated good model performance, with a station-averaged model error of 2.2 MJ m-2/day (13%). Calibration reduced errors to 1.7 MJ m -2/day (10%), and also removed temporal-related, seasonal-related, and satellite sensor-related biases. The calibrated insolation dataset will subsequently be used by state of Florida Water Management Districts to produce statewide, 2-km resolution maps of estimated daily reference and potential evapotranspiration for water management-related activities. ?? 2009 American Water Resources Association.
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Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992
Qi, Sharon L.; Konduris, Alexandria; Litke, David W.; Dupree, Jean
2002-01-01
Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land across the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a waterquality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres). This information was also compared to a similar data set based on 1980 imagery. The 1980 data classified 13.7 million acres as irrigated. Although the change in the amount of irrigated land between the two times was not substantial, the location of the irrigated land did shift from areas where there were large ground-water-level declines to other areas where ground-water levels were static or rising.
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Directory of Open Access Journals (Sweden)
Uttam Kumar
2017-10-01
Full Text Available Land cover (LC refers to the physical and biological cover present over the Earth’s surface in terms of the natural environment such as vegetation, water, bare soil, etc. Most LC features occur at finer spatial scales compared to the resolution of primary remote sensing satellites. Therefore, observed data are a mixture of spectral signatures of two or more LC features resulting in mixed pixels. One solution to the mixed pixel problem is the use of subpixel learning algorithms to disintegrate the pixel spectrum into its constituent spectra. Despite the popularity and existing research conducted on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of several subpixel learning algorithms based on least squares, sparse regression, signal–subspace and geometrical methods. Analysis of the results obtained through computer-simulated and Landsat data indicated that fully constrained least squares (FCLS outperformed the other techniques. Further, FCLS was used to unmix global Web-Enabled Landsat Data to obtain abundances of substrate (S, vegetation (V and dark object (D classes. Due to the sheer nature of data and computational needs, we leveraged the NASA Earth Exchange (NEX high-performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into four classes, namely forest, farmland, water and urban areas (in conjunction with nighttime lights data over California, USA using a random forest classifier. Validation of these LC maps with the National Land Cover Database 2011 products and North American Forest Dynamics static forest map shows a 6% improvement in unmixing-based classification relative to per-pixel classification. As such, abundance maps continue to offer a useful alternative to high-spatial-resolution classified maps for forest inventory analysis, multi
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Super-Resolution for "Jilin-1" Satellite Video Imagery via a Convolutional Network.
Xiao, Aoran; Wang, Zhongyuan; Wang, Lei; Ren, Yexian
2018-04-13
Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method's practicality. Experimental results on "Jilin-1" satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.
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Validation of Satellite Derived Cloud Properties Over the Southeastern Pacific
Ayers, J.; Minnis, P.; Zuidema, P.; Sun-Mack, S.; Palikonda, R.; Nguyen, L.; Fairall, C.
2005-12-01
Satellite measurements of cloud properties and the radiation budget are essential for understanding meso- and large-scale processes that determine the variability in climate over the southeastern Pacific. Of particular interest in this region is the prevalent stratocumulus cloud deck. The stratocumulus albedos are directly related to cloud microphysical properties that need to be accurately characterized in Global Climate Models (GCMs) to properly estimate the Earth's radiation budget. Meteorological observations in this region are sparse causing large uncertainties in initialized model fields. Remote sensing from satellites can provide a wealth of information about the clouds in this region, but it is vital to validate the remotely sensed parameters and to understand their relationship to other parameters that are not directly observed by the satellites. The variety of measurements from the R/V Roger Revelle during the 2003 STRATUS cruise and from the R/V Ron Brown during EPIC 2001 and the 2004 STRATUS cruises are suitable for validating and improving the interpretation of the satellite derived cloud properties. In this study, satellite-derived cloud properties including coverage, height, optical depth, and liquid water path are compared with in situ measurements taken during the EPIC and STRATUS cruises. The remotely sensed values are derived from Geostationary Operational Environmental Satellite (GOES) imager data, Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites, and from the Visible and Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The products from this study will include regional monthly cloud climatologies derived from the GOES data for the 2003 and 2004 cruises as well as micro and macro physical cloud property retrievals centered over the ship tracks from MODIS and VIRS.
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Hydrologic Derivatives for Modeling and Analysis—A new global high-resolution database
Verdin, Kristine L.
2017-07-17
The U.S. Geological Survey has developed a new global high-resolution hydrologic derivative database. Loosely modeled on the HYDRO1k database, this new database, entitled Hydrologic Derivatives for Modeling and Analysis, provides comprehensive and consistent global coverage of topographically derived raster layers (digital elevation model data, flow direction, flow accumulation, slope, and compound topographic index) and vector layers (streams and catchment boundaries). The coverage of the data is global, and the underlying digital elevation model is a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), GMTED2010 (Global Multi-resolution Terrain Elevation Data 2010), and the SRTM (Shuttle Radar Topography Mission). For most of the globe south of 60°N., the raster resolution of the data is 3 arc-seconds, corresponding to the resolution of the SRTM. For the areas north of 60°N., the resolution is 7.5 arc-seconds (the highest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30 arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information. This database is appropriate for use in continental-scale modeling efforts. The work described in this report was conducted by the U.S. Geological Survey in cooperation with the National Aeronautics and Space Administration Goddard Space Flight Center.
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Jacquemin, Ingrid; Henrot, Alexandra-Jane; Beckers, Veronique; Berckmans, Julie; Debusscher, Bos; Dury, Marie; Minet, Julien; Hamdi, Rafiq; Dendoncker, Nicolas; Tychon, Bernard; Hambuckers, Alain; François, Louis
2016-04-01
The interactions between land surface and climate are complex. Climate changes can affect ecosystem structure and functions, by altering photosynthesis and productivity or inducing thermal and hydric stresses on plant species. These changes then impact socio-economic systems, through e.g., lower farming or forestry incomes. Ultimately, it can lead to permanent changes in land use structure, especially when associated with other non-climatic factors, such as urbanization pressure. These interactions and changes have feedbacks on the climate systems, in terms of changing: (1) surface properties (albedo, roughness, evapotranspiration, etc.) and (2) greenhouse gas emissions (mainly CO2, CH4, N2O). In the framework of the MASC project (« Modelling and Assessing Surface Change impacts on Belgian and Western European climate »), we aim at improving regional climate model projections at the decennial scale over Belgium and Western Europe by combining high-resolution models of climate, land surface dynamics and socio-economic processes. The land surface dynamics (LSD) module is composed of a dynamic vegetation model (CARAIB) calculating the productivity and growth of natural and managed vegetation, and an agent-based model (CRAFTY), determining the shifts in land use and land cover. This up-scaled LSD module is made consistent with the surface scheme of the regional climate model (RCM: ALARO) to allow simulations of the RCM with a fully dynamic land surface for the recent past and the period 2000-2030. In this contribution, we analyze the results of the first simulations performed with the CARAIB dynamic vegetation model over Belgium at a resolution of 1km. This analysis is performed at the species level, using a set of 17 species for natural vegetation (trees and grasses) and 10 crops, especially designed to represent the Belgian vegetation. The CARAIB model is forced with surface atmospheric variables derived from the monthly global CRU climatology or ALARO outputs
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Kaya, S.; Alganci, U.; Sertel, E.; Ustundag, B.
2015-12-01
Throughout the history, agricultural activities have been performed close to urban areas. Main reason behind this phenomenon is the need of fast marketing of the agricultural production to urban residents and financial provision. Thus, using the areas nearby cities for agricultural activities brings out advantage of easy transportation of productions and fast marketing. For decades, heavy migration to cities has directly and negatively affected natural grasslands, forests and agricultural lands. This pressure has caused agricultural lands to be changed into urban areas. Dense urbanization causes increase in impervious surfaces, heat islands and many other problems in addition to destruction of agricultural lands. Considering the negative impacts of urbanization on agricultural lands and natural resources, a periodic monitoring of these changes becomes indisputably important. At this point, satellite images are known to be good data sources for land cover / use change monitoring with their fast data acquisition, large area coverages and temporal resolution properties. Classification of the satellite images provides thematic the land cover / use maps of the earth surface and changes can be determined with GIS based analysis multi-temporal maps. In this study, effects of heavy urbanization over agricultural lands in Istanbul, metropolitan city of Turkey, were investigated with use of multi-temporal Landsat TM satellite images acquired between 1984 and 2011. Images were geometrically registered to each other and classified using supervised maximum likelihood classification algorithm. Resulting thematic maps were exported to GIS environment and destructed agricultural lands by urbanization were determined using spatial analysis.
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National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of high resolution sea surface winds data produced from Synthetic Aperture Radar (SAR) on board Sentinel-1A and Sentinel-1B satellites. This...
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Muzylev, Eugene; Uspensky, Alexander; Startseva, Zoya; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey
2010-05-01
range 2.0-2.6, 2.5-3.7, and 3.5-4.9°C respectively. The dataset of remote sensing products has been compiled on the base of special technology using Internet resources, that includes MODIS-based estimates of land surface temperature (LST) Tsg, E, NDVI, LAI for the region of interest and the same vegetation seasons. Two types of MODIS-based Тsg and E estimates have been extracted from LP DAAC web-site (for separate dates of 2003-2009 time period): LST/E Daily L3 product (MOD11В1) with spatial resolution ~ 4.8 km and LST/E 5-Min L2 product (MOD11_L2) with spatial resolution ~ 1 km. The verification of Tsg estimates has been performed via comparison with analogous and collocated AVHRR-based ones. Along with this the sample of SEVIRI-based Tsg and E estimates has been accumulated for the Kursk area and surrounding territories for the time interval of several days during 2009 vegetation season. To retrieve Тsg and Е from SEVIRI/Meteosat-8, -9 data the new method has been developed. Being designed as the combination of well-known Split Window Technique and Two Temperature Method algorithms it provides the derivation of Тsg from SEVIRI/Meteosat-9 measurements carried out at three successive times (classified as 100% cloud-free) and covering the region under consideration without accurate a priory knowledge of E. Comparison of the SEVIRI-based Tsg retrievals with the independent collocated Tsg estimates gives the values of RMS deviation in the range of 0.9-1.4°C and it proves (indirectly) the efficiency of proposed approach. To assimilate satellite-derived estimates of vegetation characteristics and LST in the SVAT model some procedures have been developed. These procedures have included: 1) the replacement of LAI and B ground and point-wise estimates by their AVHRR- or MODIS-based analogues. The efficiency of such approach has been proved through comparison between satellite-derived and ground-based seasonal time behaviors of LAI and B, between satellite-derived
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Directory of Open Access Journals (Sweden)
Hassan Mohamed
2018-05-01
Full Text Available Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV ensembles for Support Vector Machines (SVM, K-Nearest Neighbor (K-NN, and Bagging (BAG classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy.
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Ferrant, S.; Gascoin, S.; Veloso, A.; Salmon-Monviola, J.; Claverie, M.; Rivalland, V.; Dedieu, G.; Demarez, V.; Ceschia, E.; Probst, J.-L.; Durand, P.; Bustillo, V.
2014-07-01
The recent and forthcoming availability of high resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the perspective offered by improving the crop growth dynamic simulation using the distributed agro-hydrological model, Topography based Nitrogen transfer and Transformation (TNT2), using LAI map series derived from 105 Formosat-2 (F2) images during the period 2006-2010. The TNT2 model (Beaujouan et al., 2002), calibrated with discharge and in-stream nitrate fluxes for the period 1985-2001, was tested on the 2006-2010 dataset (climate, land use, agricultural practices, discharge and nitrate fluxes at the outlet). A priori agricultural practices obtained from an extensive field survey such as seeding date, crop cultivar, and fertilizer amount were used as input variables. Continuous values of LAI as a function of cumulative daily temperature were obtained at the crop field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics with a priori input parameters showed an temporal shift with observed LAI profiles irregularly distributed in space (between field crops) and time (between years). By re-setting seeding date at the crop field level, we proposed an optimization method to minimize efficiently this temporal shift and better fit the crop growth against the spatial observations as well as crop production. This optimization of simulated LAI has a negligible impact on water budget at the catchment scale (1 mm yr-1 in average) but a noticeable impact on in-stream nitrogen fluxes (around 12%) which is of interest considering nitrate stream contamination issues and TNT2 model objectives. This study demonstrates the contribution of forthcoming high spatial and temporal resolution products of Sentinel-2 satellite mission in improving agro-hydrological modeling by constraining the spatial representation of crop productivity.
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Biomass estimation with high resolution satellite images: A case study of Quercus rotundifolia
Sousa, Adélia M. O.; Gonçalves, Ana Cristina; Mesquita, Paulo; Marques da Silva, José R.
2015-03-01
Forest biomass has had a growing importance in the world economy as a global strategic reserve, due to applications in bioenergy, bioproduct development and issues related to reducing greenhouse gas emissions. Current techniques used for forest inventory are usually time consuming and expensive. Thus, there is an urgent need to develop reliable, low cost methods that can be used for forest biomass estimation and monitoring. This study uses new techniques to process high spatial resolution satellite images (0.70 m) in order to assess and monitor forest biomass. Multi-resolution segmentation method and object oriented classification are used to obtain the area of tree canopy horizontal projection for Quercus rotundifolia. Forest inventory allows for calculation of tree and canopy horizontal projection and biomass, the latter with allometric functions. The two data sets are used to develop linear functions to assess above ground biomass, with crown horizontal projection as an independent variable. The functions for the cumulative values, both for inventory and satellite data, for a prediction error equal or smaller than the Portuguese national forest inventory (7%), correspond to stand areas of 0.5 ha, which include most of the Q.rotundifolia stands.
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Generation of Land Cover Maps Using High-Resolution Multispectral Aerial Cameras
DEFF Research Database (Denmark)
Höhle, Joachim
2013-01-01
. The classification had an overall accuracy of 79%. Suggestions for the improvements in the applied methodology are made. The potential of land cover maps lies in updating of topographic databases, quality control of maps, studies of town development, and other geo-spatial domain applications. The automatic...... for classification of land cover. A high degree of automation can be achieved. The obtained results of a practical example are checked with reference values derived from ortho-images in natural colour and from colour images using stereo-vision. An error matrix is applied in the evaluation of the results...
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International Nuclear Information System (INIS)
Murillo Mejia, Mario Humberto
2006-01-01
The objective was to evaluate the data obtained by sensor MODIS onboard the EOS terra satellite land cover units. The study area is the republic of Colombia in South America. The methodology consisted of analyzing the multitemporal (vegetation, soil and shade-water) fraction images and vegetation indices (NDVI) apply the lineal spectral mixture model to products derived from derived images by sensor MODIS data obtained in years 2001 and 2003. The mosaics of the original and the transformed vegetation (soil and shade-water) bands were generated for the whole study area using SPRING 4. 0 software, developed by INPE then these mosaics were segmented, classified, mapped, and edited to obtain a moderate resolution land cover map. The results derived from MODIS analysis were compared with Landsat ETM+ data acquire for a single test site. The results of the project showed the usefulness of MODIS images for large-scale land cover mapping and monitoring studies
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Total Discharge Estimation in the Korean Peninsula Using Multi-Satellite Products
Directory of Open Access Journals (Sweden)
Jae Young Seo
2017-07-01
Full Text Available Estimation of total discharge is necessary to understand the hydrological cycle and to manage water resources efficiently. However, the task is problematic in an area where ground observations are limited. The North Korea region is one example. Here, the total discharge was estimated based on the water balance using multiple satellite products. They are the terrestrial water storage changes (TWSC derived from the Gravity Recovery and Climate Experiment (GRACE, precipitation from the Tropical Rainfall Measuring Mission (TRMM, and evapotranspiration from the Moderate Resolution Imaging Spectroradiometer (MODIS. The satellite-based discharge was compared with land surface model products of the Global Land Data Assimilation System (GLDAS, and a positive relationship between the results was obtained (r = 0.70–0.86; bias = −9.08–16.99 mm/month; RMSE = 36.90–62.56 mm/month; NSE = 0.01–0.62. Among the four land surface models of GLDAS (CLM, Mosaic, Noah, and VIC, CLM corresponded best with the satellite-based discharge, satellite-based discharge has a tendency to slightly overestimate compared to model-based discharge (CLM, Mosaic, Noah, and VIC in the dry season. Also, the total discharge data based on the Precipitation-Runoff Modeling System (PRMS and the in situ discharge for major five river basins in South Korea show comparable seasonality and high correlation with the satellite-based discharge. In spite of the relatively low spatial resolution of GRACE, and loss of information incurred during the process of integrating three different satellite products, the proposed methodology can be a practical tool to estimate the total discharge with reasonable accuracy, especially in a region with scarce hydrologic data.
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Gao, Feng; DeColstoun, Eric Brown; Ma, Ronghua; Weng, Qihao; Masek, Jeffrey G.; Chen, Jin; Pan, Yaozhong; Song, Conghe
2012-01-01
Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China-Brazil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived
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Kim, Y.; Du, J.; Kimball, J. S.
2017-12-01
The landscape freeze-thaw (FT) status derived from satellite microwave remote sensing is closely linked to vegetation phenology and productivity, surface energy exchange, evapotranspiration, snow/ice melt dynamics, and trace gas fluxes over land areas affected by seasonally frozen temperatures. A long-term global satellite microwave Earth System Data Record of daily landscape freeze-thaw status (FT-ESDR) was developed using similar calibrated 37GHz, vertically-polarized (V-pol) brightness temperatures (Tb) from SMMR, SSM/I, and SSMIS sensors. The FT-ESDR shows mean annual spatial classification accuracies of 90.3 and 84.3 % for PM and AM overpass retrievals relative surface air temperature (SAT) measurement based FT estimates from global weather stations. However, the coarse FT-ESDR gridding (25-km) is insufficient to distinguish finer scale FT heterogeneity. In this study, we tested alternative finer scale FT estimates derived from two enhanced polar-grid (3.125-km and 6-km resolution), 36.5 GHz V-pol Tb records derived from calibrated AMSR-E and AMSR2 sensor observations. The daily FT estimates are derived using a modified seasonal threshold algorithm that classifies daily Tb variations in relation to grid cell-wise FT thresholds calibrated using ERA-Interim reanalysis based SAT, downscaled using a digital terrain map and estimated temperature lapse rates. The resulting polar-grid FT records for a selected study year (2004) show mean annual spatial classification accuracies of 90.1% (84.2%) and 93.1% (85.8%) for respective PM (AM) 3.125km and 6-km Tb retrievals relative to in situ SAT measurement based FT estimates from regional weather stations. Areas with enhanced FT accuracy include water-land boundaries and mountainous terrain. Differences in FT patterns and relative accuracy obtained from the enhanced grid Tb records were attributed to several factors, including different noise contributions from underlying Tb processing and spatial mismatches between Tb
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Kaiser, J.; Zhu, L.; Travis, K.; Jacob, D.
2017-12-01
In the South East United States, biogenic isoprene fuels tropospheric ozone formation, and its oxidation products contribute significantly to organic aerosol. Bottom-up emission inventories rely on very limited isoprene emission and land-cover data, yielding uncertainties of a factor of 2 or more. Here, we use formaldehyde columns from the Ozone Monitoring Instrument in a high-resolution (0.25 x 0.325o) adjoint-based inversion to constrain isoprene emissions over the SE US during Aug-Sept of 2013. We find that the MEGANv2.1 inventory is biased high over most of the SE US. Our derived scaling factors show significant spatial variability, with the largest corrections applied to Louisiana and the Edwards Plateau in Texas. We test our inversion results against a comprehensive set of isoprene oxidation product observations from the NASA SEAC4RS flight campaign. The SEAC4RS data provides new confidence in the satellite retrievals and in mechanism linking isoprene oxidation to formaldehyde production. Finally, we relate the posterior scaling factors to the underlying land-type, and examine potential sources of observed biases.
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CSIR Research Space (South Africa)
Olivier, JC
2007-11-01
Full Text Available An Artificial Neural Network (ANN) is proposed to detect human-induced land cover change using a sliding window through a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite surface reflectance pixel values. Training...
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Classification of High Spatial Resolution, Hyperspectral ...
EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result of a collaborative effort among an interdisciplinary team of scientists with the U.S. Environmental Protection Agency's (U.S. EPA's) Office of Research and Development in Cincinnati, Ohio. A primary goal of this project is to enhance the use of geography and spatial analytic tools in risk assessment, and to improve the scientific basis for risk management decisions affecting drinking water and water quality. The land use/land cover classification is derived from 82 flight lines of Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery acquired from July 24 through August 9, 2002 via fixed-wing aircraft.
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Green leaf phenology at Landsat resolution: scaling from the plot to satellite
Fisher, J. I.; Mustard, J. F.; Vadeboncour, M.
2005-12-01
Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In particular, while most phenological patterns and trends derived from satellites appear realistic and coherent, they may not reflect spatial and temporal patterns at the plot level. An obvious explanation is the drastic scale difference from plot-level to most satellite observations. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations (r2 = 0.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10-14 days over short distances (example, our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5-7 days earlier than comparable rural areas. In preliminary work, we validated the Landsat-derived metrics with similar analyses of MODIS and AVHRR, and demonstrate that aggregating diverse local phenologies into coarse grids may convolute interpretations. Despite these complications, the platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows us to effectively scale
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Automatic Blocked Roads Assessment after Earthquake Using High Resolution Satellite Imagery
Rastiveis, H.; Hosseini-Zirdoo, E.; Eslamizade, F.
2015-12-01
In 2010, an earthquake in the city of Port-au-Prince, Haiti, happened quite by chance an accident and killed over 300000 people. According to historical data such an earthquake has not occurred in the area. Unpredictability of earthquakes has necessitated the need for comprehensive mitigation efforts to minimize deaths and injuries. Blocked roads, caused by debris of destroyed buildings, may increase the difficulty of rescue activities. In this case, a damage map, which specifies blocked and unblocked roads, can be definitely helpful for a rescue team. In this paper, a novel method for providing destruction map based on pre-event vector map and high resolution world view II satellite images after earthquake, is presented. For this purpose, firstly in pre-processing step, image quality improvement and co-coordination of image and map are performed. Then, after extraction of texture descriptor from the image after quake and SVM classification, different terrains are detected in the image. Finally, considering the classification results, specifically objects belong to "debris" class, damage analysis are performed to estimate the damage percentage. In this case, in addition to the area objects in the "debris" class their shape should also be counted. The aforementioned process are performed on all the roads in the road layer.In this research, pre-event digital vector map and post-event high resolution satellite image, acquired by Worldview-2, of the city of Port-au-Prince, Haiti's capital, were used to evaluate the proposed method. The algorithm was executed on 1200×800 m2 of the data set, including 60 roads, and all the roads were labelled correctly. The visual examination have authenticated the abilities of this method for damage assessment of urban roads network after an earthquake.
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National Satellite Land Remote Sensing Data Archive
Faundeen, John L.; Longhenry, Ryan
2018-06-13
The National Satellite Land Remote Sensing Data Archive is managed on behalf of the Secretary of the Interior by the U.S. Geological Survey’s Earth Resources Observation and Science Center. The Land Remote Sensing Policy Act of 1992 (51 U.S.C. §601) directed the U.S. Department of the Interior to establish a permanent global archive consisting of imagery over land areas obtained from satellites orbiting the Earth. The law also directed the U.S. Department of the Interior, delegated to the U.S. Geological Survey, to ensure proper storage and preservation of imagery, and timely access for all parties. Since 2008, these images have been available at no cost to the user.
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Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
Directory of Open Access Journals (Sweden)
Nahuel R. Peralta
2016-10-01
Full Text Available A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington of Kansas (total of 457 ha. Three basic tests were conducted on the data: (1 spatial dependence on each of the yield and vegetation indices (VIs using Moran’s I test; (2 model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS and spatial econometric (SPL models; and (3 model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG was tested positive and statistically significant for most of the fields (p < 0.05, except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02 was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to
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Assimilation of SMOS (and SMAP) Retrieved Soil Moisture into the Land Information System
Blankenship, Clay; Zavodsky, Bradley; Case, Jonathan; Stano, Geoffrey
2016-01-01
Goal: Accurate, high-resolution (approx.3 km) soil moisture in near-real time. Situational awareness (drought assessment, flood and fire threat). Local modeling applications (to improve sfc-PBL exchanges) Method: Assimilate satellite soil moisture retrievals into a land surface model. Combines high-resolution geophysical model data with latest satellite observations.
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Open Source GIS Connectors to NASA GES DISC Satellite Data
Kempler, Steve; Pham, Long; Yang, Wenli
2014-01-01
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) houses a suite of high spatiotemporal resolution GIS data including satellite-derived and modeled precipitation, air quality, and land surface parameter data. The data are valuable to various GIS research and applications at regional, continental, and global scales. On the other hand, many GIS users, especially those from the ArcGIS community, have difficulties in obtaining, importing, and using our data due to factors such as the variety of data products, the complexity of satellite remote sensing data, and the data encoding formats. We introduce a simple open source ArcGIS data connector that significantly simplifies the access and use of GES DISC data in ArcGIS.
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The High Visible Resolution (HVR) instrument of the spot ground observation satellite
Otrio, G.
1980-01-01
Two identical high resolution cameras, capable of attaining a track width of 116 km in an almost vertical line of sight from the two 60 km images of each instrument, will be carried on the initial mission of the space observation of Earth satellite (SPOT). Specifications for the instrument, including the telescope and CCD devices are summarized. The present status of development is described including the optical characteristics, structure and thermal control, detector assembly, electronic equipment, and calibration. SPOT mission objectives include the developments relating to soil use, the exploration of EART Earth resources, the discrimination of plant species, and cartography.
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Khalilimoghadama, N.; Delavar, M. R.; Hanachi, P.
2017-09-01
The problem of overcrowding of mega cities has been bolded in recent years. To meet the need of housing this increased population, which is of great importance in mega cities, a huge number of buildings are constructed annually. With the ever-increasing trend of building constructions, we are faced with the growing trend of building infractions and illegal buildings (IBs). Acquiring multi-temporal satellite images and using change detection techniques is one of the proper methods of IB monitoring. Using the type of satellite images with different spatial and spectral resolutions has always been an issue in efficient detection of the building changes. In this research, three bi-temporal high-resolution satellite images of IRS-P5, GeoEye-1 and QuickBird sensors acquired from the west of metropolitan area of Tehran, capital of Iran, in addition to city maps and municipality property database were used to detect the under construction buildings with improved performance and accuracy. Furthermore, determining the employed bi-temporal satellite images to provide better performance and accuracy in the case of IB detection is the other purpose of this research. The Kappa coefficients of 70 %, 64 %, and 68 % were obtained for producing change image maps using GeoEye-1, IRS-P5, and QuickBird satellite images, respectively. In addition, the overall accuracies of 100 %, 6 %, and 83 % were achieved for IB detection using the satellite images, respectively. These accuracies substantiate the fact that the GeoEye-1 satellite images had the best performance among the employed images in producing change image map and detecting the IBs.
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DETECTION OF BARCHAN DUNES IN HIGH RESOLUTION SATELLITE IMAGES
Directory of Open Access Journals (Sweden)
M. A. Azzaoui
2016-06-01
Full Text Available Barchan dunes are the fastest moving sand dunes in the desert. We developed a process to detect barchans dunes on High resolution satellite images. It consisted of three steps, we first enhanced the image using histogram equalization and noise reduction filters. Then, the second step proceeds to eliminate the parts of the image having a texture different from that of the barchans dunes. Using supervised learning, we tested a coarse to fine textural analysis based on Kolomogorov Smirnov test and Youden’s J-statistic on co-occurrence matrix. As an output we obtained a mask that we used in the next step to reduce the search area. In the third step we used a gliding window on the mask and check SURF features with SVM to get barchans dunes candidates. Detected barchans dunes were considered as the fusion of overlapping candidates. The results of this approach were very satisfying in processing time and precision.
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Satellite Derived Bathymetry as a Coastal Geo-Intelligence Tool for Alaska
Ventura, D. C.
2017-12-01
What do marine rescue, navigation safety, resource management, coastal infrastructure management, climate adaptation and resilience, economic investment, habitat protection agencies and institutions all have in common? They all benefit from accurate coastal bathymetric data As Arctic-Related Incidents of National Significance (IoNS) workshop points out, reducing time and cost of collecting coastal bathymetry in the Arctic is fundamental to addressing needs of a multitude of stakeholders. Until recently, high resolution coastal data acquisition involved field mobilization of planes, vessels, and people. Given limited resources, short season and remoteness, this approach results in very modest progress toward filling the Alaska's coastal bathymetry data gap and updating vintage data from circa Captain Cook.After successfully executing Satellite Derived Bathymetry (SDB) projects in other more environmentally suitable locations, Fugro and its partner EOMAP are now assessing suitability SDB technique along the Alaska coast. This includes aaccessing archived satellite data and understanding best environmental conditions for the mapping and defining maximum mapping depth as an initial action to understand potentials for Alaska. Here we leverage the physics-based approach to satellite imagery data extraction to derive water depth and complimentary intelligence such as seafloor habitat mapping and certain water quality parameters, such as clarity, turbidity, sediment and chlorophyll-a concentrations, and seasonal changes. Both new and archive imagery are utilized as part of the process. If successful, the benefits and cost savings of this approach are enormous as repeat rate for data collects like this can be measured in months/years as opposed to decades/centuries. Arctic coasts have multiple vulnerabilities and the rate of change will continue to outpace the budgets. As innovative and learning organizations, Fugro and EOMAP strive to not only share the results of this
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Detection and Extraction of Roads from High Resolution Satellites Images with Dynamic Programming
Benzouai, Siham; Smara, Youcef
2010-12-01
The advent of satellite images allows now a regular and a fast digitizing and update of geographic data, especially roads which are very useful for Geographic Information Systems (GIS) applications such as transportation, urban pollution, geomarketing, etc. For this, several studies have been conducted to automate roads extraction in order to minimize the manual processes [4]. In this work, we are interested in roads extraction from satellite imagery with high spatial resolution (at best equal to 10 m). The method is semi automatic and follows a linear approach where road is considered as a linear object. As roads extraction is a pattern recognition problem, it is useful, above all, to characterize roads. After, we realize a pre-processing by applying an Infinite Size Edge Filter -ISEF- and processing method based on dynamic programming concept, in particular, Fishler algorithm designed by F*.
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Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan
2018-03-01
High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.
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El Bastawesy, M.; Cherif, O. H.; Sultan, M.
2017-12-01
This paper investigates the relevance of landforms to the subsidence of the Nile Delta using a high resolution topographic digital elevation model (DEM) and sets of multi-temporal Landsat satellite images. 195 topographic map sheets produced in 1946 at 1:25,000 scale were digitized, and the DEM was interpolated. The undertaken processing techniques have distinguished all the natural low-lying closed depressions from the artificial errors induced by the interpolation of the DEM. The local subsidence of these depressions from their surroundings reaches a maximum depth of 2.5 m. The regional subsidence of the Nile Delta has developed inverted topography, where the tracts occupied by the contemporary distributary channels are standing at higher elevations than the areas in between. This inversion could be related to the differences in the hydrological and sedimentological properties of underlying sediments, as the channels are underlain by water-saturated sands while the successions of clay and silt on flood plains are prone to compaction. Furthermore, the analysis of remote sensing and topographic data clearly show significant changes in the land cover and land use, particularly in the northern lagoons and adjacent sabkhas, which are dominated by numerous low subsiding depressions. The areas covered by water logging and ponds are increasing on the expense of agricultural areas, and aquaculture have been practiced instead. The precise estimation of subsidence rates and distribution should be worked out to evaluate probable changes in land cover and land use.
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Land use/land cover study of urban features using spot imagery
International Nuclear Information System (INIS)
Mahmood, S.A.; Qureshi, J.; Abbas, I.
2005-01-01
This study is based on visual interpretation and classification of the urban area of Peshawar. Cloud free satellite image of the French SPOT System in panchromatic mode at 100m/pixel spatial detail was used for this purpose. The coverage area comprised nearly (7.5 x 6)sq. km. on the ground depicting the major portion of the city. Various image interpretation elements were exploited to accomplish the study, thirteen land cover classes were identified and demarcated on a tracing sheet. Having prepared the base map. Satellite image map was constructed by assigning disparate colors to the identified features. Dimensions of some of the prominent, regular and liner features were computed from the image. The results indicate that high-resolution satellite image can be effectively used for mapping and area estimation of urban land use/land cover features. (author)
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National Oceanic and Atmospheric Administration, Department of Commerce — Synthetic Aperture Radar (SAR)-derived high resolution wind products are calculated from high resolution SAR images of normalized radar cross section (NRCS) of the...
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Analysis of satellite-derived solar irradiance over the Netherlands
Dirksen, Marieke; Fokke Meirink, Jan; Sluiter, Raymond
2017-04-01
Measurements from geostationary satellites allow the retrieval of surface solar irradiance homogeneously over large areas, thereby providing essential information for the solar energy sector. In this paper, the SICCS solar irradiance data record derived from 12 years of Meteosat Second Generation satellite measurements is analysed with a focus on the Netherlands, where the spatial resolution is about 6 by 3 km2. Extensive validation of the SICCS data with pyranometer observations is performed, indicating a bias of approximately 3 W/m2 and RMSE of 11 W/m2 for daily data. Long term averages and seasonal variations of solar irradiance show regional patterns related to the surface type (e.g., coastal waters, forests, cities). The inter-annual variability over the time frame of the data record is quantified. Methods to merge satellite and surface observations into an optimized data record are explored.
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Zhong, Bo; Chen, Wuhan; Wu, Shanlong; Liu, Qinhuo
2016-10-01
Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors don't have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called OCM (Object oriented Cloud and cloud-shadow Matching method) is presented in this paper. It firstly modified the Automatic Cloud Cover Assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold setting produces different cloud map. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Secondly, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Thirdly, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. OCM method was tested using almost 200 HJ-1/CCD images across China and the overall accuracy of cloud detection is close to 90%.
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High-Resolution airborne color video data were used to evaluate the accuracy of a land cover map of the upper San Pedro River watershed, derived from June 1997 Landsat Thematic Mapper data. The land cover map was interpreted and generated by Instituto del Medio Ambiente y el Bes...
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Thompson, O. E.
1982-01-01
The present investigation is concerned with the vertical resolving power of satellite-borne temperature sounding instruments. Information is presented on the capabilities of the High Resolution Infrared Radiation Sounder (HIRS) and a proposed sounding instrument called the Advanced Moisture and Temperature Sounder (AMTS). Two quite different methods for assessing the vertical resolving power of satellite sounders are discussed. The first is the theoretical method of Conrath (1972) which was patterned after the work of Backus and Gilbert (1968) The Backus-Gilbert-Conrath (BGC) approach includes a formalism for deriving a retrieval algorithm for optimizing the vertical resolving power. However, a retrieval algorithm constructed in the BGC optimal fashion is not necessarily optimal as far as actual temperature retrievals are concerned. Thus, an independent criterion for vertical resolving power is discussed. The criterion is based on actual retrievals of signal structure in the temperature field.
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Chen, Y.; Zhang, Y.; Gao, J.; Yuan, Y.; Lv, Z.
2018-04-01
Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.
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Land Reform and Conflict Resolution in Colombia
National Research Council Canada - National Science Library
Nieswiadomy, Mark
2003-01-01
... to change the overall high concentration of landownership, This thesis examines to what effect, if any, a redistributive land reform policy implemented amid the ongoing rural conflict would have on its resolution...
