WorldWideScience

Sample records for satellite cloud imagery

  1. Application of INSAT Satellite Cloud-Imagery Data for Site ...

    Indian Academy of Sciences (India)

    tribpo

    Application of INSAT Satellite Cloud-Imagery Data for Site Evaluation. Work of ... sources like Cyg X-3 and AM-Her binary systems (Bhat et al. 1986; Bhat et al. ... one is dealing with in the very high energy (VHE) and ultra high energy (UHE) .... shows the monthly distribution of 'spectroscopic' hours averaged over the 5-year.

  2. Automatic Mosaicking of Satellite Imagery Considering the Clouds

    Science.gov (United States)

    Kang, Yifei; Pan, Li; Chen, Qi; Zhang, Tong; Zhang, Shasha; Liu, Zhang

    2016-06-01

    With the rapid development of high resolution remote sensing for earth observation technology, satellite imagery is widely used in the fields of resource investigation, environment protection, and agricultural research. Image mosaicking is an important part of satellite imagery production. However, the existence of clouds leads to lots of disadvantages for automatic image mosaicking, mainly in two aspects: 1) Image blurring may be caused during the process of image dodging, 2) Cloudy areas may be passed through by automatically generated seamlines. To address these problems, an automatic mosaicking method is proposed for cloudy satellite imagery in this paper. Firstly, modified Otsu thresholding and morphological processing are employed to extract cloudy areas and obtain the percentage of cloud cover. Then, cloud detection results are used to optimize the process of dodging and mosaicking. Thus, the mosaic image can be combined with more clear-sky areas instead of cloudy areas. Besides, clear-sky areas will be clear and distortionless. The Chinese GF-1 wide-field-of-view orthoimages are employed as experimental data. The performance of the proposed approach is evaluated in four aspects: the effect of cloud detection, the sharpness of clear-sky areas, the rationality of seamlines and efficiency. The evaluation results demonstrated that the mosaic image obtained by our method has fewer clouds, better internal color consistency and better visual clarity compared with that obtained by traditional method. The time consumed by the proposed method for 17 scenes of GF-1 orthoimages is within 4 hours on a desktop computer. The efficiency can meet the general production requirements for massive satellite imagery.

  3. Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

    Science.gov (United States)

    Logan, T. L.; Huning, J. R.; Glackin, D. L.

    1983-01-01

    The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

  4. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

    Directory of Open Access Journals (Sweden)

    Wei Jin

    2016-12-01

    Full Text Available Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC, atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

  5. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

    Science.gov (United States)

    Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

    2016-01-01

    Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

  6. Landsat 7 ETM/1G satellite imagery - Hawaiian Islands cloud-free mosaics

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Cloud-free Landsat satellite imagery mosaics of the islands of the main 8 Hawaiian Islands (Hawaii, Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau). Landsat...

  7. Visualizing Cloud Properties and Satellite Imagery: A Tool for Visualization and Information Integration

    Science.gov (United States)

    Chee, T.; Nguyen, L.; Smith, W. L., Jr.; Spangenberg, D.; Palikonda, R.; Bedka, K. M.; Minnis, P.; Thieman, M. M.; Nordeen, M.

    2017-12-01

    Providing public access to research products including cloud macro and microphysical properties and satellite imagery are a key concern for the NASA Langley Research Center Cloud and Radiation Group. This work describes a web based visualization tool and API that allows end users to easily create customized cloud product and satellite imagery, ground site data and satellite ground track information that is generated dynamically. The tool has two uses, one to visualize the dynamically created imagery and the other to provide access to the dynamically generated imagery directly at a later time. Internally, we leverage our practical experience with large, scalable application practices to develop a system that has the largest potential for scalability as well as the ability to be deployed on the cloud to accommodate scalability issues. We build upon NASA Langley Cloud and Radiation Group's experience with making real-time and historical satellite cloud product information, satellite imagery, ground site data and satellite track information accessible and easily searchable. This tool is the culmination of our prior experience with dynamic imagery generation and provides a way to build a "mash-up" of dynamically generated imagery and related kinds of information that are visualized together to add value to disparate but related information. In support of NASA strategic goals, our group aims to make as much scientific knowledge, observations and products available to the citizen science, research and interested communities as well as for automated systems to acquire the same information for data mining or other analytic purposes. This tool and the underlying API's provide a valuable research tool to a wide audience both as a standalone research tool and also as an easily accessed data source that can easily be mined or used with existing tools.

  8. Cloud detection method for Chinese moderate high resolution satellite imagery (Conference Presentation)

    Science.gov (United States)

    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%.

  9. Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast

    International Nuclear Information System (INIS)

    Escrig, H.; Batlles, F.J.; Alonso, J.; Baena, F.M.; Bosch, J.L.; Salbidegoitia, I.B.; Burgaleta, J.I.

    2013-01-01

    Considering that clouds are the greatest causes to solar radiation blocking, short term cloud forecasting can help power plant operation and therefore improve benefits. Cloud detection, classification and motion vector determination are key to forecasting sun obstruction by clouds. Geostationary satellites provide cloud information covering wide areas, allowing cloud forecast to be performed for several hours in advance. Herein, the methodology developed and tested in this study is based on multispectral tests and binary cross correlations followed by coherence and quality control tests over resulting motion vectors. Monthly synthetic surface albedo image and a method to reject erroneous correlation vectors were developed. Cloud classification in terms of opacity and height of cloud top is also performed. A whole-sky camera has been used for validation, showing over 85% of agreement between the camera and the satellite derived cloud cover, whereas error in motion vectors is below 15%. - Highlights: ► A methodology for detection, classification and movement of clouds is presented. ► METEOSAT satellite images are used to obtain a cloud mask. ► The prediction of cloudiness is estimated with 90% in overcast conditions. ► Results for partially covered sky conditions showed a 75% accuracy. ► Motion vectors are estimated from the clouds with a success probability of 86%

  10. Do clouds save the great barrier reef? satellite imagery elucidates the cloud-SST relationship at the local scale.

    Directory of Open Access Journals (Sweden)

    Susannah M Leahy

    Full Text Available Evidence of global climate change and rising sea surface temperatures (SSTs is now well documented in the scientific literature. With corals already living close to their thermal maxima, increases in SSTs are of great concern for the survival of coral reefs. Cloud feedback processes may have the potential to constrain SSTs, serving to enforce an "ocean thermostat" and promoting the survival of coral reefs. In this study, it was hypothesized that cloud cover can affect summer SSTs in the tropics. Detailed direct and lagged relationships between cloud cover and SST across the central Great Barrier Reef (GBR shelf were investigated using data from satellite imagery and in situ temperature and light loggers during two relatively hot summers (2005 and 2006 and two relatively cool summers (2007 and 2008. Across all study summers and shelf positions, SSTs exhibited distinct drops during periods of high cloud cover, and conversely, SST increases during periods of low cloud cover, with a three-day temporal lag between a change in cloud cover and a subsequent change in SST. Cloud cover alone was responsible for up to 32.1% of the variation in SSTs three days later. The relationship was strongest in both El Niño (2005 and La Niña (2008 study summers and at the inner-shelf position in those summers. SST effects on subsequent cloud cover were weaker and more variable among study summers, with rising SSTs explaining up to 21.6% of the increase in cloud cover three days later. This work quantifies the often observed cloud cooling effect on coral reefs. It highlights the importance of incorporating local-scale processes into bleaching forecasting models, and encourages the use of remote sensing imagery to value-add to coral bleaching field studies and to more accurately predict risks to coral reefs.

  11. Feature extraction and classification of clouds in high resolution panchromatic satellite imagery

    Science.gov (United States)

    Sharghi, Elan

    The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIER®) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds. The focus of this thesis is to explore various feature extraction and classification methods to accurately distinguish clouds from ship objects. An examination of a texture analysis method, line detection using the Hough transform, and edge detection using wavelets are explored as possible feature extraction methods. The features are then supplied to a K-Nearest Neighbors (KNN) or Support Vector Machine (SVM) classifier. Parameter options for these classifiers are explored and the optimal parameters are determined.

  12. Assessment on spatiotemporal relationship between rainfall and cloud top temperature from new generation weather satellite imagery

    Science.gov (United States)

    Wei, Chiang; Yeh, Hui-Chung; Chen, Yen-Chang

    2017-04-01

    This study addressed the relationship between rainfall and cloud top temperature (CCT) from new generation satellite Himawari-8 imagery at different spatiotemporal scale. This satellite provides higher band, more bits for data format, spatial and temporal resolution compared with previous GMS series. The multi-infrared channels with 10-minute and 1-2 km resolution make it possible for rainfall estimating/forecasting in small/medium watershed. The preliminary result investigated at Chenyulan watershed (443.6 square kilometer) of Central Taiwan in 2016 Typhoon Megi shows the regression coefficient fitted by negative exponential equation of largest rainfall vs. CCT (B8 band) at pixel scale increases as time scales enlarges and reach 0.462 for 120-minute accumulative rainfall; the value (CTT of B15 band) decreases from 0.635 for 10-minute to 0.423 for 120-minute accumulative rainfall at basin-wide scale. More rainfall events for different regime are yet to evaluate to get solid results.

  13. The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

    Science.gov (United States)

    Patterson, Maria T.; Anderson, Nicholas; Bennett, Collin; Bruggemann, Jacob; Grossman, Robert L.; Handy, Matthew; Ly, Vuong; Mandl, Daniel J.; Pederson, Shane; Pivarski, James; hide

    2016-01-01

    Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for cloud-based processing of Earth satellite imagery with practical applications to aid in natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework allows batches of analytics, scanning for new data, to be applied to data as it flows in. In the Matsu Wheel, the data only need to be accessed and preprocessed once, regardless of the number or types of analytics, which can easily be slotted into the existing framework. The Matsu Wheel system provides a significantly more efficient use of computational resources over alternative methods when the data are large, have high-volume throughput, may require heavy preprocessing, and are typically used for many types of analysis. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The result products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for

  14. 7 CFR 611.22 - Availability of satellite imagery.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Availability of satellite imagery. 611.22 Section 611... § 611.22 Availability of satellite imagery. Cloud-free maps of the United States based on imagery received from a satellite are prepared and released to the pubic by NRCS. The maps offer the first image of...

  15. Investigating Gravity Waves in Polar Mesospheric Clouds Using Tomographic Reconstructions of AIM Satellite Imagery

    Science.gov (United States)

    Hart, V. P.; Taylor, M. J.; Doyle, T. E.; Zhao, Y.; Pautet, P.-D.; Carruth, B. L.; Rusch, D. W.; Russell, J. M.

    2018-01-01

    This research presents the first application of tomographic techniques for investigating gravity wave structures in polar mesospheric clouds (PMCs) imaged by the Cloud Imaging and Particle Size instrument on the NASA AIM satellite. Albedo data comprising consecutive PMC scenes were used to tomographically reconstruct a 3-D layer using the Partially Constrained Algebraic Reconstruction Technique algorithm and a previously developed "fanning" technique. For this pilot study, a large region (760 × 148 km) of the PMC layer (altitude 83 km) was sampled with a 2 km horizontal resolution, and an intensity weighted centroid technique was developed to create novel 2-D surface maps, characterizing the individual gravity waves as well as their altitude variability. Spectral analysis of seven selected wave events observed during the Northern Hemisphere 2007 PMC season exhibited dominant horizontal wavelengths of 60-90 km, consistent with previous studies. These tomographic analyses have enabled a broad range of new investigations. For example, a clear spatial anticorrelation was observed between the PMC albedo and wave-induced altitude changes, with higher-albedo structures aligning well with wave troughs, while low-intensity regions aligned with wave crests. This result appears to be consistent with current theories of PMC development in the mesopause region. This new tomographic imaging technique also provides valuable wave amplitude information enabling further mesospheric gravity wave investigations, including quantitative analysis of their hemispheric and interannual characteristics and variations.

  16. Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

    Science.gov (United States)

    Shiffman, Smadar

    2004-01-01

    Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

  17. Normalization of satellite imagery

    Science.gov (United States)

    Kim, Hongsuk H.; Elman, Gregory C.

    1990-01-01

    Sets of Thematic Mapper (TM) imagery taken over the Washington, DC metropolitan area during the months of November, March and May were converted into a form of ground reflectance imagery. This conversion was accomplished by adjusting the incident sunlight and view angles and by applying a pixel-by-pixel correction for atmospheric effects. Seasonal color changes of the area can be better observed when such normalization is applied to space imagery taken in time series. In normalized imagery, the grey scale depicts variations in surface reflectance and tonal signature of multi-band color imagery can be directly interpreted for quantitative information of the target.

  18. Thematic mapping from satellite imagery

    CERN Document Server

    Denègre, J

    2013-01-01

    Thematic Mapping from Satellite Imagery: A Guidebook discusses methods in producing maps using satellite images. The book is comprised of five chapters; each chapter covers one stage of the process. Chapter 1 tackles the satellite remote sensing imaging and its cartographic significance. Chapter 2 discusses the production processes for extracting information from satellite data. The next chapter covers the methods for combining satellite-derived information with that obtained from conventional sources. Chapter 4 deals with design and semiology for cartographic representation, and Chapter 5 pre

  19. Cloud-based Web Services for Near-Real-Time Web access to NPP Satellite Imagery and other Data

    Science.gov (United States)

    Evans, J. D.; Valente, E. G.

    2010-12-01

    We are building a scalable, cloud computing-based infrastructure for Web access to near-real-time data products synthesized from the U.S. National Polar-Orbiting Environmental Satellite System (NPOESS) Preparatory Project (NPP) and other geospatial and meteorological data. Given recent and ongoing changes in the the NPP and NPOESS programs (now Joint Polar Satellite System), the need for timely delivery of NPP data is urgent. We propose an alternative to a traditional, centralized ground segment, using distributed Direct Broadcast facilities linked to industry-standard Web services by a streamlined processing chain running in a scalable cloud computing environment. Our processing chain, currently implemented on Amazon.com's Elastic Compute Cloud (EC2), retrieves raw data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and synthesizes data products such as Sea-Surface Temperature, Vegetation Indices, etc. The cloud computing approach lets us grow and shrink computing resources to meet large and rapid fluctuations (twice daily) in both end-user demand and data availability from polar-orbiting sensors. Early prototypes have delivered various data products to end-users with latencies between 6 and 32 minutes. We have begun to replicate machine instances in the cloud, so as to reduce latency and maintain near-real time data access regardless of increased data input rates or user demand -- all at quite moderate monthly costs. Our service-based approach (in which users invoke software processes on a Web-accessible server) facilitates access into datasets of arbitrary size and resolution, and allows users to request and receive tailored and composite (e.g., false-color multiband) products on demand. To facilitate broad impact and adoption of our technology, we have emphasized open, industry-standard software interfaces and open source software. Through our work, we envision the widespread establishment of similar, derived, or interoperable systems for

  20. Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery

    Science.gov (United States)

    Helmer, E.H.; Kennaway, T.A.; Pedreros, D.H.; Clark, M.L.; Marcano-Vega, H.; Tieszen, L.L.; Ruzycki, T.R.; Schill, S.R.; Carrington, C.M.S.

    2008-01-01

    Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius, testing a more detailed classification than earlier work in the latter three islands. Secondly, we estimate the extents of land cover and protected forest by formation for five islands and ask how land cover has changed over the second half of the 20th century. The image interpretation approach combines image mosaics and ancillary geographic data, classifying the resulting set of raster data with decision tree software. Cloud-free image mosaics for one or two seasons were created by applying regression tree normalization to scene dates that could fill cloudy areas in a base scene. Such mosaics are also known as cloud-filled, cloud-minimized or cloud-cleared imagery, mosaics, or composites. The approach accurately distinguished several classes that more standard methods would confuse; the seamless mosaics aided reference data collection; and the multiseason imagery allowed us to separate drought deciduous forests and woodlands from semi-deciduous ones. Cultivated land areas declined 60 to 100 percent from about 1945 to 2000 on several islands. Meanwhile, forest cover has increased 50 to 950%. This trend will likely continue where sugar cane cultivation has dominated. Like the island of Puerto Rico, most higher-elevation forest formations are protected in formal or informal reserves. Also similarly, lowland forests, which are drier forest types on these islands, are not well represented in reserves. Former cultivated lands in lowland areas could provide lands for new reserves of drier forest types. The land-use history of these islands may provide insight for planners in countries currently considering

  1. A CLOUD BOUNDARY DETECTION SCHEME COMBINED WITH ASLIC AND CNN USING ZY-3, GF-1/2 SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    Z. Guo

    2018-04-01

    Full Text Available Remote sensing optical image cloud detection is one of the most important problems in remote sensing data processing. Aiming at the information loss caused by cloud cover, a cloud detection method based on convolution neural network (CNN is presented in this paper. Firstly, a deep CNN network is used to extract the multi-level feature generation model of cloud from the training samples. Secondly, the adaptive simple linear iterative clustering (ASLIC method is used to divide the detected images into superpixels. Finally, the probability of each superpixel belonging to the cloud region is predicted by the trained network model, thereby generating a cloud probability map. The typical region of GF-1/2 and ZY-3 were selected to carry out the cloud detection test, and compared with the traditional SLIC method. The experiment results show that the average accuracy of cloud detection is increased by more than 5 %, and it can detected thin-thick cloud and the whole cloud boundary well on different imaging platforms.

  2. Mapping the Distribution of Cloud Forests Using MODIS Imagery

    Science.gov (United States)

    Douglas, M. W.; Mejia, J.; Murillo, J.; Orozco, R.

    2007-05-01

    Tropical cloud forests - those forests that are frequently immersed in clouds or otherwise very humid, are extremely difficult to map from the ground, and are not easily distinguished in satellite imagery from other forest types, but they have a very different flora and fauna than lowland rainforest. Cloud forests, although found in many parts of the tropics, have a very restricted vertical extent and thus are also restricted horizontally. As a result, they are subject to both human disturbance (coffee growing for example) and the effects of possible climate change. Motivated by a desire to seek meteorological explanations for the distribution of cloud forests, we have begun to map cloudiness using MODIS Terra and Aqua visible imagery. This imagery, at ~1030 LT and 1330 LT, is an approximation for mid-day cloudiness. In tropical regions the amount of mid-day cloudiness strongly controls the shortwave radiation and thus the potential for evaporation (and aridity). We have mapped cloudiness using a simple algorithm that distinguishes between the cloud-free background brightness and the generally more reflective clouds to separate clouds from the underlying background. A major advantage of MODIS imagery over many other sources of satellite imagery is its high spatial resolution (~250m). This, coupled with precisely navigated images, means that detailed maps of cloudiness can be produced. The cloudiness maps can then be related to the underlying topography to further refine the location of the cloud forests. An advantage of this technique is that we are mapping the potential cloud forest, based on cloudiness, rather than the actual cloud forest, which are commonly based on forest estimates from satellite and digital elevation data. We do not derive precipitation, only estimates of daytime cloudiness. Although only a few years of MODIS imagery has been used in our studies, we will show that this is sufficient to describe the climatology of cloudiness with acceptable

  3. User Validation of VIIRS Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Don Hillger

    2015-12-01

    Full Text Available Visible/Infrared Imaging Radiometer Suite (VIIRS Imagery from the Suomi National Polar-orbiting Partnership (S-NPP satellite is the finest spatial resolution (375 m multi-spectral imagery of any operational meteorological satellite to date. The Imagery environmental data record (EDR has been designated as a Key Performance Parameter (KPP for VIIRS, meaning that its performance is vital to the success of a series of Joint Polar Satellite System (JPSS satellites that will carry this instrument. Because VIIRS covers the high-latitude and Polar Regions especially well via overlapping swaths from adjacent orbits, the Alaska theatre in particular benefits from VIIRS more than lower-latitude regions. While there are no requirements that specifically address the quality of the EDR Imagery aside from the VIIRS SDR performance requirements, the value of VIIRS Imagery to operational users is an important consideration in the Cal/Val process. As such, engaging a wide diversity of users constitutes a vital part of the Imagery validation strategy. The best possible image quality is of utmost importance. This paper summarizes the Imagery Cal/Val Team’s quality assessment in this context. Since users are a vital component to the validation of VIIRS Imagery, specific examples of VIIRS imagery applied to operational needs are presented as an integral part of the post-checkout Imagery validation.