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Directory of Open Access Journals (Sweden)
Gijs Simons
2016-03-01
Full Text Available With changes in weather patterns and intensifying anthropogenic water use, there is an increasing need for spatio-temporal information on water fluxes and stocks in river basins. The assortment of satellite-derived open-access information sources on rainfall (P and land use/land cover (LULC is currently being expanded with the application of actual evapotranspiration (ETact algorithms on the global scale. We demonstrate how global remotely sensed P and ETact datasets can be merged to examine hydrological processes such as storage changes and streamflow prior to applying a numerical simulation model. The study area is the Red River Basin in China in Vietnam, a generally challenging basin for remotely sensed information due to frequent cloud cover. Over this region, several satellite-based P and ETact products are compared, and performance is evaluated using rain gauge records and longer-term averaged streamflow. A method is presented for fusing multiple satellite-derived ETact estimates to generate an ensemble product that may be less susceptible, on a global basis, to errors in individual modeling approaches. Subsequently, monthly satellite-derived rainfall and ETact are combined to assess the water balance for individual subcatchments and types of land use, defined using a global land use classification improved based on auxiliary satellite data. It was found that a combination of TRMM rainfall and the ensemble ETact product is consistent with streamflow records in both space and time. It is concluded that monthly storage changes, multi-annual streamflow and water yield per LULC type in the Red River Basin can be successfully assessed based on currently available global satellite-derived products.
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Directory of Open Access Journals (Sweden)
Chandra Giri
2014-10-01
Full Text Available Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.
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Policy makers need to understand how land cover change alters storm water regimes, yet existing methods do not fully utilize newly available datasets to quantify storm water changes at a landscape-scale. Here, we use high-resolution, remotely-sensed land cover, imperviousness, an...
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Directory of Open Access Journals (Sweden)
M. Otto
2011-05-01
Full Text Available High Altitude Wetlands of the Andes (HAWA belong to a unique type of wetland within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand, HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 12 800 km2 situated in the Northwest of Lake Titicaca. The multi-temporal classification method used Normalized Differenced Vegetation Index (NDVI and Normalized Differenced Infrared Index (NDII data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000 and at the end of austral summer (May 2001. The mapping result indicates an unexpected high abundance of HAWA covering about 800 km2 of the study region (6 %. Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS. Analyses on the relation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43 and MODIS Eight Day Maximum Snow Extent data (MOD10A2 from 2000 to 2010. We found HAWA subtype specific dependencies on precipitation conditions. A strong relation exists between perennial HAWA and snow fall (r2: 0.82 in dry austral winter months (June to August and between temporal HAWA and precipitation (r2: 0.75 during austral summer
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Development of High Resolution Data for Irrigated Area and Cropping Patterns in India
K a, A.; Mishra, V.
2015-12-01
Information of crop phenology and its individual effect on irrigation is essential to improve the simulation of land surface states and fluxes. We use moderate resolution imaging spectroradiometer (MODIS) - Normalized difference vegetation index (NDVI) at 250 m resolution for monitoring temporal changes in irrigation and cropping patterns in India. We used the obtained dataset of cropping pattern for quantifying the effect of irrigation on land surface states and fluxes by using an uncoupled land surface model. The cropping patterns are derived by using the planting, heading, harvesting, and growing dates for each agro-ecological zone separately. Moreover, we developed a high resolution irrigated area maps for the period of 1999-2014 for India. The high resolution irrigated area was compared with relatively coarse resolution (~ 10km) irrigated area from the Food and Agricultural Organization. To identify the seasonal effects we analyzed the spatial and temporal change of irrigation and cropping pattern for different temporal seasons. The new irrigation area information along with cropping pattern was used to study the water budget in India using the Noah Land surface Model (Noah LSM) for the period of 1999-2014.
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Land Surface Temperature- Comparing Data from Polar Orbiting and Geostationary Satellites
Comyn-Platt, E.; Remedios, J. J.; Good, E. J.; Ghent, D.; Saunders, R.
2012-04-01
Land Surface Temperature (LST) is a vital parameter in Earth climate science, driving long-wave radiation exchanges that control the surface energy budget and carbon fluxes, which are important factors in Numerical Weather Prediction (NWP) and the monitoring of climate change. Satellites offer a convenient way to observe LST consistently and regularly over large areas. A comparison between LST retrieved from a Geostationary Instrument, the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), and a Polar Orbiting Instrument, the Advanced Along Track Scanning Radiometer (AATSR) is presented. Both sensors offer differing benefits. AATSR offers superior precision and spatial resolution with global coverage but given its sun-synchronous platform only observes at two local times, ~10am and ~10pm. SEVIRI provides the high-temporal resolution (every 15 minutes) required for observing diurnal variability of surface temperatures but given its geostationary platform has a poorer resolution, 3km at nadir, which declines at higher latitudes. A number of retrieval methods are applied to the raw satellite data: First order coefficient based algorithms provided on an operational basis by the LandSAF (for SEVIRI) and the University of Leicester (for AATSR); Second order coefficient based algorithms put forward by the University of Valencia; and an optimal estimation method using the 1DVar software provided by the NWP SAF. Optimal estimation is an iterative technique based upon inverse theory, thus is very useful for expanding into data assimilation systems. The retrievals are assessed and compared on both a fine scale using in-situ data from recognised validation sites and on a broad scale using two 100x100 regions such that biases can be better understood. Overall, the importance of LST lies in monitoring daily temperature extremes, e.g. for estimating permafrost thawing depth or risk of crop damage due to frost, hence the ideal dataset would use a combination of observations
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Directory of Open Access Journals (Sweden)
E. Eva Borbas
2018-04-01
Full Text Available As part of a National Aeronautics and Space Administration (NASA MEaSUREs (Making Earth System Data Records for Use in Research Environments Land Surface Temperature and Emissivity project, the Space Science and Engineering Center (UW-Madison and the NASA Jet Propulsion Laboratory (JPL developed a global monthly mean emissivity Earth System Data Record (ESDR. This new Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer and MODIS (Moderate Resolution Imaging Spectroradiometer Emissivity over Land (CAMEL ESDR was produced by merging two current state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UW BF and the JPL ASTER Global Emissivity Dataset Version 4 (GEDv4. The dataset includes monthly global records of emissivity and related uncertainties at 13 hinge points between 3.6–14.3 µm, as well as principal component analysis (PCA coefficients at 5-km resolution for the years 2000 through 2016. A high spectral resolution (HSR algorithm is provided for HSR applications. This paper describes the 13 hinge-points combination methodology and the high spectral resolutions algorithm, as well as reports the current status of the dataset.
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Next generation of global land cover characterization, mapping, and monitoring
Giri, Chandra; Pengra, Bruce; Long, J.; Loveland, Thomas R.
2013-01-01
Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).
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Validation of Satellite-Derived Sea Surface Temperatures for Waters around Taiwan
Directory of Open Access Journals (Sweden)
Ming-An Lee
2005-01-01
Full Text Available In order to validate the Advanced Very High Resolution Radiometer (AVHRR-derived sea surface temperatures (SST of the waters around Taiwan, we generated a match-up data set of 961 pairs, which included in situ SSTs and concurrent AVHRR measurements for the period of 1998 to 2002. Availability of cloud-free images, i.e., images with more than 85% of cloud-free area in their coverage, was about 2.23% of all AVHRR images during the study period. The range of in situ SSTs was from _ to _ The satellite derived-SSTs through MCSST and NLSST algorithms were linearly related to the in situ SSTs with correlation coefficients of 0.985 and 0.98, respectively. The MCSSTs and NLSSTs had small biases of 0.009 _ and 0.256 _ with root mean square deviations of 0.64 _ and 0.801 _ respectively, therefore the AVHRR-based MCSSTs and NLSSTs had high accuracy in the seas around Taiwan.
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Fine Particulate Matter Predictions Using High Resolution Aerosol Optical Depth (AOD) Retrievals
Chudnovsky, Alexandra A.; Koutrakis, Petros; Kloog, Itai; Melly, Steven; Nordio, Francesco; Lyapustin, Alexei; Wang, Jujie; Schwartz, Joel
2014-01-01
To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM(sub 2.5) concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM(sub 2.5) relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM(sub 2.5) mass concentrations are highly correlated with the actual observations, with out-of- sample R(sub 2) of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM(sub 2.5) levels.
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Scarino, B. R.; Smith, W. L., Jr.; Minnis, P.; Bedka, K. M.
2017-12-01
Atmospheric models rely on high-accuracy, high-resolution initial radiometric and surface conditions for better short-term meteorological forecasts, as well as improved evaluation of global climate models. Continuous remote sensing of the Earth's energy budget, as conducted by the Clouds and Earth's Radiant Energy System (CERES) project, allows for near-realtime evaluation of cloud and surface radiation properties. It is unfortunately common for there to be bias between atmospheric/surface radiation models and Earth-observations. For example, satellite-observed surface skin temperature (Ts), an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface, can be biased due to atmospheric adjustment assumptions and anisotropy effects. Similarly, models are potentially biased by errors in initial conditions and regional forcing assumptions, which can be mitigated through assimilation with true measurements. As such, when frequent, broad-coverage, and accurate retrievals of satellite Ts are available, important insights into model estimates of Ts can be gained. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared method to produce anisotropy-corrected Ts over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) satellite imagers. Regional and diurnal changes in model land surface temperature (LST) performance can be assessed owing to the somewhat continuous measurements of the LST offered by GEO satellites - measurements which are accurate to within 0.2 K. A seasonal, hourly comparison of satellite-observed LST with the NASA Goddard Earth Observing System Version 5 (GEOS-5) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA) LST estimates is conducted to reveal regional and diurnal biases. This assessment is an important first step for evaluating the effectiveness of Ts assimilation, as well for determining the
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Moisture convergence using satellite-derived wind fields - A severe local storm case study
Negri, A. J.; Vonder Haar, T. H.
1980-01-01
Five-minute interval 1-km resolution SMS visible channel data were used to derive low-level wind fields by tracking small cumulus clouds on NASA's Atmospheric and Oceanographic Information Processing System. The satellite-derived wind fields were combined with surface mixing ratios to derive horizontal moisture convergence in the prestorm environment of April 24, 1975. Storms began developing in an area extending from southwest Oklahoma to eastern Tennessee 2 h subsequent to the time of the derived fields. The maximum moisture convergence was computed to be 0.0022 g/kg per sec and areas of low-level convergence of moisture were in general indicative of regions of severe storm genesis. The resultant moisture convergence fields derived from two wind sets 20 min apart were spatially consistent and reflected the mesoscale forcing of ensuing storm development. Results are discussed with regard to possible limitations in quantifying the relationship between low-level flow and between low-level flow and satellite-derived cumulus motion in an antecedent storm environment.
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Ferrant, S.; Gascoin, S.; Veloso, A.; Salmon-Monviola, J.; Claverie, M.; Rivalland, V.; Dedieu, G.; Demarez, V.; Ceschia, E.; Probst, J.-L.; Durand, P.; Bustillo, V.
2014-12-01
The growing availability of high-resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the possibilities offered for improving crop-growth dynamic simulation with the distributed agro-hydrological model: topography-based nitrogen transfer and transformation (TNT2). We used a leaf area index (LAI) map series derived from 105 Formosat-2 (F2) images covering the period 2006-2010. The TNT2 model (Beaujouan et al., 2002), calibrated against discharge and in-stream nitrate fluxes for the period 1985-2001, was tested on the 2005-2010 data set (climate, land use, agricultural practices, and discharge and nitrate fluxes at the outlet). Data from the first year (2005) were used to initialize the hydrological model. A priori agricultural practices obtained from an extensive field survey, such as seeding date, crop cultivar, and amount of fertilizer, were used as input variables. Continuous values of LAI as a function of cumulative daily temperature were obtained at the crop-field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics using the a priori input parameters displayed temporal shifts from those observed LAI profiles that are irregularly distributed in space (between field crops) and time (between years). By resetting the seeding date at the crop-field level, we have developed an optimization method designed to efficiently minimize this temporal shift and better fit the crop growth against both the spatial observations and crop production. This optimization of simulated LAI has a negligible impact on water budgets at the catchment scale (1 mm yr-1 on average) but a noticeable impact on in-stream nitrogen fluxes (around 12%), which is of interest when considering nitrate stream contamination issues and the objectives of TNT2 modeling. This study demonstrates the potential contribution of the forthcoming high spatial and temporal resolution
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Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Forman, Barton A.; Draper, Clara S.; Liu, Qing
2013-01-01
A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.
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Temporal resolution requirements of satellite constellations for 30 m global burned area mapping
Melchiorre, A.; Boschetti, L.
2017-12-01
Global burned area maps have been generated systematically with daily, coarse resolution satellite data (Giglio et al. 2013). The production of moderate resolution (10 - 30 m) global burned area products would meet the needs of several user communities: improved carbon emission estimations due to heterogeneous landscapes and for local scale air quality and fire management applications (Mouillot et al. 2014; van der Werf et al. 2010). While the increased spatial resolution reduces the influence of mixed burnt/unburnt pixels and it would increase the spectral separation of burned areas, moderate resolution satellites have reduced temporal resolution (10 - 16 days). Fire causes a land-cover change spectrally visible for a period ranging from a few weeks in savannas to over a year in forested ecosystems (Roy et al. 2010); because clouds, smoke, and other optically thick aerosols limit the number of available observations (Roy et al. 2008; Smith and Wooster 2005), burned areas might disappear before they are observed by moderate resolution sensors. Data fusion from a constellation of different sensors has been proposed to overcome these limits (Boschetti et al. 2015; Roy 2015). In this study, we estimated the probability of moderate resolution satellites and virtual constellations (including Landsat-8/9, Sentinel-2A/B) to provide sufficient observations for burned area mapping globally, and by ecosystem. First, we estimated the duration of the persistence of the signal associated with burned areas by combining the MODIS Global Burned Area and the Nadir BRDF-Adjusted Reflectance Product by characterizing the post-fire trends in reflectance to determine the length of the period in which the burn class is spectrally distinct from the unburned and, therefore, detectable. The MODIS-Terra daily cloud data were then used to estimate the probability of cloud cover. The cloud probability was used at each location to estimate the minimum revisit time needed to obtain at least one
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Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network
Directory of Open Access Journals (Sweden)
Aoran Xiao
2018-04-01
Full Text Available Super-resolution for satellite video attaches much significance to earth observation accuracy, and the special imaging and transmission conditions on the video satellite pose great challenges to this task. The existing deep convolutional neural-network-based methods require pre-processing or post-processing to be adapted to a high-resolution size or pixel format, leading to reduced performance and extra complexity. To this end, this paper proposes a five-layer end-to-end network structure without any pre-processing and post-processing, but imposes a reshape or deconvolution layer at the end of the network to retain the distribution of ground objects within the image. Meanwhile, we formulate a joint loss function by combining the output and high-dimensional features of a non-linear mapping network to precisely learn the desirable mapping relationship between low-resolution images and their high-resolution counterparts. Also, we use satellite video data itself as a training set, which favors consistency between training and testing images and promotes the method’s practicality. Experimental results on “Jilin-1” satellite video imagery show that this method demonstrates a superior performance in terms of both visual effects and measure metrics over competing methods.
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Directory of Open Access Journals (Sweden)
Yingpin Yang
2017-12-01
Full Text Available Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents an improved identification procedure for geo-parcel based crop identification by combining fine-resolution images and multi-source medium-resolution images. GF-2 images with fine spatial resolution of 0.8 m provided agricultural farming plot boundaries, and GF-1 (16 m and Landsat 8 OLI data were used to transform the geo-parcel based enhanced vegetation index (EVI time-series. In this study, we propose a piecewise EVI time-series smoothing method to fit irregular time profiles, especially for crop rotation situations. Global EVI time-series were divided into several temporal segments, from which phenological metrics could be derived. This method was applied to Lixian, where crop rotation was the common practice of growing different types of crops, in the same plot, in sequenced seasons. After collection of phenological features and multi-temporal spectral information, Random Forest (RF was performed to classify crop types, and the overall accuracy was 93.27%. Moreover, an analysis of feature significance showed that phenological features were of greater importance for distinguishing agricultural land cover compared to temporal spectral information. The identification results indicated that the integration of high spatial-temporal resolution imagery is promising for geo-parcel based crop identification and that the newly proposed smoothing method is effective.
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Land science with Sentinel-2 and Sentinel-3 data series synergy
Moreno, Jose; Guanter, Luis; Alonso, Luis; Gomez, Luis; Amoros, Julia; Camps, Gustavo; Delegido, Jesus
2010-05-01
Although the GMES/Sentinel satellite series were primarily designed to provide observations for operational services and routine applications, there is a growing interest in the scientific community towards the usage of Sentinel data for more advanced and innovative science. Apart from the improved spatial and spectral capabilities, the availability of consistent time series covering a period of over 20 years opens possibilities never explored before, such as systematic data assimilation approaches exploiting the time-series concept, or the incorporation in the modelling approaches of processes covering time scales from weeks to decades. Sentinel-3 will provide continuity to current ENVISAT MERIS/AATSR capabilities. The results already derived from MERIS/AATRS will be more systematically exploited by using OLCI in synergy with SLST. Particularly innovative is the case of Sentinel-2, which is specifically designed for land applications. Built on a constellation of two satellites operating simultaneously to provide 5 days geometric revisit time, the Sentinel-2 system will providing global and systematic acquisitions with high spatial resolution and with a high revisit time tailored towards the needs of land monitoring. Apart from providing continuity to Landsat and SPOT time series, the Sentinel-2 Multi-Spectral Instrument (MSI) incorporates new narrow bands around the red-edge for improved retrievals of biophysical parameters. The limitations imposed by the need of a proper cloud screening and atmospheric corrections have represented a serious constraint in the past for optical data. The fact that both Sentinel-2 and 3 have dedicated bands to allow such needed corrections for optical data represents an important step towards a proper exploitation, guarantying consistent time series showing actual variability in land surface conditions without the artefacts introduced by the atmosphere. Expected operational products (such as Land Cover maps, Leaf Area Index
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On land-use modeling: A treatise of satellite imagery data and misclassification error
Sandler, Austin M.
Recent availability of satellite-based land-use data sets, including data sets with contiguous spatial coverage over large areas, relatively long temporal coverage, and fine-scale land cover classifications, is providing new opportunities for land-use research. However, care must be used when working with these datasets due to misclassification error, which causes inconsistent parameter estimates in the discrete choice models typically used to model land-use. I therefore adapt the empirical correction methods developed for other contexts (e.g., epidemiology) so that they can be applied to land-use modeling. I then use a Monte Carlo simulation, and an empirical application using actual satellite imagery data from the Northern Great Plains, to compare the results of a traditional model ignoring misclassification to those from models accounting for misclassification. Results from both the simulation and application indicate that ignoring misclassification will lead to biased results. Even seemingly insignificant levels of misclassification error (e.g., 1%) result in biased parameter estimates, which alter marginal effects enough to affect policy inference. At the levels of misclassification typical in current satellite imagery datasets (e.g., as high as 35%), ignoring misclassification can lead to systematically erroneous land-use probabilities and substantially biased marginal effects. The correction methods I propose, however, generate consistent parameter estimates and therefore consistent estimates of marginal effects and predicted land-use probabilities.
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Pai, H.; Tyler, S.
2017-12-01
Small, unmanned aerial systems (sUAS) are quickly becoming a cost-effective and easily deployable tool for high spatial resolution environmental sensing. Land surface studies from sUAS imagery have largely focused on accurate topographic mapping, quantifying geomorphologic changes, and classification/identification of vegetation, sediment, and water quality tracers. In this work, we explore a further application of sUAS-derived topographic mapping to a two-dimensional (2-d), depth-averaged river hydraulic model (Flow and Sediment Transport with Morphological Evolution of Channels, FaSTMECH) along a short, meandering reach of East River, Colorado. On August 8, 2016, we flew a sUAS as part of the Center for Transformative Environmental Monitoring Programs with a consumer-grade visible camera and created a digital elevation map ( 1.5 cm resolution; 5 cm accuracy; 500 m long river corridor) with Agisoft Photoscan software. With the elevation map, we created a longitudinal water surface elevation (WSE) profile by manually delineating the bank-water interface and river bathymetry by applying refraction corrections for more accurate water depth estimates, an area of ongoing research for shallow and clear river systems. We tested both uncorrected and refraction-corrected bathymetries with the steady-state, 2-d model, applying sensitivities for dissipation parameters (bed roughness and eddy characteristics). Model performance was judged from the WSE data and measured stream velocities. While the models converged, performance and insights from model output could be improved with better bed roughness characterization and additional water depth cross-validation for refraction corrections. Overall, this work shows the applicability of sUAS-derived products to a multidimensional river model, where bathymetric data of high resolution and accuracy are key model input requirements.
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A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data
Stibig, H.-J.; Belward, A.S.; Roy, P.S.; Rosalina-Wasrin, U.; Agrawal, S.; Joshi, P.K.; ,; Beuchle, R.; Fritz, S.; Mubareka, S.; Giri, C.
2007-01-01
Aim Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project.Location The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east.Methods The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’.Results The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics.Main conclusions The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub
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Lo, Chor Pang
1996-01-01
The main objective of this research is to apply airborne high-resolution thermal infrared imagery for urban heat island studies, using Huntsville, AL, a medium-sized American city, as the study area. The occurrence of urban heat islands represents human-induced urban/rural contrast, which is caused by deforestation and the replacement of the land surface by non-evaporating and non-porous materials such as asphalt and concrete. The result is reduced evapotranspiration and more rapid runoff of rain water. The urban landscape forms a canopy acting as a transitional zone between the atmosphere and the land surface. The composition and structure of this canopy have a significant impact on the thermal behavior of the urban environment. Research on the trends of surface temperature at rapidly growing urban sites in the United States during the last 30 to 50 years suggests that significant urban heat island effects have caused the temperatures at these sites to rise by 1 to 2 C. Urban heat islands have caused changes in urban precipitation and temperature that are at least similar to, if not greater than, those predicted to develop over the next 100 years by global change models. Satellite remote sensing, particularly NOAA AVHRR thermal data, has been used in the study of urban heat islands. Because of the low spatial resolution (1.1 km at nadir) of the AVHRR data, these studies can only examine and map the phenomenon at the macro-level. The present research provides the rare opportunity to utilize 5-meter thermal infrared data acquired from an airplane to characterize more accurately the thermal responses of different land cover types in the urban landscape as input to urban heat island studies.
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Baker, R. David; Wang, Yansen; Tao, Wei-Kuo; Wetzel, Peter; Belcher, Larry R.
2004-01-01
High-resolution mesoscale model simulations of the 6-7 May 2000 Missouri flash flood event were performed to test the impact of model initialization and land surface treatment on timing, intensity, and location of extreme precipitation. In this flash flood event, a mesoscale convective system (MCS) produced over 340 mm of rain in roughly 9 hours in some locations. Two different types of model initialization were employed: 1) NCEP global reanalysis with 2.5-degree grid spacing and 12-hour temporal resolution, and 2) Eta reanalysis with 40- km grid spacing and $hour temporal resolution. In addition, two different land surface treatments were considered. A simple land scheme. (SLAB) keeps soil moisture fixed at initial values throughout the simulation, while a more sophisticated land model (PLACE) allows for r interactive feedback. Simulations with high-resolution Eta model initialization show considerable improvement in the intensity of precipitation due to the presence in the initialization of a residual mesoscale convective vortex (hlCV) from a previous MCS. Simulations with the PLACE land model show improved location of heavy precipitation. Since soil moisture can vary over time in the PLACE model, surface energy fluxes exhibit strong spatial gradients. These surface energy flux gradients help produce a strong low-level jet (LLJ) in the correct location. The LLJ then interacts with the cold outflow boundary of the MCS to produce new convective cells. The simulation with both high-resolution model initialization and time-varying soil moisture test reproduces the intensity and location of observed rainfall.
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Super-resolution post-processing for satellites with yaw-steering capability
CSIR Research Space (South Africa)
Van den Dool, R
2012-10-01
Full Text Available We describe a method for improving Earth observation satellite image resolution, for specific areas of interest where the sensor design resolution is insufficient. Our method may be used for satellites with yaw-steering capability, such as Nigeria...
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Global Land Survey Impervious Mapping Project Web Site
DeColstoun, Eric Brown; Phillips, Jacqueline
2014-01-01
The Global Land Survey Impervious Mapping Project (GLS-IMP) aims to produce the first global maps of impervious cover at the 30m spatial resolution of Landsat. The project uses Global Land Survey (GLS) Landsat data as its base but incorporates training data generated from very high resolution commercial satellite data and using a Hierarchical segmentation program called Hseg. The web site contains general project information, a high level description of the science, examples of input and output data, as well as links to other relevant projects.
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Sekertekin, A.; Kutoglu, S.; Kaya, S.; Marangoz, A. M.
2014-12-01
In recent years, climate change is one of the most important problems that the ecological system of the world has been encountering. Global warming and climate change have been studied frequently by all disciplines all over the world and Geomatics Engineering also contributes to such studies by means of remote sensing, global positioning system etc. Monitoring Land Surface Temperature (LST) via remote sensing satellites is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and there are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. Generally three algorithms are used to obtain LST by using Landsat 5 TM data. These algorithms are radiative transfer equation method, single channel method and mono-window algorithm. Radiative transfer equation method is not applicable because during the satellite pass, atmospheric parameters must be measured in-situ. In this research, mono window algorithm was implemented to Landsat 5 TM image. Besides, meteorological data such as humidity and temperature are used in the algorithm. Acquisition date of the image is 28.08.2011 and our study area is Zonguldak, Turkey. High resolution images are used to investigate the relationships between LST and land cover type. As a result of these analyses, area with vegetation cover has approximately 5 ºC lower temperature than the city center and arid land. Because different surface properties like reinforced concrete construction, green zones and sandbank are all together in city center, LST differs about 10 ºC in the city center. The temperature around some places in thermal power plant region Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature because of land cover structure. Thanks to this
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Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai
2010-08-01
In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.
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Spectroscopic Characterization of GEO Satellites with Gunma LOW Resolution Spectrograph
Endo, T.; Ono, H.; Hosokawa, M.; Ando, T.; Takanezawa, T.; Hashimoto, O.
The spectroscopic observation is potentially a powerful tool for understanding the Geostationary Earth Orbit (GEO) objects. We present here the results of an investigation of energy spectra of GEO satellites obtained from a groundbased optical telescope. The spectroscopic observations were made from April to June 2016 with the Gunma LOW resolution Spectrograph and imager (GLOWS) at the Gunma Astronomical Observatory (GAO) in JAPAN. The observation targets consist of eleven different satellites: two weather satellites, four communications satellites, and five broadcasting satellites. All the spectra of those GEO satellites are inferred to be solar-like. A number of well-known absorption features such as H-alpha, H-beta, Na-D,water vapor and oxygen molecules are clearly seen in thewavelength range of 4,000 - 8,000 Å. For comparison, we calculated the intensity ratio of the spectra of GEO satellites to that of the Moon which is the natural satellite of the earth. As a result, the following characteristics were obtained. 1) Some variations are seen in the strength of absorption features of water vapor and oxygen originated by the telluric atmosphere, but any other characteristic absorption features were not found. 2) For all observed satellites, the intensity ratio of the spectrum of GEO satellites decrease as a function of wavelength or to be flat. It means that the spectral reflectance of satellite materials is bluer than that of the Moon. 3) A characteristic dip at around 4,800 Å is found in all observed spectra of a weather satellite. Based on these observations, it is indicated that the characteristics of the spectrum are mainly derived from the solar panels because the apparent area of the solar cell is probably larger than that of the satellite body.
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Exploration of satellite-derived data products for atmospheric turbulence studies
CSIR Research Space (South Africa)
Griffith, DJ
2014-09-01
Full Text Available reasonable proxy in the absence of in-situ measurements. 3.2 ORNL DAAC The Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) provides a global subsetting and time-series derivation for Moderate Resolution Imaging Spectrometer... (MODIS) data from the NASA Terra and Aqua satellite platforms. The products available for subsetting and time-series generation from the ORNL DAAC are given in Table 2. Moreover, this MODIS facility is available programmatically using the Simple Object...
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Bricher, Phillippa K.; Lucieer, Arko; Shaw, Justine; Terauds, Aleks; Bergstrom, Dana M.
2013-01-01
Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments. PMID:23940805
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Directory of Open Access Journals (Sweden)
Charles R. Lane
2014-12-01
Full Text Available Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2 for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA. We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85 for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.
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Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.
2015-04-01
To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is
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Giri, Chandra; Long, Jordan
2014-01-01
Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.
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Karakacan Kuzucu, A.; Bektas Balcik, F.
2017-11-01
Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.
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CSIR Research Space (South Africa)
Kleynhans, W
2010-01-01
Full Text Available A method for detecting land cover change using NDVI timeseries data derived fromMODerate-resolution Imaging Spectroradiometer (MODIS) satellite data is proposed. The algorithm acts as a per pixel change alarm and takes as input the NDVI time...
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Statistics of Deep Convection in the Congo Basin Derived From High-Resolution Simulations.
White, B.; Stier, P.; Kipling, Z.; Gryspeerdt, E.; Taylor, S.
2016-12-01
Convection transports moisture, momentum, heat and aerosols through the troposphere, and so the temporal variability of convection is a major driver of global weather and climate. The Congo basin is home to some of the most intense convective activity on the planet and is under strong seasonal influence of biomass burning aerosol. However, deep convection in the Congo basin remains under studied compared to other regions of tropical storm systems, especially when compared to the neighbouring, relatively well-understood West African climate system. We use the WRF model to perform a high-resolution, cloud-system resolving simulation to investigate convective storm systems in the Congo. Our setup pushes the boundaries of current computational resources, using a 1 km grid length over a domain covering millions of square kilometres and for a time period of one month. This allows us to draw statistical conclusions on the nature of the simulated storm systems. Comparing data from satellite observations and the model enables us to quantify the diurnal variability of deep convection in the Congo basin. This approach allows us to evaluate our simulations despite the lack of in-situ observational data. This provides a more comprehensive analysis of the diurnal cycle than has previously been shown. Further, we show that high-resolution convection-permitting simulations performed over near-seasonal timescales can be used in conjunction with satellite observations as an effective tool to evaluate new convection parameterisations.
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Directory of Open Access Journals (Sweden)
Paulo Costa de Oliveira Filho
2012-11-01
Full Text Available The objective of this research was to present a detailed diagnosis for use and occupation of urban land aimed at different levels of sealing, in a downtownIrati area of 14 blocks, totaling 0.23 km2, from the Quickbird satellite images with spatial resolution of 0.60 m, by the method of interpretation and vectorization on the screen followed by classification. The area occupied by the classes that represent the highest level of waterproof is 33,218% of the total study area, since the area occupied by classes representing less impermeable level is 22,488% of the same area. The results show that the study area is well sealed.
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Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.
2018-06-01
Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.
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Energy Technology Data Exchange (ETDEWEB)
Hollmann, R. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Atmosphaerenphysik
2000-07-01
Since forty years instruments onboard satellites have been demonstrated their usefulness for many applications in the field of meteorology and oceanography. Several experiments, like ERBE, are dedicated to establish a climatology of the global Earth radiation budget at the top of the atmosphere. Now the focus has been changed to the regional scale, e.g. GEWEX with its regional sub-experiments like BALTEX. To obtain a regional radiation budget for Europe in the first part of the work the well calibrated measurements from ScaRaB (scanner for radiation budget) are used to derive a narrow-to-broadband conversion, which is applicable to the AVHRR (advanced very high resolution radiometer). It is shown, that the accuracy of the method is in the order of that from SCaRaB itself. In the second part of the work, results of REMO have been compared with measurements of ScaRaB and AVHRR for March 1994. The model reproduces the measurements overall well, but it is overestimating the cold areas and underestimating the warm areas in the longwave spectral domain. Similarly it is overestimating the dark areas and underestimating the bright areas in the solar spectral domain. (orig.)
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The high-resolution version of TM5-MP for optimized satellite retrievals: description and validation
Williams, Jason E.; Folkert Boersma, K.; Le Sager, Phillipe; Verstraeten, Willem W.
2017-02-01
We provide a comprehensive description of the high-resolution version of the TM5-MP global chemistry transport model, which is to be employed for deriving highly resolved vertical profiles of nitrogen dioxide (NO2), formaldehyde (CH2O), and sulfur dioxide (SO2) for use in satellite retrievals from platforms such as the Ozone Monitoring Instrument (OMI) and the Sentinel-5 Precursor, and the TROPOspheric Monitoring Instrument (tropOMI). Comparing simulations conducted at horizontal resolutions of 3° × 2° and 1° × 1° reveals differences of ±20 % exist in the global seasonal distribution of 222Rn, being larger near specific coastal locations and tropical oceans. For tropospheric ozone (O3), analysis of the chemical budget terms shows that the impact on globally integrated photolysis rates is rather low, in spite of the higher spatial variability of meteorological data fields from ERA-Interim at 1° × 1°. Surface concentrations of O3 in high-NOx regions decrease between 5 and 10 % at 1° × 1° due to a reduction in NOx recycling terms and an increase in the associated titration term of O3 by NO. At 1° × 1°, the net global stratosphere-troposphere exchange of O3 decreases by ˜ 7 %, with an associated shift in the hemispheric gradient. By comparing NO, NO2, HNO3 and peroxy-acetyl-nitrate (PAN) profiles against measurement composites, we show that TM5-MP captures the vertical distribution of NOx and long-lived NOx reservoirs at background locations, again with modest changes at 1° × 1°. Comparing monthly mean distributions in lightning NOx and applying ERA-Interim convective mass fluxes, we show that the vertical re-distribution of lightning NOx changes with enhanced release of NOx in the upper troposphere. We show that surface mixing ratios in both NO and NO2 are generally underestimated in both low- and high-NOx scenarios. For Europe, a negative bias exists for [NO] at the surface across the whole domain, with lower biases at 1° × 1° at only ˜ 20
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Directory of Open Access Journals (Sweden)
Stefanie M. Herrmann
2014-09-01
Full Text Available Mounting evidence from satellite observations of a re-greening across much of the Sahel and Sudan zones over the past three decades has raised questions about the extent and reversibility of desertification. Historical ground data that could help in interpreting the re-greening are scarce. To fill that void, we tapped into the collective memories of local land users from central and western Senegal in 39 focus groups and assessed the spatial association between their perceptions of vegetation changes over time and remote sensing-derived trends. To provide context to the vegetation changes, we also explored the land users' perspective on the evolution of other environmental and human variables that are potentially related to the greening, using participatory research methods. While increases in vegetation were confirmed by the study participants for certain areas, which spatially corresponded to satellite-observed re-greening, vegetation degradation dominated their perceptions of change. This degradation, although spatially extensive according to land users, flies under the radar of coarse-resolution remote sensing data because it is not necessarily associated with a decrease in biomass but rather with undesired changes in species composition. Few significant differences were found in the perceived trends of population pressure, environmental, and livelihood variables between communities that have greened up according to satellite data and those that have not. Our findings challenge the prevailing chain of assumptions of the satellite-observed greening trend indicating an improvement of environmental conditions in the sense of a rehabilitation of the vegetation cover after the great droughts of the 1970s and 1980s, and the improvement of environmental conditions possibly translating into more stable livelihoods and greater well-being of the populations. For monitoring desertification and rehabilitation, there is a need to develop remote sensing
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Directory of Open Access Journals (Sweden)
M. Ashrafi
2005-01-01
Full Text Available Energy maps are important for ionosphere-magnetosphere coupling studies, because quantitative determination of field-aligned currents requires knowledge of the conductances and their spatial gradients. By combining imaging riometer absorption and all-sky auroral optical data it is possible to produce high temporal and spatial resolution maps of the Maxwellian characteristic energy of precipitating electrons within a 240240 common field of view. These data have been calibrated by inverting EISCAT electron density profiles into equivalent energy spectra. In this paper energy maps produced by ground-based instruments (optical and riometer are compared with DMSP satellite data during geomagnetic conjunctions. For the period 1995-2002, twelve satellite passes over the ground-based instruments' field of view for the cloud-free conditions have been considered. Four of the satellite conjunctions occurred during moderate geomagnetic, steady-state conditions and without any ion precipitation. In these cases with Maxwellian satellite spectra, there is 71% agreement between the characteristic energies derived from the satellite and the ground-based energy map method.