  4. Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands—Application to Suomi NPP VIIRS Images over Fennoscandia

    Directory of Open Access Journals (Sweden)

    Eija Parmes

    2017-08-01

    Full Text Available In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite’s VIIRS (Visible Infrared Imaging Radiometer Suite satellite images. The proposed cloud and shadow detection method has two distinct features when compared to many other methods. First, the method does not use the thermal bands and can thus be applied to other sensors which do not contain thermal channels, such as Sentinel-2 data. Secondly, the method uses the ratio between blue and green reflectance to detect shadows. Seven hundred and forty-seven VIIRS images over Fennoscandia from August 2014 to April 2016 were processed to train and develop the method. Twenty four points from every tenth of the images were used in accuracy assessment. These 1752 points were interpreted visually to cloud, cloud shadow and clear classes, then compared to the output of the cloud and shadow detection. The comparison on VIIRS images showed 94.2% correct detection rates and 11.1% false alarms for clouds, and respectively 36.1% and 82.7% for shadows. The results on cloud detection were similar to state-of-the-art methods. Shadows showed correctly on the northern edge of the clouds, but many shadows were wrongly assigned to other classes in some cases (e.g., to water class on lake and forest boundary, or with shadows over cloud. This may be due to the low spatial resolution of VIIRS images, where shadows are only a few pixels wide and contain lots of mixed pixels.

  5. Satellite Imagery Analysis for Automated Global Food Security Forecasting

    Science.gov (United States)

    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.

  6. Proposed Use of the NASA Ames Nebula Cloud Computing Platform for Numerical Weather Prediction and the Distribution of High Resolution Satellite Imagery

    Science.gov (United States)

    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.

  7. Satellite imagery in safeguards: progress and prospects

    International Nuclear Information System (INIS)

    Niemeyer, I.; Listner, C.

    2013-01-01

    The use of satellite imagery has become very important for the verification of the safeguards implementation under the Nuclear Non-Proliferation Treaty (NPT). The main applications of satellite imagery are to verify the correctness and completeness of the member states' declarations, and to provide preparatory information for inspections, complimentary access and other technical visits. If the area of interest is not accessible, remote sensing sensors provide one of the few opportunities of gathering data for nuclear monitoring, as for example in Iraq between 1998 and 2002 or currently in North Korea. Satellite data of all available sensor types contains a considerable amount of safeguard-relevant information. Very high-resolution optical satellite imagery provides the most detailed spatial information on nuclear sites and activities up to 0.41 m resolution, together with up to 8 spectral bands from the visible light and near infrared. Thermal infrared (TIR) images can indicate the operational status of nuclear facilities and help to identify undeclared activities. Hyper-spectral imagery allows a quantitative estimation of geophysical, geochemical and biochemical characteristics of the earth's surface and is therefore useful for assessing, for example, surface cover changes due to drilling, mining and milling activities. Synthetic Aperture Radar (SAR) image data up to 1 m spatial resolution provides an all-weather, day and night monitoring capability. However, the absence (or existence) of nuclear activities can never be confirmed completely based on satellite imagery. (A.C.)

  8. Motion/imagery secure cloud enterprise architecture analysis

    Science.gov (United States)

    DeLay, John L.

    2012-06-01

    Cloud computing with storage virtualization and new service-oriented architectures brings a new perspective to the aspect of a distributed motion imagery and persistent surveillance enterprise. Our existing research is focused mainly on content management, distributed analytics, WAN distributed cloud networking performance issues of cloud based technologies. The potential of leveraging cloud based technologies for hosting motion imagery, imagery and analytics workflows for DOD and security applications is relatively unexplored. This paper will examine technologies for managing, storing, processing and disseminating motion imagery and imagery within a distributed network environment. Finally, we propose areas for future research in the area of distributed cloud content management enterprises.

  9. Satellite remote sensing and cloud modeling of St. Anthony, Minnesota storm clouds and dew point depression

    Science.gov (United States)

    Hung, R. J.; Tsao, Y. D.

    1988-01-01

    Rawinsonde data and geosynchronous satellite imagery were used to investigate the life cycles of St. Anthony, Minnesota's severe convective storms. It is found that the fully developed storm clouds, with overshooting cloud tops penetrating above the tropopause, collapsed about three minutes before the touchdown of the tornadoes. Results indicate that the probability of producing an outbreak of tornadoes causing greater damage increases when there are higher values of potential energy storage per unit area for overshooting cloud tops penetrating the tropopause. It is also found that there is less chance for clouds with a lower moisture content to be outgrown as a storm cloud than clouds with a higher moisture content.

  10. Satellite imagery in a nuclear age

    International Nuclear Information System (INIS)

    Baines, P.J.

    1998-01-01

    Increasingly, high resolution satellite imaging systems are becoming available from multiple and diverse sources with capabilities useful for answering security questions. With increased supply, data availability and data authenticity may be assured. In a commercial market a supplier can ill afford the loss in market share that would result from any falsification of data. Similarly rising competitors willing to sell imagery of national security sites will decrease the tendency to endure self-imposed restrictions on sales of those sites. International organizations operating in the security interests of all nations might also gain preferential access. Costa for imagery will also fall to the point were individuals can afford purchases of satellite images. International organizations will find utility in exploiting imagery for solving international security problems. Housed within international organizations possessing competent staff, procedures, and 'shared destiny' stakes in resolving compliance discrepancies, the use of satellite imagery may provide a degree of stability in a world in which individuals, non-governmental organizations and governments may choose to exploit the available information for political gain. The use of satellite imagery outside these international organizations might not necessarily be aimed at seeking mutually beneficial solutions for international problems

  11. Monitoring Areal Snow Cover Using NASA Satellite Imagery

    Science.gov (United States)

    Harshburger, Brian J.; Blandford, Troy; Moore, Brandon

    2011-01-01

    The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data

  12. VERTICAL ACCURACY COMPARISON OF DIGITAL ELEVATION MODEL FROM LIDAR AND MULTITEMPORAL SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    J. Octariady

    2017-05-01

    Full Text Available Digital elevation model serves to illustrate the appearance of the earth's surface. DEM can be produced from a wide variety of data sources including from radar data, LiDAR data, and stereo satellite imagery. Making the LiDAR DEM conducted using point cloud data from LiDAR sensor. Making a DEM from stereo satellite imagery can be done using same temporal or multitemporal stereo satellite imagery. How much the accuracy of DEM generated from multitemporal stereo stellite imagery and LiDAR data is not known with certainty. The study was conducted using LiDAR DEM data and multitemporal stereo satellite imagery DEM. Multitemporal stereo satellite imagery generated semi-automatically by using 3 scene stereo satellite imagery with acquisition 2013–2014. The high value given each of DEM serve as the basis for calculating high accuracy DEM respectively. The results showed the high value differences in the fraction of the meter between LiDAR DEM and multitemporal stereo satellite imagery DEM.

  13. Satellite imagery and the Department of Safeguards

    International Nuclear Information System (INIS)

    Chitumbo, K.; Bunney, J.; Leve, G.; Robb, S.

    2001-01-01

    Full text: The presentation examines some of the challenges the Satellite Imagery and Analysis Laboratory (SIAL) is facing in supporting Strengthened Safeguards. It focuses on the analytical process, starting with specifying initial tasking and continuing through to end products that are a direct result of in-house analysis. In addition it also evaluates the advantages and disadvantages of SIAL's mission and introduces external forces that the agency must consider, but cannot itself, predict or control. Although SIAL's contribution to tasks relating to Article 2a(iii) of the Additional Protocol are known and are presently of great benefit to operations areas, this is only one aspect of its work. SIAL's ability to identify and analyze historical satellite imagery data has the advantage of permitting operations to take a more in depth view of a particular area of interest's (AOI) development, and thus may permit operations to confirm or refute specific assertions relating to the AOI's function or abilities. These assertions may originate in-house or may be open source reports the agency feels it is obligated to explore. SIAL's mission is unique in the world of imagery analysis. Its aim is to support all operations areas equally and in doing so it must maintain global focus. The task is tremendous, but the resultant coverage and concentration of unique expertise will allow SIAL to develop and provide operations with datasets that can be exploited in standalone mode or be incorporated into new cutting edge tools to be developed in SGIT. At present SIAL relies on two remote sensors, IKONOS-2 and EROS-AI, for present high- resolution imagery data and is using numerous sources for historical, pre 1999, data. A multiplicity of sources for high-resolution data is very important to SIAL, but is something that it cannot influence. It is hoped that the planned launch of two new sensors by Summer 2002 will be successful and will offer greater flexibility for image collection

  14. Validation of Cloud Properties From Multiple Satellites Using CALIOP Data

    Science.gov (United States)

    Yost, Christopher R.; Minnis, Patrick; Bedka, Kristopher M.; Heck, Patrick W.; Palikonda, Rabindra; Sun-Mack, Sunny; Trepte, Qing

    2016-01-01

    The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) is routinely applied to multispectral imagery from several geostationary and polar-orbiting imagers to retrieve cloud properties for weather and climate applications. Validation of the retrievals with independent datasets is continuously ongoing in order to understand differences caused by calibration, spatial resolution, viewing geometry, and other factors. The CALIOP instrument provides a decade of detailed cloud observations which can be used to evaluate passive imager retrievals of cloud boundaries, thermodynamic phase, cloud optical depth, and water path on a global scale. This paper focuses on comparisons of CALIOP retrievals to retrievals from MODIS, VIIRS, AVHRR, GOES, SEVIRI, and MTSAT. CALIOP is particularly skilled at detecting weakly-scattering cirrus clouds with optical depths less than approx. 0.5. These clouds are often undetected by passive imagers and the effect this has on the property retrievals is discussed.

  15. Photogrammetric Processing Using ZY-3 Satellite Imagery

    Science.gov (United States)

    Kornus, W.; Magariños, A.; Pla, M.; Soler, E.; Perez, F.

    2015-03-01

    This paper evaluates the stereoscopic capacities of the Chinese sensor ZiYuan-3 (ZY-3) for the generation of photogrammetric products. The satellite was launched on January 9, 2012 and carries three high-resolution panchromatic cameras viewing in forward (22º), nadir (0º) and backward direction (-22º) and an infrared multi-spectral scanner (IRMSS), which is slightly looking forward (6º). The ground sampling distance (GSD) is 2.1m for the nadir image, 3.5m for the two oblique stereo images and 5.8m for the multispectral image. The evaluated ZY-3 imagery consists of a full set of threefold-stereo and a multi-spectral image covering an area of ca. 50km x 50km north-west of Barcelona, Spain. The complete photogrammetric processing chain was executed including image orientation, the generation of a digital surface model (DSM), radiometric image correction, pansharpening, orthoimage generation and digital stereo plotting. All 4 images are oriented by estimating affine transformation parameters between observed and nominal RPC (rational polynomial coefficients) image positions of 17 ground control points (GCP) and a subsequent calculation of refined RPC. From 10 independent check points RMS errors of 2.2m, 2.0m and 2.7m in X, Y and H are obtained. Subsequently, a DSM of 5m grid spacing is generated fully automatically. A comparison with the Lidar data results in an overall DSM accuracy of approximately 3m. In moderate and flat terrain higher accuracies in the order of 2.5m and better are achieved. In a next step orthoimages from the high resolution nadir image and the multispectral image are generated using the refined RPC geometry and the DSM. After radiometric corrections a fused high resolution colour orthoimage with 2.1m pixel size is created using an adaptive HSL method. The pansharpen process is performed after the individual geocorrection due to the different viewing angles between the two images. In a detailed analysis of the colour orthoimage artifacts are

  16. Essential climatic variables estimation with satellite imagery

    Science.gov (United States)

    Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

    2016-12-01

    According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

  17. An efficient cloud detection method for high resolution remote sensing panchromatic imagery

    Science.gov (United States)

    Li, Chaowei; Lin, Zaiping; Deng, Xinpu

    2018-04-01

    In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

  18. Providing Access and Visualization to Global Cloud Properties from GEO Satellites

    Science.gov (United States)

    Chee, T.; Nguyen, L.; Minnis, P.; Spangenberg, D.; Palikonda, R.; Ayers, J. K.

    2015-12-01

    Providing public access to cloud macro and microphysical properties is a key concern for the NASA Langley Research Center Cloud and Radiation Group. This work describes a tool and method that allows end users to easily browse and access cloud information that is otherwise difficult to acquire and manipulate. The core of the tool is an application-programming interface that is made available to the public. One goal of the tool is to provide a demonstration to end users so that they can use the dynamically generated imagery as an input into their own work flows for both image generation and cloud product requisition. This project builds upon NASA Langley Cloud and Radiation Group's experience with making real-time and historical satellite cloud product imagery accessible and easily searchable. As we see the increasing use of virtual supply chains that provide additional value at each link there is value in making satellite derived cloud product information available through a simple access method as well as allowing users to browse and view that imagery as they need rather than in a manner most convenient for the data provider. Using the Open Geospatial Consortium's Web Processing Service as our access method, we describe a system that uses a hybrid local and cloud based parallel processing system that can return both satellite imagery and cloud product imagery as well as the binary data used to generate them in multiple formats. The images and cloud products are sourced from multiple satellites and also "merged" datasets created by temporally and spatially matching satellite sensors. Finally, the tool and API allow users to access information that spans the time ranges that our group has information available. In the case of satellite imagery, the temporal range can span the entire lifetime of the sensor.

  19. Evaluation of terrestrial photogrammetric point clouds derived from thermal imagery

    Science.gov (United States)

    Metcalf, Jeremy P.; Olsen, Richard C.

    2016-05-01

    Computer vision and photogrammetric techniques have been widely applied to digital imagery producing high density 3D point clouds. Using thermal imagery as input, the same techniques can be applied to infrared data to produce point clouds in 3D space, providing surface temperature information. The work presented here is an evaluation of the accuracy of 3D reconstruction of point clouds produced using thermal imagery. An urban scene was imaged over an area at the Naval Postgraduate School, Monterey, CA, viewing from above as with an airborne system. Terrestrial thermal and RGB imagery were collected from a rooftop overlooking the site using a FLIR SC8200 MWIR camera and a Canon T1i DSLR. In order to spatially align each dataset, ground control points were placed throughout the study area using Trimble R10 GNSS receivers operating in RTK mode. Each image dataset is processed to produce a dense point cloud for 3D evaluation.

  20. ACCURACY COMPARISON OF VHR SYSTEMATIC-ORTHO SATELLITE IMAGERIES AGAINST VHR ORTHORECTIFIED IMAGERIES USING GCP

    Directory of Open Access Journals (Sweden)

    E. Widyaningrum

    2016-06-01

    Full Text Available The Very High Resolution (VHR satellite imageries such us Pleiades, WorldView-2, GeoEye-1 used for precise mapping purpose must be corrected from any distortion to achieve the expected accuracy. Orthorectification is performed to eliminate geometric errors of the VHR satellite imageries. Orthorectification requires main input data such as Digital Elevation Model (DEM and Ground Control Point (GCP. The VHR systematic-ortho imageries were generated using SRTM 30m DEM without using any GCP data. The accuracy value differences of VHR systematic-ortho imageries and VHR orthorectified imageries using GCP currently is not exactly defined. This study aimed to identified the accuracy comparison of VHR systematic-ortho imageries against orthorectified imageries using GCP. Orthorectified imageries using GCP created by using Rigorous model. Accuracy evaluation is calculated by using several independent check points.

  1. Generative Street Addresses from Satellite Imagery

    Directory of Open Access Journals (Sweden)

    İlke Demir

    2018-03-01

    Full Text Available We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph- and proximity-based algorithms. We also extend our addressing scheme to (i cover inaccessible areas following similar design principles; (ii be inclusive and flexible for changes on the ground; and (iii lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions.

  2. Evaluation of Future Internet Technologies for Processing and Distribution of Satellite Imagery

    Science.gov (United States)

    Becedas, J.; Perez, R.; Gonzalez, G.; Alvarez, J.; Garcia, F.; Maldonado, F.; Sucari, A.; Garcia, J.

    2015-04-01

    Satellite imagery data centres are designed to operate a defined number of satellites. For instance, difficulties when new satellites have to be incorporated in the system appear. This occurs because traditional infrastructures are neither flexible nor scalable. With the appearance of Future Internet technologies new solutions can be provided to manage large and variable amounts of data on demand. These technologies optimize resources and facilitate the appearance of new applications and services in the traditional Earth Observation (EO) market. The use of Future Internet technologies for the EO sector were validated with the GEO-Cloud experiment, part of the Fed4FIRE FP7 European project. This work presents the final results of the project, in which a constellation of satellites records the whole Earth surface on a daily basis. The satellite imagery is downloaded into a distributed network of ground stations and ingested in a cloud infrastructure, where the data is processed, stored, archived and distributed to the end users. The processing and transfer times inside the cloud, workload of the processors, automatic cataloguing and accessibility through the Internet are evaluated to validate if Future Internet technologies present advantages over traditional methods. Applicability of these technologies is evaluated to provide high added value services. Finally, the advantages of using federated testbeds to carry out large scale, industry driven experiments are analysed evaluating the feasibility of an experiment developed in the European infrastructure Fed4FIRE and its migration to a commercial cloud: SoftLayer, an IBM Company.

  3. Commercial Satellite Imagery Analysis for Countering Nuclear Proliferation

    Science.gov (United States)

    Albright, David; Burkhard, Sarah; Lach, Allison

    2018-05-01

    High-resolution commercial satellite imagery from a growing number of private satellite companies allows nongovernmental analysts to better understand secret or opaque nuclear programs of countries in unstable or tense regions, called proliferant states. They include North Korea, Iran, India, Pakistan, and Israel. By using imagery to make these countries’ aims and capabilities more transparent, nongovernmental groups like the Institute for Science and International Security have affected the policies of governments and the course of public debate. Satellite imagery work has also strengthened the efforts of the International Atomic Energy Agency, thereby helping this key international agency build its case to mount inspections of suspect sites and activities. This work has improved assessments of the nuclear capabilities of proliferant states. Several case studies provide insight into the use of commercial satellite imagery as a key tool to educate policy makers and affect policy.

  4. Feature Detection Systems Enhance Satellite Imagery

    Science.gov (United States)

    2009-01-01

    In 1963, during the ninth orbit of the Faith 7 capsule, astronaut Gordon Cooper skipped his nap and took some photos of the Earth below using a Hasselblad camera. The sole flier on the Mercury-Atlas 9 mission, Cooper took 24 photos - never-before-seen images including the Tibetan plateau, the crinkled heights of the Himalayas, and the jagged coast of Burma. From his lofty perch over 100 miles above the Earth, Cooper noted villages, roads, rivers, and even, on occasion, individual houses. In 1965, encouraged by the effectiveness of NASA s orbital photography experiments during the Mercury and subsequent Gemini manned space flight missions, U.S. Geological Survey (USGS) director William Pecora put forward a plan for a remote sensing satellite program that would collect information about the planet never before attainable. By 1972, NASA had built and launched Landsat 1, the first in a series of Landsat sensors that have combined to provide the longest continuous collection of space-based Earth imagery. The archived Landsat data - 37 years worth and counting - has provided a vast library of information allowing not only the extensive mapping of Earth s surface but also the study of its environmental changes, from receding glaciers and tropical deforestation to urban growth and crop harvests. Developed and launched by NASA with data collection operated at various times by the Agency, the National Oceanic and Atmospheric Administration (NOAA), Earth Observation Satellite Company (EOSAT, a private sector partnership that became Space Imaging Corporation in 1996), and USGS, Landsat sensors have recorded flooding from Hurricane Katrina, the building boom in Dubai, and the extinction of the Aral Sea, offering scientists invaluable insights into the natural and manmade changes that shape the world. Of the seven Landsat sensors launched since 1972, Landsat 5 and Landsat 7 are still operational. Though both are in use well beyond their intended lifespans, the mid

  5. Detection of pear thrips damage using satellite imagery data

    Science.gov (United States)

    James E. Vogelmann; Barrett N. Rock

    1991-01-01

    This study evaluates the potential of measuring, mapping and monitoring sugar maple damage caused by pear thrips in southern Vermont and northwestern Massachusetts using satellite imagery data. Landsat Thematic Mapper (TM) data were obtained during a major thrips infestation in June 1988, and were compared with satellite data acquired during June 1984 (before pear...