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Directory of Open Access Journals (Sweden)
M. Ashrafi
2005-01-01
Full Text Available Energy maps are important for ionosphere-magnetosphere coupling studies, because quantitative determination of field-aligned currents requires knowledge of the conductances and their spatial gradients. By combining imaging riometer absorption and all-sky auroral optical data it is possible to produce high temporal and spatial resolution maps of the Maxwellian characteristic energy of precipitating electrons within a 240240 common field of view. These data have been calibrated by inverting EISCAT electron density profiles into equivalent energy spectra. In this paper energy maps produced by ground-based instruments (optical and riometer are compared with DMSP satellite data during geomagnetic conjunctions. For the period 1995-2002, twelve satellite passes over the ground-based instruments' field of view for the cloud-free conditions have been considered. Four of the satellite conjunctions occurred during moderate geomagnetic, steady-state conditions and without any ion precipitation. In these cases with Maxwellian satellite spectra, there is 71% agreement between the characteristic energies derived from the satellite and the ground-based energy map method.
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De Roeck, Els; Van Coillie, Frieke; De Wulf, Robert; Soenen, Karen; Charlier, Johannes; Vercruysse, Jozef; Hantson, Wouter; Ducheyne, Els; Hendrickx, Guy
2014-12-01
The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke, as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m2 and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.
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Sun, Y. S.; Zhang, L.; Xu, B.; Zhang, Y.
2018-04-01
The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image - GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.
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Use of satellite data for the monitoring of species on Saba and St. Eustatius
Smith, S.R.; Mücher, C.A.; Debrot, A.O.; Roupioz, L.F.S.; Meesters, H.W.G.; Hazeu, G.W.; Davaasuren, N.
2013-01-01
The present study examines the possibility to identify the different land cover types (natural and artificial) on very high resolution satellite images of the Caribbean islands Bonaire, Saba and St. Eustatius. Linking species habitat requirements with associated land cover types allows for the
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Kanaparthi, M. B.
2017-12-01
In India urban population is growing day by day which is causing air pollution less air quality finally leading to climate change and global warming. To mitigate the effect of the climate change we need to plant more trees in the urban area. The objective of this study is develop a plan to improve the urban Green Infrastructure (GI) to fight against the climate change and global warming. Improving GI is a challenging and difficult task in the urban areas because land unavailability of land, to overcome the problem greenways is a good the solution. Greenway is a linear open space developed along the rivers, canals, roads in the urban areas to form a network of green spaces. Roads are the most common structures in the urban area. The idea is to develop the greenways alongside the road to connecting the different green spaces. Tree crowns will act as culverts to connect the green spaces. This will require the spatial structure of the green space, distribution of trees along the roads and the gap areas along the road where more trees can be planted. This can be achieved with help of high resolution Satellite Imagery and the object extraction techniques. This study was carried in the city Bhimavaram which is located in state Andhra Pradesh. The final outcome of this study is potential gap areas for planting trees in the city.
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Directory of Open Access Journals (Sweden)
N. Peter Benny
2015-01-01
Full Text Available The present study analyses the mean and seasonal mesoscale surface circulation of the Northern South China Sea (NSCS and determines the influence of El Niño/SouthernNiño/Southern Oscillation (ENSO. High resolution Eulerian velocity field is derived by combining the available satellite tracked surface drifter data with satellite altimetry during 1993 - 2012. The wind driven current is computed employing the weekly ocean surface mean wind fields derived from the scatterometers on board ERS 1/2, QuikSCAT and ASCAT. The derived mean velocity field exhibits strong boundary currents and broad zonal flow across NSCS. The anomalous field is quite strong in the southern part and the Seasonal circulation clearly depicts the monsoonal forcing. Eddy Kinetic Energy (EKE distribution and its spatial and temporal structures are determined employing Empirical Orthogonal Function (EOF analysis. The ENSO influence on NSCS surface circulation has been analyzed using monthly absolute geostrophic velocity fields during 1996 - 1999.
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Directory of Open Access Journals (Sweden)
A-Ra Cho
2013-08-01
Full Text Available We evaluated the precision of land surface temperature (LST operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS. The split-window (SW-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4. The estimation capabilities of the COMS SW (CSW LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE = 1.41 K, correlation coefficient = 0.99; however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were −1.009 K, 2.613 K and 0.988, respectively.
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Directory of Open Access Journals (Sweden)
Lili Li
2015-06-01
Full Text Available Satellite remote sensing offers an effective approach to estimate indicators of air quality on a large scale. It is critically significant for air quality monitoring in areas experiencing rapid urbanization and consequently severe air pollution, like the Pearl River Delta (PRD in China. This paper starts with examining ground observations of particulate matter (PM and the relationship between PM10 (particles smaller than 10 μm and aerosol optical thickness (AOT by analyzing observations on the sampling sites in the PRD. A linear regression (R2 = 0.51 is carried out using MODIS-derived 500 m-resolution AOT and PM10 concentration from monitoring stations. Data of atmospheric boundary layer (ABL height and relative humidity are used to make vertical and humidity corrections on AOT. Results after correction show higher correlations (R2 = 0.55 between extinction coefficient and PM10. However, coarse spatial resolution of meteorological data affects the smoothness of retrieved maps, which suggests high-resolution and accurate meteorological data are critical to increase retrieval accuracy of PM. Finally, the model provides the spatial distribution maps of instantaneous and yearly average PM10 over the PRD. It is proved that observed PM10 is more relevant to yearly mean AOT than instantaneous values.
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HIGH RESOLUTION SEAMLESS DOM GENERATION OVER CHANG'E-5 LANDING AREA USING LROC NAC IMAGES
Directory of Open Access Journals (Sweden)
K. Di
2018-04-01
Full Text Available Chang’e-5, China’s first sample return lunar mission, will be launched in 2019, and the planned landing area is near Mons Rümker in Oceanus Procellarum. High-resolution and high-precision mapping of the landing area is of great importance for supporting scientific analysis and safe landing. This paper proposes a systematic method for large area seamless digital orthophoto map (DOM generation, and presents the mapping result of Chang’e-5 landing area using over 700 LROC NAC images. The developed method mainly consists of two stages of data processing: stage 1 includes subarea block adjustment with rational function model (RFM and seamless subarea DOM generation; stage 2 includes whole area adjustment through registration of the subarea DOMs with thin plate spline model and seamless DOM mosaicking. The resultant seamless DOM coves a large area (20° longitude × 4° latitude and is tied to the widely used reference DEM – SLDEM2015. As a result, the RMS errors of the tie points are all around half pixel in image space, indicating a high internal precision; the RMS errors of the control points are about one grid cell size of SLDEM2015, indicating that the resultant DOM is tied to SLDEM2015 well.
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High Resolution Seamless Dom Generation Over CHANG'E-5 Landing Area Using Lroc Nac Images
Di, K.; Jia, M.; Xin, X.; Liu, B.; Liu, Z.; Peng, M.; Yue, Z.
2018-04-01
Chang'e-5, China's first sample return lunar mission, will be launched in 2019, and the planned landing area is near Mons Rümker in Oceanus Procellarum. High-resolution and high-precision mapping of the landing area is of great importance for supporting scientific analysis and safe landing. This paper proposes a systematic method for large area seamless digital orthophoto map (DOM) generation, and presents the mapping result of Chang'e-5 landing area using over 700 LROC NAC images. The developed method mainly consists of two stages of data processing: stage 1 includes subarea block adjustment with rational function model (RFM) and seamless subarea DOM generation; stage 2 includes whole area adjustment through registration of the subarea DOMs with thin plate spline model and seamless DOM mosaicking. The resultant seamless DOM coves a large area (20° longitude × 4° latitude) and is tied to the widely used reference DEM - SLDEM2015. As a result, the RMS errors of the tie points are all around half pixel in image space, indicating a high internal precision; the RMS errors of the control points are about one grid cell size of SLDEM2015, indicating that the resultant DOM is tied to SLDEM2015 well.
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Simons, G.W.H.; Bastiaanssen, W.G.M.; Ngô, L.A.; Hain, C.R.; Anderson, M.; Senay, G.
2016-01-01
With changes in weather patterns and intensifying anthropogenic water use, there is an increasing need for spatio-temporal information on water fluxes and stocks in river basins. The assortment of satellite-derived open-access information sources on rainfall (P) and land use/land cover (LULC) is
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Land cover mapping of Greater Mesoamerica using MODIS data
Giri, Chandra; Jenkins, Clinton N.
2005-01-01
A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: (i) qualitative assessment provided good insight in identifying and correcting gross errors; (ii) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest (r2 = 0.88), shrubland (r2 = 0.75), and cropland (r2 = 0.97) but weak positive association for grassland (r2 = 0.26); and (iii) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.
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Use of high resolution satellite images for monitoring of earthquakes and volcano activity.
Arellano-Baeza, Alonso A.
Our studies have shown that the strain energy accumulation deep in the Earth's crust that precedes a strong earthquake can be detected by applying a lineament extraction technique to the high-resolution multispectral satellite images. A lineament is a straight or a somewhat curved feature in a satellite image, which it is possible to detect by a special processing of images based on directional filtering and or Hough transform. We analyzed tens of earthquakes occurred in the Pacific coast of the South America with the Richter scale magnitude ˜4.5, using ASTER/TERRA multispectral satellite images for detection and analysis of changes in the system of lineaments previous to a strong earthquake. All events were located in the regions with small seasonal variations and limited vegetation to facilitate the tracking of features associated with the seismic activity only. It was found that the number and orientation of lineaments changed significantly about one month before an earthquake approximately, and a few months later the system returns to its initial state. This effect increases with the earthquake magnitude. It also was shown that the behavior of lineaments associated to the volcano seismic activity is opposite to that obtained previously for earthquakes. This discrepancy can be explained assuming that in the last case the main reason of earthquakes is compression and accumulation of strength in the Earth's crust due to subduction of tectonic plates, whereas in the first case we deal with the inflation of a volcano edifice due to elevation of pressure and magma intrusion. The results obtained made it possible to include this research as a part of scientific program of Chilean Remote Sensing Satellite mission to be launched in 2010.
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Debats, Stephanie Renee
Smallholder farms dominate in many parts of the world, including Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural landcover. In this thesis, we developed a benchmark labeled data set of high-resolution satellite imagery of agricultural fields in South Africa. We presented a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. The algorithm achieved similar high performance across agricultural types, including spectrally indistinct smallholder fields, and demonstrated the ability to generalize across large geographic areas. In sensitivity analyses, we determined multi-temporal images provided greater performance gains than the addition of multi-spectral bands. We also demonstrated how active learning can be incorporated in the algorithm to create smaller, more efficient training data sets, which reduced computational resources, minimized the need for humans to hand-label data, and boosted performance. We designed a patch-based uncertainty metric to drive the active learning framework, based on the regular grid of a crowdsourcing platform, and demonstrated how subject matter experts can be replaced with fleets of crowdsourcing workers. Our active learning algorithm achieved similar performance as an algorithm trained with randomly selected data, but with 62% less data samples. This thesis furthers the goal of providing accurate agricultural landcover maps, at a scale that is relevant for the dominant smallholder class. Accurate maps are crucial for monitoring and promoting agricultural production. Furthermore, improved agricultural landcover maps will aid a host of other applications, including landcover change assessments, cadastral surveys to strengthen smallholder land rights, and constraints for crop modeling
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A new digital land mobile satellite system
Schneider, Philip
A description is given of the different digital services planned to be carried over existing and planned mobile satellite systems. These systems are then compared with analog services in terms of bandwidth and power efficiency. This comparison provides the rationale for the establishment of a digital land mobile satellite service (DLMSS) to use frequencies that are currently available but not yet assigned to a domestic mobile satellite system in the United States. The focus here is on the expected advantages of digital transmission techniques in accommodating additional mobile satellite systems in this portion of the spectrum, and how such techniques can fully satisfy voice, data and facsimile mobile communications requirements in a cost effective manner. A description is given of the system architecture of the DMLSS service proposed by the Geostar Messaging Corporation (GMC) and the market potential of DLMSS.
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Linking Satellite Derived Land Surface Temperature with Cholera: A Case Study for South Sudan
Aldaach, H. S. V.; Jutla, A.; Akanda, A. S.; Colwell, R. R.
2014-12-01
A sudden onset of cholera in South Sudan, in April 2014 in Northern Bari in Juba town resulted in more than 400 cholera cases after four weeks of initial outbreak with a case of fatality rate of CFR 5.4%. The total number of reported cholera cases for the period of April to July, 2014 were 5,141 including 114 deaths. With the limited efficacy of cholera vaccines, it is necessary to develop mechanisms to predict cholera occurrence and thereafter devise intervention strategies for mitigating impacts of the disease. Hydroclimatic processes, primarily precipitation and air temperature are related to epidemic and episodic outbreak of cholera. However, due to coarse resolution of both datasets, it is not possible to precisely locate the geographical location of disease. Here, using Land Surface Temperature (LST) from MODIS sensors, we have developed an algorithm to identify regions susceptible for cholera. Conditions for occurrence of cholera were detectable at least one month in advance in South Sudan and were statistically sensitive to hydroclimatic anomalies of land surface and air temperature, and precipitation. Our results indicate significant spatial and temporal averaging required to infer usable information from LST over South Sudan. Preliminary results that geographically location of cholera outbreak was identifiable within 1km resolution of the LST data.
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Seto, Karen Ching-Yee
Over the last two decades, rapid rates of economic growth in the People's Republic of China have converted large areas of natural ecosystems and agricultural lands to urban uses. The size and rate of these land-use changes may affect local and regional climate, biogeochemistry, and food supply. To assess these impacts, both the amount of land converted and its relation to socioeconomic drivers must be determined. This research combines satellite remote sensing, which is used to monitor land conversion, with socioeconomic data to model the economic and demographic drivers of land-use change in the Pearl River Delta of Southern China. This research modifies existing techniques and develops new methods to assess the type, amount, and timing of land-use change from annual Landsat Thematic Mapper (TM) images from 1988 to 1996. During this period, most of the land-use change is conversion of agricultural land to urban areas. Results indicate that urban areas, increased by over 300% between 1988 and 1996. Field assessments confirm these results and indicate that the land-use change map is highly accurate at 93.5%. To use these data as inputs to statistical models, the year of land conversion derived from satellite imagery must be unbiased. A new method that uses time series techniques identifies the date at which land-use changes occur from a sequential series of TM images. The accuracy and bias of the dates of change identified compare favorably to a more conventional remote sensing change detection technique and may have the additional advantages of reducing efforts required to assemble training data and to correct for atmospheric effects. Data on the quantity of land-use change and the timing of these changes are used in conjunction with socioeconomic data to estimate statistical models that identify and quantify the demographic and economic changes on two types of land conversion: urbanization of agricultural land and urbanization of natural vegetation. Results
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Amatya, Pukar Man; Ma, Yaoming; Han, Cunbo; Wang, Binbin; Devkota, Lochan Prasad
2015-12-01
Novice efforts have been made in order to study the regional distribution of land surface heat fluxes on the southern side of the central Himalayas utilizing high-resolution remotely sensed products, but these have been on instantaneous scale. In this study the Surface Energy Balance System model is used to obtain annual averaged maps of the land surface heat fluxes for 11 years (2003-2013) and study their annual trends on the central Himalayan region. The maps were derived at 5 km resolution using monthly input products ranging from satellite derived to Global Land Data Assimilation System meteorological data. It was found that the net radiation flux is increasing as a result of decreasing precipitation (drier environment). The sensible heat flux did not change much except for the northwestern High Himalaya and High Mountains. In northwestern High Himalaya sensible heat flux is decreasing because of decrease in wind speed, ground-air temperature difference, and increase in winter precipitation, whereas in High Mountains it is increasing due to increase in ground-air temperature difference and high rate of deforestation. The latent heat flux has an overall increasing trend with increase more pronounced in the lower regions compared to high elevated regions. It has been reported that precipitation is decreasing with altitude in this region. Therefore, the increasing trend in latent heat flux can be attributed to increase in net radiation flux under persistent forest cover and irrigation land used for agriculture.
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Generation and Assessment of Urban Land Cover Maps Using High-Resolution Multispectral Aerial Images
DEFF Research Database (Denmark)
Höhle, Joachim; Höhle, Michael
2013-01-01
a unique method for the automatic generation of urban land cover maps. In the present paper, imagery of a new medium-format aerial camera and advanced geoprocessing software are applied to derive normalized digital surface models and vegetation maps. These two intermediate products then become input...... to a tree structured classifier, which automatically derives land cover maps in 2D or 3D. We investigate the thematic accuracy of the produced land cover map by a class-wise stratified design and provide a method for deriving necessary sample sizes. Corresponding survey adjusted accuracy measures...... and their associated confidence intervals are used to adequately reflect uncertainty in the assessment based on the chosen sample size. Proof of concept for the method is given for an urban area in Switzerland. Here, the produced land cover map with six classes (building, wall and carport, road and parking lot, hedge...
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High-resolution forest carbon stocks and emissions in the Amazon.
Asner, Gregory P; Powell, George V N; Mascaro, Joseph; Knapp, David E; Clark, John K; Jacobson, James; Kennedy-Bowdoin, Ty; Balaji, Aravindh; Paez-Acosta, Guayana; Victoria, Eloy; Secada, Laura; Valqui, Michael; Hughes, R Flint
2010-09-21
Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.
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Modelling Participatory Geographic Information System for Customary Land Conflict Resolution
Gyamera, E. A.; Arko-Adjei, A.; Duncan, E. E.; Kuma, J. S. Y.
2017-11-01
Since land contributes to about 73 % of most countries Gross Domestic Product (GDP), attention on land rights have tremendously increased globally. Conflicts over land have therefore become part of the major problems associated with land administration. However, the conventional mechanisms for land conflict resolution do not provide satisfactory result to disputants due to various factors. This study sought to develop a Framework of using Participatory Geographic Information System (PGIS) for customary land conflict resolution. The framework was modelled using Unified Modelling Language (UML). The PGIS framework, called butterfly model, consists of three units namely, Social Unit (SU), Technical Unit (TU) and Decision Making Unit (DMU). The name butterfly model for land conflict resolution was adopted for the framework based on its features and properties. The framework has therefore been recommended to be adopted for land conflict resolution in customary areas.
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Wang, W.; Wang, Y.; Hashimoto, H.; Li, S.; Takenaka, H.; Higuchi, A.; Lyapustin, A.; Nemani, R. R.
2017-12-01
The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor land surface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of-atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked ("remembered") over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance
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Volcanic SO2 fluxes derived from satellite data: a survey using OMI, GOME-2, IASI and MODIS
Directory of Open Access Journals (Sweden)
N. Theys
2013-06-01
Full Text Available Sulphur dioxide (SO2 fluxes of active degassing volcanoes are routinely measured with ground-based equipment to characterize and monitor volcanic activity. SO2 of unmonitored volcanoes or from explosive volcanic eruptions, can be measured with satellites. However, remote-sensing methods based on absorption spectroscopy generally provide integrated amounts of already dispersed plumes of SO2 and satellite derived flux estimates are rarely reported. Here we review a number of different techniques to derive volcanic SO2 fluxes using satellite measurements of plumes of SO2 and investigate the temporal evolution of the total emissions of SO2 for three very different volcanic events in 2011: Puyehue-Cordón Caulle (Chile, Nyamulagira (DR Congo and Nabro (Eritrea. High spectral resolution satellite instruments operating both in the ultraviolet-visible (OMI/Aura and GOME-2/MetOp-A and thermal infrared (IASI/MetOp-A spectral ranges, and multispectral satellite instruments operating in the thermal infrared (MODIS/Terra-Aqua are used. We show that satellite data can provide fluxes with a sampling of a day or less (few hours in the best case. Generally the flux results from the different methods are consistent, and we discuss the advantages and weaknesses of each technique. Although the primary objective of this study is the calculation of SO2 fluxes, it also enables us to assess the consistency of the SO2 products from the different sensors used.
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Weiqi Zhou; Austin Troy; Morgan Grove
2008-01-01
Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...
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Assessing the consistency of UAV-derived point clouds and images acquired at different altitudes
Ozcan, O.
2016-12-01
Unmanned Aerial Vehicles (UAVs) offer several advantages in terms of cost and image resolution compared to terrestrial photogrammetry and satellite remote sensing system. Nowadays, UAVs that bridge the gap between the satellite scale and field scale applications were initiated to be used in various application areas to acquire hyperspatial and high temporal resolution imageries due to working capacity and acquiring in a short span of time with regard to conventional photogrammetry methods. UAVs have been used for various fields such as for the creation of 3-D earth models, production of high resolution orthophotos, network planning, field monitoring and agricultural lands as well. Thus, geometric accuracy of orthophotos and volumetric accuracy of point clouds are of capital importance for land surveying applications. Correspondingly, Structure from Motion (SfM) photogrammetry, which is frequently used in conjunction with UAV, recently appeared in environmental sciences as an impressive tool allowing for the creation of 3-D models from unstructured imagery. In this study, it was aimed to reveal the spatial accuracy of the images acquired from integrated digital camera and the volumetric accuracy of Digital Surface Models (DSMs) which were derived from UAV flight plans at different altitudes using SfM methodology. Low-altitude multispectral overlapping aerial photography was collected at the altitudes of 30 to 100 meters and georeferenced with RTK-GPS ground control points. These altitudes allow hyperspatial imagery with the resolutions of 1-5 cm depending upon the sensor being used. Preliminary results revealed that the vertical comparison of UAV-derived point clouds with respect to GPS measurements pointed out an average distance at cm-level. Larger values are found in areas where instantaneous changes in surface are present.
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Evaluation of satellite derived spectral diffuse attenuation coefficients
Digital Repository Service at National Institute of Oceanography (India)
Suresh, T.; Talaulikar, M.; Desa, E.; Mascarenhas, A.A.M.Q.; Matondkar, S.G.P.
, 443, 490, 510, 555 and 670 nm derived from the ocean color satellite sensor, SeaWiFS with the in-situ measured values from the Arabian Sea is compared. The satellite derived values are found to be comparable to the measured values in the lower...
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Directory of Open Access Journals (Sweden)
Yang Bai
2015-04-01
Full Text Available As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST retrieved from Thermal Infra-Red (TIR images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD and absolute average difference (AAD are smaller than 1.7 K, where the correlation coefficient (CC and root-mean-square error (RMSE are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.
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Directory of Open Access Journals (Sweden)
Zhiyong Lv
2018-03-01
Full Text Available In recent decades, land cover change detection (LCCD using very high-spatial resolution (VHR remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution, respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type
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Spatial resolution influence on the identification of land cover classes in the Amazon environment
Directory of Open Access Journals (Sweden)
PONZONI FLÁVIO J.
2002-01-01
Full Text Available To evaluate the role played by the spatial resolution in distinguishing land cover classes in the Amazon region, different levels of spatial resolution (60, 100, 120, 200 and 250 meters were simulated from a Landsat_5 Thematic Mapper (TM image. Thematic maps were produced by visual interpretation from the original (30 x 30 meters and simulated set of images. The map legend included primary forest, old and young woody secondary succession, and non-forest. The results indicated that for the discrimination between primary forest and non-forest, spatial resolution did not have great influence for pixel size equal or lower than 200 meters. The contrary was verified for the identification of old and young woody secondary vegetation due to their occurrence in small polygons. To avoid significant changes in the calculated area of these land cover types, a spatial resolution better than 100 meters is required. This result is an indication that the use of the future Brazilian remote sensing satellite (SSR-1 for secondary succession identification may be unreliable, especially for latitudes between S10degrees and S15degrees where critical areas of deforestation are located and pixel size is expected to vary within the same scene from 100 meters (S10degrees to 200 meters (S15degrees.
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Soti, Valérie; Chevalier, Véronique; Maura, Jonathan; Bégué, Agnès; Lelong, Camille; Lancelot, Renaud; Thiongane, Yaya; Tran, Annelise
2013-03-01
Dynamics of most of vector-borne diseases are strongly linked to global and local environmental changes. Landscape changes are indicators of human activities or natural processes that are likely to modify the ecology of the diseases. Here, a landscape approach developed at a local scale is proposed for extracting mosquito favourable biotopes, and for testing ecological parameters when identifying risk areas of Rift Valley fever (RVF) transmission. The study was carried out around Barkedji village, Ferlo region, Senegal. In order to test whether pond characteristics may influence the density and the dispersal behaviour of RVF vectors, and thus the spatial variation in RVFV transmission, we used a very high spatial resolution remote sensing image (2.4 m resolution) provided by the Quickbird sensor to produce a detailed land-cover map of the study area. Based on knowledge of vector and disease ecology, seven landscape attributes were defined at the pond level and computed from the land-cover map. Then, the relationships between landscape attributes and RVF serologic incidence rates in small ruminants were analyzed through a beta-binomial regression. Finally, the best statistical model according to the Akaike Information Criterion corrected for small samples (AICC), was used to map areas at risk for RVF. Among the derived landscape variables, the vegetation density index (VDI) computed within a 500 m buffer around ponds was positively correlated with serologic incidence (premote sensing data for identifying environmental risk factors and mapping RVF risk areas at a local scale.
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Leng, Pei; Li, Zhao-Liang; Duan, Si-Bo; Gao, Mao-Fang; Huo, Hong-Yuan
2017-09-01
Soil moisture has long been recognized as one of the essential variables in the water cycle and energy budget between Earth's surface and atmosphere. The present study develops a practical approach for deriving all-weather soil moisture using combined satellite images and gridded meteorological products. In this approach, soil moisture over the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky pixels are estimated from the Vegetation Index/Temperature (VIT) trapezoid scheme in which theoretical dry and wet edges were determined pixel to pixel by China Meteorological Administration Land Data Assimilation System (CLDAS) meteorological products, including air temperature, solar radiation, wind speed and specific humidity. For cloudy pixels, soil moisture values are derived by the calculation of surface and aerodynamic resistances from wind speed. The approach is capable of filling the soil moisture gaps over remaining cloudy pixels by traditional optical/thermal infrared methods, allowing for a spatially complete soil moisture map over large areas. Evaluation over agricultural fields indicates that the proposed approach can produce an overall generally reasonable distribution of all-weather soil moisture. An acceptable accuracy between the estimated all-weather soil moisture and in-situ measurements at different depths could be found with an Root Mean Square Error (RMSE) varying from 0.067 m3/m3 to 0.079 m3/m3 and a slight bias ranging from 0.004 m3/m3 to -0.011 m3/m3. The proposed approach reveals significant potential to derive all-weather soil moisture using currently available satellite images and meteorological products at a regional or global scale in future developments.
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Spennemann, P. C.; Salvia, M.; Ruscica, R. C.; Sörensson, A. A.; Grings, F.; Karszenbaum, H.
2018-02-01
In regions of strong Land-Atmosphere (L-A) interaction, soil moisture (SM) conditions can impact the atmosphere through modulating the land surface fluxes. The importance of the identification of L-A interaction regions lies in the potential improvement of the weather/seasonal forecast and the better understanding of the physical mechanisms involved. This study aims to compare the terrestrial segment of the L-A interaction from satellite products and climate models, motivated by previous modeling studies pointing out southeastern South America (SESA) as a L-A hotspot during austral summer. In addition, the L-A interaction under dry or wet anomalous conditions over SESA is analyzed. To identify L-A hotspots the AMSRE-LPRM SM and MODIS land surface temperature products; coupled climate models and uncoupled land surface models were used. SESA highlights as a strong L-A interaction hotspot when employing different metrics, temporal scales and independent datasets, showing consistency between models and satellite estimations. Both AMSRE-LPRM bands (X and C) are consistent showing a strong L-A interaction hotspot over the Pampas ecoregion. Intensification and a larger spatial extent of the L-A interaction for dry summers was observed in both satellite products and models compared to wet summers. These results, which were derived from measured physical variables, are encouraging and promising for future studies analyzing L-A interactions. L-A interaction analysis is proposed here as a meeting point between remote sensing and climate modelling communities of Argentina, within a region with the highest agricultural and livestock production of the continent, but with an important lack of in-situ SM observations.
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Shinomiya, Kazufusa; Zaima, Kazumasa; Harada, Yukina; Yasue, Miho; Harikai, Naoki; Tokura, Koji; Ito, Yoichiro
2017-01-20
Coil satellite centrifuge (CSC) produces the complex satellite motion consisting of the triplicate rotation of the coiled column around three axes including the sun axis (the angular velocity, ω 1 ), the planet axis (ω 2 ) and the satellite axis (the central axis of the column) (ω 3 ) according to the following formula: ω 1 =ω 2 +ω 3 . Improved peak resolution in the separation of 4-methylumbelliferyl sugar derivatives was achieved using the conventional multilayer coiled columns with ethyl acetate/1-butanol/water (3: 2: 5, v/v) for the lower mobile phase at the combination of the rotation speeds (ω 1 , ω 2 , ω 3 )=(300, 150, 150rpm), and (1:4:5, v/v) for the upper mobile phase at (300:100:200rpm). The effect of the satellite motion on the peak resolution and the stationary phase retention was evaluated by each CSC separation with the different rotation speeds of ω 2 and ω 3 under the constant revolution speed at ω 1 =300rpm. With the lower mobile phase, almost constant peak resolution and stationary phase retention were yielded regardless of the change of ω 2 and ω 3 , while with the upper mobile phase these two values were sensitively varied according to the different combination of ω 2 and ω 3 . For example, when ω 2 =147 or 200rpm is used, no stationary phase was retained in the coiled column while ω 2 =150rpm could retain enough volume of stationary phase for separation. On the other hand, the combined rotation speeds at (ω 1 , ω 2 , ω 3 )=(300, 300, 0rpm) or (300, 0, 300rpm) produced insufficient peak resolution regardless of the choice of the mobile phase apparently due to the lack of rotation speed except at (300, 0, 300rpm) with the upper mobile phase. At lower rotation speed of ω 1 =300rpm, better peak resolution and stationary phase retention were obtained by the satellite motion (ω 3 ) than by the planetary motion (ω 2 ), or ω 3 >ω 2 . The effect of the hydrophobicity of the two-phase solvent systems on the stationary phase
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Monitoring of oil pollution in the Arabian Gulf based on medium resolution satellite imagery
Zhao, J.; Ghedira, H.
2013-12-01
A large number of inland and offshore oil fields are located in the Arabian Gulf where about 25% of the world's oil is produced by the countries surrounding the Arabian Gulf region. Almost all of this oil production is shipped by sea worldwide through the Strait of Hormuz making the region vulnerable to environmental and ecological threats that might arise from accidental or intentional oil spills. Remote sensing technologies have the unique capability to detect and monitor oil pollutions over large temporal and spatial scales. Synoptic satellite imaging can date back to 1972 when Landsat-1 was launched. Landsat satellite missions provide long time series of imagery with a spatial resolution of 30 m. MODIS sensors onboard NASA's Terra and Aqua satellites provide a wide and frequent coverage at medium spatial resolution, i.e. 250 m and 500, twice a day. In this study, the capability of medium resolution MODIS and Landsat data in detecting and monitoring oil pollutions in the Arabian Gulf was tested. Oil spills and slicks show negative or positive contrasts in satellite derived RGB images compared with surrounding clean waters depending on the solar/viewing geometry, oil thickness and evolution, etc. Oil-contaminated areas show different spectral characteristics compared with surrounding waters. Rayleigh-corrected reflectance at the seven medium resolution bands of MODIS is lower in oil affected areas. This is caused by high light absorption of oil slicks. 30-m Landsat image indicated the occurrence of oil spill on May 26 2000 in the Arabian Gulf. The oil spill showed positive contrast and lower temperature than surrounding areas. Floating algae index (FAI) images are also used to detect oil pollution. Oil-contaminated areas were found to have lower FAI values. To track the movement of oil slicks found on October 21 2007, ocean circulations from a HYCOM model were examined and demonstrated that the oil slicks were advected toward the coastal areas of United Arab
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Directory of Open Access Journals (Sweden)
Wenquan Zhu
Full Text Available Carbon Flux Phenology (CFP can affect the interannual variation in Net Ecosystem Exchange (NEE of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands, using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU by more than 70% and End of Carbon Uptake (ECU by more than 60%. The Root Mean Square Error (RMSE of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.
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Incorporating Open Source Data for Bayesian Classification of Urban Land Use From VHR Stereo Images
Li, Mengmeng; De Beurs, Kirsten M.; Stein, Alfred; Bijker, Wietske
2017-01-01
This study investigates the incorporation of open source data into a Bayesian classification of urban land use from very high resolution (VHR) stereo satellite images. The adopted classification framework starts from urban land cover classification, proceeds to building-type characterization, and
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Fisher, Jeremy Isaac
Important systematic shifts in ecosystem function are often masked by natural variability. The rich legacy of over two decades of continuous satellite observations provides an important database for distinguishing climatological and anthropogenic ecosystem changes. Examples from semi-arid Sudanian West Africa and New England (USA) illustrate the response of vegetation to climate and land-use. In Burkina Faso, West Africa, pastoral and agricultural practices compete for land area, while degradation may follow intensification. The Nouhao Valley is a natural experiment in which pastoral and agricultural land uses were allocated separate, coherent reserves. Trajectories of annual net primary productivity were derived from 18 years of coarse-grain (AVHRR) satellite data. Trends suggested that pastoral lands had responded rigorously to increasing rainfall after the 1980's droughts. A detailed analysis at Landsat resolution (30m) indicated that the increased vegetative cover was concentrated in the river basins of the pastoral region, implying a riparian wood expansion. In comparison, riparian cover was reduced in agricultural regions. We suggest that broad-scale patterns of increasing semi-arid West African greenness may be indicative of climate variability, whereas local losses may be anthropogenic in nature. The contiguous deciduous forests, ocean proximity, topography, and dense urban developments of New England provide an ideal landscape to examine influences of climate variability and the impact of urban development vegetation response. Spatial and temporal patterns of interannual climate variability were examined via green leaf phenology. Phenology, or seasonal growth and senescence, is driven by deficits of light, temperature, and water. In temperate environments, phenology variability is driven by interannual temperature and precipitation shifts. Average and interannual phenology analyses across southern New England were conducted at resolutions of 30m (Landsat
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Directory of Open Access Journals (Sweden)
Elizabeth J. Botha
2016-05-01
Full Text Available Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals were quantified. Two WorldView-2 satellite images were acquired, within one minute of each other, with Image 1 placed in a near-optimal sun-sensor geometric configuration and Image 2 placed close to the specular point of the Bidirectional Reflectance Distribution Function (BRDF. Image 2 had higher total environmental noise due to increased surface glint and higher atmospheric path-scattering. Generally, depths were under-estimated from Image 2, compared to Image 1. A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase of the maximum depth to which accurate depth estimations were returned. This case-study indicates that critical analysis of individual images, accounting for the entire sun elevation and azimuth and satellite sensor pointing and geometry as well as anticipated wave height and direction, is required to ensure an image is fit for purpose for aquatic data analysis.
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Vegetation Fraction Mapping with High Resolution Multispectral Data in the Texas High Plains
Oshaughnessy, S. A.; Gowda, P. H.; Basu, S.; Colaizzi, P. D.; Howell, T. A.; Schulthess, U.
2010-12-01
Land surface models use vegetation fraction to more accurately partition latent, sensible and soil heat fluxes from a partially vegetated surface as it affects energy and moisture exchanges between the earth’s surface and atmosphere. In recent years, there is interest to integrate vegetation fraction data into intelligent irrigation scheduling systems to avoid false positive signals to irrigate. Remote sensing can facilitate the collection of vegetation fraction information on individual fields over large areas in a timely and cost-effective manner. In this study, we developed and evaluated a set of vegetation fraction models using least square regression and artificial neural network (ANN) techniques using RapidEye satellite data (6.5 m spatial resolution and on-demand temporal resolution). Four images were acquired during the 2010 summer growing season, covering bare soil to full crop cover conditions, over the USDA-ARS-Conservation and Production Research Laboratory in Bushland, Texas [350 11' N, 1020 06' W; 1,170 m elevation MSL]. Spectral signatures were extracted from 25 ground truth locations with geographic coordinates. Vegetation fraction information was derived from digital photos taken at the time of image acquisition using a supervised classification technique. Comparison of performance statistics indicate that ANN performed slightly better than least square regression models.
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Thomas, N.; Rueda, X.; Lambin, E.; Mendenhall, C. D.
2012-12-01
Large intact forested regions of the world are known to be critical to maintaining Earth's climate, ecosystem health, and human livelihoods. Remote sensing has been successfully implemented as a tool to monitor forest cover and landscape dynamics over broad regions. Much of this work has been done using coarse resolution sensors such as AVHRR and MODIS in combination with moderate resolution sensors, particularly Landsat. Finer scale analysis of heterogeneous and fragmented landscapes is commonly performed with medium resolution data and has had varying success depending on many factors including the level of fragmentation, variability of land cover types, patch size, and image availability. Fine scale tree cover in mixed agricultural areas can have a major impact on biodiversity and ecosystem sustainability but may often be inadequately captured with the global to regional (coarse resolution and moderate resolution) satellite sensors and processing techniques widely used to detect land use and land cover changes. This study investigates whether advanced remote sensing methods are able to assess and monitor percent tree canopy cover in spatially complex human-dominated agricultural landscapes that prove challenging for traditional mapping techniques. Our study areas are in high altitude, mixed agricultural coffee-growing regions in Costa Rica and the Colombian Andes. We applied Random Forests regression tree analysis to Landsat data along with additional spectral, environmental, and spatial variables to predict percent tree canopy cover at 30m resolution. Image object-based texture, shape, and neighborhood metrics were generated at the Landsat scale using eCognition and included in the variable suite. Training and validation data was generated using high resolution imagery from digital aerial photography at 1m to 2.5 m resolution. Our results are promising with Pearson's correlation coefficients between observed and predicted percent tree canopy cover of .86 (Costa
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Use of satellite data for the monitoring of species on Saba and St. Eustatius
Smith, S.R.; Mücher, C.A.; Debrot, A.O.; Roupioz, L.F.S.; Meesters, H.W.G.; Hazeu, G.W.; Davaasuren, N.
2013-01-01
The present study examines the possibility to identify the different land cover types (natural and artificial) on very high resolution satellite images of the Caribbean islands Bonaire, Saba and St. Eustatius. Linking species habitat requirements with associated land cover types allows for the identification of their potential occurence on the islands.
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Use of high-resolution satellite images for detection of geothermal reservoirs
Arellano-Baeza, A. A.
2012-12-01
Chile has an enormous potential to use the geothermal resources for electric energy generation. The main geothermal fields are located in the Central Andean Volcanic Chain in the North, between the Central valley and the border with Argentina in the center, and in the fault system Liquiñe-Ofqui in the South of the country. High resolution images from the LANDSAT and ASTER satellites have been used to delineate the geological structures related to the Calerias geothermal field located at the northern end of the Southern Volcanic Zone of Chile and Puchuldiza geothermal field located in the Region of Tarapaca. It was done by applying the lineament extraction technique developed by author. These structures have been compared with the distribution of main geological structures obtained in the fields. It was found that the lineament density increases in the areas of the major heat flux indicating that the lineament analysis could be a power tool for the detection of faults and joint zones associated to the geothermal fields.
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Van Gordon, M.; Van Gordon, S.; Min, A.; Sullivan, J.; Weiner, Z.; Tappan, G. G.
2017-12-01
Using support vector machine (SVM) learning and high-accuracy hand-classified maps, we have developed a publicly available land cover classification tool for the West African Sahel. Our classifier produces high-resolution and regionally calibrated land cover maps for the Sahel, representing a significant contribution to the data available for this region. Global land cover products are unreliable for the Sahel, and accurate land cover data for the region are sparse. To address this gap, the U.S. Geological Survey and the Regional Center for Agriculture, Hydrology and Meteorology (AGRHYMET) in Niger produced high-quality land cover maps for the region via hand-classification of Landsat images. This method produces highly accurate maps, but the time and labor required constrain the spatial and temporal resolution of the data products. By using these hand-classified maps alongside SVM techniques, we successfully increase the resolution of the land cover maps by 1-2 orders of magnitude, from 2km-decadal resolution to 30m-annual resolution. These high-resolution regionally calibrated land cover datasets, along with the classifier we developed to produce them, lay the foundation for major advances in studies of land surface processes in the region. These datasets will provide more accurate inputs for food security modeling, hydrologic modeling, analyses of land cover change and climate change adaptation efforts. The land cover classification tool we have developed will be publicly available for use in creating additional West Africa land cover datasets with future remote sensing data and can be adapted for use in other parts of the world.
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DEFF Research Database (Denmark)
Schumacher, Paul; Mislimshoeva, Bunafsha; Brenning, Alexander
2016-01-01
to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically...... red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data...
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Global mobile satellite communications theory for maritime, land and aeronautical applications
Ilčev, Stojče Dimov
2017-01-01
This book discusses current theory regarding global mobile satellite communications (GMSC) for maritime, land (road and rail), and aeronautical applications. It covers how these can enable connections between moving objects such as ships, road and rail vehicles and aircrafts on one hand, and on the other ground telecommunications subscribers through the medium of communications satellites, ground earth stations, Terrestrial Telecommunication Networks (TTN), Internet Service Providers (ISP) and other wireless and landline telecommunications providers. This new edition covers new developments and initiatives that have resulted in land and aeronautical applications and the introduction of new satellite constellations in non-geostationary orbits and projects of new hybrid satellite constellations. The book presents current GMSC trends, mobile system concepts and network architecture using a simple mode of style with understandable technical information, characteristics, graphics, illustrations and mathematics equ...
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Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús
2011-09-01
This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.
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Huang, Xin; Chen, Huijun; Gong, Jianya
2018-01-01
Spaceborne multi-angle images with a high-resolution are capable of simultaneously providing spatial details and three-dimensional (3D) information to support detailed and accurate classification of complex urban scenes. In recent years, satellite-derived digital surface models (DSMs) have been increasingly utilized to provide height information to complement spectral properties for urban classification. However, in such a way, the multi-angle information is not effectively exploited, which is mainly due to the errors and difficulties of the multi-view image matching and the inaccuracy of the generated DSM over complex and dense urban scenes. Therefore, it is still a challenging task to effectively exploit the available angular information from high-resolution multi-angle images. In this paper, we investigate the potential for classifying urban scenes based on local angular properties characterized from high-resolution ZY-3 multi-view images. Specifically, three categories of angular difference features (ADFs) are proposed to describe the angular information at three levels (i.e., pixel, feature, and label levels): (1) ADF-pixel: the angular information is directly extrapolated by pixel comparison between the multi-angle images; (2) ADF-feature: the angular differences are described in the feature domains by comparing the differences between the multi-angle spatial features (e.g., morphological attribute profiles (APs)). (3) ADF-label: label-level angular features are proposed based on a group of urban primitives (e.g., buildings and shadows), in order to describe the specific angular information related to the types of primitive classes. In addition, we utilize spatial-contextual information to refine the multi-level ADF features using superpixel segmentation, for the purpose of alleviating the effects of salt-and-pepper noise and representing the main angular characteristics within a local area. The experiments on ZY-3 multi-angle images confirm that the proposed
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Meng, Y.; Cao, Y.; Tian, H.; Han, Z.
2018-04-01
In recent decades, land reclamation activities have been developed rapidly in Chinese coastal regions, especially in Bohai Bay. The land reclamation areas can effectively alleviate the contradiction between land resources shortage and human needs, but some idle lands that left unused after the government making approval the usage of sea areas are also supposed to pay attention to. Due to the particular features of land coverage identification in large regions, traditional monitoring approaches are unable to perfectly meet the needs of effectively and quickly land use classification. In this paper, Gaofen-1 remotely sensed satellite imagery data together with sea area usage ownership data were used to identify the land use classifications and find out the idle land resources. It can be seen from the result that most of the land use types and idle land resources can be identified precisely.
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A multi-temporal analysis approach for land cover mapping in support of nuclear incident response
Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.
2012-06-01
Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.
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Energy Technology Data Exchange (ETDEWEB)
Demirel, N.; Duzgun, S.; Kemal Emil, M. [Middle East Technical Univ., Ankara (Turkey). Dept. of Mining Engineering
2010-07-01
Changes in the landcover and landuse of a mine area can be caused by surface mining activities, exploitation of ore and stripping and dumping overburden. In order to identify the long-term impacts of mining on the environment and land cover, these changes must be continuously monitored. A facility to regularly observe the progress of surface mining and reclamation is important for effective enforcement of mining and environmental regulations. Remote sensing provides a powerful tool to obtain rigorous data and reduce the need for time-consuming and expensive field measurements. The purpose of this study was to conduct post classification change detection for identifying, quantifying, and analyzing the spatial response of landscape due to surface lignite coal mining activities in Goynuk, Bolu, Turkey, from 2004 to 2008. The paper presented the research algorithm which involved acquiring multi temporal high resolution satellite data; preprocessing the data; performing image classification using maximum likelihood classification algorithm and performing accuracy assessment on the classification results; performing post classification change detection algorithm; and analyzing the results. Specifically, the paper discussed the study area, data and methodology, and image preprocessing using radiometric correction. Image classification and change detection were also discussed. It was concluded that the mine and dump area decreased by 192.5 ha from 2004 to 2008 and was caused by the diminishing reserves in the area and decline in the required production. 5 refs., 2 tabs., 4 figs.
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High-resolution surface analysis for extended-range downscaling with limited-area atmospheric models
Separovic, Leo; Husain, Syed Zahid; Yu, Wei; Fernig, David
2014-12-01
High-resolution limited-area model (LAM) simulations are frequently employed to downscale coarse-resolution objective analyses over a specified area of the globe using high-resolution computational grids. When LAMs are integrated over extended time frames, from months to years, they are prone to deviations in land surface variables that can be harmful to the quality of the simulated near-surface fields. Nudging of the prognostic surface fields toward a reference-gridded data set is therefore devised in order to prevent the atmospheric model from diverging from the expected values. This paper presents a method to generate high-resolution analyses of land-surface variables, such as surface canopy temperature, soil moisture, and snow conditions, to be used for the relaxation of lower boundary conditions in extended-range LAM simulations. The proposed method is based on performing offline simulations with an external surface model, forced with the near-surface meteorological fields derived from short-range forecast, operational analyses, and observed temperatures and humidity. Results show that the outputs of the surface model obtained in the present study have potential to improve the near-surface atmospheric fields in extended-range LAM integrations.
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Enhancing Conservation with High Resolution Productivity Datasets for the Conterminous United States
Robinson, Nathaniel Paul
Human driven alteration of the earth's terrestrial surface is accelerating through land use changes, intensification of human activity, climate change, and other anthropogenic pressures. These changes occur at broad spatio-temporal scales, challenging our ability to effectively monitor and assess the impacts and subsequent conservation strategies. While satellite remote sensing (SRS) products enable monitoring of the earth's terrestrial surface continuously across space and time, the practical applications for conservation and management of these products are limited. Often the processes driving ecological change occur at fine spatial resolutions and are undetectable given the resolution of available datasets. Additionally, the links between SRS data and ecologically meaningful metrics are weak. Recent advances in cloud computing technology along with the growing record of high resolution SRS data enable the development of SRS products that quantify ecologically meaningful variables at relevant scales applicable for conservation and management. The focus of my dissertation is to improve the applicability of terrestrial gross and net primary productivity (GPP/NPP) datasets for the conterminous United States (CONUS). In chapter one, I develop a framework for creating high resolution datasets of vegetation dynamics. I use the entire archive of Landsat 5, 7, and 8 surface reflectance data and a novel gap filling approach to create spatially continuous 30 m, 16-day composites of the normalized difference vegetation index (NDVI) from 1986 to 2016. In chapter two, I integrate this with other high resolution datasets and the MOD17 algorithm to create the first high resolution GPP and NPP datasets for CONUS. I demonstrate the applicability of these products for conservation and management, showing the improvements beyond currently available products. In chapter three, I utilize this dataset to evaluate the relationships between land ownership and terrestrial production
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A European satellite-derived UV climatology available for impact studies
International Nuclear Information System (INIS)
Verdebout, J.
2004-01-01
This paper presents a satellite-derived climatology of the surface UV radiation, intended to support impact studies on the environment and human health. As of today, the dataset covers the period from 1 January 1984 to 31 August 2003, with daily dose maps covering Europe with a spatial resolution of 0.05 deg.. A comparison between the modelled erythemal daily dose and measurements in Ispra yields an r.m.s value with a relative difference of 29% and a bias of 3%. The seemingly large dispersion is, however, due to a restricted number of days for which the relative difference is very high. The climatological dataset documents systematic patterns in the geographical distribution of the surface UV radiation due to cloudiness, altitude and snow. It also shows a large year-to-year variability in monthly doses of up to ±50% in spring and ±30% in summer. (authors)
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Gibe, Hezron P.; Cayetano, Mylene G.
2017-09-01
Exposure to particulate matter (PM) is a serious environmental problem in many urban areas on Earth. In the Philippines, most existing studies and emission inventories have mainly focused on point and mobile sources, while research involving human exposures to particulate pollutants is rare. This paper presents a method for estimating the amount of fine particulate (PM2.5) emissions in a test study site in the city of Cabanatuan, Nueva Ecija, in the Philippines, by utilizing local emission factors, regionally procured data, and land cover/land use (activity data) interpreted from satellite imagery. Geographic information system (GIS) software was used to map the estimated emissions in the study area. The present results suggest that vehicular emissions from motorcycles and tricycles, as well as fuels used by households (charcoal) and burning of agricultural waste, largely contribute to PM2.5 emissions in Cabanatuan. Overall, the method used in this study can be applied in other small urbanizing cities, as long as on-site specific activity, emission factor, and satellite-imaged land cover data are available.
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High-resolution mapping of forest carbon stocks in the Colombian Amazon
Directory of Open Access Journals (Sweden)
G. P. Asner
2012-07-01
Full Text Available High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40% of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i employing a universal approach to airborne LiDAR-calibration with limited field data; (ii quantifying environmental controls over carbon densities; and (iii developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.
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A Decade of High-Resolution Arctic Sea Ice Measurements from Airborne Altimetry
Duncan, K.; Farrell, S. L.; Connor, L. N.; Jackson, C.; Richter-Menge, J.
2017-12-01
Satellite altimeters carried on board ERS-1,-2, EnviSat, ICESat, CryoSat-2, AltiKa and Sentinel-3 have transformed our ability to map the thickness and volume of the polar sea ice cover, on seasonal and decadal time-scales. The era of polar satellite altimetry has coincided with a rapid decline of the Arctic ice cover, which has thinned, and transitioned from a predominantly multi-year to first-year ice cover. In conjunction with basin-scale satellite altimeter observations, airborne surveys of the Arctic Ocean at the end of winter are now routine. These surveys have been targeted to monitor regions of rapid change, and are designed to obtain the full snow and ice thickness distribution, across a range of ice types. Sensors routinely deployed as part of NASA's Operation IceBridge (OIB) campaigns include the Airborne Topographic Mapper (ATM) laser altimeter, the frequency-modulated continuous-wave snow radar, and the Digital Mapping System (DMS). Airborne measurements yield high-resolution data products and thus present a unique opportunity to assess the quality and characteristics of the satellite observations. We present a suite of sea ice data products that describe the snow depth and thickness of the Arctic ice cover during the last decade. Fields were derived from OIB measurements collected between 2009-2017, and from reprocessed data collected during ad-hoc sea ice campaigns prior to OIB. Our bespoke algorithms are designed to accommodate the heterogeneous sea ice surface topography, that varies at short spatial scales. We assess regional and inter-annual variability in the sea ice thickness distribution. Results are compared to satellite-derived ice thickness fields to highlight the sensitivities of satellite footprints to the tails of the thickness distribution. We also show changes in the dynamic forcing shaping the ice pack over the last eight years through an analysis of pressure-ridge sail-height distributions and surface roughness conditions
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Generating High-Resolution Lake Bathymetry over Lake Mead using the ICESat-2 Airborne Simulator
Li, Y.; Gao, H.; Jasinski, M. F.; Zhang, S.; Stoll, J.
2017-12-01
Precise lake bathymetry (i.e., elevation/contour) mapping is essential for optimal decision making in water resources management. Although the advancement of remote sensing has made it possible to monitor global reservoirs from space, most of the existing studies focus on estimating the elevation, area, and storage of reservoirs—and not on estimating the bathymetry. This limitation is attributed to the low spatial resolution of satellite altimeters. With the significant enhancement of ICESat-2—the Ice, Cloud & Land Elevation Satellite #2, which is scheduled to launch in 2018—producing satellite-based bathymetry becomes feasible. Here we present a pilot study for deriving the bathymetry of Lake Mead by combining Landsat area estimations with airborne elevation data using the prototype of ICESat-2—the Multiple Altimeter Beam Experimental Lidar (MABEL). First, an ISODATA classifier was adopted to extract the lake area from Landsat images during the period from 1982 to 2017. Then the lake area classifications were paired with MABEL elevations to establish an Area-Elevation (AE) relationship, which in turn was applied to the classification contour map to obtain the bathymetry. Finally, the Lake Mead bathymetry image was embedded onto the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), to replace the existing constant values. Validation against sediment survey data indicates that the bathymetry derived from this study is reliable. This algorithm has the potential for generating global lake bathymetry when ICESat-2 data become available after next year's launch.
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A flexible spatiotemporal method for fusing satellite images with different resolutions
Xiaolin Zhu; Eileen H. Helmer; Feng Gao; Desheng Liu; Jin Chen; Michael A. Lefsky
2016-01-01
Studies of land surface dynamics in heterogeneous landscapes often require remote sensing datawith high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial...
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Yeom, J. M.
2017-12-01
Recently developed Korea Multi-Purpose Satellite-3A (KOMPSAT-3A), which is a continuation of the KOMPSAT-1, 2 and 3 earth observation satellite (EOS) programs from the Korea Aerospace Research Institute (KARI) was launched on March, 25 2015 on a Dnepr-1 launch vehicle from the Jasny Dombarovsky site in Russia. After launched, KARI performed in-orbit-test (IOT) including radiometric calibration for 6 months from 14 Apr. to 4 Sep. 2015. KOMPSAT-3A is equipped with two distinctive sensors; one is a high resolution multispectral optical sensor, namely the Advances Earth Image Sensor System-A (AEISS-A) and the other is the Scanner Infrared Imaging System (SIIS). In this study, we focused on the radiometric calibration of AEISS-A. The multispectral wavelengths of AEISS-A are covering three visible regions: blue (450 - 520 nm), green (520 - 600 nm), red (630 - 690 nm), one near infrared (760 - 900 nm) with a 2.0 m spatial resolution at nadir, whereas the panchromatic imagery (450 - 900 nm) has a 0.5 m resolution. Those are the same spectral response functions were same with KOMPSAT-3 multispectral and panchromatic bands but the spatial resolutions are improved. The main mission of KOMPSAT-3A is to develop for Geographical Information System (GIS) applications in environmental, agriculture, and oceanographic sciences, as well as natural hazard monitoring.
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McCabe, Matthew F.; Houborg, Rasmus; Lucieer, Arko
2016-10-01
With global population projected to approach 9 billion by 2050, it has been estimated that a 40% increase in cereal production will be required to satisfy the worlds growing nutritional demands. Any such increases in agricultural productivity are likely to occur within a system that has limited room for growth and in a world with a climate that is different from that of today. Fundamental to achieving food and water security, is the capacity to monitor the health and condition of agricultural systems. While space-agency based satellites have provided the backbone for earth observation over the last few decades, many developments in the field of high-resolution earth observation have been advanced by the commercial sector. These advances relate not just to technological developments in the use of unmanned aerial vehicles (UAVs), but also the advent of nano-satellite constellations that offer a radical shift in the way earth observations are now being retrieved. Such technologies present opportunities for improving our description of the water, energy and carbon cycles. Efforts towards developing new observational techniques and interpretative frameworks are required to provide the tools and information needed to improve the management and security of agricultural and related sectors. These developments are one of the surest ways to better manage, protect and preserve national food and water resources. Here we review the capabilities of recently deployed satellite systems and UAVs and examine their potential for application in precision agriculture.
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McCabe, Matthew
2016-10-25
With global population projected to approach 9 billion by 2050, it has been estimated that a 40% increase in cereal production will be required to satisfy the worlds growing nutritional demands. Any such increases in agricultural productivity are likely to occur within a system that has limited room for growth and in a world with a climate that is different from that of today. Fundamental to achieving food and water security, is the capacity to monitor the health and condition of agricultural systems. While space-Agency based satellites have provided the backbone for earth observation over the last few decades, many developments in the field of high-resolution earth observation have been advanced by the commercial sector. These advances relate not just to technological developments in the use of unmanned aerial vehicles (UAVs), but also the advent of nano-satellite constellations that offer a radical shift in the way earth observations are now being retrieved. Such technologies present opportunities for improving our description of the water, energy and carbon cycles. Efforts towards developing new observational techniques and interpretative frameworks are required to provide the tools and information needed to improve the management and security of agricultural and related sectors. These developments are one of the surest ways to better manage, protect and preserve national food and water resources. Here we review the capabilities of recently deployed satellite systems and UAVs and examine their potential for application in precision agriculture.
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LaRue, Michelle A.; Stapleton, Seth P.; Porter, Claire; Atkinson, Stephen N.; Atwood, Todd C.; Dyck, Markus; Lecomte, Nicolas
2015-01-01
High-resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited. With polar bears (Ursus maritimus), the technique has only proven effective on landscapes with little topographic relief that are devoid of snow and ice, and time-consuming manual review of imagery is required to identify bears. Here, we evaluated mechanisms to further develop methods for satellite imagery by examining data from Rowley Island, Canada. We attempted to automate and expedite detection via a supervised spectral classification and image differencing to expedite image review. We also assessed what proportion of a region should be sampled to obtain reliable estimates of density and abundance. Although the spectral signature of polar bears differed from nontarget objects, these differences were insufficient to yield useful results via a supervised classification process. Conversely, automated image differencing—or subtracting one image from another—correctly identified nearly 90% of polar bear locations. This technique, however, also yielded false positives, suggesting that manual review will still be required to confirm polar bear locations. On Rowley Island, bear distribution approximated a Poisson distribution across a range of plot sizes, and resampling suggests that sampling >50% of the site facilitates reliable estimation of density (CV large-scale applications remain limited because of the challenges in automation and the limited environments in which the method can be effectively applied. Improvements in resolution may expand opportunities for its future uses.
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LaRue, Michelle A.; Stapleton, Seth P.; Porter, Claire; Atkinson, Stephen N.; Atwood, Todd C.; Dyck, Markus; Lecomte, Nicolas
2015-01-01
High-resolution satellite imagery is a promising tool for providing coarse information about polar species abundance and distribution, but current applications are limited. With polar bears (Ursus maritimus), the technique has only proven effective on landscapes with little topographic relief that are devoid of snow and ice, and time-consuming manual review of imagery is required to identify bears. Here, we evaluated mechanisms to further develop methods for satellite imagery by examining data from Rowley Island, Canada. We attempted to automate and expedite detection via a supervised spectral classification and image differencing to expedite image review. We also assessed what proportion of a region should be sampled to obtain reliable estimates of density and abundance. Although the spectral signature of polar bears differed from nontarget objects, these differences were insufficient to yield useful results via a supervised classification process. Conversely, automated image differencing—or subtracting one image from another—correctly identified nearly 90% of polar bear locations. This technique, however, also yielded false positives, suggesting that manual review will still be required to confirm polar bear locations. On Rowley Island, bear distribution approximated a Poisson distribution across a range of plot sizes, and resampling suggests that sampling >50% of the site facilitates reliable estimation of density (CV in certain areas, but large-scale applications remain limited because of the challenges in automation and the limited environments in which the method can be effectively applied. Improvements in resolution may expand opportunities for its future uses.
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A Multi-stage Method to Extract Road from High Resolution Satellite Image
International Nuclear Information System (INIS)
Zhijian, Huang; Zhang, Jinfang; Xu, Fanjiang
2014-01-01
Extracting road information from high-resolution satellite images is complex and hardly achieves by exploiting only one or two modules. This paper presents a multi-stage method, consisting of automatic information extraction and semi-automatic post-processing. The Multi-scale Enhancement algorithm enlarges the contrast of human-made structures with the background. The Statistical Region Merging segments images into regions, whose skeletons are extracted and pruned according to geometry shape information. Setting the start and the end skeleton points, the shortest skeleton path is constructed as a road centre line. The Bidirectional Adaptive Smoothing technique smoothens the road centre line and adjusts it to right position. With the smoothed line and its average width, a Buffer algorithm reconstructs the road region easily. Seen from the last results, the proposed method eliminates redundant non-road regions, repairs incomplete occlusions, jumps over complete occlusions, and reserves accurate road centre lines and neat road regions. During the whole process, only a few interactions are needed
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Ribeiro, F.; Roberts, D. A.; Hess, L. L.; Davis, F. W.; Caylor, K. K.; Nackoney, J.; Antunes Daldegan, G.
2017-12-01
Savannas are heterogeneous landscapes consisting of highly mixed land cover types that lack clear distinct boundaries. The Brazilian Cerrado is a Neotropical savanna considered a biodiversity hotspot for conservation due to its biodiversity richness and rapid transformation of its landscape by crop and pasture activities. The Cerrado is one of the most threatened Brazilian biomes and only 2.2% of its original extent is strictly protected. Accurate mapping and monitoring of its ecosystems and adjacent land use are important to select areas for conservation and to improve our understanding of the dynamics in this biome. Land cover mapping of savannas is difficult due to spectral similarity between land cover types resulting from similar vegetation structure, floristically similar components, generalization of land cover classes, and heterogeneity usually expressed as small patch sizes within the natural landscape. These factors are the major contributor to misclassification and low map accuracies among remote sensing studies in savannas. Specific challenges to map the Cerrado's land cover types are related to the spectral similarity between classes of land use and natural vegetation, such as natural grassland vs. cultivated pasture, and forest ecosystem vs. crops. This study seeks to classify and evaluate the land cover patterns across an area ranked as having extremely high priority for future conservation in the Cerrado. The main objective of this study is to identify the representativeness of each vegetation type across the landscape using high to moderate spatial resolution imagery using an automated scheme. A combination of pixel-based and object-based approaches were tested using RapidEye 3A imagery (5m spatial resolution) to classify the Cerrado's major land cover types. The random forest classifier was used to map the major ecosystems present across the area, and demonstrated to have an effective result with 68% of overall accuracy. Post
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Corucci, Linda; Masini, Andrea; Cococcioni, Marco
2011-01-01
This paper addresses bathymetry estimation from high resolution multispectral satellite images by proposing an accurate supervised method, based on a neuro-fuzzy approach. The method is applied to two Quickbird images of the same area, acquired in different years and meteorological conditions, and is validated using truth data. Performance is studied in different realistic situations of in situ data availability. The method allows to achieve a mean standard deviation of 36.7 cm for estimated water depths in the range [-18, -1] m. When only data collected along a closed path are used as a training set, a mean STD of 45 cm is obtained. The effect of both meteorological conditions and training set size reduction on the overall performance is also investigated.
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High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at s...
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Scarino, B. R.; Minnis, P.; Yost, C. R.; Chee, T.; Palikonda, R.
2015-12-01
Single-channel algorithms for satellite thermal-infrared- (TIR-) derived land and sea surface skin temperature (LST and SST) are advantageous in that they can be easily applied to a variety of satellite sensors. They can also accommodate decade-spanning instrument series, particularly for periods when split-window capabilities are not available. However, the benefit of one unified retrieval methodology for all sensors comes at the cost of critical sensitivity to surface emissivity (ɛs) and atmospheric transmittance estimation. It has been demonstrated that as little as 0.01 variance in ɛs can amount to more than a 0.5-K adjustment in retrieved LST values. Atmospheric transmittance requires calculations that employ vertical profiles of temperature and humidity from numerical weather prediction (NWP) models. Selection of a given NWP model can significantly affect LST and SST agreement relative to their respective validation sources. Thus, it is necessary to understand the accuracies of the retrievals for various NWP models to ensure the best LST/SST retrievals. The sensitivities of the single-channel retrievals to surface emittance and NWP profiles are investigated using NASA Langley historic land and ocean clear-sky skin temperature (Ts) values derived from high-resolution 11-μm TIR brightness temperature measured from geostationary satellites (GEOSat) and Advanced Very High Resolution Radiometers (AVHRR). It is shown that mean GEOSat-derived, anisotropy-corrected LST can vary by up to ±0.8 K depending on whether CERES or MODIS ɛs sources are used. Furthermore, the use of either NOAA Global Forecast System (GFS) or NASA Goddard Modern-Era Retrospective Analysis for Research and Applications (MERRA) for the radiative transfer model initial atmospheric state can account for more than 0.5-K variation in mean Ts. The results are compared to measurements from the Surface Radiation Budget Network (SURFRAD), an Atmospheric Radiation Measurement (ARM) Program ground
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Limaye, Ashutosh S.; Molthan, Andrew L.; Srikishen, Jayanthi
2010-01-01
The development of the Nebula Cloud Computing Platform at NASA Ames Research Center provides an open-source solution for the deployment of scalable computing and storage capabilities relevant to the execution of real-time weather forecasts and the distribution of high resolution satellite data to the operational weather community. Two projects at Marshall Space Flight Center may benefit from use of the Nebula system. The NASA Short-term Prediction Research and Transition (SPoRT) Center facilitates the use of unique NASA satellite data and research capabilities in the operational weather community by providing datasets relevant to numerical weather prediction, and satellite data sets useful in weather analysis. SERVIR provides satellite data products for decision support, emphasizing environmental threats such as wildfires, floods, landslides, and other hazards, with interests in numerical weather prediction in support of disaster response. The Weather Research and Forecast (WRF) model Environmental Modeling System (WRF-EMS) has been configured for Nebula cloud computing use via the creation of a disk image and deployment of repeated instances. Given the available infrastructure within Nebula and the "infrastructure as a service" concept, the system appears well-suited for the rapid deployment of additional forecast models over different domains, in response to real-time research applications or disaster response. Future investigations into Nebula capabilities will focus on the development of a web mapping server and load balancing configuration to support the distribution of high resolution satellite data sets to users within the National Weather Service and international partners of SERVIR.
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Directory of Open Access Journals (Sweden)
Byongjun Hwang
2017-07-01
Full Text Available In this study, we present an algorithm for summer sea ice conditions that semi-automatically produces the floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar data. Currently, floe size distribution data from satellite images are very rare in the literature, mainly due to the lack of a reliable algorithm to produce such data. Here, we developed the algorithm by combining various image analysis methods, including Kernel Graph Cuts, distance transformation and watershed transformation, and a rule-based boundary revalidation. The developed algorithm has been validated against the ground truth that was extracted manually with the aid of 1-m resolution visible satellite data. Comprehensive validation analysis has shown both perspectives and limitations. The algorithm tends to fail to detect small floes (mostly less than 100 m in mean caliper diameter compared to ground truth, which is mainly due to limitations in water-ice segmentation. Some variability in the power law exponent of floe size distribution is observed due to the effects of control parameters in the process of de-noising, Kernel Graph Cuts segmentation, thresholds for boundary revalidation and image resolution. Nonetheless, the algorithm, for floes larger than 100 m, has shown a reasonable agreement with ground truth under various selections of these control parameters. Considering that the coverage and spatial resolution of satellite Synthetic Aperture Radar data have increased significantly in recent years, the developed algorithm opens a new possibility to produce large volumes of floe size distribution data, which is essential for improving our understanding and prediction of the Arctic sea ice cover
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Directory of Open Access Journals (Sweden)
Federico Preti
2013-09-01
Full Text Available The soil erosion in the vineyards is a critical issue that could affect their productivity, but also, when the cultivation is organized in terraces, increase the risk due to derived slope failure processes. If terraces are not correctly designed or maintained, a progressively increasing of gully erosion affects the structure of the walls. The results of this process is the increasing of connectivity and runoff. In order to overcome such issues it is really important to recognize in detail all the surface drainage paths, thus providing a basis upon which develop a suitable drainage system or provide structural measures for the soil erosion risk mitigation. In the last few years, the airborne LiDAR technology led to a dramatic increase in terrain information. Airborne LiDAR and Terrestrial Laser Scanner derived high-resolution Digital Terrain Models (DTMs have opened avenues for hydrologic and geomorphologic studies (Tarolli et al., 2009. In general, all the main surface process signatures are correctly recognized using a DTM with cell sizes of 1 m. However sub-meter grid sizes may be more suitable in those situations where the analysis of micro topography related to micro changes is critical for slope failures risk assessment or for the design of detailed drainage flow paths. The Terrestrial Laser Scanner (TLS has been proven to be an useful tool for such detailed field survey. In this work, we test the effectiveness of high resolution topography derived by airborne LiDAR and TLS for the recognition of areas subject to soil erosion risk in a typical terraced vineyard landscape of “Chianti Classico” (Tuscany, Italy. The algorithm proposed by Tarolli et al. (2013, for the automatic recognition of anthropic feature induced flow direction changes, has been tested. The results underline the effectiveness of LiDAR and TLS data in the analysis of soil erosion signatures in vineyards, and indicate the high resolution topography as a useful tool to
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Directory of Open Access Journals (Sweden)
Yassine Charabi
2016-11-01
Full Text Available A bankable solar radiation database is required for the financial viability of solar energy project. Accurate estimation of solar energy resources in a country is very important for proper siting, sizing and life cycle cost analysis of solar energy systems. During the last decade an important progress has been made to develop multiple solar irradiance database (Global Horizontal Irradiance (GHI and Direct Normal Irradiance (DNI, using satellite of different resolution and sophisticated models. This paper assesses the performance of High-resolution solar irradiance derived with dynamical downscaling Numerical Weather Prediction model with, GIS topographical solar radiation model, satellite data and ground measurements, for the production of bankable solar radiation datasets. For this investigation, NWP model namely Consortium for Small-scale Modeling (COSMO is used for the dynamical downscaling of solar radiation. The obtained results increase confidence in solar radiation data base obtained from dynamical downscaled NWP model. The mean bias of dynamical downscaled NWP model is small, on the order of a few percents for GHI, and it could be ranked as a bankable datasets. Fortunately, these data are usually archived in the meteorological department and gives a good idea of the hourly, monthly, and annual incident energy. Such short time-interval data are valuable in designing and operating the solar energy facility. The advantage of the NWP model is that it can be used for solar radiation forecast since it can estimate the weather condition within the next 72–120 hours. This gives a reasonable estimation of the solar radiation that in turns can be used to forecast the electric power generation by the solar power plant.
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Kamangir, H.; Momeni, M.; Satari, M.
2017-09-01
This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.
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Wasowski, Janusz; Nutricato, Raffaele; Nitti, Davide Oscar; Bovenga, Fabio; Chiaradia, Maria Teresa; Piard, Boby Emmanuel; Mondesir, Philemon
2015-04-01
Synthetic aperture radar (SAR) multi-temporal interferometry (MTI) is one of the most promising satellite-based remote sensing techniques for fostering new opportunities in landslide hazard detection and assessment. MTI is attractive because it can provide very precise quantitative information on slow slope displacements of the ground surface over huge areas with limited vegetation cover. Although MTI is a mature technique, we are only beginning to realize the benefits of the high-resolution imagery that is currently acquired by the new generation radar satellites (e.g., COSMO-SkyMed, TerraSAR-X). In this work we demonstrate the potential of high resolution X-band MTI for wide-area detection of slope instability hazards even in tropical environments that are typically very harsh (eg. coherence loss) for differential interferometry applications. This is done by presenting an example from the island of Haiti, a tropical region characterized by dense and rapidly growing vegetation, as well as by significant climatic variability (two rainy seasons) with intense precipitation events. Despite the unfavorable setting, MTI processing of nearly 100 COSMO-SkyMed (CSK) mages (2011-2013) resulted in the identification of numerous radar targets even in some rural (inhabited) areas thanks to the high resolution (3 m) of CSK radar imagery, the adoption of a patch wise processing SPINUA approach and the presence of many man-made structures dispersed in heavily vegetated terrain. In particular, the density of the targets resulted suitable for the detection of some deep-seated and shallower landslides, as well as localized, very slow slope deformations. The interpretation and widespread exploitation of high resolution MTI data was facilitated by Google EarthTM tools with the associated high resolution optical imagery. Furthermore, our reconnaissance in situ checks confirmed that MTI results provided useful information on landslides and marginally stable slopes that can represent a
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Estimating NOx emissions and surface concentrations at high spatial resolution using OMI
Goldberg, D. L.; Lamsal, L. N.; Loughner, C.; Swartz, W. H.; Saide, P. E.; Carmichael, G. R.; Henze, D. K.; Lu, Z.; Streets, D. G.