  6. 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.

  7. Mapping Urban Tree Canopy Cover Using Fused Airborne LIDAR and Satellite Imagery Data

    Science.gov (United States)

    Parmehr, Ebadat G.; Amati, Marco; Fraser, Clive S.

    2016-06-01

    Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.

  8. Biomass burning - Combustion emissions, satellite imagery, and biogenic emissions

    Science.gov (United States)

    Levine, Joel S.; Cofer, Wesley R., III; Winstead, Edward L.; Rhinehart, Robert P.; Cahoon, Donald R., Jr.; Sebacher, Daniel I.; Sebacher, Shirley; Stocks, Brian J.

    1991-01-01

    After detailing a technique for the estimation of the instantaneous emission of trace gases produced by biomass burning, using satellite imagery, attention is given to the recent discovery that burning results in significant enhancement of biogenic emissions of N2O, NO, and CH4. Biomass burning accordingly has an immediate and long-term impact on the production of atmospheric trace gases. It is presently demonstrated that satellite imagery of fires may be used to estimate combustion emissions, and could be used to estimate long-term postburn biogenic emission of trace gases to the atmosphere.

  9. Burn severity mapping using simulation modeling and satellite imagery

    Science.gov (United States)

    Eva C. Karau; Robert E. Keane

    2010-01-01

    Although burn severity maps derived from satellite imagery provide a landscape view of fire impacts, fire effects simulation models can provide spatial fire severity estimates and add a biotic context in which to interpret severity. In this project, we evaluated two methods of mapping burn severity in the context of rapid post-fire assessment for four wildfires in...

  10. A Commercial Architecture for Satellite Imagery

    National Research Council Canada - National Science Library

    Didier, Christopher J

    2006-01-01

    .... This study focuses on the concept of the U.S. government purchasing proven and successful commercial satellites with minimal non-recurring engineering costs to help augment current national systems...

  11. Detecting long-duration cloud contamination in hyper-temporal NDVI imagery

    NARCIS (Netherlands)

    Ali, A.; de Bie, C.A.J.M.; Skidmore, A.K.

    2013-01-01

    Cloud contamination impacts on the quality of hyper-temporal NDVI imagery and its subsequent interpretation. Short-duration cloud impacts are easily removed by using quality flags and an upper envelope filter, but long-duration cloud contamination of NDVI imagery remains. In this paper, an approach

  12. Cloud Forecasting and 3-D Radiative Transfer Model Validation using Citizen-Sourced Imagery

    Science.gov (United States)

    Gasiewski, A. J.; Heymsfield, A.; Newman Frey, K.; Davis, R.; Rapp, J.; Bansemer, A.; Coon, T.; Folsom, R.; Pfeufer, N.; Kalloor, J.

    2017-12-01

    Cloud radiative feedback mechanisms are one of the largest sources of uncertainty in global climate models. Variations in local 3D cloud structure impact the interpretation of NASA CERES and MODIS data for top-of-atmosphere radiation studies over clouds. Much of this uncertainty results from lack of knowledge of cloud vertical and horizontal structure. Surface-based data on 3-D cloud structure from a multi-sensor array of low-latency ground-based cameras can be used to intercompare radiative transfer models based on MODIS and other satellite data with CERES data to improve the 3-D cloud parameterizations. Closely related, forecasting of solar insolation and associated cloud cover on time scales out to 1 hour and with spatial resolution of 100 meters is valuable for stabilizing power grids with high solar photovoltaic penetrations. Data for cloud-advection based solar insolation forecasting with requisite spatial resolution and latency needed to predict high ramp rate events obtained from a bottom-up perspective is strongly correlated with cloud-induced fluctuations. The development of grid management practices for improved integration of renewable solar energy thus also benefits from a multi-sensor camera array. The data needs for both 3D cloud radiation modelling and solar forecasting are being addressed using a network of low-cost upward-looking visible light CCD sky cameras positioned at 2 km spacing over an area of 30-60 km in size acquiring imagery on 30 second intervals. Such cameras can be manufactured in quantity and deployed by citizen volunteers at a marginal cost of 200-400 and operated unattended using existing communications infrastructure. A trial phase to understand the potential utility of up-looking multi-sensor visible imagery is underway within this NASA Citizen Science project. To develop the initial data sets necessary to optimally design a multi-sensor cloud camera array a team of 100 citizen scientists using self-owned PDA cameras is being

  13. Biomass burning: Combustion emissions, satellite imagery, and biogenic emissions

    International Nuclear Information System (INIS)

    Levine, J.S.; Cofer, W.R III; Rhinehart, R.P.; Cahoon, D.R. J.; Winstead, E.L.; Sebacher, S.; Sebacher, D.I.; Stocks, B.J.

    1991-01-01

    This chapter deals with two different, but related, aspects of biomass burning. The first part of the chapter deals with a technique to estimate the instantaneous emissions of trace gases produced by biomass burning using satellite imagery. The second part of the chapter concerns the recent discovery that burning results in significantly enhanced biogenic emissions of N 2 O, NO, and CH 4 . Hence, biomass burning has both an immediate and long-term impact on the production of trace gases to the atmosphere. The objective of this research is to better assess and quantify the role of this research is to better assess and quantify the role and impact of biomass as a driver for global change. It will be demonstrated that satellite imagery of fires may be used to estimate combustion emissions and may in the future be used to estimate the long-term postburn biogenic emissions of trace gases to the atmosphere

  14. IAEA Safeguards: Cost/benefit analysis of commercial satellite imagery

    International Nuclear Information System (INIS)

    Andersson, Christer

    1999-03-01

    A major milestone in the efforts to strengthen the Safeguards System was reached in May 1997 when the Board of Governors approved a 'Model Protocol Additional to Safeguards Agreements'. The Protocol provides the legal basis necessary to enhance the Agency's ability to detect undeclared nuclear material and activities by using information available from open sources to complement the declarations made by Member States. Commercially available high-resolution satellite data has emerged as one potential complementary open information source to support the traditional and extended Safeguard activities of IAEA. This document constitutes a first report from SSC Satellitbild giving the Agency tentative and initial estimates of the potential cost and time-savings possible with the new proposed technology. The initial cost/benefit simulation will be further finalised in the following 'Implementation Blueprint' study. The general foundation and starting point for the cost/benefit calculation is to simulate a new efficient and relatively small 'imagery unit' within the IAEA, capable of performing advanced image processing as a tool for various safeguards tasks. The image processing capacity is suggested to be task- and interpretation-oriented. The study was performed over a period of 1,5 weeks in late 1998, and is based upon interviews of IAEA staff, reviews of existing IAEA documentation as well as from SSC Satellitbild's long-standing experience of satellite imagery and field missions. The cost/benefit analysis is based on a spreadsheet simulation of five potential applications of commercial satellite imagery: Reference information; Confirmation of Agency acquired and Member State supplied data; Change detection and on-going monitoring; Assessing open source information available to the Agency; Detecting undeclared activities and undeclared sites. The study confirms that the proposed concept of a relatively small 'imagery unit' using high-resolution data will be a sound and

  15. IAEA Safeguards: Cost/benefit analysis of commercial satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Andersson, Christer [SSC Satellitbild AB, Kiruna (Sweden)

    1999-03-01

    A major milestone in the efforts to strengthen the Safeguards System was reached in May 1997 when the Board of Governors approved a `Model Protocol Additional to Safeguards Agreements`. The Protocol provides the legal basis necessary to enhance the Agency`s ability to detect undeclared nuclear material and activities by using information available from open sources to complement the declarations made by Member States. Commercially available high-resolution satellite data has emerged as one potential complementary open information source to support the traditional and extended Safeguard activities of IAEA. This document constitutes a first report from SSC Satellitbild giving the Agency tentative and initial estimates of the potential cost and time-savings possible with the new proposed technology. The initial cost/benefit simulation will be further finalised in the following `Implementation Blueprint` study. The general foundation and starting point for the cost/benefit calculation is to simulate a new efficient and relatively small `imagery unit` within the IAEA, capable of performing advanced image processing as a tool for various safeguards tasks. The image processing capacity is suggested to be task- and interpretation-oriented. The study was performed over a period of 1,5 weeks in late 1998, and is based upon interviews of IAEA staff, reviews of existing IAEA documentation as well as from SSC Satellitbild`s long-standing experience of satellite imagery and field missions. The cost/benefit analysis is based on a spreadsheet simulation of five potential applications of commercial satellite imagery: Reference information; Confirmation of Agency acquired and Member State supplied data; Change detection and on-going monitoring; Assessing open source information available to the Agency; Detecting undeclared activities and undeclared sites. The study confirms that the proposed concept of a relatively small `imagery unit` using high-resolution data will be a sound and

  16. VHR satellite imagery for humanitarian crisis management: a case study

    Science.gov (United States)

    Bitelli, Gabriele; Eleias, Magdalena; Franci, Francesca; Mandanici, Emanuele

    2017-09-01

    During the last years, remote sensing data along with GIS have been largely employed for supporting emergency management activities. In this context, the use of satellite images and derived map products has become more common also in the different phases of humanitarian crisis response. In this work very high resolution satellite imagery was processed to assess the evolution of Za'atari Refugee Camp, built in Jordan in 2012 by the UN Refugee Agency to host Syrian refugees. Multispectral satellite scenes of the Za'atari area were processed by means of object-based classifications. The main aim of the present work is the development of a semiautomated procedure for multi-temporal camp monitoring with particular reference to the dwellings detection. Whilst in the emergency mapping domain automation of feature extraction is widely investigated, in the field of humanitarian missions the information is often extracted by means of photointerpretation of the satellite data. This approach requires time for the interpretation; moreover, it is not reliable enough in complex situations, where features of interest are often small, heterogeneous and inconsistent. Therefore, the present paper discusses a methodology to obtain information for assisting humanitarian crisis management, using a semi-automatic classification approach applied to satellite imagery.