2017-12-01
In many instances, NOx emissions are not measured at the source. In these cases, remote sensing techniques are extremely useful in quantifying NOx emissions. Using an exponential modified Gaussian (EMG) fitting of oversampled Ozone Monitoring Instrument (OMI) NO2 data, we estimate NOx emissions and lifetimes in regions where these emissions are uncertain. This work also presents a new high-resolution OMI NO2 dataset derived from the NASA retrieval that can be used to estimate surface level concentrations in the eastern United States and South Korea. To better estimate vertical profile shape factors, we use high-resolution model simulations (Community Multi-scale Air Quality (CMAQ) and WRF-Chem) constrained by in situ aircraft observations to re-calculate tropospheric air mass factors and tropospheric NO2 vertical columns during summertime. The correlation between our satellite product and ground NO2 monitors in urban areas has improved dramatically: r2 = 0.60 in new product, r2 = 0.39 in operational product, signifying that this new product is a better indicator of surface concentrations than the operational product. Our work emphasizes the need to use both high-resolution and high-fidelity models in order to re-calculate vertical column data in areas with large spatial heterogeneities in NOx emissions. The methodologies developed in this work can be applied to other world regions and other satellite data sets to produce high-quality region-specific emissions estimates.
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High resolution radar satellite imagery analysis for safeguards applications
Energy Technology Data Exchange (ETDEWEB)
Minet, Christian; Eineder, Michael [German Aerospace Center, Remote Sensing Technology Institute, Department of SAR Signal Processing, Wessling, (Germany); Rezniczek, Arnold [UBA GmbH, Herzogenrath, (Germany); Niemeyer, Irmgard [Forschungszentrum Juelich, Institue of Energy and Climate Research, IEK-6: Nuclear Waste Management and Reactor Safety, Juelich, (Germany)
2011-12-15
For monitoring nuclear sites, the use of Synthetic Aperture Radar (SAR) imagery shows essential promises. Unlike optical remote sensing instruments, radar sensors operate under almost all weather conditions and independently of the sunlight, i.e. time of the day. Such technical specifications are required both for continuous and for ad-hoc, timed surveillance tasks. With Cosmo-Skymed, TerraSARX and Radarsat-2, high-resolution SAR imagery with a spatial resolution up to 1m has recently become available. Our work therefore aims to investigate the potential of high-resolution TerraSAR data for nuclear monitoring. This paper focuses on exploiting amplitude of a single acquisition, assessing amplitude changes and phase differences between two acquisitions, and PS-InSAR processing of an image stack.
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Just, Allan C.; Wright, Robert O.; Schwartz, Joel; Coull, Brent A.; Baccarelli, Andrea A.; Tellez-Rojo, Martha María; Moody, Emily; Wang, Yujie; Lyapustin, Alexei; Kloog, Itai
2015-01-01
Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to...
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Tropical forest carbon assessment: integrating satellite and airborne mapping approaches
International Nuclear Information System (INIS)
Asner, Gregory P
2009-01-01
Large-scale carbon mapping is needed to support the UNFCCC program to reduce deforestation and forest degradation (REDD). Managers of forested land can potentially increase their carbon credits via detailed monitoring of forest cover, loss and gain (hectares), and periodic estimates of changes in forest carbon density (tons ha -1 ). Satellites provide an opportunity to monitor changes in forest carbon caused by deforestation and degradation, but only after initial carbon densities have been assessed. New airborne approaches, especially light detection and ranging (LiDAR), provide a means to estimate forest carbon density over large areas, which greatly assists in the development of practical baselines. Here I present an integrated satellite-airborne mapping approach that supports high-resolution carbon stock assessment and monitoring in tropical forest regions. The approach yields a spatially resolved, regional state-of-the-forest carbon baseline, followed by high-resolution monitoring of forest cover and disturbance to estimate carbon emissions. Rapid advances and decreasing costs in the satellite and airborne mapping sectors are already making high-resolution carbon stock and emissions assessments viable anywhere in the world.
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An operational framework for object-based land use classification of heterogeneous rural landscapes
DEFF Research Database (Denmark)
Watmough, Gary Richard; Palm, Cheryl; Sullivan, Clare
2017-01-01
The characteristics of very high resolution (VHR) satellite data are encouraging development agencies to investigate its use in monitoring and evaluation programmes. VHR data pose challenges for land use classification of heterogeneous rural landscapes as it is not possible to develop generalised...... and transferable land use classification definitions and algorithms. We present an operational framework for classifying VHR satellite data in heterogeneous rural landscapes using an object-based and random forest classifier. The framework overcomes the challenges of classifying VHR data in anthropogenic...
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High resolution X-ray scattering techniques for studying the sliding CDWS distortions, in NbSe sub 3
Rideau, D; Currat, R; Requardt, H; Nad, F Y; Lorenzo, J E; Brazovskii, S; Detlefs, C; Grübel, G
2001-01-01
The phase gradient in a sliding-charge density wave (CDW), which is observable as a longitudinal shift, q propor to partial deriv phi/partial deriv x, of the CDW satellite peak position, is due to the conversion free-electrons CDW-condensate, at the current electrodes. Using high resolution X-ray scattering techniques and time-resolved techniques, we monitor, on thin NbSe sub 3 whiskers, the shift, q(x), and its relaxation, q(t), upon switching off the current.
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Directory of Open Access Journals (Sweden)
Antonio T. Monteiro
2017-01-01
Full Text Available Invasion by non-native tree species is an environmental and societal challenge requiring predictive tools to assess invasion dynamics. The frequent scale mismatch between such tools and on-ground conservation is currently limiting invasion management. This study aimed to reduce these scale mismatches, assess the success of non-native tree invasion and determine the environmental factors associated to it. A hierarchical scaling approach combining species distribution models (SDMs and satellite mapping at very high resolution (VHR was developed to assess invasion by Acacia dealbata in Peneda-Gerês National Park, the only national park in Portugal. SDMs were first used to predict the climatically suitable areas for A. dealdata and satellite mapping with the random-forests classifier was then applied to WorldView-2 very-high resolution imagery to determine whether A. dealdata had actually colonized the predicted areas (invasion success. Environmental attributes (topographic, disturbance and canopy-related differing between invaded and non-invaded vegetated areas were then analyzed. The SDM results indicated that most (67% of the study area was climatically suitable for A. dealbata invasion. The onset of invasion was documented to 1905 and satellite mapping highlighted that 12.6% of study area was colonized. However, this species had only colonized 62.5% of the maximum potential range, although was registered within 55.6% of grid cells that were considerable unsuitable. Across these areas, the specific success rate of invasion was mostly below 40%, indicating that A. dealbata invasion was not dominant and effective management may still be possible. Environmental attributes related to topography (slope, canopy (normalized difference vegetation index (ndvi, land surface albedo and disturbance (historical burnt area differed between invaded and non-invaded vegetated area, suggesting that landscape attributes may alter at specific locations with Acacia
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Hassaballah, Khalid; Mohamed, Yasir; Uhlenbrook, Stefan; Biro, Khalid
2017-10-01
Understanding the land use and land cover changes (LULCCs) and their implication on surface hydrology of the Dinder and Rahad basins (D&R, approximately 77 504 km2) is vital for the management and utilization of water resources in the basins. Although there are many studies on LULCC in the Blue Nile Basin, specific studies on LULCC in the D&R are still missing. Hence, its impact on streamflow is unknown. The objective of this paper is to understand the LULCC in the Dinder and Rahad and its implications on streamflow response using satellite data and hydrological modelling. The hydrological model has been derived by different sets of land use and land cover maps from 1972, 1986, 1998 and 2011. Catchment topography, land cover and soil maps are derived from satellite images and serve to estimate model parameters. Results of LULCC detection between 1972 and 2011 indicate a significant decrease in woodland and an increase in cropland. Woodland decreased from 42 to 14 % and from 35 to 14 % for Dinder and Rahad, respectively. Cropland increased from 14 to 47 % and from 18 to 68 % in Dinder and Rahad, respectively. The model results indicate that streamflow is affected by LULCC in both the Dinder and the Rahad rivers. The effect of LULCC on streamflow is significant during 1986 and 2011. This could be attributed to the severe drought during the mid-1980s and the recent large expansion in cropland.
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Alpine cloud climatology using long-term NOAA-AVHRR satellite data
Energy Technology Data Exchange (ETDEWEB)
Kaestner, M.; Kriebel, K.T.
2000-07-01
Three different climates have been identified by our evaluation of AVHRR (advanced very high resolution radiometer) data using APOLLO (AVHRR processing scheme over land, clouds and ocean) for a five-years cloud climatology of the Alpine region. The cloud cover data from four layers were spatially averaged in boxes of 15 km by 14 km. The study area only comprises 540 km by 560 km, but contains regions with moderate, Alpine and Mediterranean climate. Data from the period July 1989 until December 1996 have been considered. The temporal resolution is one scene per day, the early afternoon pass, yielding monthly means of satellite derived cloud coverages 5% to 10% above the daily mean compared to conventional surface observation. At nonvegetated sites the cloudiness is sometimes significantly overestimated. Averaging high resolution cloud data seems to be superior to low resolution measurements of cloud properties and averaging is favourable in topographical homogeneous regions only. The annual course of cloud cover reveals typical regional features as foehn or temporal singularities as the so-called Christmas thaw. The cloud cover maps in spatially high resolution show local luff/lee features which outline the orography. Less cloud cover is found over the Alps than over the forelands in winter, an accumulation of thick cirrus is found over the High Alps and an accumulation of thin cirrus north of the Alps. (orig.)
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Detection of a weak meddy-like anomaly from high-resolution satellite SST maps
Directory of Open Access Journals (Sweden)
Mikhail Emelianov
2012-09-01
Full Text Available Despite the considerable impact of meddies on climate through the long-distance transport of properties, a consistent observation of meddy generation and propagation in the ocean is rather elusive. Meddies propagate at about 1000 m below the ocean surface, so satellite sensors are not able to detect them directly and finding them in the open ocean is more fortuitous than intentional. However, a consistent census of meddies and their paths is required in order to gain knowledge about their role in transporting properties such as heat and salt. In this paper we propose a new methodology for processing high-resolution sea surface temperature maps in order to detect meddy-like anomalies in the open ocean on a near-real-time basis. We present an example of detection, involving an atypical meddy-like anomaly that was confirmed as such by in situ measurements.
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Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad
2018-06-01
Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.
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Spatial, Temporal and Spectral Satellite Image Fusion via Sparse Representation
Song, Huihui
-MODIS image pairs, we build the corresponding relationship between the difference images of MODIS and ETM+ by training a low- and high-resolution dictionary pair from the given prior image pairs. In the second scenario, i.e., only one Landsat- MODIS image pair being available, we directly correlate MODIS and ETM+ data through an image degradation model. Then, the fusion stage is achieved by super-resolving the MODIS image combining the high-pass modulation in a two-layer fusion framework. Remarkably, the proposed spatial-temporal fusion methods form a unified framework for blending remote sensing images with phenology change or land-cover-type change. Based on the proposed spatial-temporal fusion models, we propose to monitor the land use/land cover changes in Shenzhen, China. As a fast-growing city, Shenzhen faces the problem of detecting the rapid changes for both rational city planning and sustainable development. However, the cloudy and rainy weather in region Shenzhen located makes the capturing circle of high-quality satellite images longer than their normal revisit periods. Spatial-temporal fusion methods are capable to tackle this problem by improving the spatial resolution of images with coarse spatial resolution but frequent temporal coverage, thereby making the detection of rapid changes possible. On two Landsat-MODIS datasets with annual and monthly changes, respectively, we apply the proposed spatial-temporal fusion methods to the task of multiple change detection. Afterward, we propose a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning and sparse non-negative matrix factorization. By combining the spectral information from hyperspectral image, which is characterized by low spatial resolution but high spectral resolution and abbreviated as LSHS, and the spatial information from multispectral image, which is featured by high spatial resolution but low spectral
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Hardman, M.; Brodzik, M. J.; Long, D. G.; Paget, A. C.; Armstrong, R. L.
2015-12-01
Beginning in 1978, the satellite passive microwave data record has been a mainstay of remote sensing of the cryosphere, providing twice-daily, near-global spatial coverage for monitoring changes in hydrologic and cryospheric parameters that include precipitation, soil moisture, surface water, vegetation, snow water equivalent, sea ice concentration and sea ice motion. Currently available global gridded passive microwave data sets serve a diverse community of hundreds of data users, but do not meet many requirements of modern Earth System Data Records (ESDRs) or Climate Data Records (CDRs), most notably in the areas of intersensor calibration, quality-control, provenance and consistent processing methods. The original gridding techniques were relatively primitive and were produced on 25 km grids using the original EASE-Grid definition that is not easily accommodated in modern software packages. Further, since the first Level 3 data sets were produced, the Level 2 passive microwave data on which they were based have been reprocessed as Fundamental CDRs (FCDRs) with improved calibration and documentation. We are funded by NASA MEaSUREs to reprocess the historical gridded data sets as EASE-Grid 2.0 ESDRs, using the most mature available Level 2 satellite passive microwave (SMMR, SSM/I-SSMIS, AMSR-E) records from 1978 to the present. We have produced prototype data from SSM/I and AMSR-E for the year 2003, for review and feedback from our Early Adopter user community. The prototype data set includes conventional, low-resolution ("drop-in-the-bucket" 25 km) grids and enhanced-resolution grids derived from the two candidate image reconstruction techniques we are evaluating: 1) Backus-Gilbert (BG) interpolation and 2) a radiometer version of Scatterometer Image Reconstruction (SIR). We summarize our temporal subsetting technique, algorithm tuning parameters and computational costs, and include sample SSM/I images at enhanced resolutions of up to 3 km. We are actively
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Satellite Imagery Analysis for Automated Global Food Security Forecasting
Moody, D.; Brumby, S. P.; Chartrand, R.; Keisler, R.; Mathis, M.; Beneke, C. M.; Nicholaeff, D.; Skillman, S.; Warren, M. S.; Poehnelt, J.
2017-12-01
The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and that can scale with the high-rate and dimensionality of imagery being collected. We focus on the problem of monitoring food crop productivity across the Middle East and North Africa, and show how an analysis-ready, multi-sensor data platform enables quick prototyping of satellite imagery analysis algorithms, from land use/land cover classification and natural resource mapping, to yearly and monthly vegetative health change trends at the structural field level.
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Elmes, A.; Rogan, J.; Williams, C. A.; Martin, D. G.; Ratick, S.; Nowak, D.
2015-12-01
Urban tree canopy (UTC) coverage is a critical component of sustainable urban areas. Trees provide a number of important ecosystem services, including air pollution mitigation, water runoff control, and aesthetic and cultural values. Critically, urban trees also act to mitigate the urban heat island (UHI) effect by shading impervious surfaces and via evaporative cooling. The cooling effect of urban trees can be seen locally, with individual trees reducing home HVAC costs, and at a citywide scale, reducing the extent and magnitude of an urban areas UHI. In order to accurately model the ecosystem services of a given urban forest, it is essential to map in detail the condition and composition of these trees at a fine scale, capturing individual tree crowns and their vertical structure. This paper presents methods for delineating UTC and measuring canopy structure at fine spatial resolution (body of methods, relying on a data fusion method to combine the information contained in high resolution WorldView-3 satellite imagery and aerial lidar data using an object-based image classification approach. The study area, Worcester, MA, has recently undergone a large-scale tree removal and reforestation program, following a pest eradication effort. Therefore, the urban canopy in this location provides a wide mix of tree age class and functional type, ideal for illustrating the effectiveness of the proposed methods. Early results show that the object-based classifier is indeed capable of identifying individual tree crowns, while continued research will focus on extracting crown structural characteristics using lidar-derived metrics. Ultimately, the resulting fine resolution UTC map will be compared with previously created UTC maps of the same area but for earlier dates, producing a canopy change map corresponding to the Worcester area tree removal and replanting effort.
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Lee, Mihye; Kloog, Itai; Chudnovsky, Alexandra; Lyapustin, Alexei; Wang, Yujie; Melly, Steven; Coull, Brent; Koutrakis, Petros; Schwartz, Joel
2016-01-01
Numerous studies have demonstrated that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM(sub 2.5) to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM(sub 2.5) exposures. In this paper, we used AOD data with other PM(sub 2.5) variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM(sub 2.5) at a 1 sq km resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM(sub 2.5) measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 cu micrograms for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM(sub 2.5) concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM(sub 2.5). Our model results will also extend the existing studies on PM(sub 2.5) which have mostly focused on urban areas because of the paucity of monitors in rural areas.
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Earth mapping - aerial or satellite imagery comparative analysis
Fotev, Svetlin; Jordanov, Dimitar; Lukarski, Hristo
Nowadays, solving the tasks for revision of existing map products and creation of new maps requires making a choice of the land cover image source. The issue of the effectiveness and cost of the usage of aerial mapping systems versus the efficiency and cost of very-high resolution satellite imagery is topical [1, 2, 3, 4]. The price of any remotely sensed image depends on the product (panchromatic or multispectral), resolution, processing level, scale, urgency of task and on whether the needed image is available in the archive or has to be requested. The purpose of the present work is: to make a comparative analysis between the two approaches for mapping the Earth having in mind two parameters: quality and cost. To suggest an approach for selection of the map information sources - airplane-based or spacecraft-based imaging systems with very-high spatial resolution. Two cases are considered: area that equals approximately one satellite scene and area that equals approximately the territory of Bulgaria.
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Maschhoff, K. R.; Polizotti, J. J.; Aumann, H. H.; Susskind, J.
2016-01-01
MISTiC(TM) Winds is an approach to improve short-term weather forecasting based on a miniature high resolution, wide field, thermal emission spectrometry instrument that will provide global tropospheric vertical profiles of atmospheric temperature and humidity at high (3-4 km) horizontal and vertical ( 1 km) spatial resolution. MISTiCs extraordinarily small size, payload mass of less than 15 kg, and minimal cooling requirements can be accommodated aboard a 27U-class CubeSat or an ESPA-Class micro-satellite. Low fabrication and launch costs enable a LEO sunsynchronous sounding constellation that would collectively provide frequent IR vertical profiles and vertically resolved atmospheric motion vector wind observations in the troposphere. These observations are highly complementary to present and emerging environmental observing systems, and would provide a combination of high vertical and horizontal resolution not provided by any other environmental observing system currently in operation. The spectral measurements that would be provided by MISTiC Winds are similar to those of NASA's AIRS that was built by BAE Systems and operates aboard the AQUA satellite. These new observations, when assimilated into high resolution numerical weather models, would revolutionize short-term and severe weather forecasting, save lives, and support key economic decisions in the energy, air transport, and agriculture arenasat much lower cost than providing these observations from geostationary orbit. In addition, this observation capability would be a critical tool for the study of transport processes for water vapor, clouds, pollution, and aerosols. Key remaining technical risks are being reduced through laboratory and airborne testing under NASA's Instrument Incubator Program.
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Maschhoff, K. R.; Polizotti, J. J.; Aumann, H. H.; Susskind, J.
2016-09-01
MISTiCTM Winds is an approach to improve short-term weather forecasting based on a miniature high resolution, wide field, thermal emission spectrometry instrument that will provide global tropospheric vertical profiles of atmospheric temperature and humidity at high (3-4 km) horizontal and vertical ( 1 km) spatial resolution. MISTiC's extraordinarily small size, payload mass of less than 15 kg, and minimal cooling requirements can be accommodated aboard a 27U-class CubeSat or an ESPA-Class micro-satellite. Low fabrication and launch costs enable a LEO sunsynchronous sounding constellation that would collectively provide frequent IR vertical profiles and vertically resolved atmospheric motion vector wind observations in the troposphere. These observations are highly complementary to present and emerging environmental observing systems, and would provide a combination of high vertical and horizontal resolution not provided by any other environmental observing system currently in operation. The spectral measurements that would be provided by MISTiC Winds are similar to those of NASA's AIRS that was built by BAE Systems and operates aboard the AQUA satellite. These new observations, when assimilated into high resolution numerical weather models, would revolutionize short-term and severe weather forecasting, save lives, and support key economic decisions in the energy, air transport, and agriculture arenas-at much lower cost than providing these observations from geostationary orbit. In addition, this observation capability would be a critical tool for the study of transport processes for water vapor, clouds, pollution, and aerosols. Key remaining technical risks are being reduced through laboratory and airborne testing under NASA's Instrument Incubator Program.
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Kurczyński, Zdzisław; Różycki, Sebastian; Bylina, Paweł
2017-12-01
To produce orthophotomaps or digital elevation models, the most commonly used method is photogrammetric measurement. However, the use of aerial images is not easy in polar regions for logistical reasons. In these areas, remote sensing data acquired from satellite systems is much more useful. This paper presents the basic technical requirements of different products which can be obtain (in particular orthoimages and digital elevation model (DEM)) using Very-High-Resolution Satellite (VHRS) images. The study area was situated in the vicinity of the Henryk Arctowski Polish Antarctic Station on the Western Shore of Admiralty Bay, King George Island, Western Antarctic. Image processing was applied on two triplets of images acquired by the Pléiades 1A and 1B in March 2013. The results of the generation of orthoimages from the Pléiades systems without control points showed that the proposed method can achieve Root Mean Squared Error (RMSE) of 3-9 m. The presented Pléiades images are useful for thematic remote sensing analysis and processing of measurements. Using satellite images to produce remote sensing products for polar regions is highly beneficial and reliable and compares well with more expensive airborne photographs or field surveys.
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The implementation of sea ice model on a regional high-resolution scale
Prasad, Siva; Zakharov, Igor; Bobby, Pradeep; McGuire, Peter
2015-09-01
The availability of high-resolution atmospheric/ocean forecast models, satellite data and access to high-performance computing clusters have provided capability to build high-resolution models for regional ice condition simulation. The paper describes the implementation of the Los Alamos sea ice model (CICE) on a regional scale at high resolution. The advantage of the model is its ability to include oceanographic parameters (e.g., currents) to provide accurate results. The sea ice simulation was performed over Baffin Bay and the Labrador Sea to retrieve important parameters such as ice concentration, thickness, ridging, and drift. Two different forcing models, one with low resolution and another with a high resolution, were used for the estimation of sensitivity of model results. Sea ice behavior over 7 years was simulated to analyze ice formation, melting, and conditions in the region. Validation was based on comparing model results with remote sensing data. The simulated ice concentration correlated well with Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Ocean and Sea Ice Satellite Application Facility (OSI-SAF) data. Visual comparison of ice thickness trends estimated from the Soil Moisture and Ocean Salinity satellite (SMOS) agreed with the simulation for year 2010-2011.
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Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.
2011-01-01
This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.
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Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio
Directory of Open Access Journals (Sweden)
Miao Zhang
2014-06-01
Full Text Available Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images and seasonal dynamics (based on crop condition profiles. Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite charge-coupled device (HJ-1 CCD images at 30-m resolution. Using the land map’s data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i pixels with only uncropped arable land are not included in the assessment; and (ii the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of
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Alvarez, Gabriel O.
2018-05-01
Hydrocarbon exploration in Argentina started long before the IGM created a single, high-precision geodetic reference network for the whole country. Several geodetic surveys were conducted in every producing basin, which have ever since then supported well placement. Currently, every basin has a huge amount of information referenced to the so-called "local" geodetic systems, such as Chos Malal - Quiñi Huao in the Neuquén Basin, and Pampa del Castillo in the San Jorge Basin, which differ to a greater or lesser extent from the national Campo Inchauspe datum established by the IGM in 1969 as the official geodetic network. However, technology development over the last few years and the expansion of satellite positioning systems such as GPS resulted in a new world geodetic order. Argentina rapidly joined this new geodetic order through the implementation of a new national geodetic system by the IGM: POSGAR network, which replaced the old national Campo Inchauspe system. However, this only helped to worsen the data georeferencing issue for oil companies, as a third reference system was added to each basin. Now every basin has a local system, the national system until 1997 (Campo Inchauspe), and finally the newly created POSGAR network national satellite system, which is geocentric unlike the former two planimetric datums. The purpose of this paper is to identify and allocate geodetic systems of coordinates to historical wells, whose geodetic system is missing or has been erroneously allocated, by using currently available technological resources such as geographic information systems and high-resolution satellite imagery.
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Recent developments in high-resolution global altimetric gravity field modeling
DEFF Research Database (Denmark)
Andersen, Ole Baltazar; Knudsen, Per; Berry, P. A .M.
2010-01-01
older gravity fields show accuracy improvement of the order of 20-40% due to a combination of retracking, enhanced processing, and the use of the new EGM2008 geoid model. In coastal and polar regions, accuracy improved in many places by 40-50% (or more) compared with older global marine gravity fields.......In recent years, dedicated effort has been made to improve high-resolution global marine gravity fields. One new global field is the Danish National Space Center (DNSC) 1-minute grid called DNSC08GRA, released in 2008. DNSC08GRA was derived from double-retracked satellite altimetry, mainly from...... the ERS-1 geodetic mission data, augmented with new retracked GEOSAT data which have significantly enhanced the range and hence the gravity field accuracy. DNSC08GRA is the first high-resolution global gravity field to cover the entire Arctic Ocean all the way to the North Pole. Comparisons with other...
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Land-mobile satellite excess path loss measurements
Hess, G. C.
1980-05-01
An experiment conducted with the ATS-6 satellite to determine the additional path loss over free-space loss experienced by land-mobile communication links is described. This excess path loss is measured as a function of 1) local environment, 2) vehicle heading, 3) link frequency, 4) satellite elevation angle, and 5) street side. A statistical description of excess loss developed from the data shows that the first two parameters dominate. Excess path loss on the order of 25 dB is typical in urban situations, but decreases to under 10 dB in suburban/rural areas. Spaced antenna selection diversity is found to provide only a slight decrease (4 dB, typically) in the urban excess path loss observed. Level crossing rates are depressed in satellite links relative to those of Rayleigh-faded terrestrial links, but increases in average fade durations tend to offset that advantage. The measurements show that the excess path loss difference between 860-MHz links and 1550-MHz links is generally negligible.
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An Integrated High Resolution Hydrometeorological Modeling Testbed using LIS and WRF
Kumar, Sujay V.; Peters-Lidard, Christa D.; Eastman, Joseph L.; Tao, Wei-Kuo
2007-01-01
Scientists have made great strides in modeling physical processes that represent various weather and climate phenomena. Many modeling systems that represent the major earth system components (the atmosphere, land surface, and ocean) have been developed over the years. However, developing advanced Earth system applications that integrates these independently developed modeling systems have remained a daunting task due to limitations in computer hardware and software. Recently, efforts such as the Earth System Modeling Ramework (ESMF) and Assistance for Land Modeling Activities (ALMA) have focused on developing standards, guidelines, and computational support for coupling earth system model components. In this article, the development of a coupled land-atmosphere hydrometeorological modeling system that adopts these community interoperability standards, is described. The land component is represented by the Land Information System (LIS), developed by scientists at the NASA Goddard Space Flight Center. The Weather Research and Forecasting (WRF) model, a mesoscale numerical weather prediction system, is used as the atmospheric component. LIS includes several community land surface models that can be executed at spatial scales as fine as 1km. The data management capabilities in LIS enable the direct use of high resolution satellite and observation data for modeling. Similarly, WRF includes several parameterizations and schemes for modeling radiation, microphysics, PBL and other processes. Thus the integrated LIS-WRF system facilitates several multi-model studies of land-atmosphere coupling that can be used to advance earth system studies.
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Directory of Open Access Journals (Sweden)
Shimelis B. Gebere
2015-09-01
Full Text Available Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM 3B42, the Global Satellite Mapping of Precipitation (GSMaP_MVK+, and the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Networks (PERSIANN at daily, monthly, and seasonal time scales against rain gauge records over data-scarce parts of Eastern Ethiopia. TRMM 3B42 rain products show relatively better performance at the three time scales, while PERSIANN did much better than GSMaP. At the daily time scale, TRMM correctly detected 88% of the rainfall from the rain gauge. The correlation at the monthly time scale also revealed that the TRMM has captured the observed rainfall better than the other two. For Belg (short rain and Kiremt (long rain seasons, the TRMM did better than the others by far. However, during Bega (dry season, PERSIANN showed a relatively good estimate. At all-time scales, noticing the bias, TRMM tends to overestimate, while PERSIANN and GSMaP tend to underestimate the rainfall. The overall result suggests that monthly and seasonal TRMM rainfall performed better than daily rainfall. It has also been found that both GSMaP and PERSIANN performed better in relatively flat areas than mountainous areas. Before the practical use of TRMM, the RMSE value needs to be improved by considering the topography of the study area or adjusting the bias.
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ASTER-Derived High-Resolution Ice Surface Temperature for the Arctic Coast
Directory of Open Access Journals (Sweden)
Young-Sun Son
2018-04-01
Full Text Available Ice surface temperature (IST controls the rate of sea ice growth and the heat exchange between the atmosphere and ocean. In this study, high-resolution IST using the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER thermal infrared region (TIR images was retrieved to observe the thermal change of coastal sea ice. The regression coefficients of the multi-channel equation using ASTER brightness temperatures ( B T and MODIS ISTs were derived. MODIS IST products (MOD29 were used as an in situ temperature substitute. The ASTER IST using five channels from band 10 ( B T 10 to band 14 ( B T 14 showed an RMSE of 0.746 K for the validation images on the Alaskan coast. The uncertainty of the two-channel ( B T 13 and B T 14 ASTER IST was 0.497 K, which was better than that of the five-channel. We thus concluded that the two-channel equation using ASTER B T 13 and B T 14 was an optimal model for the surface temperature retrieval of coastal sea ice. The two-channel ASTER IST showed similar accuracy at higher latitudes than in Alaska. Therefore, ASTER-derived IST with 90 m spatial resolution can be used to observe small-scale thermal variations on the sea ice surface along the Arctic coast.
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Cook, Bruce D.; Bolstad, Paul V.; Naesset, Erik; Anderson, Ryan S.; Garrigues, Sebastian; Morisette, Jeffrey T.; Nickeson, Jaime; Davis, Kenneth J.
2009-01-01
Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.
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Satellite-derived vertical profiles of temperature and dew point for mesoscale weather forecast
Masselink, Thomas; Schluessel, P.
1995-12-01
Weather forecast-models need spatially high resolutioned vertical profiles of temperature and dewpoint for their initialisation. These profiles can be supplied by a combination of data from the Tiros-N Operational Vertical Sounder (TOVS) and the imaging Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA polar orbiting sate!- lites. In cloudy cases the profiles derived from TOVS data only are of insufficient accuracy. The stanthrd deviations from radiosonde ascents or numerical weather analyses likely exceed 2 K in temperature and 5Kin dewpoint profiles. It will be shown that additional cloud information as retrieved from AVHIRR allows a significant improvement in theaccuracy of vertical profiles. The International TOVS Processing Package (ITPP) is coupled to an algorithm package called AVHRR Processing scheme Over cLouds, Land and Ocean (APOLLO) where parameters like cloud fraction and cloud-top temperature are determined with higher accuracy than obtained from TOVS retrieval alone. Furthermore, a split-window technique is applied to the cloud-free AVHRR imagery in order to derive more accurate surface temperatures than can be obtained from the pure TOVS retrieval. First results of the impact of AVHRR cloud detection on the quality of the profiles are presented. The temperature and humidity profiles of different retrieval approaches are validated against analyses of the European Centre for Medium-Range Weatherforecasts.
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Directory of Open Access Journals (Sweden)
Shaowei Ning
2016-10-01
Full Text Available The Global Precipitation Mission (GPM Core Observatory that was launched on 27 February 2014 ushered in a new era for estimating precipitation from satellites. Based on their high spatial–temporal resolution and near global coverage, satellite-based precipitation products have been applied in many research fields. The goal of this study was to quantitatively compare two of the latest GPM-era satellite precipitation products (GPM IMERG and GSMap-Gauge Ver. 6 with a network of 840 precipitation gauges over the Chinese mainland. Direct comparisons of satellite-based precipitation products with rain gauge observations over a 20 month period from April 2014 to November 2015 at 0.1° and daily/monthly resolutions showed the following results: Both of the products were capable of capturing the overall spatial pattern of the 20 month mean daily precipitation, which was characterized by a decreasing trend from the southeast to the northwest. GPM IMERG overestimated precipitation by approximately 0.09 mm/day while GSMap-Gauge Ver. 6 underestimated precipitation by −0.04 mm/day. The two satellite-based precipitation products performed better over wet southern regions than over dry northern regions. They also showed better performance in summer than in winter. In terms of mean error, root mean square error, correlation coefficient, and probability of detection, GSMap-Gauge was better able to estimate precipitation and had more stable quality results than GPM IMERG on both daily and monthly scales. GPM IMERG was more sensitive to conditions of no rain or light rainfall and demonstrated good capability of capturing the behavior of extreme precipitation events. Overall, the results revealed some limitations of these two latest satellite-based precipitation products when used over the Chinese mainland, helping to characterize some of the error features in these datasets for potential users.
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Arctide2017, a high-resolution regional tidal model in the Arctic Ocean
DEFF Research Database (Denmark)
Cancet, M.; Andersen, O. B.; Lyard, F.
2018-01-01
The Arctic Ocean is a challenging region for tidal modelling. The accuracy of the global tidal models decreases by several centimeters in the Polar Regions, which has a large impact on the quality of the satellite altimeter sea surface heights and the altimetry-derived products. NOVELTIS, DTU Space...... and LEGOS have developed Arctide2017, a regional, high-resolution tidal atlas in the Arctic Ocean, in the framework of an extension of the CryoSat Plus for Ocean (CP4O) ESA STSE (Support to Science Element) project. In particular, this atlas benefits from the assimilation of the most complete satellite...... assimilation and validation. This paper presents the implementation methodology and the performance of this new regional tidal model in the Arctic Ocean, compared to the existing global and regional tidal models....
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Estimating Global Impervious Surface based on Social-economic Data and Satellite Observations
Zeng, Z.; Zhang, K.; Xue, X.; Hong, Y.
2016-12-01
Impervious surface areas around the globe are expanding and significantly altering the surface energy balance, hydrology cycle and ecosystem services. Many studies have underlined the importance of impervious surface, r from hydrological modeling to contaminant transport monitoring and urban development estimation. Therefore accurate estimation of the global impervious surface is important for both physical and social sciences. Given the limited coverage of high spatial resolution imagery and ground survey, using satellite remote sensing and geospatial data to estimate global impervious areas is a practical approach. Based on the previous work of area-weighted imperviousness for north branch of the Chicago River provided by HDR, this study developed a method to determine the percentage of impervious surface using latest global land cover categories from multi-source satellite observations, population density and gross domestic product (GDP) data. Percent impervious surface at 30-meter resolution were mapped. We found that 1.33% of the CONUS (105,814 km2) and 0.475% of the land surface (640,370km2) are impervious surfaces. To test the utility and practicality of the proposed method, National Land Cover Database (NLCD) 2011 percent developed imperviousness for the conterminous United States was used to evaluate our results. The average difference between the derived imperviousness from our method and the NLCD data across CONUS is 1.14%, while difference between our results and the NLCD data are within ±1% over 81.63% of the CONUS. The distribution of global impervious surface map indicates that impervious surfaces are primarily concentrated in China, India, Japan, USA and Europe where are highly populated and/or developed. This study proposes a straightforward way of mapping global imperviousness, which can provide useful information for hydrologic modeling and other applications.