  17. Satellite Imagery Assisted Road-Based Visual Navigation System

    Science.gov (United States)

    Volkova, A.; Gibbens, P. W.

    2016-06-01

    There is a growing demand for unmanned aerial systems as autonomous surveillance, exploration and remote sensing solutions. Among the key concerns for robust operation of these systems is the need to reliably navigate the environment without reliance on global navigation satellite system (GNSS). This is of particular concern in Defence circles, but is also a major safety issue for commercial operations. In these circumstances, the aircraft needs to navigate relying only on information from on-board passive sensors such as digital cameras. An autonomous feature-based visual system presented in this work offers a novel integral approach to the modelling and registration of visual features that responds to the specific needs of the navigation system. It detects visual features from Google Earth* build a feature database. The same algorithm then detects features in an on-board cameras video stream. On one level this serves to localise the vehicle relative to the environment using Simultaneous Localisation and Mapping (SLAM). On a second level it correlates them with the database to localise the vehicle with respect to the inertial frame. The performance of the presented visual navigation system was compared using the satellite imagery from different years. Based on comparison results, an analysis of the effects of seasonal, structural and qualitative changes of the imagery source on the performance of the navigation algorithm is presented. * The algorithm is independent of the source of satellite imagery and another provider can be used

  18. Landslide detection using very high-resolution satellite imageries

    Science.gov (United States)

    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.

  19. Processing Satellite Imagery To Detect Waste Tire Piles

    Science.gov (United States)

    Skiles, Joseph; Schmidt, Cynthia; Wuinlan, Becky; Huybrechts, Catherine

    2007-01-01

    A methodology for processing commercially available satellite spectral imagery has been developed to enable identification and mapping of waste tire piles in California. The California Integrated Waste Management Board initiated the project and provided funding for the method s development. The methodology includes the use of a combination of previously commercially available image-processing and georeferencing software used to develop a model that specifically distinguishes between tire piles and other objects. The methodology reduces the time that must be spent to initially survey a region for tire sites, thereby increasing inspectors and managers time available for remediation of the sites. Remediation is needed because millions of used tires are discarded every year, waste tire piles pose fire hazards, and mosquitoes often breed in water trapped in tires. It should be possible to adapt the methodology to regions outside California by modifying some of the algorithms implemented in the software to account for geographic differences in spectral characteristics associated with terrain and climate. The task of identifying tire piles in satellite imagery is uniquely challenging because of their low reflectance levels: Tires tend to be spectrally confused with shadows and deep water, both of which reflect little light to satellite-borne imaging systems. In this methodology, the challenge is met, in part, by use of software that implements the Tire Identification from Reflectance (TIRe) model. The development of the TIRe model included incorporation of lessons learned in previous research on the detection and mapping of tire piles by use of manual/ visual and/or computational analysis of aerial and satellite imagery. The TIRe model is a computational model for identifying tire piles and discriminating between tire piles and other objects. The input to the TIRe model is the georeferenced but otherwise raw satellite spectral images of a geographic region to be surveyed

  20. Development of methods for inferring cloud thickness and cloud-base height from satellite radiance data

    Science.gov (United States)

    Smith, William L., Jr.; Minnis, Patrick; Alvarez, Joseph M.; Uttal, Taneil; Intrieri, Janet M.; Ackerman, Thomas P.; Clothiaux, Eugene

    1993-01-01

    Cloud-top height is a major factor determining the outgoing longwave flux at the top of the atmosphere. The downwelling radiation from the cloud strongly affects the cooling rate within the atmosphere and the longwave radiation incident at the surface. Thus, determination of cloud-base temperature is important for proper calculation of fluxes below the cloud. Cloud-base altitude is also an important factor in aircraft operations. Cloud-top height or temperature can be derived in a straightforward manner using satellite-based infrared data. Cloud-base temperature, however, is not observable from the satellite, but is related to the height, phase, and optical depth of the cloud in addition to other variables. This study uses surface and satellite data taken during the First ISCCP Regional Experiment (FIRE) Phase-2 Intensive Field Observation (IFO) period (13 Nov. - 7 Dec. 1991, to improve techniques for deriving cloud-base height from conventional satellite data.

  1. Automated Generation of the Alaska Coastline Using High-Resolution Satellite Imagery

    Science.gov (United States)

    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.

  2. Volumetric Forest Change Detection Through Vhr Satellite Imagery

    Science.gov (United States)

    Akca, Devrim; Stylianidis, Efstratios; Smagas, Konstantinos; Hofer, Martin; Poli, Daniela; Gruen, Armin; Sanchez Martin, Victor; Altan, Orhan; Walli, Andreas; Jimeno, Elisa; Garcia, Alejandro

    2016-06-01

    Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView - 3, SPOT - 5 HRS, SPOT - 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in

  3. 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

  4. Satellite Imagery Production and Processing Using Apache Hadoop

    Science.gov (United States)

    Hill, D. V.; Werpy, J.

    2011-12-01

    The United States Geological Survey's (USGS) Earth Resources Observation and Science (EROS) Center Land Science Research and Development (LSRD) project has devised a method to fulfill its processing needs for Essential Climate Variable (ECV) production from the Landsat archive using Apache Hadoop. Apache Hadoop is the distributed processing technology at the heart of many large-scale, processing solutions implemented at well-known companies such as Yahoo, Amazon, and Facebook. It is a proven framework and can be used to process petabytes of data on thousands of processors concurrently. It is a natural fit for producing satellite imagery and requires only a few simple modifications to serve the needs of science data processing. This presentation provides an invaluable learning opportunity and should be heard by anyone doing large scale image processing today. The session will cover a description of the problem space, evaluation of alternatives, feature set overview, configuration of Hadoop for satellite image processing, real-world performance results, tuning recommendations and finally challenges and ongoing activities. It will also present how the LSRD project built a 102 core processing cluster with no financial hardware investment and achieved ten times the initial daily throughput requirements with a full time staff of only one engineer. Satellite Imagery Production and Processing Using Apache Hadoop is presented by David V. Hill, Principal Software Architect for USGS LSRD.

  5. Classification of Clouds and Deep Convection from GEOS-5 Using Satellite Observations

    Science.gov (United States)

    Putman, William; Suarez, Max

    2010-01-01

    With the increased resolution of global atmospheric models and the push toward global cloud resolving models, the resemblance of model output to satellite observations has become strikingly similar. As we progress with our adaptation of the Goddard Earth Observing System Model, Version 5 (GEOS-5) as a high resolution cloud system resolving model, evaluation of cloud properties and deep convection require in-depth analysis beyond a visual comparison. Outgoing long-wave radiation (OLR) provides a sufficient comparison with infrared (IR) satellite imagery to isolate areas of deep convection. We have adopted a binning technique to generate a series of histograms for OLR which classify the presence and fraction of clear sky versus deep convection in the tropics that can be compared with a similar analyses of IR imagery from composite Geostationary Operational Environmental Satellite (GOES) observations. We will present initial results that have been used to evaluate the amount of deep convective parameterization required within the model as we move toward cloud system resolving resolutions of 10- to 1-km globally.

  6. Daytime Low Stratiform Cloud Detection on AVHRR Imagery

    Directory of Open Access Journals (Sweden)

    Jan Pawel Musial

    2014-06-01

    Full Text Available The near-real time retrieval of low stratiform cloud (LSC coverage is of vital interest for such disciplines as meteorology, transport safety, economy and air quality. Within this scope, a novel methodology is proposed which provides the LSC occurrence probability estimates for a satellite scene. The algorithm is suited for the 1 × 1 km Advanced Very High Resolution Radiometer (AVHRR data and was trained and validated against collocated SYNOP observations. Utilisation of these two combined data sources requires a formulation of constraints in order to discriminate cases where the LSC is overlaid by higher clouds. The LSC classification process is based on six features which are first converted to the integer form by step functions and combined by means of bitwise operations. Consequently, a set of values reflecting a unique combination of those features is derived which is further employed to extract the LSC occurrence probability estimates from the precomputed look-up vectors (LUV. Although the validation analyses confirmed good performance of the algorithm, some inevitable misclassification with other optically thick clouds were reported. Moreover, the comparison against Polar Platform System (PPS cloud-type product revealed superior classification accuracy. From the temporal perspective, the acquired results reported a presence of diurnal and annual LSC probability cycles over Europe.

  7. Comparison of Cloud Properties from CALIPSO-CloudSat and Geostationary Satellite Data

    Science.gov (United States)

    Nguyen, L.; Minnis, P.; Chang, F.; Winker, D.; Sun-Mack, S.; Spangenberg, D.; Austin, R.

    2007-01-01

    Cloud properties are being derived in near-real time from geostationary satellite imager data for a variety of weather and climate applications and research. Assessment of the uncertainties in each of the derived cloud parameters is essential for confident use of the products. Determination of cloud amount, cloud top height, and cloud layering is especially important for using these real -time products for applications such as aircraft icing condition diagnosis and numerical weather prediction model assimilation. Furthermore, the distribution of clouds as a function of altitude has become a central component of efforts to evaluate climate model cloud simulations. Validation of those parameters has been difficult except over limited areas where ground-based active sensors, such as cloud radars or lidars, have been available on a regular basis. Retrievals of cloud properties are sensitive to the surface background, time of day, and the clouds themselves. Thus, it is essential to assess the geostationary satellite retrievals over a variety of locations. The availability of cloud radar data from CloudSat and lidar data from CALIPSO make it possible to perform those assessments over each geostationary domain at 0130 and 1330 LT. In this paper, CloudSat and CALIPSO data are matched with contemporaneous Geostationary Operational Environmental Satellite (GOES), Multi-functional Transport Satellite (MTSAT), and Meteosat-8 data. Unlike comparisons with cloud products derived from A-Train imagers, this study considers comparisons of nadir active sensor data with off-nadir retrievals. These matched data are used to determine the uncertainties in cloud-top heights and cloud amounts derived from the geostationary satellite data using the Clouds and the Earth s Radiant Energy System (CERES) cloud retrieval algorithms. The CERES multi-layer cloud detection method is also evaluated to determine its accuracy and limitations in the off-nadir mode. The results will be useful for

  8. 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

  9. Hurricane Satellite (HURSAT) from International Satellite Cloud Climatology Project (ISCCP) B1, Version 6

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Hurricane Satellite (HURSAT) from derived International Satellite Cloud Climatology Project (ISCCP) B1 observations of tropical cyclones worldwide. The B1 data...

  10. Users, uses, and value of Landsat satellite imagery: results from the 2012 survey of users

    Science.gov (United States)

    Miller, Holly M.; Richardson, Leslie A.; Koontz, Stephen R.; Loomis, John; Koontz, Lynne

    2013-01-01

    Landsat satellites have been operating since 1972, providing a continuous global record of the Earth’s land surface. The imagery is currently available at no cost through the U.S. Geological Survey (USGS). Social scientists at the USGS Fort Collins Science Center conducted an extensive survey in early 2012 to explore who uses Landsat imagery, how they use the imagery, and what the value of the imagery is to them. The survey was sent to all users registered with USGS who had accessed Landsat imagery in the year prior to the survey and over 11,000 current Landsat imagery users responded. The results of the survey revealed that respondents from many sectors use Landsat imagery in myriad project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance of and dependence on the imagery, the numerous environmental and societal benefits observed from projects using Landsat imagery, the potential negative impacts on users’ work if Landsat imagery was no longer available, and the substantial aggregated annual economic benefit from the imagery. These results represent only the value of Landsat to users registered with USGS; further research would help to determine what the value of the imagery is to a greater segment of the population, such as downstream users of the imagery and imagery-derived products.

  11. Surface Characteristics of Green Island Wakes from Satellite Imagery

    Science.gov (United States)

    Cheng, Kai-Ho; Hsu, Po-Chun; Ho, Chung-Ru

    2017-04-01

    Characteristics of an island wake induced by the Kuroshio Current flows pass by Green Island, a small island 40 km off southeast of Taiwan is investigated by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. The MODIS sea surface temperature (SST) and chlorophyll-a (chl-a) imagery is produced at 250-meter resolution from 2014 to 2015 using the SeaDAS software package which is developed by the National Aeronautics and Space Administration. The wake occurrence is 59% observed from SST images during the data span. The average cooling area is 190 km2, but the area is significantly changed with wind directions. The wake area is increased during southerly winds and is reduced during northerly winds. Besides, the average cooling SST was about 2.1 oC between the front and rear island. Comparing the temperature difference between the wake and its left side, the difference is 1.96 oC. In addition, the wakes have 1 3 times higher than normal in chlorophyll concentration. The results indicate the island mass effect makes the surface water of Green island wake colder and chl-a higher.

  12. Study of cloud properties using airborne and satellite measurements

    Science.gov (United States)

    Boscornea, Andreea; Stefan, Sabina; Vajaiac, Sorin Nicolae

    2014-08-01

    The present study investigates cloud microphysics properties using aircraft and satellite measurements. Cloud properties were drawn from data acquired both from in situ measurements with state of the art airborne instrumentation and from satellite products of the MODIS06 System. The used aircraft was ATMOSLAB - Airborne Laboratory for Environmental Atmospheric Research, property of the National Institute for Aerospace Research "Elie Carafoli" (INCAS), Bucharest, Romania, which is specially equipped for this kind of research. The main tool of the airborne laboratory is a Cloud, Aerosol and Precipitation Spectrometer - CAPS (30 bins, 0.51- 50 μm). The data was recorded during two flights during the winter 2013-2014, over a flat region in the south-eastern part of Romania (between Bucharest and Constanta). The analysis of cloud particle size variations and cloud liquid water content provided by CAPS can explain cloud processes, and can also indicate the extent of aerosols effects on clouds. The results, such as cloud coverage and/or cloud types, microphysical parameters of aerosols on the one side and the cloud microphysics parameters obtained from aircraft flights on the other side, was used to illustrate the importance of microphysics cloud properties for including the radiative effects of clouds in the regional climate models.

  13. Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers

    Science.gov (United States)

    Rosenfeld, Daniel; Zheng, Youtong; Hashimshoni, Eyal; Pöhlker, Mira L.; Jefferson, Anne; Pöhlker, Christopher; Yu, Xing; Zhu, Yannian; Liu, Guihua; Yue, Zhiguo; Fischman, Baruch; Li, Zhanqing; Giguzin, David; Goren, Tom; Artaxo, Paulo; Pöschl, Ulrich

    2016-01-01

    Quantifying the aerosol/cloud-mediated radiative effect at a global scale requires simultaneous satellite retrievals of cloud condensation nuclei (CCN) concentrations and cloud base updraft velocities (Wb). Hitherto, the inability to do so has been a major cause of high uncertainty regarding anthropogenic aerosol/cloud-mediated radiative forcing. This can be addressed by the emerging capability of estimating CCN and Wb of boundary layer convective clouds from an operational polar orbiting weather satellite. Our methodology uses such clouds as an effective analog for CCN chambers. The cloud base supersaturation (S) is determined by Wb and the satellite-retrieved cloud base drop concentrations (Ndb), which is the same as CCN(S). Validation against ground-based CCN instruments at Oklahoma, at Manaus, and onboard a ship in the northeast Pacific showed a retrieval accuracy of ±25% to ±30% for individual satellite overpasses. The methodology is presently limited to boundary layer not raining convective clouds of at least 1 km depth that are not obscured by upper layer clouds, including semitransparent cirrus. The limitation for small solar backscattering angles of <25° restricts the satellite coverage to ∼25% of the world area in a single day. PMID:26944081

  14. Upper atmospheric gravity wave details revealed in nightglow satellite imagery

    Science.gov (United States)

    Miller, Steven D.; Straka, William C.; Yue, Jia; Smith, Steven M.; Alexander, M. Joan; Hoffmann, Lars; Setvák, Martin; Partain, Philip T.

    2015-01-01

    Gravity waves (disturbances to the density structure of the atmosphere whose restoring forces are gravity and buoyancy) comprise the principal form of energy exchange between the lower and upper atmosphere. Wave breaking drives the mean upper atmospheric circulation, determining boundary conditions to stratospheric processes, which in turn influence tropospheric weather and climate patterns on various spatial and temporal scales. Despite their recognized importance, very little is known about upper-level gravity wave characteristics. The knowledge gap is mainly due to lack of global, high-resolution observations from currently available satellite observing systems. Consequently, representations of wave-related processes in global models are crude, highly parameterized, and poorly constrained, limiting the description of various processes influenced by them. Here we highlight, through a series of examples, the unanticipated ability of the Day/Night Band (DNB) on the NOAA/NASA Suomi National Polar-orbiting Partnership environmental satellite to resolve gravity structures near the mesopause via nightglow emissions at unprecedented subkilometric detail. On moonless nights, the Day/Night Band observations provide all-weather viewing of waves as they modulate the nightglow layer located near the mesopause (∼90 km above mean sea level). These waves are launched by a variety of physical mechanisms, ranging from orography to convection, intensifying fronts, and even seismic and volcanic events. Cross-referencing the Day/Night Band imagery with conventional thermal infrared imagery also available helps to discern nightglow structures and in some cases to attribute their sources. The capability stands to advance our basic understanding of a critical yet poorly constrained driver of the atmospheric circulation. PMID:26630004

  15. Comparisons of Satellite-Deduced Overlapping Cloud Properties and CALIPSO CloudSat Data

    Science.gov (United States)

    Chang, Fu-Lung; Minnis, Patrick; Lin, Bing; Sun-Mack, Sunny

    2010-01-01

    Introduction to the overlapped cloud properties derived from polar-orbiting (MODIS) and geostationary (GOES-12, -13, Meteosat-8, -9, etc.) meteorological satellites, which are produced at the NASA Langley Research Center (LaRC) cloud research & development team (NASA lead scientist: Dr. Patrick Minnis). Comparison of the LaRC CERES MODIS Edition-3 overlapped cloud properties to the CALIPSO and the CloudSat active sensing data. High clouds and overlapped clouds occur frequently as deduced by CALIPSO (44 & 25%), CloudSat (25 & 4%), and MODIS (37 & 6%). Large fractions of optically-thin cirrus and overlapped clouds are deduced from CALIPSO, but much smaller fractions are from CloudSat and MODIS. For overlapped clouds, the averaged upper-layer CTHs are about 12.8 (CALIPSO), 10.9 (CloudSat) and 10 km (MODIS), and the averaged lower-layer CTHs are about 3.6 (CALIPSO), 3.2 (CloudSat) and 3.9 km (MODIS). Based on comparisons of upper and lower-layer cloud properties as deduced from the MODIS, CALIPSO and CloudSat data, more enhanced passive satellite methods for retrieving thin cirrus and overlapped cloud properties are needed and are under development.

  16. Validation of Satellite Derived Cloud Properties Over the Southeastern Pacific

    Science.gov (United States)

    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.