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Zhang, Xueying; Chu, Yiyi; Wang, Yuxuan; Zhang, Kai
2018-08-01
The regulatory monitoring data of particulate matter with an aerodynamic diameter images retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites. We then developed mixed-effects models based on AODs, land use features, geographic characteristics, and weather conditions, and the day-specific as well as site-specific random effects to estimate the PM 2.5 concentrations (μg/m 3 ) in the state of Texas during the period 2008-2013. The mixed-effects models' performance was evaluated using the coefficient of determination (R 2 ) and square root of the mean squared prediction error (RMSPE) from ten-fold cross-validation, which randomly selected 90% of the observations for training purpose and 10% of the observations for assessing the models' true prediction ability. Mixed-effects regression models showed good prediction performance (R 2 values from 10-fold cross validation: 0.63-0.69). The model performance varied by regions and study years, and the East region of Texas, and year of 2009 presented relatively higher prediction precision (R 2 : 0.62 for the East region; R 2 : 0.69 for the year of 2009). The PM 2.5 concentrations generated through our developed models at 1-km grid cells in the state of Texas showed a decreasing trend from 2008 to 2013 and a higher reduction of predicted PM 2.5 in more polluted areas. Our findings suggest that mixed-effects regression models developed based on MAIAC AOD are a feasible approach to predict ground-level PM 2.5 in Texas. Predicted PM 2.5 concentrations at the 1-km resolution on a daily basis can be used for epidemiological studies to investigate short- and long-term health impact of PM 2.5 in Texas. Copyright © 2017 Elsevier B.V. All rights reserved.
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Directory of Open Access Journals (Sweden)
J. Etchanchu
2017-11-01
Full Text Available Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA land surface model included in the EXternalized SURface (SURFEX modeling platform. The study focuses on the effect of the leaf area index (LAI spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m. The study area is an agricultural zone in southwestern France covering 576 km2 (24 km × 24 km. An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE. Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE in the simulated evapotranspiration. This
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Land use/cover classification in the Brazilian Amazon using satellite images.
Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira
2012-09-01
Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.
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Influence of satellite-derived photolysis rates and NOx emissions on Texas ozone modeling
Tang, W.; Cohan, D. S.; Pour-Biazar, A.; Lamsal, L. N.; White, A. T.; Xiao, X.; Zhou, W.; Henderson, B. H.; Lash, B. F.
2015-02-01
Uncertain photolysis rates and emission inventory impair the accuracy of state-level ozone (O3) regulatory modeling. Past studies have separately used satellite-observed clouds to correct the model-predicted photolysis rates, or satellite-constrained top-down NOx emissions to identify and reduce uncertainties in bottom-up NOx emissions. However, the joint application of multiple satellite-derived model inputs to improve O3 state implementation plan (SIP) modeling has rarely been explored. In this study, Geostationary Operational Environmental Satellite (GOES) observations of clouds are applied to derive the photolysis rates, replacing those used in Texas SIP modeling. This changes modeled O3 concentrations by up to 80 ppb and improves O3 simulations by reducing modeled normalized mean bias (NMB) and normalized mean error (NME) by up to 0.1. A sector-based discrete Kalman filter (DKF) inversion approach is incorporated with the Comprehensive Air Quality Model with extensions (CAMx)-decoupled direct method (DDM) model to adjust Texas NOx emissions using a high-resolution Ozone Monitoring Instrument (OMI) NO2 product. The discrepancy between OMI and CAMx NO2 vertical column densities (VCDs) is further reduced by increasing modeled NOx lifetime and adding an artificial amount of NO2 in the upper troposphere. The region-based DKF inversion suggests increasing NOx emissions by 10-50% in most regions, deteriorating the model performance in predicting ground NO2 and O3, while the sector-based DKF inversion tends to scale down area and nonroad NOx emissions by 50%, leading to a 2-5 ppb decrease in ground 8 h O3 predictions. Model performance in simulating ground NO2 and O3 are improved using sector-based inversion-constrained NOx emissions, with 0.25 and 0.04 reductions in NMBs and 0.13 and 0.04 reductions in NMEs, respectively. Using both GOES-derived photolysis rates and OMI-constrained NOx emissions together reduces modeled NMB and NME by 0.05, increases the model
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Husnayaen; Rimba, A. Besse; Osawa, Takahiro; Parwata, I. Nyoman Sudi; As-syakur, Abd. Rahman; Kasim, Faizal; Astarini, Ida Ayu
2018-04-01
Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68 km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters = 53%, CVI 7 parameters = 93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7 km of the total 48.7 km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.
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Satellite derived bathymetry: mapping the Irish coastline
Monteys, X.; Cahalane, C.; Harris, P.; Hanafin, J.
2017-12-01
Ireland has a varied coastline in excess of 3000 km in length largely characterized by extended shallow environments. The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods or airborne LiDAR techniques as demonstrated in the Irish INFOMAR program. Thus, large coastal areas in Ireland, and much of the coastal zone worldwide remain unmapped using modern techniques and is poorly understood. Earth Observations (EO) missions are currently being used to derive timely, cost effective, and quality controlled information for mapping and monitoring coastal environments. Different wavelengths of the solar light penetrate the water column to different depths and are routinely sensed by EO satellites. A large selection of multispectral imagery (MS) from many platforms were examined, as well as from small aircrafts and drones. A number of bays representing very different coastal environments were explored in turn. The project's workflow is created by building a catalogue of satellite and field bathymetric data to assess the suitability of imagery captured at a range of spatial, spectral and temporal resolutions. Turbidity indices are derived from the multispectral information. Finally, a number of spatial regression models using water-leaving radiance parameters and field calibration data are examined. Our assessment reveals that spatial regression algorithms have the potential to significantly improve the accuracy of the predictions up to 10m WD and offer a better handle on the error and uncertainty budget. The four spatial models investigated show better adjustments than the basic non-spatial model. Accuracy of the predictions is better than 10% WD at 95% confidence. Future work will focus on improving the accuracy of the predictions incorporating an analytical model in conjunction with improved empirical methods. The recently launched ESA Sentinel 2 will become the
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High Resolution Digital Elevation Models of Pristine Explosion Craters
Farr, T. G.; Krabill, W.; Garvin, J. B.
2004-01-01
In order to effectively capture a realistic terrain applicable to studies of cratering processes and landing hazards on Mars, we have obtained high resolution digital elevation models of several pristine explosion craters at the Nevada Test Site. We used the Airborne Terrain Mapper (ATM), operated by NASA's Wallops Flight Facility to obtain DEMs with 1 m spacing and 10 cm vertical errors of 4 main craters and many other craters and collapse pits. The main craters that were mapped are Sedan, Scooter, Schooner, and Danny Boy. The 370 m diameter Sedan crater, located on Yucca Flat, is the largest and freshest explosion crater on Earth that was formed under conditions similar to hypervelocity impact cratering. As such, it is effectively pristine, having been formed in 1962 as a result of a controlled detonation of a 100 kiloton thermonuclear device, buried at the appropriate equivalent depth of burst required to make a simple crater. Sedan was formed in alluvium of mixed lithology and subsequently studied using a variety of field-based methods. Nearby secondary craters were also formed at the time and were also mapped by ATM. Adjacent to Sedan and also in alluvium is Scooter, about 90 m in diameter and formed by a high-explosive event. Schooner (240 m) and Danny Boy (80 m) craters were also important targets for ATM as they were excavated in hard basalt and therefore have much rougher ejecta. This will allow study of ejecta patterns in hard rock as well as engineering tests of crater and rock avoidance and rover trafficability. In addition to the high resolution DEMs, crater geometric characteristics, RMS roughness maps, and other higher-order derived data products will be generated using these data. These will provide constraints for models of landing hazards on Mars and for rover trafficability. Other planned studies will include ejecta size-frequency distribution at the resolution of the DEM and at finer resolution through air photography and field measurements
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Use of the high-resolution satellite images for detection of fractures related to the ore deposits
Cruz-Mondaca, M.; Soto-Pinto, C. A.; Arellano-Baeza, A. A.
2012-12-01
The Aster and GeoEye satellite high-resolution images were used to detect the structures related to the fracturing of the upper crust in the North of Chile. In particular, lineament analysis has been applied to detect the presence of epithermal fluids of low sulfurization associated with the Paleozoic ore deposits. These results have been compared with the location of the minerals altered by the presence of geothermal fluids detected using the spectral libraries. Later, the presence of fractures has been corroborated during recognition of fractures in situ and the geochemical analysis of samples of minerals altered by the presence of fluids. It was shown that the results obtained are relevant for the gold vein detection.
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Kharbouche, Said; Muller, Jan-Peter; Gatebe, Charles K.; Scanlon, Tracy; Banks, Andrew C.
2017-01-01
CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May 2008, a CAR flight campaign took place over the well-known calibration and validation site of Railroad Valley in Nevada (38.504 deg N, 115.692 deg W).The campaign coincided with the overpasses of several key EO (Earth Observation) satellites such as Landsat-7, Envisat and Terra. Thus, there are nearly simultaneous measurements from these satellites and the CAR airborne sensor over the same calibration site. The CAR spectral bands are close to those of most EO satellites. CAR has the ability to cover the whole range of azimuth view angles and a variety of zenith angles depending on altitude and, as a consequence, the biases seen between satellite and CAR measurements due to both unmatched spectral bands and unmatched angles can be significantly reduced. A comparison is presented here between CARs land surface reflectance (BRF or Bidirectional Reflectance Factor) with those derived from Terra/MODIS (MOD09 and MAIAC), Terra/MISR, Envisat/MERIS and Landsat-7. In this study, we utilized CAR data from low altitude flights (approx. 180 m above the surface) in order to minimize the effects of the atmosphere on these measurements and then obtain a valuable ground-truth data set of surface reflectance. Furthermore, this study shows that differences between measurements caused by surface heterogeneity can be tolerated, thanks to the high homogeneity of the study site on the one hand, and on the other hand, to the spatial sampling and the large number of CAR samples. These results demonstrate that satellite BRF measurements over this site are in good agreement with CAR with variable biases across different spectral bands. This is most likely due to residual aerosol
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Skurikhin, A. N.; Gangodagamage, C.; Rowland, J. C.; Wilson, C. J.
2013-12-01
Arctic lowland landscapes underlain by permafrost are often characterized by polygon-like patterns such as ice-wedge polygons outlined by networks of ice wedges and complemented with polygon rims, troughs, shallow ponds and thermokarst lakes. Polygonal patterns and corresponding features are relatively easy to recognize in high spatial resolution satellite imagery by a human, but their automated recognition is challenging due to the variability in their spectral appearance, the irregularity of individual trough spacing and orientation within the patterns, and a lack of unique spectral response attributable to troughs with widths commonly between 1 m and 2 m. Accurate identification of fine scale elements of ice-wedge polygonal tundra is important as their imprecise recognition may bias estimates of water, heat and carbon fluxes in large-scale climate models. Our focus is on the problem of identification of Arctic polygonal tundra fine-scale landscape elements (as small as 1 m - 2 m width). The challenge of the considered problem is that while large water bodies (e.g. lakes and rivers) can be recognized based on spectral response, reliable recognition of troughs is more difficult. Troughs do not have unique spectral signature, their appearance is noisy (edges are not strong), their width is small, and they often form connected networks with ponds and lakes, and thus they have overlapping spectral response with other water bodies and surrounding non-water bodies. We present a semi-automated approach to identify and classify Arctic polygonal tundra landscape components across the range of spatial scales, such as troughs, ponds, river- and lake-like objects, using high spatial resolution satellite imagery. The novelty of the approach lies in: (1) the combined use of segmentation and shape-based classification to identify a broad range of water bodies, including troughs, and (2) the use of high-resolution WorldView-2 satellite imagery (with resolution of 0.6 m) for this
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Black Sea impact on its west-coast land surface temperature
Cheval, Sorin; Constantin, Sorin
2018-03-01
This study investigates the Black Sea influence on the thermal characteristics of its western hinterland based on satellite imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The marine impact on the land surface temperature (LST) values is detected at daily, seasonal and annual time scales, and a strong linkage with the land cover is demonstrated. The remote sensing products used within the study supply LST data with complete areal coverage during clear sky conditions at 1-km spatial resolution, which is appropriate for climate studies. The sea influence is significant up to 4-5 km, by daytime, while the nighttime influence is very strong in the first 1-2 km, and it gradually decreases westward. Excepting the winter, the daytime temperature increases towards the plateau with the distance from the sea, e.g. with a gradient of 0.9 °C/km in the first 5 km in spring or with 0.7 °C/km in summer. By nighttime, the sea water usually remains warmer than the contiguous land triggering higher LST values in the immediate proximity of the coastline in all seasons, e.g. mean summer LST is 19.0 °C for the 1-km buffer, 16.6 °C for the 5-km buffer and 16.0 °C for the 10-km buffer. The results confirm a strong relationship between the land cover and thermal regime in the western hinterland of the Black Sea coast. The satellite-derived LST and air temperature values recorded at the meteorological stations are highly correlated for similar locations, but the marine influence propagates differently, pledging for distinct analysis. Identified anomalies in the general observed trends are investigated in correlation with sea surface temperature dynamics in the coastal area.
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International Nuclear Information System (INIS)
Bolle, H.J.; Katergiannakis, U.; Billing, H.; Koslowsky, D.; Langer, I.; Tonn, W.
1993-01-01
In large-scale field experiments, methods were validated with whose aid characteristics of the terrestrial surfaces can be derived from satellite data; these characteristics are required for the exploration of the global change. The report gives an overview. The following topics are treated: Problems of calibration of satellite sensors; the geographical matching of ground observations to the satellite measurements; necessary corrections; dimensional integration of the data up to the dimensions of raster grids of global climate models. The report discusses in detail in what manner the remote exploration data can be connected with information on the terrestrial surfaces, in particular with energy balances. Few experiments only have been executed up to now within the framework of land surface climatology; however, they contributed a great deal to the better understanding of linking satellite data with terrestrial surface processes. If one wants to apply the elaborated methods globally wants, one needs, however, complex algorithms as well as - at least for the time being - constant quality control in the different landscape regions of the earth. (orig.) [de
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International Nuclear Information System (INIS)
Cosentino, B.L.; Lillesand, T.M.
1991-01-01
Attention is given to an initial research project being performed by the University of Wisconsin-Madison, Environmental Remote Sensing Center in conjunction with seven local, state, and federal agencies to implement automated statewide land cover mapping using satellite remote sensing and geographical information system (GIS) techniques. The basis, progress, and future research needs for this mapping program are presented. The research efforts are directed toward strategies that integrate satellite remote sensing and GIS techniques in the generation of land cover data for multiple users of land cover information. The project objectives are to investigate methodologies that integrate satellite data with other imagery and spatial data resident in emerging GISs in the state for particular program needs, and to develop techniques that can improve automated land cover mapping efficiency and accuracy. 10 refs
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Directory of Open Access Journals (Sweden)
Kun Hu
2016-09-01
Full Text Available High precision geometric rectification of High Resolution Satellite Imagery (HRSI is the basis of digital mapping and Three-Dimensional (3D modeling. Taking advantage of line features as basic geometric control conditions instead of control points, the Line-Based Transformation Model (LBTM provides a practical and efficient way of image rectification. It is competent to build the mathematical relationship between image space and the corresponding object space accurately, while it reduces the workloads of ground control and feature recognition dramatically. Based on generalization and the analysis of existing LBTMs, a novel rigorous LBTM is proposed in this paper, which can further eliminate the geometric deformation caused by sensor inclination and terrain variation. This improved nonlinear LBTM is constructed based on a generalized point strategy and resolved by least squares overall adjustment. Geo-positioning accuracy experiments with IKONOS, GeoEye-1 and ZiYuan-3 satellite imagery are performed to compare rigorous LBTM with other relevant line-based and point-based transformation models. Both theoretic analysis and experimental results demonstrate that the rigorous LBTM is more accurate and reliable without adding extra ground control. The geo-positioning accuracy of satellite imagery rectified by rigorous LBTM can reach about one pixel with eight control lines and can be further improved by optimizing the horizontal and vertical distribution of control lines.
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Fita, L.; Romero, R.; Luque, A.; Ramis, C.
2009-08-01
The scarcity of meteorological observations in maritime areas is a well-known problem that can be an important limitation in the study of different phenomena. Tropical-like storms or medicanes developed over the Mediterranean sea are intense storms with some similarities to the tropical ones. Although they do not reach the hurricane intensity, their potential for damage is very high, due to the densely populated Mediterranean coastal regions. In this study, the two notable cases of medicane development which occurred in the western Mediterranean basin in September 1996 and October 2003, are considered. The capability of mesoscale numerical models to simulate general aspects of such a phenomena has been previously shown. With the aim of improving the numerical results, an adjustment of the humidity vertical profiles in MM5 simulations is performed by means of satellite derived precipitation. Convective and stratiform precipitation types obtained from satellite images are used to individually adjust the profiles. Lightning hits are employed to identify convective grid points. The adjustment of the vertical humidity profiles is carried out in the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses used as initial conditions for the simulations. Analyses nudging to ECMWF analyses and to the satellite-based humidity-corrected version of these analyses has also been applied using Four Dimensional Data Assimilation (FDDA). An additional adjustment is applied as observation nudging of satellite/lightning information at different time and spatial resolutions. Statistical parameters are proposed and tested as an objective way to intercompare satellite-derived and simulated trajectories. Simulations of medicanes exhibit a strong sensitivity to vertical humidity profiles. Trajectories of the storms are improved or worsened by using FDDA. A case dependence is obtained on the characteristics of the humidity-corrected medicanes. FDDA sensitivity on temporal and
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Directory of Open Access Journals (Sweden)
L. Fita
2009-08-01
Full Text Available The scarcity of meteorological observations in maritime areas is a well-known problem that can be an important limitation in the study of different phenomena. Tropical-like storms or medicanes developed over the Mediterranean sea are intense storms with some similarities to the tropical ones. Although they do not reach the hurricane intensity, their potential for damage is very high, due to the densely populated Mediterranean coastal regions. In this study, the two notable cases of medicane development which occurred in the western Mediterranean basin in September 1996 and October 2003, are considered. The capability of mesoscale numerical models to simulate general aspects of such a phenomena has been previously shown. With the aim of improving the numerical results, an adjustment of the humidity vertical profiles in MM5 simulations is performed by means of satellite derived precipitation. Convective and stratiform precipitation types obtained from satellite images are used to individually adjust the profiles. Lightning hits are employed to identify convective grid points. The adjustment of the vertical humidity profiles is carried out in the European Centre for Medium-Range Weather Forecasts (ECMWF analyses used as initial conditions for the simulations. Analyses nudging to ECMWF analyses and to the satellite-based humidity-corrected version of these analyses has also been applied using Four Dimensional Data Assimilation (FDDA. An additional adjustment is applied as observation nudging of satellite/lightning information at different time and spatial resolutions. Statistical parameters are proposed and tested as an objective way to intercompare satellite-derived and simulated trajectories. Simulations of medicanes exhibit a strong sensitivity to vertical humidity profiles. Trajectories of the storms are improved or worsened by using FDDA. A case dependence is obtained on the characteristics of the humidity-corrected medicanes. FDDA sensitivity
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Energy Technology Data Exchange (ETDEWEB)
Fita, L.; Romero, R.; Luque, A.; Ramis, C. [Univ. de les Illes Balears, Palma de Mallorca (Spain). Grup de Meteorologia
2009-07-01
The scarcity of meteorological observations in maritime areas is a well-known problem that can be an important limitation in the study of different phenomena. Tropical-like storms or medicanes developed over the Mediterranean sea are intense storms with some similarities to the tropical ones. Although they do not reach the hurricane intensity, their potential for damage is very high, due to the densely populated Mediterranean coastal regions. In this study, the two notable cases of medicane development which occurred in the western Mediterranean basin in September 1996 and October 2003, are considered. The capability of mesoscale numerical models to simulate general aspects of such a phenomena has been previously shown. With the aim of improving the numerical results, an adjustment of the humidity vertical profiles in MM5 simulations is performed by means of satellite derived precipitation. Convective and stratiform precipitation types obtained from satellite images are used to individually adjust the profiles. Lightning hits are employed to identify convective grid points. The adjustment of the vertical humidity profiles is carried out in the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses used as initial conditions for the simulations. Analyses nudging to ECMWF analyses and to the satellite-based humidity-corrected version of these analyses has also been applied using Four Dimensional Data Assimilation (FDDA). An additional adjustment is applied as observation nudging of satellite/lightning information at different time and spatial resolutions. Statistical parameters are proposed and tested as an objective way to intercompare satellite-derived and simulated trajectories. Simulations of medicanes exhibit a strong sensitivity to vertical humidity profiles. Trajectories of the storms are improved or worsened by using FDDA. A case dependence is obtained on the characteristics of the humidity-corrected medicanes. FDDA sensitivity on temporal and
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The long-term Global LAnd Surface Satellite (GLASS) product suite and applications
Liang, S.
2015-12-01
Our Earth's environment is experiencing rapid changes due to natural variability and human activities. To monitor, understand and predict environment changes to meet the economic, social and environmental needs, use of long-term high-quality satellite data products is critical. The Global LAnd Surface Satellite (GLASS) product suite, generated at Beijing Normal University, currently includes 12 products, including leaf area index (LAI), broadband shortwave albedo, broadband longwave emissivity, downwelling shortwave radiation and photosynthetically active radiation, land surface skin temperature, longwave net radiation, daytime all-wave net radiation, fraction of absorbed photosynetically active radiation absorbed by green vegetation (FAPAR), fraction of green vegetation coverage, gross primary productivity (GPP), and evapotranspiration (ET). Most products span from 1981-2014. The algorithms for producing these products have been published in the top remote sensing related journals and books. More and more applications have being reported in the scientific literature. The GLASS products are freely available at the Center for Global Change Data Processing and Analysis of Beijing Normal University (http://www.bnu-datacenter.com/), and the University of Maryland Global Land Cover Facility (http://glcf.umd.edu). After briefly introducing the basic characteristics of GLASS products, we will present some applications on the long-term environmental changes detected from GLASS products at both global and local scales. Detailed analysis of regional hotspots, such as Greenland, Tibetan plateau, and northern China, will be emphasized, where environmental changes have been mainly associated with climate warming, drought, land-atmosphere interactions, and human activities.
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The 2010 Pakistan floods: high-resolution simulations with the WRF model
Viterbo, Francesca; Parodi, Antonio; Molini, Luca; Provenzale, Antonello; von Hardenberg, Jost; Palazzi, Elisa
2013-04-01
Estimating current and future water resources in high mountain regions with complex orography is a difficult but crucial task. In particular, the French-Italian project PAPRIKA is focused on two specific regions in the Hindu-Kush -- Himalaya -- Karakorum (HKKH)region: the Shigar basin in Pakistan, at the feet of K2, and the Khumbu valley in Nepal, at the feet of Mount Everest. In this framework, we use the WRF model to simulate precipitation and meteorological conditions with high resolution in areas with extreme orographic slopes, comparing the model output with station and satellite data. Once validated the model, we shall run a set of three future time-slices at very high spatial resolution, in the periods 2046-2050, 2071-2075 and 2096-2100, nested in different climate change scenarios (EXtreme PREcipitation and Hydrological climate Scenario Simulations -EXPRESS-Hydro project). As a prelude to this study, here we discuss the simulation of specific, high-intensity rainfall events in this area. In this paper we focus on the 2010 Pakistan floods which began in late July 2010, producing heavy monsoon rains in the Khyber Pakhtunkhwa, Sindh, Punjab and Balochistan regions of Pakistan and affecting the Indus River basin. Approximately one-fifth of Pakistan's total land area was underwater, with a death toll of about 2000 people. This event has been simulated with the WRF model (version 3.3.) in cloud-permitting mode (d01 14 km and d02 3.5 km): different convective closures and microphysics parameterization have been used. A deeper understanding of the processes responsible for this event has been gained through comparison with rainfall depth observations, radiosounding data and geostationary/polar satellite images.
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A new CM SAF Solar Surface Radiation Climate Data Set derived from Meteosat Satellite Observations
Trentmann, J.; Mueller, R. W.; Pfeifroth, U.; Träger-Chatterjee, C.; Cremer, R.
2014-12-01
The incoming surface solar radiation has been defined as an essential climate variable by GCOS. It is mandatory to monitor this part of the earth's energy balance, and thus gain insights on the state and variability of the climate system. In addition, data sets of the surface solar radiation have received increased attention over the recent years as an important source of information for the planning of solar energy applications. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving surface solar radiation from geostationary and polar-orbiting satellite instruments. While CM SAF is focusing on the generation of high-quality long-term climate data records, also operationally data is provided in short time latency within 8 weeks. Here we present SARAH (Solar Surface Radiation Dataset - Heliosat), i.e. the new CM SAF Solar Surface Radiation data set based on Meteosat satellite observations. SARAH provides instantaneous, daily- and monthly-averaged data of the effective cloud albedo (CAL), the direct normalized solar radiation (DNI) and the solar irradiance (SIS) from 1983 to 2013 for the full view of the Meteosat satellite (i.e, Europe, Africa, parts of South America, and the Atlantic ocean). The data sets are generated with a high spatial resolution of 0.05 deg allowing for detailed regional studies, and are available in netcdf-format at no cost without restrictions at www.cmsaf.eu. We provide an overview of the data sets, including a validation against reference measurements from the BSRN and GEBA surface station networks.
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Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki
2015-01-01
In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments. PMID:26378532
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A European Land Mobile Satellite System via EMS
Ananasso, Fulvio; Mistretta, Ignazio
1991-10-01
The paper analyzes the technical and market issues that influence the strategy of implementation of a Land Mobile Satellite System via the payload EMS (European Mobile System) embarked on ITALSAT F-2. The final goal is to determine services, network architecture, and added value chain that make LMSS via EMS profitable for a typical telecommunication company.
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High-Resolution Near Real-Time Drought Monitoring in South Asia
Aadhar, S.; Mishra, V.
2017-12-01
Drought in South Asia affect food and water security and pose challenges for millions of people. For policy-making, planning and management of water resources at the sub-basin or administrative levels, high-resolution datasets of precipitation and air temperature are required in near-real time. Here we develop a high resolution (0.05 degree) bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia. Moreover, the dataset can be used to monitor climatic extremes (heat waves, cold waves, dry and wet anomalies) in South Asia. A distribution mapping method was applied to correct bias in precipitation and air temperature (maximum and minimum), which performed well compared to the other bias correction method based on linear scaling. Bias-corrected precipitation and temperature data were used to estimate Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to assess the historical and current drought conditions in South Asia. We evaluated drought severity and extent against the satellite-based Normalized Difference Vegetation Index (NDVI) anomalies and satellite-driven Drought Severity Index (DSI) at 0.05˚. We find that the bias-corrected high-resolution data can effectively capture observed drought conditions as shown by the satellite-based drought estimates. High resolution near real-time dataset can provide valuable information for decision-making at district and sub- basin levels.
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Cardille, J. A.; Lee, J.
2017-12-01
With the opening of the Landsat archive, there is a dramatically increased potential for creating high-quality time series of land use/land-cover (LULC) classifications derived from remote sensing. Although LULC time series are appealing, their creation is typically challenging in two fundamental ways. First, there is a need to create maximally correct LULC maps for consideration at each time step; and second, there is a need to have the elements of the time series be consistent with each other, without pixels that flip improbably between covers due only to unavoidable, stray classification errors. We have developed the Bayesian Updating of Land Cover - Unsupervised (BULC-U) algorithm to address these challenges simultaneously, and introduce and apply it here for two related but distinct purposes. First, with minimal human intervention, we produced an internally consistent, high-accuracy LULC time series in rapidly changing Mato Grosso, Brazil for a time interval (1986-2000) in which cropland area more than doubled. The spatial and temporal resolution of the 59 LULC snapshots allows users to witness the establishment of towns and farms at the expense of forest. The new time series could be used by policy-makers and analysts to unravel important considerations for conservation and management, including the timing and location of past development, the rate and nature of changes in forest connectivity, the connection with road infrastructure, and more. The second application of BULC-U is to sharpen the well-known GlobCover 2009 classification from 300m to 30m, while improving accuracy measures for every class. The greatly improved resolution and accuracy permits a better representation of the true LULC proportions, the use of this map in models, and quantification of the potential impacts of changes. Given that there may easily be thousands and potentially millions of images available to harvest for an LULC time series, it is imperative to build useful algorithms
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Müllerová, Jana; Brůna, Josef; Dvořák, Petr; Bartaloš, Tomáš; Vítková, Michaela
2016-06-01
Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.
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Towards scale-independent land-surface flux estimates in Noah-MP
Thober, Stephan; Mizukami, Naoki; Samaniego, Luis; Attinger, Sabine; Clark, Martyn; Cuntz, Matthias
2017-04-01
Land-surface models use a variety of process representations to calculate terrestrial energy, water and biogeochemical fluxes. These process descriptions are usually derived from point measurements which are, in turn, scaled to much larger resolutions ranging from 1 km in catchment hydrology to 100 km in climate modelling. Both, hydrologic and climate models are nowadays run on different spatial resolutions, using the exactly same land surface representations. A fundamental criterion for the physical consistency of land-surface simulations across scales is that a flux estimated over a given area is independent of the spatial model resolution (i.e., the flux-matching criterion). The Noah-MP land surface model considers only one soil and land cover type per model grid cell without any representation of their subgrid variability, implying a weak flux-matching. A fractional approach simulates the subgrid variability but it requires a higher computational demand than using effective parameters and it is used only for land cover in current land surface schemes. A promising approach to derive scale-independent parameters is the Multiscale Parameter Regionalization (MPR) technique, which consists of two steps: first, it applies transfer functions directly to high-resolution data (such as 100 m soil maps) to derive high-resolution model parameter fields, acknowledging the full subgrid variability. Second, it upscales these high-resolution parameter fields to the model resolution by using appropriate upscaling operators. MPR has shown to improve substantially the scalability of the mesoscale Hydrologic Models mHM (Samaniego et al., 2010 WRR). Here, we apply the MPR technique to the Noah-MP land-surface model for a large sample of basins distributed across the contiguous USA. Specifically, we evaluate the flux-matching criterion for several hydrologic fluxes such as evapotranspiration and drainage at scales ranging from 3 km to 48 km. We investigate the impact of different
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Wind Atlas for the Gulf of Suez Satellite Imagery and Analyses
DEFF Research Database (Denmark)
Hasager, Charlotte Bay
(SAR) data derived from the European Remote Sensing Satellite (ERS) have been used to make wind speed maps for the Gulf of Suez. 2. “Land cover from Landsat TM imagery”. Landsat Thematic Mapper(TM) data have been used to establish true- and false-colour land cover maps, as well as land cover...... classification maps. 3. “Reporting on satellite information for the Wind Atlas for Egypt”. Along-Track Scanning Radiometer (ATSR) data from the European Remote Sensing Satellite (ERS) have been used to map the sea- and land-surface temperatures and albedos....
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Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye; Lillo-Saavedra, Mario; Lagos, Octavio
2017-04-01
these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated rainfall in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, while CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR datasets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of rainfall geographically specially in the North zone and seasonally during the summer and spring months in the other zones.
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Can MODIS detect trends in aerosol optical depth over land?
Fan, Xuehua; Xia, Xiang'ao; Chen, Hongbin
2018-02-01
The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's Aqua satellite has been collecting valuable data about the Earth system for more than 14 years, and one of the benefits of this is that it has made it possible to detect the long-term variation in aerosol loading across the globe. However, the long-term aerosol optical depth (AOD) trends derived from MODIS need careful validation and assessment, especially over land. Using AOD products with at least 70 months' worth of measurements collected during 2002-15 at 53 Aerosol Robotic Network (AERONET) sites over land, Mann-Kendall (MK) trends in AOD were derived and taken as the ground truth data for evaluating the corresponding results from MODIS onboard Aqua. The results showed that the AERONET AOD trends over all sites in Europe and North America, as well as most sites in Africa and Asia, can be reproduced by MODIS/Aqua. However, disagreement in AOD trends between MODIS and AERONET was found at a few sites in Australia and South America. The AOD trends calculated from AERONET instantaneous data at the MODIS overpass times were consistent with those from AERONET daily data, which suggests that the AOD trends derived from satellite measurements of 1-2 overpasses may be representative of those from daily measurements.
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Thober, S.; Mizukami, N.; Samaniego, L. E.; Attinger, S.; Clark, M. P.; Cuntz, M.
2016-12-01
Land-surface models use a variety of process representations to calculate terrestrial energy, water and biogeochemical fluxes. These process descriptions are usually derived from point measurements but are scaled to much larger resolutions in applications that range from about 1 km in catchment hydrology to 100 km in climate modelling. Both, hydrologic and climate models are nowadays run on different spatial resolutions, using the exact same land surface representations. A fundamental criterion for the physical consistency of land-surface simulations across scales is that a flux estimated over a given area is independent of the spatial model resolution (i.e., the flux-matching criterion). The Noah-MP land surface model considers only one soil and land cover type per model grid cell without any representation of subgrid variability, implying a weak flux-matching. A fractional approach simulates subgrid variability but it requires a higher computational demand than using effective parameters and it is used only for land cover in current land surface schemes. A promising approach to derive scale-independent parameters is the Multiscale Parameter Regionalization (MPR) technique, which consists of two steps: first, it applies transfer functions directly to high-resolution data (such as 100 m soil maps) to derive high-resolution model parameter fields, acknowledging the full subgrid variability. Second, it upscales these high-resolution parameter fields to the model resolution by using appropriate upscaling operators. MPR has shown to improve substantially the scalability of hydrologic models. Here, we apply the MPR technique to the Noah-MP land-surface model for a large sample of basins distributed across the contiguous USA. Specifically, we evaluate the flux-matching criterion for several hydrologic fluxes such as evapotranspiration and total runoff at scales ranging from 3 km to 48 km. We also investigate a p-norm scaling operator that goes beyond the current
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International Nuclear Information System (INIS)
Abdou Azaz, L.K.
2008-01-01
Alexandria is the second largest urban governorate in Egypt and has seen significant urban growth in its modern and contemporary history. This study investigates the urban growth phenomenon in Alexandria, Egypt, using the integration of remote sensing and GIS. The urban physical expansion and change were detected using Landsat satellite images. The satellite images of years 1984 and 1993 were first geo referenced, achieving a very small RMSE that provided high accuracy data for satellite image analysis. Then, the images were classified using a tailored classification scheme with accuracy of 93.82% and 95.27% for 1984 and 1993 images respectively. This high accuracy enabled detecting land use/land cover changes with high confidence using a post-classification comparison method. One of the most important findings here is the loss of cultivated land in favour of urban expansion. If the current loss rates continued, 75% of green lands would be lost by year 2191. These hazardous rates call for an urban growth management policy that can preserve such valuable resources to achieve sustainable urban development. Modelling techniques can help in defining the scenarios of urban growth. In this study, the SLEUTH urban growth model was applied to predict future urban expansion in Alexandria until the year 2055. The application of this model in Alexandria of Egypt with its different environmental characteristics is the first application outside USA and Europe. The results revealed that future urban growth would continue along the edges of the current urban extent. This means that the cultivated lands in the east and the southeast of the city will be decreased. To deal with such crisis, there is a serious need for a comprehensive urban growth management programme that can be based on the best practices in similar situations
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Integration between terrestrial-based and satellite-based land mobile communications systems
Arcidiancono, Antonio
1990-01-01
A survey is given of several approaches to improving the performance and marketability of mobile satellite systems (MSS). The provision of voice/data services in the future regional European Land Mobile Satellite System (LMSS), network integration between the Digital Cellular Mobile System (GSM) and LMSS, the identification of critical areas for the implementation of integrated GSM/LMSS areas, space segment scenarios, LMSS for digital trunked private mobile radio (PMR) services, and code division multiple access (CDMA) techniques for a terrestrial/satellite system are covered.
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Comparison of different "along the track" high resolution satellite stereo-pair for DSM extraction
Nikolakopoulos, Konstantinos G.
2013-10-01
The possibility to create DEM from stereo pairs is based on the Pythagoras theorem and on the principles of photogrammetry that are applied to aerial photographs stereo pairs for the last seventy years. The application of these principles to digital satellite stereo data was inherent in the first satellite missions. During the last decades the satellite stereo-pairs were acquired across the track in different days (SPOT, ERS etc.). More recently the same-date along the track stereo-data acquisition seems to prevail (Terra ASTER, SPOT5 HRS, Cartosat, ALOS Prism) as it reduces the radiometric image variations (refractive effects, sun illumination, temporal changes) and thus increases the correlation success rate in any image matching.Two of the newest satellite sensors with stereo collection capability is Cartosat and ALOS Prism. Both of them acquire stereopairs along the track with a 2,5m spatial resolution covering areas of 30X30km. In this study we compare two different satellite stereo-pair collected along the track for DSM creation. The first one is created from a Cartosat stereopair and the second one from an ALOS PRISM triplet. The area of study is situated in Chalkidiki Peninsula, Greece. Both DEMs were created using the same ground control points collected with a Differential GPS. After a first control for random or systematic errors a statistical analysis was done. Points of certified elevation have been used to estimate the accuracy of these two DSMs. The elevation difference between the different DEMs was calculated. 2D RMSE, correlation and the percentile value were also computed and the results are presented.
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High-resolution mapping of biomass burning emissions in tropical regions across three continents
Shi, Yusheng; Matsunaga, Tsuneo; Saito, Makoto
2015-04-01
Biomass burning emissions from open vegetation fires (forest fires, savanna fires, agricultural waste burning), human waste and biofuel combustion contain large amounts of trace gases (e.g., CO2, CH4, and N2O) and aerosols (BC and OC), which significantly impact ecosystem productivity, global atmospheric chemistry, and climate . With the help of recently released satellite products, biomass density based on satellite and ground-based observation data, and spatial variable combustion factors, this study developed a new high-resolution emissions inventory for biomass burning in tropical regions across three continents in 2010. Emissions of trace gases and aerosols from open vegetation burning are estimated from burned areas, fuel loads, combustion factors, and emission factors. Burned areas were derived from MODIS MCD64A1 burned area product, fuel loads were mapped from biomass density data sets for herbaceous and tree-covered land based on satellite and ground-based observation data. To account for spatial heterogeneity in combustion factors, global fractional tree cover (MOD44B) and vegetation cover maps (MCD12Q1) were introduced to estimate the combustion factors in different regions by using their relationship with tree cover under less than 40%, between 40-60% and above 60% conditions. For emission factors, the average values for each fuel type from field measurements are used. In addition to biomass burning from open vegetation fires, the emissions from human waste (residential and dump) burning and biofuel burning in 2010 were also estimated for 76 countries in tropical regions across the three continents and then allocated into each pixel with 1 km grid based on the population density (Gridded Population of the World v3). Our total estimates for the tropical regions across the three continents in 2010 were 17744.5 Tg CO2, 730.3 Tg CO, 32.0 Tg CH4, 31.6 Tg NOx, 119.2 Tg NMOC, 6.3 Tg SO2, 9.8 NH3 Tg, 81.8 Tg PM2.5, 48.0 Tg OC, and 5.7 Tg BC, respectively. Open
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Chatterjee, R. S.; Singh, Narendra; Thapa, Shailaja; Sharma, Dravneeta; Kumar, Dheeraj
2017-06-01
The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVITHM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is
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Evaluation of ALOS PALSAR Data for High-Resolution Mapping of Vegetated Wetlands in Alaska
Directory of Open Access Journals (Sweden)
Daniel Clewley
2015-06-01
Full Text Available As the largest natural source of methane, wetlands play an important role in the carbon cycle. High-resolution maps of wetland type and extent are required to quantify wetland responses to climate change. Mapping northern wetlands is particularly important because of a disproportionate increase in temperatures at higher latitudes. Synthetic aperture radar data from a spaceborne platform can be used to map wetland types and dynamics over large areas. Following from earlier work by Whitcomb et al. (2009 using Japanese Earth Resources Satellite (JERS-1 data, we applied the “random forests” classification algorithm to variables from L-band ALOS PALSAR data for 2007, topographic data (e.g., slope, elevation and locational information (latitude, longitude to derive a map of vegetated wetlands in Alaska, with a spatial resolution of 50 m. We used the National Wetlands Inventory and National Land Cover Database (for upland areas to select training and validation data and further validated classification results with an independent dataset that we created. A number of improvements were made to the method of Whitcomb et al. (2009: (1 more consistent training data in upland areas; (2 better distribution of training data across all classes by taking a stratified random sample of all available training pixels; and (3 a more efficient implementation, which allowed classification of the entire state as a single entity (rather than in separate tiles, which eliminated discontinuities at tile boundaries. The overall accuracy for discriminating wetland from upland was 95%, and the accuracy at the level of wetland classes was 85%. The total area of wetlands mapped was 0.59 million km2, or 36% of the total land area of the state of Alaska. The map will be made available to download from NASA’s wetland monitoring website.
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Roads Data Conflation Using Update High Resolution Satellite Images
Abdollahi, A.; Riyahi Bakhtiari, H. R.
2017-11-01
Urbanization, industrialization and modernization are rapidly growing in developing countries. New industrial cities, with all the problems brought on by rapid population growth, need infrastructure to support the growth. This has led to the expansion and development of the road network. A great deal of road network data has made by using traditional methods in the past years. Over time, a large amount of descriptive information has assigned to these map data, but their geometric accuracy and precision is not appropriate to today's need. In this regard, the improvement of the geometric accuracy of road network data by preserving the descriptive data attributed to them and updating of the existing geo databases is necessary. Due to the size and extent of the country, updating the road network maps using traditional methods is time consuming and costly. Conversely, using remote sensing technology and geographic information systems can reduce costs, save time and increase accuracy and speed. With increasing the availability of high resolution satellite imagery and geospatial datasets there is an urgent need to combine geographic information from overlapping sources to retain accurate data, minimize redundancy, and reconcile data conflicts. In this research, an innovative method for a vector-to-imagery conflation by integrating several image-based and vector-based algorithms presented. The SVM method for image classification and Level Set method used to extract the road the different types of road intersections extracted from imagery using morphological operators. For matching the extracted points and to find the corresponding points, matching function which uses the nearest neighborhood method was applied. Finally, after identifying the matching points rubber-sheeting method used to align two datasets. Two residual and RMSE criteria used to evaluate accuracy. The results demonstrated excellent performance. The average root-mean-square error decreased from 11.8 to 4.1 m.
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2016-06-13
1735-2015 © Author(s) 2015. CC Attribution 3.0 License. Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice...concentration data into the US Navy’s ice forecast systems P. G. Posey1, E. J. Metzger1, A. J. Wallcraft1, D. A. Hebert1, R. A. Allard1, O. M. Smedstad2...error within the US Navy’s operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration
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Raju, P. L. N.; Chaudhary, H.; Jha, A. K.
2014-11-01
These High resolution satellite data with metadata information is used to extract the height of the building using shadow. Proposed approach divides into two phases 1) rooftop and shadow extraction and 2) height estimation. Firstly the rooftop and shadow region were extracted by manual/ automatic methods using Example - Based and Rule - Based approaches. After feature extraction next step is estimating height of the building by taking rooftop in association with shadow using Ratio Method and by using the relation between sun-satellite geometry. The performance analysis shows the total mean error of height is 0.67 m from ratio method, 1.51 m from Example - Based Approach and 0.96 m from Rule - Based Approach. Analysis concluded that Ratio Method i.e. manual method is best for height estimation but it is time consuming so the automatic Rule Based approach is best for height estimation in comparison to Example Based Approach because it require more knowledge and selection of more training samples as well as slows the processing rate of the method.
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Clark, M. L.; Kilham, N. E.
2015-12-01
Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as
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Malatesta, Luca; Attorre, Fabio; Altobelli, Alfredo; Adeeb, Ahmed; De Sanctis, Michele; Taleb, Nadim M.; Scholte, Paul T.; Vitale, Marcello
2013-01-01
Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.
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Lee, I.-Chieh
manually connected, for its length was less than 3% of the total shoreline length in our dataset. Secondly, the parameters for satellite image classification needed to be manually determined. The need for manpower was significantly less compared to the ground surveying or aerial photogrammetry. The first phase of shoreline extraction was to utilize Normalized Difference Vegetation Index (NDVI), Mean-Shift segmentation on the coordinate (X, Y, Z), and attributes (multispectral bands from satellite images) of the LiDAR points to classify each LiDAR point into land or water surface. Boundary of the land points were then traced to create the shoreline. The second phase of shoreline extraction solely from satellite images utilized spectrum, NDVI, and shadow analysis to classify the satellite images into classes. These classes were then refined by mean-shift segmentation on the panchromatic band. By tracing the boundary of the water surface, the shoreline can be created. Since these two shorelines may represent different shoreline instances in time, evaluating the changes of shoreline was the first to be done. Then an independent scenario analysis and a procedure are performed for the shoreline of each of the three conditions: in the process of erosion, in the process of accession, and remaining the same. With these three conditions, we could analysis the actual terrain type and correct the classification errors to obtain a more accurate shoreline. Meanwhile, methods of evaluating the quality of shorelines had also been discussed. The experiment showed that there were three indicators could best represent the quality of the shoreline. These indicators were: (1) shoreline accuracy, (2) land area difference between extracted shoreline and ground truth shoreline, and (3) bias factor from shoreline quality metrics.
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Analysis of Multipath Mitigation Techniques with Land Mobile Satellite Channel Model
Directory of Open Access Journals (Sweden)
M. Z. H. Bhuiyan J. Zhang
2012-12-01
is of utmost importance to analyze the performance of different multipath mitigation techniques in some realistic measurement-based channel models, for example, the Land Mobile Satellite (LMS channel model [1]-[4], developed at the German Aerospace Center (DLR. The DLR LMS channel model is widely used for simulating the positioning accuracy of mobile satellite navigation receivers in urban outdoor scenarios. The main objective of this paper is to present a comprehensive analysis of some of the most promising techniques with the DLR LMS channel model in varying multipath scenarios. Four multipath mitigation techniques are chosen herein for performance comparison, namely, the narrow Early-Minus-Late (nEML, the High Resolution Correlator, the C/N0-based two stage delay tracking technique, and the Reduced Search Space Maximum Likelihood (RSSML delay estimator. The first two techniques are the most popular and traditional ones used in nowadays GNSS receivers, whereas the later two techniques are comparatively new and are advanced techniques, recently proposed by the authors. In addition, the implementation of the RSSML is optimized here for a narrow-bandwidth receiver configuration in the sense that it now requires a significantly less number of correlators and memory than its original implementation. The simulation results show that the reduced-complexity RSSML achieves the best multipath mitigation performance in moderate-to-good carrier-to-noise density ratio with the DLR LMS channel model in varying multipath scenarios.
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Directory of Open Access Journals (Sweden)
Stacy Jupiter
2011-03-01
Full Text Available The use of marine spatial planning for zoning multi-use areas is growing in both developed and developing countries. Comprehensive maps of marine resources, including those important for local fisheries management and biodiversity conservation, provide a crucial foundation of information for the planning process. Using a combination of field and high spatial resolution satellite data, we use an empirical procedure to create a bathymetric map (RMSE 1.76 m and object-based image analysis to produce accurate maps of geomorphic and benthic coral reef classes (Kappa values of 0.80 and 0.63; 9 and 33 classes, respectively covering a large (>260 km2 traditional fisheries management area in Fiji. From these maps, we derive per-pixel information on habitat richness, structural complexity, coral cover and the distance from land, and use these variables as input in models to predict fish species richness, diversity and biomass. We show that random forest models outperform five other model types, and that all three fish community variables can be satisfactorily predicted from the high spatial resolution satellite data. We also show geomorphic zone to be the most important predictor on average, with secondary contributions from a range of other variables including benthic class, depth, distance from land, and live coral cover mapped at coarse spatial scales, suggesting that data with lower spatial resolution and lower cost may be sufficient for spatial predictions of the three fish community variables.
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TESTFIELD TRENTO: GEOMETRIC EVALUATION OF VERY HIGH RESOLUTION SATELLITE IMAGERY
Directory of Open Access Journals (Sweden)
G. Agugiaro
2012-07-01
Full Text Available Today the use of spaceborne Very High Spatial Resolution (VHSR optical sensors for automatic 3D information extraction is increasing in the scientific and civil communities. The 3D Optical Metrology (3DOM Unit of the Bruno Kessler Foundation (FBK in Trento (Italy has collected stereo VHSR satellite imagery, as well as aerial and terrestrial data over Trento, with the aim to create a complete data collection with state-of-the-art datasets for investigations on image analysis, automatic digital surface model (DSM generation, 2D/3D feature extraction, city modelling and data fusion. The testfield region covers the city of Trento, characterised by very dense urban (historical centre, residential and industrial areas, and the surrounding hills and steep mountains (approximate height range 200-2100 m with cultivations, forests and bare soil. This paper reports the analysis conducted in FBK on the VHSR spaceborne imagery of Trento testfield for 3D information extraction. The data include two stereo-pairs acquired by WorldView-2 in August 2010 and by GeoEye-1 in September 2011 in panchromatic and multispectral mode, together with their original Rational Polynomial Coefficients (RPC, and the position and description of well distributed ground points. For reference and validation, a DSM from airborne LiDAR acquisition is used. The paper gives details on the project and the dataset characteristics. The results achieved by 3DOM on DSM extraction from WorldView-2 and GeoEye-1 stereo-pairs are shown and commented.
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Jacobs, Suzanne R.; Weeser, Björn; Guzha, Alphonce C.; Rufino, Mariana C.; Butterbach-Bahl, Klaus; Windhorst, David; Breuer, Lutz
2018-03-01
Land use change alters nitrate (NO3-N) dynamics in stream water by changing nitrogen cycling, nutrient inputs, uptake and hydrological flow paths. There is little empirical evidence of these processes for East Africa. We collected a unique 2 year high-resolution data set to assess the effects of land use (i.e., natural forest, smallholder agriculture and commercial tea plantations) on NO3-N dynamics in three subcatchments within a headwater catchment in the Mau Forest Complex, Kenya's largest tropical montane forest. The natural forest subcatchment had the lowest NO3-N concentrations (0.44 ± 0.043 mg N L-1) with no seasonal variation. NO3-N concentrations in the smallholder agriculture (1.09 ± 0.11 mg N L-1) and tea plantation (2.13 ± 0.19 mg N L-1) subcatchments closely followed discharge patterns, indicating mobilization of NO3-N during the rainy seasons. Hysteresis patterns of rainfall events indicate a shift from subsurface flow in the natural forest to surface runoff in agricultural subcatchments. Distinct peaks in NO3-N concentrations were observed during rainfall events after a longer dry period in the forest and tea subcatchments. The high-resolution data set enabled us to identify differences in NO3-N transport of catchments under different land use, such as enhanced NO3-N inputs to the stream during the rainy season and higher annual export in agricultural subcatchments (4.9 ± 0.3 to 12.0 ± 0.8 kg N ha-1 yr-1) than in natural forest (2.6 ± 0.2 kg N ha-1 yr-1). This emphasizes the usefulness of our monitoring approach to improve the understanding of land use effects on riverine N exports in tropical landscapes, but also the need to apply such methods in other regions.
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C-CAP Land Cover, Kauai, Hawaii
National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land derived from high resolution imagery and was analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine...
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C-CAP Land Cover, Niihau, Hawaii
National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land derived from high resolution imagery and was analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine...
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Satellite image time series simulation for environmental monitoring
Guo, Tao
2014-11-01
The performance of environmental monitoring heavily depends on the availability of consecutive observation data and it turns out an increasing demand in remote sensing community for satellite image data in the sufficient resolution with respect to both spatial and temporal requirements, which appear to be conflictive and hard to tune tradeoffs. Multiple constellations could be a solution if without concerning cost, and thus it is so far interesting but very challenging to develop a method which can simultaneously improve both spatial and temporal details. There are some research efforts to deal with the problem from various aspects, a type of approaches is to enhance the spatial resolution using techniques of super resolution, pan-sharpen etc. which can produce good visual effects, but mostly cannot preserve spectral signatures and result in losing analytical value. Another type is to fill temporal frequency gaps by adopting time interpolation, which actually doesn't increase informative context at all. In this paper we presented a novel method to generate satellite images in higher spatial and temporal details, which further enables satellite image time series simulation. Our method starts with a pair of high-low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and the temporal change is then projected onto high resolution data plane and assigned to each high resolution pixel referring the predefined temporal change patterns of each type of ground objects to generate a simulated high resolution data. A preliminary experiment shows that our method can simulate a high resolution data with a good accuracy. We consider the contribution of our method is to enable timely monitoring of temporal changes through analysis of low resolution images time series only, and usage of
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Adachi, T.; Said, R.; Cummer, S. A.; Li, J.; Takahashi, Y.; Hsu, R.; Su, H.; Chen, A. B.; Mende, S. B.; Frey, H. U.
2010-12-01
Comparative studies on the electrical properties of oceanic and continental lightning are crucial to elucidate air discharge processes occurring under different conditions. Past studies however have primarily focused on continental lightning because of the limited coverage of ground-based instruments. Recent satellite measurements by FORMOSAT-2/ISUAL provided a new way to survey the global characteristics of lightning and transient luminous events regardless of land and ocean. In this study, we analyze ISUAL/spectrophotometer data to clarify the electrical properties of lightning on a global level. Based on the results obtained by Cummer et al. [2006] and Adachi et al. [2009], the OI-777.4nm emission intensity is used to infer lightning electrical parameters. Results show a clear land-ocean contrast on the parameters of lightning discharge: in oceanic lightning, peak luminosity is 60 % higher and the time scale of return stroke is 30 % shorter. These results suggest higher peak current in oceanic lightning, which is consistent with the fact that elves, EMP-driven phenomena, also tend to occur over the ocean [Chen et al., 2008]. Further analysis of lightning events occurring around the Caribbean Sea shows that the transition-line of lightning electrical properties is precisely located along the coastline. We suggest that the differences in these electrical properties may be due to the boundary conditions (conductivity, surface terrain, etc). In this talk, based on the calibration with NLDN and Duke magnetometer data, current moment change and charge moment change will be globally evaluated using a complete set of the ISUAL-observed lightning events.
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Brasseur, P.; Verron, J. A.; Djath, B.; Duran, M.; Gaultier, L.; Gourdeau, L.; Melet, A.; Molines, J. M.; Ubelmann, C.
2014-12-01
The upcoming high-resolution SWOT altimetry satellite will provide an unprecedented description of the ocean dynamic topography for studying sub- and meso-scale processes in the ocean. But there is still much uncertainty on the signal that will be observed. There are many scientific questions that are unresolved about the observability of altimetry at vhigh resolution and on the dynamical role of the ocean meso- and submesoscales. In addition, SWOT data will raise specific problems due to the size of the data flows. These issues will probably impact the data assimilation approaches for future scientific or operational oceanography applications. In this work, we propose to use a high-resolution numerical model of the Western Pacific Solomon Sea as a regional laboratory to explore such observability and dynamical issues, as well as new data assimilation challenges raised by SWOT. The Solomon Sea connects subtropical water masses to the equatorial ones through the low latitude western boundary currents and could potentially modulate the tropical Pacific climate. In the South Western Pacific, the Solomon Sea exhibits very intense eddy kinetic energy levels, while relatively little is known about the mesoscale and submesoscale activities in this region. The complex bathymetry of the region, complicated by the presence of narrow straits and numerous islands, raises specific challenges. So far, a Solomon sea model configuration has been set up at 1/36° resolution. Numerical simulations have been performed to explore the meso- and submesoscales dynamics. The numerical solutions which have been validated against available in situ data, show the development of small scale features, eddies, fronts and filaments. Spectral analysis reveals a behavior that is consistent with the SQG theory. There is a clear evidence of energy cascade from the small scales including the submesoscales, although those submesoscales are only partially resolved by the model. In parallel
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High-resolution satellite image segmentation using Hölder exponents
Indian Academy of Sciences (India)
Keywords. High resolution image; texture analysis; segmentation; IKONOS; Hölder exponent; cluster. ... are that. • it can be used as a tool to measure the roughness ... uses reinforcement learning to learn the reward values of ..... The numerical.
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Hu, Yuanyuan; Zhong, Lei; Ma, Yaoming; Zou, Mijun; Xu, Kepiao; Huang, Ziyu; Feng, Lu
2018-01-28
During the process of land-atmosphere interaction, one of the essential parameters is the land surface temperature (LST). The LST has high temporal variability, especially in its diurnal cycle, which cannot be acquired by polar-orbiting satellites. Therefore, it is of great practical significance to retrieve LST data using geostationary satellites. According to the data of FengYun 2C (FY-2C) satellite and the measurements from the Enhanced Observing Period (CEOP) of the Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), a regression approach was utilized in this research to optimize the split window algorithm (SWA). The thermal infrared data obtained by the Chinese geostationary satellite FY-2C over the Tibetan Plateau (TP) was used to estimate the hourly LST time series. To decrease the effects of cloud, the 10-day composite hourly LST data were obtained through the approach of maximal value composite (MVC). The derived LST was used to compare with the product of MODIS LST and was also validated by the field observation. The results show that the LST retrieved through the optimized SWA and in situ data has a better consistency (with correlation coefficient (R), mean absolute error (MAE), mean bias (MB), and root mean square error (RMSE) values of 0.987, 1.91 K, 0.83 K and 2.26 K, respectively) than that derived from Becker and Li's SWA and MODIS LST product, which means that the modified SWA can be applied to achieve plateau-scale LST. The diurnal variation of the LST and the hourly time series of the LST over the Tibetan Plateau were also obtained. The diurnal range of LST was found to be clearly affected by the influence of the thawing and freezing process of soil and the summer monsoon evolution. The comparison between the seasonal and diurnal variations of LST at four typical underlying surfaces over the TP indicate that the variation of LST is closely connected with the underlying surface types as well. The diurnal variation of LST is
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Exploring image data assimilation in the prospect of high-resolution satellite oceanic observations
Durán Moro, Marina; Brankart, Jean-Michel; Brasseur, Pierre; Verron, Jacques
2017-07-01
Satellite sensors increasingly provide high-resolution (HR) observations of the ocean. They supply observations of sea surface height (SSH) and of tracers of the dynamics such as sea surface salinity (SSS) and sea surface temperature (SST). In particular, the Surface Water Ocean Topography (SWOT) mission will provide measurements of the surface ocean topography at very high-resolution (HR) delivering unprecedented information on the meso-scale and submeso-scale dynamics. This study investigates the feasibility to use these measurements to reconstruct meso-scale features simulated by numerical models, in particular on the vertical dimension. A methodology to reconstruct three-dimensional (3D) multivariate meso-scale scenes is developed by using a HR numerical model of the Solomon Sea region. An inverse problem is defined in the framework of a twin experiment where synthetic observations are used. A true state is chosen among the 3D multivariate states which is considered as a reference state. In order to correct a first guess of this true state, a two-step analysis is carried out. A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a reduced-order Kalman filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis. Experiments show that the proposed technique succeeds in correcting a multivariate state using meso-scale and submeso-scale information contained in HR SSH and image structure observations. It also demonstrates how the surface information can be used to reconstruct the ocean state below
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National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land cover derived from high resolution imagery according to the Coastal Change Analysis Program (C-CAP) protocol. This data set utilized 1...
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The development of a land use inventory for regional planning using satellite imagery
Hessling, A. H.; Mara, T. G.
1975-01-01
Water quality planning in Ohio, Kentucky, and Indiana is reviewed in terms of use of land use data and satellite imagery. A land use inventory applicable to water quality planning and developed through computer processing of LANDSAT-1 imagery is described.
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Kaiser, Jennifer; Jacob, Daniel J.; Zhu, Lei; Travis, Katherine R.; Fisher, Jenny A.; González Abad, Gonzalo; Zhang, Lin; Zhang, Xuesong; Fried, Alan; Crounse, John D.; St. Clair, Jason M.; Wisthaler, Armin
2018-04-01
Isoprene emissions from vegetation have a large effect on atmospheric chemistry and air quality. Bottom-up isoprene emission inventories used in atmospheric models are based on limited vegetation information and uncertain land cover data, leading to potentially large errors. Satellite observations of atmospheric formaldehyde (HCHO), a high-yield isoprene oxidation product, provide top-down information to evaluate isoprene emission inventories through inverse analyses. Past inverse analyses have however been hampered by uncertainty in the HCHO satellite data, uncertainty in the time- and NOx-dependent yield of HCHO from isoprene oxidation, and coarse resolution of the atmospheric models used for the inversion. Here we demonstrate the ability to use HCHO satellite data from OMI in a high-resolution inversion to constrain isoprene emissions on ecosystem-relevant scales. The inversion uses the adjoint of the GEOS-Chem chemical transport model at 0.25° × 0.3125° horizontal resolution to interpret observations over the southeast US in August-September 2013. It takes advantage of concurrent NASA SEAC4RS aircraft observations of isoprene and its oxidation products including HCHO to validate the OMI HCHO data over the region, test the GEOS-Chem isoprene oxidation mechanism and NOx environment, and independently evaluate the inversion. This evaluation shows in particular that local model errors in NOx concentrations propagate to biases in inferring isoprene emissions from HCHO data. It is thus essential to correct model NOx biases, which was done here using SEAC4RS observations but can be done more generally using satellite NO2 data concurrently with HCHO. We find in our inversion that isoprene emissions from the widely used MEGAN v2.1 inventory are biased high over the southeast US by 40 % on average, although the broad-scale distributions are correct including maximum emissions in Arkansas/Louisiana and high base emission factors in the oak-covered Ozarks of southeast
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Migratory herbivorous waterfowl track satellite-derived green wave index.
Directory of Open Access Journals (Sweden)
Mitra Shariatinajafabadi
Full Text Available Many migrating herbivores rely on plant biomass to fuel their life cycles and have adapted to following changes in plant quality through time. The green wave hypothesis predicts that herbivorous waterfowl will follow the wave of food availability and quality during their spring migration. However, testing this hypothesis is hampered by the large geographical range these birds cover. The satellite-derived normalized difference vegetation index (NDVI time series is an ideal proxy indicator for the development of plant biomass and quality across a broad spatial area. A derived index, the green wave index (GWI, has been successfully used to link altitudinal and latitudinal migration of mammals to spatio-temporal variations in food quality and quantity. To date, this index has not been used to test the green wave hypothesis for individual avian herbivores. Here, we use the satellite-derived GWI to examine the green wave hypothesis with respect to GPS-tracked individual barnacle geese from three flyway populations (Russian n = 12, Svalbard n = 8, and Greenland n = 7. Data were collected over three years (2008-2010. Our results showed that the Russian and Svalbard barnacle geese followed the middle stage of the green wave (GWI 40-60%, while the Greenland geese followed an earlier stage (GWI 20-40%. Despite these differences among geese populations, the phase of vegetation greenness encountered by the GPS-tracked geese was close to the 50% GWI (i.e. the assumed date of peak nitrogen concentration, thereby implying that barnacle geese track high quality food during their spring migration. To our knowledge, this is the first time that the migration of individual avian herbivores has been successfully studied with respect to vegetation phenology using the satellite-derived GWI. Our results offer further support for the green wave hypothesis applying to long-distance migrants on a larger scale.
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Lin, T.; Laporte, N. T.
2003-12-01
Compared to the traditional validation methods, aerial videography is a relatively inexpensive and time-efficient approach to collect "field" data for validating satellite-derived land cover map over large areas. In particular, this approach is valuable in remote and inaccessible locations. In the Sangha Tri-National Park region of Central Africa, where road access is limited to industrial logging sites, we are using IKONOS imagery and aerial videography to assess the accuracy of Landsat-derived land cover maps. As part of a NASA Land Cover Land Use Change project (INFORMS) and in collaboration with the Wildlife Conservation Society in the Republic of Congo, over 1500km of aerial video transects were collected in the Spring of 2001. The use of MediaMapper software combined with a VMS 200 video mapping system enabled the collection of aerial transects to be registered with geographic locations from a Geographic Positioning System. Video frame were extracted, visually interpreted, and compared to land cover types mapped by Landsat. We addressed the limitations of accuracy assessment using aerial-base data and its potential for improving vegetation mapping in tropical rain forests. The results of the videography and IKONOS image analysis demonstrate the utility of very high resolution imagery for map validation and forest resource assessment.
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de Baan, Laura; Curran, Michael; Rondinini, Carlo; Visconti, Piero; Hellweg, Stefanie; Koellner, Thomas
2015-02-17
Agricultural land use is a main driver of global biodiversity loss. The assessment of land use impacts in decision-support tools such as life cycle assessment (LCA) requires spatially explicit models, but existing approaches are either not spatially differentiated or modeled at very coarse scales (e.g., biomes or ecoregions). In this paper, we develop a high-resolution (900 m) assessment method for land use impacts on biodiversity based on habitat suitability models (HSM) of mammal species. This method considers potential land use effects on individual species, and impacts are weighted by the species' conservation status and global rarity. We illustrate the method using a case study of crop production in East Africa, but the underlying HSMs developed by the Global Mammals Assessment are available globally. We calculate impacts of three major export crops and compare the results to two previously developed methods (focusing on local and regional impacts, respectively) to assess the relevance of the methodological innovations proposed in this paper. The results highlight hotspots of product-related biodiversity impacts that help characterize the links among agricultural production, consumption, and biodiversity loss.
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Lee, Mihye; Kloog, Itai; Chudnovsky, Alexandra; Lyapustin, Alexei; Wang, Yujie; Melly, Steven; Coull, Brent; Koutrakis, Petros; Schwartz, Joel
2016-01-01
Numerous studies have demonstrated that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM2.5 to assess personal exposure; however, induces measurement error. Land use regression provides spatially resolved predictions but land use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM2.5 exposures. In this paper, we used AOD data with other PM2.5 variables such as meteorological variables, land use regression, and spatial smoothing to predict daily concentrations of PM2.5 at a 1 km2 resolution of the southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 through 2011. We divided the study area into 3 regions and applied separate mixed-effect models to calibrate AOD using ground PM2.5 measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors (RMSPE) of 2.89, 2.51, and 2.82 μg/m3 for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM2.5 concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM2.5. Our model results will also extend the existing studies on PM2.5 which have mostly focused on urban areas due to the paucity of monitors in rural areas. PMID:26082149
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Herold, M.; Woodcock, C.; Stehman, S.; Nightingale, J.; Friedl, M.; Schmullius, C.
2010-12-01
A global effort to assess the accuracy of existing and future land cover products derived from a variety of satellite sensors over a range of spatial resolutions is being led by the Land Cover Implementation Team (LC-IT) of GOFC/GOLD (Global Observation of Land Cover Dynamics) in conjunction with the CEOS (Committee on Earth Observation Satellites) WGCV (Working Group on Calibration and Validation) LPV (Land Product Validation) subgroup. The first phase of this effort is complete and culminated in a publication of community consensus "best practices" for validation of global land cover datasets (2). The next phase is to implement the recommendations outlined in the "best practices" document. A "living database" of global randomized sample sites will form the basis of accuracy assessment for a host of global land cover products (GLC2000, MODIS land cover, GLOBCOVER, United Nation's Forest Resource Assessment (FRA2010), and the Mid-Decadal Global Land Survey. This "living dataset" will also be a community resource available for use in validation of regional or national mapping efforts using LCCS (UN FAO's Land Cover Classification System). Based on the known accuracy of existing land cover products, GOFC/GOLD will to develop and update a "best currently available" global land cover map. Individual geographic regions may be selected from different land cover products (global, national or regional), or they may be combined in various ways
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Determination of atmospheric aerosol properties over land using satellite measurements
Kokhanovsky, A.A.; Leeuw, G. de
2009-01-01
Mostly, aerosol properties are poorly understood because the aerosol properties are very sparse. The first workshop on the determination of atmospheric aerosol properties over land using satellite measurements is convened in Bremen, Germany. In this workshop, the topics of discussions included a
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Uncertainties and applications of satellite-derived coastal water quality products
Zheng, Guangming; DiGiacomo, Paul M.
2017-12-01
Recent and forthcoming launches of a plethora of ocean color radiometry sensors, coupled with increasingly adopted free and open data policies are expected to boost usage of satellite ocean color data and drive the demand to use these data in a quantitative and routine manner. Here we review factors that introduce uncertainties to various satellite-derived water quality products and recommend approaches to minimize the uncertainty of a specific product. We show that the regression relationships between remote-sensing reflectance and water turbidity (in terms of nephelometric units) established for different regions tend to converge and therefore it is plausible to develop a global satellite water turbidity product derived using a single algorithm. In contrast, solutions to derive suspended particulate matter concentration are much less generalizable; in one case it might be more accurate to estimate this parameter based on satellite-derived particulate backscattering coefficient, whereas in another the nonagal particulate absorption coefficient might be a better proxy. Regarding satellite-derived chlorophyll concentration, known to be subject to large uncertainties in coastal waters, studies summarized here clearly indicate that the accuracy of classical reflectance band-ratio algorithms depends largely on the contribution of phytoplankton to total light absorption coefficient as well as the degree of correlation between phytoplankton and the dominant nonalgal contributions. Our review also indicates that currently available satellite-derived water quality products are restricted to optically significant materials, whereas many users are interested in toxins, nutrients, pollutants, and pathogens. Presently, proxies or indicators for these constituents are inconsistently (and often incorrectly) developed and applied. Progress in this general direction will remain slow unless, (i) optical oceanographers and environmental scientists start collaborating more closely
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Downgrading recent estimates of land available for biofuel production.
Fritz, Steffen; See, Linda; van der Velde, Marijn; Nalepa, Rachel A; Perger, Christoph; Schill, Christian; McCallum, Ian; Schepaschenko, Dmitry; Kraxner, Florian; Cai, Ximing; Zhang, Xiao; Ortner, Simone; Hazarika, Rubul; Cipriani, Anna; Di Bella, Carlos; Rabia, Ahmed H; Garcia, Alfredo; Vakolyuk, Mar'yana; Singha, Kuleswar; Beget, Maria E; Erasmi, Stefan; Albrecht, Franziska; Shaw, Brian; Obersteiner, Michael
2013-02-05
Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development.
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Young, Steven
2017-05-02
When nature and humanity change Earth’s landscapes - through flood or fire, public policy, natural resources management, or economic development - the results are often dramatic and lasting.Wildfires can reshape ecosystems. Hurricanes with names like Sandy or Katrina will howl for days while altering the landscape for years. One growing season in the evolution of drought-resistant genetics can transform semiarid landscapes into farm fields.In the past, valuable land cover maps created for understanding the effects of those events - whether changes in wildlife habitat, water-quality impacts, or the role land use and land cover play in affecting weather and climate - came out at best every 5 to 7 years. Those high quality, high resolution maps were good, but users always craved more: even higher quality data, additional land cover and land change variables, more detailed legends, and most importantly, more frequent land change information.Now a bold new initiative called Land Change Monitoring, Assessment, and Projection (LCMAP) promises to fulfill that demand.Developed at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, LCMAP provides definitive, timely information on how, why, and where the planet is changing. LCMAP’s continuous monitoring process can detect changes as they happen every day that Landsat satellites acquire clear observations. The result will be to place near real-time information in the hands of land and resource managers who need to understand the effects these changes have on landscapes.
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Matsui, Toshihisa; Chern, Jiun-Dar; Tao, Wei-Kuo; Lang, Stephen E.; Satoh, Masaki; Hashino, Tempei; Kubota, Takuji
2016-01-01
A 14-year climatology of Tropical Rainfall Measuring Mission (TRMM) collocated multi-sensor signal statistics reveal a distinct land-ocean contrast as well as geographical variability of precipitation type, intensity, and microphysics. Microphysics information inferred from the TRMM precipitation radar and Microwave Imager (TMI) show a large land-ocean contrast for the deep category, suggesting continental convective vigor. Over land, TRMM shows higher echo-top heights and larger maximum echoes, suggesting taller storms and more intense precipitation, as well as larger microwave scattering, suggesting the presence of morelarger frozen convective hydrometeors. This strong land-ocean contrast in deep convection is invariant over seasonal and multi-year time-scales. Consequently, relatively short-term simulations from two global storm-resolving models can be evaluated in terms of their land-ocean statistics using the TRMM Triple-sensor Three-step Evaluation via a satellite simulator. The models evaluated are the NASA Multi-scale Modeling Framework (MMF) and the Non-hydrostatic Icosahedral Cloud Atmospheric Model (NICAM). While both simulations can represent convective land-ocean contrasts in warm precipitation to some extent, near-surface conditions over land are relatively moisture in NICAM than MMF, which appears to be the key driver in the divergent warm precipitation results between the two models. Both the MMF and NICAM produced similar frequencies of large CAPE between land and ocean. The dry MMF boundary layer enhanced microwave scattering signals over land, but only NICAM had an enhanced deep convection frequency over land. Neither model could reproduce a realistic land-ocean contrast in in deep convective precipitation microphysics. A realistic contrast between land and ocean remains an issue in global storm-resolving modeling.
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A prototype method for diagnosing high ice water content probability using satellite imager data
Yost, Christopher R.; Bedka, Kristopher M.; Minnis, Patrick; Nguyen, Louis; Strapp, J. Walter; Palikonda, Rabindra; Khlopenkov, Konstantin; Spangenberg, Douglas; Smith, William L., Jr.; Protat, Alain; Delanoe, Julien
2018-03-01
Recent studies have found that ingestion of high mass concentrations of ice particles in regions of deep convective storms, with radar reflectivity considered safe for aircraft penetration, can adversely impact aircraft engine performance. Previous aviation industry studies have used the term high ice water content (HIWC) to define such conditions. Three airborne field campaigns were conducted in 2014 and 2015 to better understand how HIWC is distributed in deep convection, both as a function of altitude and proximity to convective updraft regions, and to facilitate development of new methods for detecting HIWC conditions, in addition to many other research and regulatory goals. This paper describes a prototype method for detecting HIWC conditions using geostationary (GEO) satellite imager data coupled with in situ total water content (TWC) observations collected during the flight campaigns. Three satellite-derived parameters were determined to be most useful for determining HIWC probability: (1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, (2) tropopause-relative infrared brightness temperature, and (3) daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IR-only information. The products show the greatest capability for discriminating TWC ≥ 0.5 g m-3. Product validation remains challenging due to vertical TWC uncertainties and the typically coarse spatio-temporal resolution of the GEO data.
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Maxwell, S.K.; Meliker, J.R.; Goovaerts, P.
2010-01-01
In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.
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GEONEX: Land Monitoring From a New Generation of Geostationary Satellite Sensors
Nemani, Ramakrishna; Lyapustin, Alexei; Wang, Weile; Wang, Yujie; Hashimoto, Hirofumi; Li, Shuang; Ganguly, Sangram; Michaelis, Andrew; Higuchi, Atsushi; Takaneka, Hideaki;
2017-01-01
The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval
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GEONEX: Land monitoring from a new generation of geostationary satellite sensors
Nemani, R. R.; Lyapustin, A.; Wang, W.; Ganguly, S.; Wang, Y.; Michaelis, A.; Hashimoto, H.; Li, S.; Higuchi, A.; Huete, A. R.; Yeom, J. M.; camacho De Coca, F.; Lee, T. J.; Takenaka, H.
2017-12-01
The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval
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Energy Technology Data Exchange (ETDEWEB)
Li, Xiaoma; Zhou, Yuyu; Asrar, Ghassem R.; Zhu, Zhengyuan
2018-03-01
High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate Resolution Imaging Spectroradiometer (MODIS) LST is one of such high spatiotemporal datasets that are widely used. But, it has large amount of missing values primarily because of clouds. Gapfilling the missing values is an important approach to create high spatiotemporal LST datasets. However current gapfilling methods have limitations in terms of accuracy and time required to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybrid method by integrating a combination of daily merging, spatiotemporal gapfilling, and temporal interpolation methods, to create a high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas for the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates that its root mean squared error (RMSE) to be 3.3K for mid-daytime (1:30 pm) and 2.7K for mid-13 nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying effects of urbanization (e.g., urban heat island) and the related impacts on people, ecosystems, energy systems and other infrastructure for cities.
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Welle, Paul D.; Mauter, Meagan S.
2017-09-01
This work introduces a generalizable approach for estimating the field-scale agricultural yield losses due to soil salinization. When integrated with regional data on crop yields and prices, this model provides high-resolution estimates for revenue losses over large agricultural regions. These methods account for the uncertainty inherent in model inputs derived from satellites, experimental field data, and interpreted model results. We apply this method to estimate the effect of soil salinity on agricultural outputs in California, performing the analysis with both high-resolution (i.e. field scale) and low-resolution (i.e. county-scale) data sources to highlight the importance of spatial resolution in agricultural analysis. We estimate that soil salinity reduced agricultural revenues by 3.7 billion (1.7-7.0 billion) in 2014, amounting to 8.0 million tons of lost production relative to soil salinities below the crop-specific thresholds. When using low-resolution data sources, we find that the costs of salinization are underestimated by a factor of three. These results highlight the need for high-resolution data in agro-environmental assessment as well as the challenges associated with their integration.
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Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Vasilenko, Eugene; Volkova, Elena; Kukharsky, Alexander
2017-04-01
The model of water and heat exchange between vegetation covered territory and atmosphere (LSM, Land Surface Model) for vegetation season has been developed to calculate soil water content, evapotranspiration, infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat balances components as well as soil surface and vegetation cover temperatures and depth distributions of moisture and temperature. The LSM is suited for utilizing satellite-derived estimates of precipitation, land surface temperature and vegetation characteristics and soil surface humidity for each pixel. Vegetation and meteorological characteristics being the model parameters and input variables, correspondingly, have been estimated by ground observations and thematic processing measurement data of scanning radiometers AVHRR/NOAA, SEVIRI/Meteosat-9, -10 (MSG-2, -3) and MSU-MR/Meteor-M № 2. Values of soil surface humidity has been calculated from remote sensing data of scatterometers ASCAT/MetOp-A, -B. The case study has been carried out for the territory of part of the agricultural Central Black Earth Region of European Russia with area of 227300 km2 located in the forest-steppe zone for years 2012-2015 vegetation seasons. The main objectives of the study have been: - to built estimates of precipitation, land surface temperatures (LST) and vegetation characteristics from MSU-MR measurement data using the refined technologies (including algorithms and programs) of thematic processing satellite information matured on AVHRR and SEVIRI data. All technologies have been adapted to the area of interest; - to investigate the possibility of utilizing satellite-derived estimates of values above in the LSM including verification of obtained estimates and development of procedure of their inputting into the model. From the AVHRR data there have been built the estimates of precipitation, three types of LST: land skin temperature Tsg, air temperature at a level of
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A high-resolution ambient seismic noise model for Europe
Kraft, Toni
2014-05-01
measurement precision (i.e. earthquake location), while considering this extremely complex boundary condition. To solve this problem I have developed a high-resolution ambient seismic noise model for Europe. The model is based on land-use data derived from satellite imagery by the EU-project CORINE in a resolution of 100x100m. The the CORINE data consists of several land-use classes, which, besides others, contain: industrial areas, mines, urban fabric, agricultural areas, permanent corps, forests and open spaces. Additionally, open GIS data for highways, and major and minor roads and railway lines were included from the OpenStreetMap project (www.openstreetmap.org). This data was divided into three classes that represent good, intermediate and bad ambient conditions of the corresponding land-use class based on expert judgment. To account for noise propagation away from its source a smoothing operator was applied to individual land-use noise-fields. Finally, the noise-fields were stacked to obtain an European map of ambient noise conditions. A calibration of this map with data of existing seismic stations Europe allowed me to estimate the expected noise level in actual ground motion units for the three ambient noise condition classes of the map. The result is a high-resolution ambient seismic noise map, that allows the network designer to make educated predictions on the expected noise level for arbitrary location in Europe. The ambient noise model was successfully tested in several network optimization projects in Switzerland and surrounding countries and will hopefully be a valuable contribution to improving the data quality of microseismic monitoring networks in Europe.
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A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification
Directory of Open Access Journals (Sweden)
Huai Yu
2016-03-01
Full Text Available Scene classification plays an important role in understanding high-resolution satellite (HRS remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT, which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.
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LOTUS— Preparing Sentinel-3 SAR Altimetry Processing for Ocean and Land
DEFF Research Database (Denmark)
Knudsen, Per; Andersen, Ole Baltazar; Nielsen, Karina
2016-01-01
methods and processing chains need to be developed. Subsequently, new potential Copernicus products should be developed that utilize the improved alongtrack resolution over both the oceans and over land. The main objective of the LOTUS project is to prepare the scientific and operational use of data from......The Sentinel-3 satellite mission with its SRAL instrumentation contains new features compared to the conventional radar altimeter mission that form the basis for new innovative scientific analyses of both ocean and inland water levels. To utilize the full potential of the new data source, new...... that they will be used for commercial activities. LOTUS will develop processing scheme for extracting high-resolution sea surface heights, wave heights and wind speeds from SAR mode data. Over land, the LOTUS will develop processing scheme for extracting high-resolution river and lake heights, soil moisture, and snow...
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TREND ASSESSMENT OF SPATIO-TEMPORAL CHANGE OF TEHRAN HEAT ISLAND USING SATELLITE IMAGES
Directory of Open Access Journals (Sweden)
M. R. Saradjian
2015-12-01
Full Text Available Numerous investigations on Urban Heat Island (UHI show that land cover change is the main factor of increasing Land Surface Temperature (LST in urban areas, especially conversion of vegetation and bare soil to concrete, asphalt and other man-made structures. On the other hand, other human activities like those which cause to burning fossil fuels, that increase the amount of carbon dioxide, may raise temperature in global scale in comparison with small scales (urban areas. In this study, multiple satellite images with different spatial and temporal resolutions have been used to determine Land Surface Temperature (LST variability in Tehran metropolitan area. High temporal resolution of AVHRR images have been used as the main data source when investigating temperature variability in the urban area. The analysis shows that UHI appears more significant at afternoon and night hours. But the urban class temperature is almost equal to its surrounding vegetation and bare soil classes at around noon. It also reveals that there is no specific difference in UHI intense during the days throughout the year. However, it can be concluded that in the process of city expansion in years, UHI has been grown both spatially and in magnitude. In order to locate land-cover types and relate them to LST, Thematic Mapper (TM images have been exploited. The influence of elevation on the LST has also been studied, using digital elevation model derived from SRTM database.
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C-CAP Land Cover, Big Island, Hawaii
National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land derived from high resolution imagery and was analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine...
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C-CAP Land Cover, Kauai, Hawaii 2005
National Oceanic and Atmospheric Administration, Department of Commerce — This data set consists of land derived from high resolution imagery and was analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine...
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Directory of Open Access Journals (Sweden)
Ali M. Al-Saegh
2015-01-01
Full Text Available Recent advances in satellite to land mobile terminal services and technologies, which utilize high frequencies with directional antennas, have made the design of an appropriate model for land mobile satellite (LMS channels a necessity. This paper presents LMS channel model at Ku-band with features that enhance accuracy, comprehensiveness, and reliability. The effect of satellite tracking loss at different mobile terminal speeds is considered for directional mobile antenna systems, a reliable tropospheric scintillation model for an LMS scenario at tropical and temperate regions is presented, and finally a new quality indicator module for different modulation and coding schemes is included. The proposed extended LMS channel (ELMSC model is designed based on actual experimental measurements and can be applied to narrow- and wide-band signals at different regions and at different speeds and multichannel states. The proposed model exhibits lower root mean square error (RMSE and significant performance observation compared with the conventional model in terms of the signal fluctuations, fade depth, signal-to-noise ratio (SNR, and quality indicators accompanied for several transmission schemes.
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John C. Lehrter; John C. Lehrter; Chengfeng Le
2017-01-01
Relationships between satellite-derived water quality variables and river discharges, concentrations and loads of nutrients, organic carbon, and sediments were investigated over a 9-year period (2003–2011) in Pensacola Bay, Florida, USA. These analyses were conducted to better understand which river forcing factors were the primary drivers of estuarine variability in several water quality variables. Remote sensing reflectance time-series data were retrieved from the MEdium Resolution Imaging ...
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Rapid prototyping of soil moisture estimates using the NASA Land Information System
Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.
2007-12-01
The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.
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DEFF Research Database (Denmark)
Lindbäck, K.; Pettersson, R.; Doyle, S. H.
2014-01-01
We present ice thickness and bed topography maps with high spatial resolution (250 to 500 m) of a and-terminating section of the Greenland Ice Sheet derived from combined ground-based and airborne radar surveys. The data have a total area of ~12000 km2 and cover the whole ablation area of the out...
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Sulistiyono, N.; Basyuni, M.; Slamet, B.
2018-03-01
Green open space (GOS) is one of the requirements where a city is comfortable to stay. GOS might reduce land surface temperature (LST) and air pollution. Medan is one of the biggest towns in Indonesia that experienced rapid development. However, the early development tends to neglect the GOS existence for the city. The objective of the study is to determine the distribution of land surface temperature and the relationship between the normalized difference vegetation index (NDVI) and the priority of GOS development in Medan City using imagery-based satellite Landsat 8. The method approached to correlate the distribution of land surface temperature derived from the value of digital number band 10 with the NDVI which was from the ratio of groups five and four on satellite images of Landsat 8. The results showed that the distribution of land surface temperature in the Medan City in 2016 ranged 20.57 - 33.83 °C. The relationship between the distribution of LST distribution with NDVI was reversed with a negative correlation of -0.543 (sig 0,000). The direction of GOS in Medan City is therefore developed on the allocation of LST and divided into three priority classes namely first priority class had 5,119.71 ha, the second priority consisted of 16,935.76 ha, and third priority of 6,118.50 ha.
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Directory of Open Access Journals (Sweden)
Taïs Grippa
2018-06-01
Full Text Available Up-to-date and reliable land-use information is essential for a variety of applications such as planning or monitoring of the urban environment. This research presents a workflow for mapping urban land use at the street block level, with a focus on residential use, using very-high resolution satellite imagery and derived land-cover maps as input. We develop a processing chain for the automated creation of street block polygons from OpenStreetMap and ancillary data. Spatial metrics and other street block features are computed, followed by feature selection that reduces the initial datasets by more than 80%, providing a parsimonious, discriminative, and redundancy-free set of features. A random forest (RF classifier is used for the classification of street blocks, which results in accuracies of 84% and 79% for five and six land-use classes, respectively. We exploit the probabilistic output of RF to identify and relabel blocks that have a high degree of uncertainty. Finally, the thematic precision of the residential blocks is refined according to the proportion of the built-up area. The output data and processing chains are made freely available. The proposed framework is able to process large datasets, given that the cities in the case studies, Dakar and Ouagadougou, cover more than 1000 km2 in total, with a spatial resolution of 0.5 m.
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Method for validating cloud mask obtained from satellite measurements using ground-based sky camera.
Letu, Husi; Nagao, Takashi M; Nakajima, Takashi Y; Matsumae, Yoshiaki
2014-11-01
Error propagation in Earth's atmospheric, oceanic, and land surface parameters of the satellite products caused by misclassification of the cloud mask is a critical issue for improving the accuracy of satellite products. Thus, characterizing the accuracy of the cloud mask is important for investigating the influence of the cloud mask on satellite products. In this study, we proposed a method for validating multiwavelength satellite data derived cloud masks using ground-based sky camera (GSC) data. First, a cloud cover algorithm for GSC data has been developed using sky index and bright index. Then, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived cloud masks by two cloud-screening algorithms (i.e., MOD35 and CLAUDIA) were validated using the GSC cloud mask. The results indicate that MOD35 is likely to classify ambiguous pixels as "cloudy," whereas CLAUDIA is likely to classify them as "clear." Furthermore, the influence of error propagations caused by misclassification of the MOD35 and CLAUDIA cloud masks on MODIS derived reflectance, brightness temperature, and normalized difference vegetation index (NDVI) in clear and cloudy pixels was investigated using sky camera data. It shows that the influence of the error propagation by the MOD35 cloud mask on the MODIS derived monthly mean reflectance, brightness temperature, and NDVI for clear pixels is significantly smaller than for the CLAUDIA cloud mask; the influence of the error propagation by the CLAUDIA cloud mask on MODIS derived monthly mean cloud products for cloudy pixels is significantly smaller than that by the MOD35 cloud mask.
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Directory of Open Access Journals (Sweden)
Chandi Witharana
2016-04-01
Full Text Available The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.
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Chen, Gongbo; Knibbs, Luke D; Zhang, Wenyi; Li, Shanshan; Cao, Wei; Guo, Jianping; Ren, Hongyan; Wang, Boguang; Wang, Hao; Williams, Gail; Hamm, N A S; Guo, Yuming
2018-02-01
PM 1 might be more hazardous than PM 2.5 (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM 1 concentrations and its health effects are limited due to a lack of PM 1 monitoring data. To estimate spatial and temporal variations of PM 1 concentrations in China during 2005-2014 using satellite remote sensing, meteorology, and land use information. Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM 1 data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability. The results of 10-fold cross-validation showed R 2 and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m 3 , respectively. For seasonal prediction, the R 2 and RMSE were 77% and 11.4 μg/m 3 , respectively. The predicted annual mean concentration of PM 1 across China was 26.9 μg/m 3 . The PM 1 level was highest in winter while lowest in summer. Generally, the PM 1 levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM 1 levels increased substantially in the South-Western Hebei and Beijing-Tianjin region. GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM 1 . Ambient PM 1 reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM 1 . Copyright © 2017 Elsevier Ltd. All rights reserved.
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Kadlec, J.; Ames, D. P.
2014-12-01
The aim of the presented work is creating a freely accessible, dynamic and re-usable snow cover map of the world by combining snow extent and snow depth datasets from multiple sources. The examined data sources are: remote sensing datasets (MODIS, CryoLand), weather forecasting model outputs (OpenWeatherMap, forecast.io), ground observation networks (CUAHSI HIS, GSOD, GHCN, and selected national networks), and user-contributed snow reports on social networks (cross-country and backcountry skiing trip reports). For adding each type of dataset, an interface and an adapter is created. Each adapter supports queries by area, time range, or combination of area and time range. The combined dataset is published as an online snow cover mapping service. This web service lowers the learning curve that is required to view, access, and analyze snow depth maps and snow time-series. All data published by this service are licensed as open data; encouraging the re-use of the data in customized applications in climatology, hydrology, sports and other disciplines. The initial version of the interactive snow map is on the website snow.hydrodata.org. This website supports the view by time and view by site. In view by time, the spatial distribution of snow for a selected area and time period is shown. In view by site, the time-series charts of snow depth at a selected location is displayed. All snow extent and snow depth map layers and time series are accessible and discoverable through internationally approved protocols including WMS, WFS, WCS, WaterOneFlow and WaterML. Therefore they can also be easily added to GIS software or 3rd-party web map applications. The central hypothesis driving this research is that the integration of user contributed data and/or social-network derived snow data together with other open access data sources will result in more accurate and higher resolution - and hence more useful snow cover maps than satellite data or government agency produced data by
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New satellite altimetry products for coastal oceans
Dufau, Claire; Mercier, F.; Ablain, M.; Dibarboure, G.; Carrere, L.; Labroue, S.; Obligis, E.; Sicard, P.; Thibaut, P.; Birol, F.; Bronner, E.; Lombard, A.; Picot, N.
Since the launch of Topex-Poseidon in 1992, satellite altimetry has become one of the most essential elements of the Earth's observing system. Its global view of the ocean state has permitted numerous improvements in the environment understanding, particularly in the global monitoring of climate changes and ocean circulation. Near the coastlines where human activities have a major impact on the ocean, satellite altimeter techniques are unfortunately limited by a growth of their error budget. This quality loss is due to land contamination in the altimetric and radiometric footprints but also to inaccurate geophysical corrections (tides, high-frequency processes linked to atmospheric forcing).Despite instrumental perturbations by emerged lands until 10 km (altimeter) and 50 km (radiometer) off the coasts, measurements are made and may contain useful information for coastal studies. In order to recover these data close to the coast, the French Spatial Agency (CNES) has funded the development of the PISTACH prototype dedicated to Jason-2 altimeter processing in coastal ocean. Since November 2008, these new satellite altimeter products have been providing new retracking solutions, several state-of-the-art or with higher resolution corrections in addition to standard fields. This presentation will present and illustrate this new set of satellite data for the coastal oceans.
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Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Schwartz, Joel
2012-08-15
Although meteorological stations provide accurate air temperature observations, their spatial coverage is limited and thus often insufficient for epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate near surface air temperature (Ta). However, the derivation of Ta from surface temperature (Ts) measured by satellites is far from being straightforward. In this study, we present a novel approach that incorporates land use regression, meteorological variables and spatial smoothing to first calibrate between Ts and Ta on a daily basis and then predict Ta for days when satellite Ts data were not available. We applied mixed regression models with daily random slopes to calibrate Moderate Resolution Imaging Spectroradiometer (MODIS) Ts data with monitored Ta measurements for 2003. Then, we used a generalized additive mixed model with spatial smoothing to estimate Ta in days with missing Ts. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (mean out-of-sample R(2)=0.946 and R(2)=0.941 respectively). Furthermore, based on the high quality predictions we investigated the spatial patterns of Ta within the study domain as they relate to urban vs. non-urban land uses. Copyright © 2012 Elsevier B.V. All rights reserved.
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Land cover change of watersheds in Southern Guam from 1973 to 2001.
Wen, Yuming; Khosrowpanah, Shahram; Heitz, Leroy
2011-08-01
Land cover change can be caused by human-induced activities and natural forces. Land cover change in watershed level has been a main concern for a long time in the world since watersheds play an important role in our life and environment. This paper is focused on how to apply Landsat Multi-Spectral Scanner (MSS) satellite image of 1973 and Landsat Thematic Mapper (TM) satellite image of 2001 to determine the land cover changes of coastal watersheds from 1973 to 2001. GIS and remote sensing are integrated to derive land cover information from Landsat satellite images of 1973 and 2001. The land cover classification is based on supervised classification method in remote sensing software ERDAS IMAGINE. Historical GIS data is used to replace the areas covered by clouds or shadows in the image of 1973 to improve classification accuracy. Then, temporal land cover is utilized to determine land cover change of coastal watersheds in southern Guam. The overall classification accuracies for Landsat MSS image of 1973 and Landsat TM image of 2001 are 82.74% and 90.42%, respectively. The overall classification of Landsat MSS image is particularly satisfactory considering its coarse spatial resolution and relatively bad data quality because of lots of clouds and shadows in the image. Watershed land cover change in southern Guam is affected greatly by anthropogenic activities. However, natural forces also affect land cover in space and time. Land cover information and change in watersheds can be applied for watershed management and planning, and environmental modeling and assessment. Based on spatio-temporal land cover information, the interaction behavior between human and environment may be evaluated. The findings in this research will be useful to similar research in other tropical islands.
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Satellite Estimation of Daily Land Surface Water Vapor Pressure Deficit from AMSR- E
Jones, L. A.; Kimball, J. S.; McDonald, K. C.; Chan, S. K.; Njoku, E. G.; Oechel, W. C.
2007-12-01
Vapor pressure deficit (VPD) is a key variable for monitoring land surface water and energy exchanges, and estimating plant water stress. Multi-frequency day/night brightness temperatures from the Advanced Microwave Scanning Radiometer on EOS Aqua (AMSR-E) were used to estimate daily minimum and average near surface (2 m) air temperatures across a North American boreal-Arctic transect. A simple method for determining daily mean VPD (Pa) from AMSR-E air temperature retrievals was developed and validated against observations across a regional network of eight study sites ranging from boreal grassland and forest to arctic tundra. The method assumes that the dew point and minimum daily air temperatures tend to equilibrate in areas with low night time temperatures and relatively moist conditions. This assumption was tested by comparing the VPD algorithm results derived from site daily temperature observations against results derived from AMSR-E retrieved temperatures alone. An error analysis was conducted to determine the amount of error introduced in VPD estimates given known levels of error in satellite retrieved temperatures. Results indicate that the assumption generally holds for the high latitude study sites except for arid locations in mid-summer. VPD estimates using the method with AMSR-E retrieved temperatures compare favorably with site observations. The method can be applied to land surface temperature retrievals from any sensor with day and night surface or near-surface thermal measurements and shows potential for inferring near-surface wetness conditions where dense vegetation may hinder surface soil moisture retrievals from low-frequency microwave sensors. This work was carried out at The University of Montana, at San Diego State University, and at the Jet Propulsion Laboratory, California Institute of Technology, under contract to the National Aeronautics and Space Administration.
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LAKE ICE DETECTION IN LOW-RESOLUTION OPTICAL SATELLITE IMAGES
Directory of Open Access Journals (Sweden)
M. Tom
2018-05-01
Full Text Available Monitoring and analyzing the (decreasing trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m–1000 m satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal. Only the cloud-free (clean pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM. We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.
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Lake Ice Detection in Low-Resolution Optical Satellite Images
Tom, M.; Kälin, U.; Sütterlin, M.; Baltsavias, E.; Schindler, K.
2018-05-01
Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m-1000 m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.
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Albergel, C.; Dorigo, W.; Balsamo, G.; Sabatar, J; de Rosnay, P.; Isaksen, I; Brocca, L; de Jeu, R.A.M.; Wagner, W.
2013-01-01
Soil moisture from ERA-Land, a revised version of the land surface components of the European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim), is used to monitor at a global scale the consistency of a new microwave based multi-satellite surface soil moisture date set
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Directory of Open Access Journals (Sweden)
N. M. S. M. Kadhim
2015-03-01
Full Text Available Very-High-Resolution (VHR satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour, the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates
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Kadhim, N. M. S. M.; Mourshed, M.; Bray, M. T.
2015-03-01
Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of
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Ryan, Jonathan C.; Hubbard, Alun; Box, Jason E.; Brough, Stephen; Cameron, Karen; Cook, Joseph M.; Cooper, Matthew; Doyle, Samuel H.; Edwards, Arwyn; Holt, Tom; Irvine-Fynn, Tristram; Jones, Christine; Pitcher, Lincoln H.; Rennermalm, Asa K.; Smith, Laurence C.; Stibal, Marek; Snooke, Neal
2017-05-01
Measurements of albedo are a prerequisite for modelling surface melt across the Earth's cryosphere, yet available satellite products are limited in spatial and/or temporal resolution. Here, we present a practical methodology to obtain centimetre resolution albedo products with accuracies of 5% using consumer-grade digital camera and unmanned aerial vehicle (UAV) technologies. Our method comprises a workflow for processing, correcting and calibrating raw digital images using a white reference target, and upward and downward shortwave radiation measurements from broadband silicon pyranometers. We demonstrate the method with a set of UAV sorties over the western, K-sector of the Greenland Ice Sheet. The resulting albedo product, UAV10A1, covers 280 km2, at a resolution of 20 cm per pixel and has a root-mean-square difference of 3.7% compared to MOD10A1 and 4.9% compared to ground-based broadband pyranometer measurements. By continuously measuring downward solar irradiance, the technique overcomes previous limitations due to variable illumination conditions during and between surveys over glaciated terrain. The current miniaturization of multispectral sensors and incorporation of upward facing radiation sensors on UAV packages means that this technique will likely become increasingly attractive in field studies and used in a wide range of applications for high temporal and spatial resolution surface mapping of debris, dust, cryoconite and bioalbedo and for directly constraining surface energy balance models.
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Lemon, Mary Grace T.; Allen, Scott T.; Edwards, Brandon L.; King, Sammy L.; Keim, Richard F.
2016-01-01
Decreased water availability due to hydrologic modifications, groundwater withdrawal, and climate change threaten bottomland hardwood (BLH) forest communities. We used satellite-derived (MODIS) land-surface temperature (LST) data to investigate spatial heterogeneity of canopy temperature (an indicator of plant-water status) in a floodplain forest of the upper Sabine River for 2008–2014. High LST pixels were generally further from the river and at higher topographic locations, indicating lower water-availability. Increasing rainfall-derived soil moisture corresponded with decreased heterogeneity of LST between pixels but there was weaker association between Sabine River stage and heterogeneity. Stronger dependence of LST convergence on rainfall rather than river flow suggests that some regions are less hydrologically connected to the river, and vegetation may rely on local precipitation and other contributions to the riparian aquifer to replenish soil moisture. Observed LST variations associated with hydrology encourage further investigation of the utility of this approach for monitoring forest stress, especially with considerations of climate change and continued river management.
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Polar bears from space: Assessing satellite imagery as a tool to track Arctic wildlife
Stapleton, Seth P.; LaRue, Michelle A.; Lecomte, Nicolas; Atkinson, Stephen N.; Garshelis, David L.; Porter, Claire; Atwood, Todd C.
2014-01-01
Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark- recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105) was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152). Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.
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Polar bears from space: assessing satellite imagery as a tool to track Arctic wildlife.
Directory of Open Access Journals (Sweden)
Seth Stapleton
Full Text Available Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark-recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105 was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152. Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.
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Polar bears from space: assessing satellite imagery as a tool to track Arctic wildlife.
Stapleton, Seth; LaRue, Michelle; Lecomte, Nicolas; Atkinson, Stephen; Garshelis, David; Porter, Claire; Atwood, Todd
2014-01-01
Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark-recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105) was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152). Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.
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Propagation considerations in land mobile satellite transmission
Vogel, W. J.; Smith, E. K.
1985-01-01
It appears likely that the Land Mobile Satellite Services (LMSS) will be authorized by the FCC for operation in the 800 to 900 MHz (UHF) and possibly near 1500 MHz (L-band). Propagation problems are clearly an important factor in the effectiveness of this service, but useful measurements are few, and produced contradictory interpretations. A first order overview of existing measurements is presented with particular attention to the first two NASA balloon to mobile vehicle propagation experiments. Some physical insight into the interpretation of propagation effects in LMSS transmissions is provided.
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Satellite bands of the RbCs molecule in the range of highly excited states
Energy Technology Data Exchange (ETDEWEB)
Rakić, Mario; Beuc, Robert; Skenderović, Hrvoje, E-mail: hrvoje@ifs.hr [Institute of Physics, Bijenička cesta 46, Zagreb 10000 (Croatia); Bouloufa-Maafa, Nadia; Dulieu, Olivier; Vexiau, Romain [Laboratoire Aimé Cotton, CNRS, Université Paris-Sud, ENS Cachan, Université Paris-Saclay, Bât. 505, Campus d’Orsay, Orsay Cedex 91405 (France); Pichler, Goran [Physics Department, Kuwait University, PO Box 5969, Safat—13060 (Kuwait)
2016-05-28
We report on the observation of three RbCs satellite bands in the blue and green ranges of the visible spectrum. Absorption measurements are performed using all-sapphire cell filled with a mixture of Rb and Cs. We compare high resolution absorption spectrum of Rb-Cs vapor mixture with pure Rb and Cs vapor spectra from the literature. After detailed analysis, the new satellite bands of RbCs molecule at 418.3 nm, 468.3, and 527.5 nm are identified. The origin of these bands is discussed by direct comparison with difference potentials derived from quantum chemistry calculations of RbCs potential energy curves. These bands originate from the lower Rydberg states of the RbCs molecule. This study thus provides further insight into photoassociation of lower Rydberg molecular states, approximately between Cs(7s) + Rb(5s) and Cs(6s) + Rb(6p) asymptotes, in ultracold gases.
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Ultra-high resolution protein crystallography
International Nuclear Information System (INIS)
Takeda, Kazuki; Hirano, Yu; Miki, Kunio
2010-01-01
Many protein structures have been determined by X-ray crystallography and deposited with the Protein Data Bank. However, these structures at usual resolution (1.5< d<3.0 A) are insufficient in their precision and quantity for elucidating the molecular mechanism of protein functions directly from structural information. Several studies at ultra-high resolution (d<0.8 A) have been performed with synchrotron radiation in the last decade. The highest resolution of the protein crystals was achieved at 0.54 A resolution for a small protein, crambin. In such high resolution crystals, almost all of hydrogen atoms of proteins and some hydrogen atoms of bound water molecules are experimentally observed. In addition, outer-shell electrons of proteins can be analyzed by the multipole refinement procedure. However, the influence of X-rays should be precisely estimated in order to derive meaningful information from the crystallographic results. In this review, we summarize refinement procedures, current status and perspectives for ultra high resolution protein crystallography. (author)
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Directory of Open Access Journals (Sweden)
Fraser Baker
2018-03-01
Full Text Available Domestic gardens are an important component of cities, contributing significantly to urban green infrastructure (GI and its associated ecosystem services. However, domestic gardens are incredibly heterogeneous which presents challenges for quantifying their GI contribution and associated benefits for sustainable urban development. This study applies an innovative methodology that combines citizen science data with high resolution image analysis to create a garden dataset in the case study city of Manchester, UK. An online Citizen Science Survey (CSS collected estimates of proportional coverage for 10 garden land surface types from 1031 city residents. High resolution image analysis was conducted to validate the CSS estimates, and to classify 7 land surface cover categories for all garden parcels in the city. Validation of the CSS land surface estimations revealed a mean accuracy of 76.63% (s = 15.24%, demonstrating that citizens are able to provide valid estimates of garden surface coverage proportions. An Object Based Image Analysis (OBIA classification achieved an estimated overall accuracy of 82%, with further processing required to classify shadow objects. CSS land surface estimations were then extrapolated across the entire classification through calculation of within image class proportions, to provide the proportional coverage of 10 garden land surface types (buildings, hard impervious surfaces, hard pervious surfaces, bare soil, trees, shrubs, mown grass, rough grass, cultivated land, water within every garden parcel in the city. The final dataset provides a better understanding of the composition of GI in domestic gardens and how this varies across the city. An average garden in Manchester has 50.23% GI, including trees (16.54%, mown grass (14.46%, shrubs (9.19%, cultivated land (7.62%, rough grass (1.97% and water (0.45%. At the city scale, Manchester has 49.0% GI, and around one fifth (20.94% of this GI is contained within domestic
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Mach, Douglas M.; Blakeslee, Richard J.; Bateman, Monte G.
2010-01-01
Using rotating vane electric field mills and Gerdien capacitors, we measured the electric field profile and conductivity during 850 overflights of electrified shower clouds and thunderstorms spanning regions including the Southeastern United States, the Western Atlantic Ocean, the Gulf of Mexico, Central America and adjacent oceans, Central Brazil, and the South Pacific. The overflights include storms over land and ocean, with and without lightning, and with positive and negative fields above the storms. The measurements were made with the NASA ER-2 and the Altus-II high altitude aircrafts. Peak electric fields, with lightning transients removed, ranged from -1.0 kV/m to 16 kV/m, with a mean value of 0.9 kV/m. The median peak field was 0.29 kV/m. Integrating our electric field and conductivity data, we determined total conduction currents and flash rates for each overpass. With knowledge of the storm location (land or ocean) and type (with or without lightning), we determine the mean currents by location and type. The mean current for ocean storms with lightning is 1.6 A while the mean current for land storms with lightning is 1.0 A. The mean current for oceanic storms without lightning (i.e., electrified shower clouds) is 0.39 A and the mean current for land storms without lightning is 0.13 A. Thus, on average, land storms with or without lightning have about half the mean current as their corresponding oceanic storm counterparts. Over three-quarters (78%) of the land storms had detectable lightning, while less than half (43%) of the oceanic storms had lightning. We did not find any significant regional or latitudinal based patterns in our total conduction currents. By combining the aircraft derived storm currents and flash rates with diurnal lightning statistics derived from the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) low Earth orbiting satellites, we reproduce the diurnal variation in the global electric circuit (i.e., the Carnegie