  17. DEVELOPMENT OF IMPROVED TECHNIQUES FOR SATELLITE REMOTE SENSING OF CLOUDS AND RADIATION USING ARM DATA, FINAL REPORT

    Energy Technology Data Exchange (ETDEWEB)

    Minnis, Patrick [NASA Langley Research Center, Hampton, VA

    2013-06-28

    During the period, March 1997 – February 2006, the Principal Investigator and his research team co-authored 47 peer-reviewed papers and presented, at least, 138 papers at conferences, meetings, and workshops that were supported either in whole or in part by this agreement. We developed a state-of-the-art satellite cloud processing system that generates cloud properties over the Atmospheric Radiation (ARM) surface sites and surrounding domains in near-real time and outputs the results on the world wide web in image and digital formats. When the products are quality controlled, they are sent to the ARM archive for further dissemination. These products and raw satellite images can be accessed at http://cloudsgate2.larc.nasa.gov/cgi-bin/site/showdoc?docid=4&cmd=field-experiment-homepage&exp=ARM and are used by many in the ARM science community. The algorithms used in this system to generate cloud properties were validated and improved by the research conducted under this agreement. The team supported, at least, 11 ARM-related or supported field experiments by providing near-real time satellite imagery, cloud products, model results, and interactive analyses for mission planning, execution, and post-experiment scientific analyses. Comparisons of cloud properties derived from satellite, aircraft, and surface measurements were used to evaluate uncertainties in the cloud properties. Multiple-angle satellite retrievals were used to determine the influence of cloud structural and microphysical properties on the exiting radiation field.

  18. Detection of Tropical Overshooting Cloud Tops Using Himawari-8 Imagery

    Directory of Open Access Journals (Sweden)

    Miae Kim

    2017-07-01

    Full Text Available Abstract: Overshooting convective cloud Top (OT-accompanied clouds can cause severe weather conditions, such as lightning, strong winds, and heavy rainfall. The distribution and behavior of OTs can affect regional and global climate systems. In this paper, we propose a new approach for OT detection by using machine learning methods with multiple infrared images and their derived features. Himawari-8 satellite images were used as the main input data, and binary detection (OT or nonOT with class probability was the output of the machine learning models. Three machine learning techniques—random forest (RF, extremely randomized trees (ERT, and logistic regression (LR—were used to develop OT classification models to distinguish OT from non-OT. The hindcast validation over the Southeast Asia and West Pacific regions showed that RF performed best, resulting in a mean probabilities of detection (POD of 77.06% and a mean false alarm ratio (FAR of 36.13%. Brightness temperature at 11.2 μm (Tb11 and its standard deviation (STD in a 3 × 3 window size were identified as the most contributing variables for discriminating OT and nonOT classes. The proposed machine learning-based OT detection algorithms produced promising results comparable to or even better than the existing approaches, which are the infrared window (IRW-texture and water vapor (WV minus IRW brightness temperature difference (BTD methods.

  19. Earth mapping - aerial or satellite imagery comparative analysis

    Science.gov (United States)

    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.

  20. 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.

  1. An Automatic Cloud Detection Method for ZY-3 Satellite

    Directory of Open Access Journals (Sweden)

    CHEN Zhenwei

    2015-03-01

    Full Text Available Automatic cloud detection for optical satellite remote sensing images is a significant step in the production system of satellite products. For the browse images cataloged by ZY-3 satellite, the tree discriminate structure is adopted to carry out cloud detection. The image was divided into sub-images and their features were extracted to perform classification between clouds and grounds. However, due to the high complexity of clouds and surfaces and the low resolution of browse images, the traditional classification algorithms based on image features are of great limitations. In view of the problem, a prior enhancement processing to original sub-images before classification was put forward in this paper to widen the texture difference between clouds and surfaces. Afterwards, with the secondary moment and first difference of the images, the feature vectors were extended in multi-scale space, and then the cloud proportion in the image was estimated through comprehensive analysis. The presented cloud detection algorithm has already been applied to the ZY-3 application system project, and the practical experiment results indicate that this algorithm is capable of promoting the accuracy of cloud detection significantly.

  2. AUTOMATIC CLOUD DETECTION FROM MULTI-TEMPORAL SATELLITE IMAGES: TOWARDS THE USE OF PLÉIADES TIME SERIES

    Directory of Open Access Journals (Sweden)

    N. Champion

    2012-08-01

    Full Text Available Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, developed at the French Mapping agency. Our approach is based on the availability of multi-temporal satellite images (i.e. time series that generally contain between 5 and 10 images and is based on a region-growing procedure. Seeds (corresponding to clouds are firstly extracted through a pixel-to-pixel comparison between the images contained in time series (the presence of a cloud is here assumed to be related to a high variation of reflectance between two images. Clouds are then delineated finely using a dedicated region-growing algorithm. The method, originally designed for panchromatic SPOT5-HRS images, is tested in this paper using time series with 9 multi-temporal satellite images. Our preliminary experiments show the good performances of our method. In a near future, the method will be applied to Pléiades images, acquired during the in-flight commissioning phase of the satellite (launched at the end of 2011. In that context, this is a particular goal of this paper to show to which extent and in which way our method can be adapted to this kind of imagery.

  3. Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery. Caribbean Journal of Science. 44(2):175-198.

    Science.gov (United States)

    E.H. Helmer; T.A. Kennaway; D.H. Pedreros; M.L. Clark; H. Marcano-Vega; L.L. Tieszen; S.R. Schill; C.M.S. Carrington

    2008-01-01

    Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius...

  4. Nearshore Benthic Habitats of Timor-Leste Derived from WorldView-2 Satellite Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Benthic habitat classes were derived for nearshore waters (< 20 m depths) around Timor-Leste from DigitalGlobe WorldView-2 satellite imagery, acquired from Jan 26...

  5. Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

    Data.gov (United States)

    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...

  6. The development of a land use inventory for regional planning using satellite imagery

    Science.gov (United States)

    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.

  7. Integrating satellite imagery with simulation modeling to improve burn severity mapping

    Science.gov (United States)

    Eva C. Karau; Pamela G. Sikkink; Robert E. Keane; Gregory K. Dillon

    2014-01-01

    Both satellite imagery and spatial fire effects models are valuable tools for generating burn severity maps that are useful to fire scientists and resource managers. The purpose of this study was to test a new mapping approach that integrates imagery and modeling to create more accurate burn severity maps. We developed and assessed a statistical model that combines the...

  8. A Study on the Use of Commercial Satellite Imagery for Monitoring of Yongbyon Nuclear Activities

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jung Hyun; Kim, Min Soo [Korea Institute of Nuclear Nonproliferation and Control Daejeon (Korea, Republic of)

    2014-10-15

    It is particularly useful for the areas that are hard to access, such as the DPRK. On April 2009, North Korea expelled IAEA inspectors and USA disabling team at Yongbyon. Since then, there is not much left except for satellite imagery analysis. In this paper, we focused on the growing role and importance of commercial satellite imagery analysis for detecting and identifying nuclear activities at Yongbyon. For this, we examined monitoring capability of commercial satellite imagery status of commercial satellite imagery analysis to monitor the Yongbyon nuclear site. And we suggested several recommendations for enhancing the monitoring and analyzing capability. Current commercial satellite imagery has proven effective in monitoring for Yongbyon nuclear activities, especially change detection including the new construction activities. But identification and technical analysis of the operation status is still limited. In case of North Korea, operation status of 5 MWe reactor should be clearly identified to assess its plutonium production capability and to set up the negotiation strategy. To enhance the monitoring capability, we need much more thermal infrared imagery and radar imagery.

  9. Vessel and oil spill early detection using COSMO satellite imagery

    Science.gov (United States)

    Revollo, Natalia V.; Delrieux, Claudio A.

    2017-10-01

    Oil spillage is one of the most common sources of environmental damage in places where coastal wild life is found in natural reservoirs. This is especially the case in the Patagonian coast, with a littoral more than 5000 km long and a surface above a million and half square km. In addition, furtive fishery activities in Argentine waters are depleting the food supplies of several species, altering the ecological equilibrium. For this reason, early oil spills and vessel detection is an imperative surveillance task for environmental and governmental authorities. However, given the huge geographical extension, human assisted monitoring is unfeasible, and therefore real time remote sensing technologies are the only operative and economically feasible solution. In this work we describe the theoretical foundations and implementation details of a system specifically designed to take advantage of the SAR imagery delivered by two satellite constellations (the SAOCOM mission, developed by the Argentine Space Agency, and the COSMO mission, developed by the Italian Space Agency), to provide real-time detection of vessels and oil spills. The core of the system is based on pattern recognition over a statistical characterization of the texture patterns arising in the positive and negative conditions (i.e., vessel, oil, or plain sea surfaces). Training patterns were collected from a large number of previously reported contacts tagged by experts in the National Commission on Space Activities (CONAE). The resulting system performs well above the sensitivity and specificity of other avalilable systems.

  10. Properties of CIRRUS Overlapping Clouds as Deduced from the GOES-12 Imagery Data

    Science.gov (United States)

    Chang, Fu-Lung; Minnis, Patrick; Lin, Bing; Sun-Mack, Sunny; Khaiyer, Mandana

    2006-01-01

    Understanding the impact of cirrus clouds on modifying both the solar reflected and terrestrial emitted radiations is crucial for climate studies. Unlike most boundary layer stratus and stratocumulus clouds that have a net cooling effect on the climate, high-level thin cirrus clouds can have a warming effect on our climate. Many research efforts have been devoted to retrieving cirrus cloud properties due to their ubiquitous presence. However, using satellite observations to detect and/or retrieve cirrus cloud properties faces two major challenges. First, they are often semitransparent at visible to infrared wavelengths; and secondly, they often occur over a lower cloud system. The overlapping of high-level cirrus and low-level stratus cloud poses a difficulty in determining the individual cloud top altitudes and optical properties, especially when the signals from cirrus clouds are overwhelmed by the signals of stratus clouds. Moreover, the operational satellite retrieval algorithms, which often assume only single layer cloud in the development of cloud retrieval techniques, cannot resolve the cloud overlapping situation properly. The new geostationary satellites, starting with the Twelfth Geostationary Operational Environmental Satellite (GOES-12), are providing a new suite of imager bands that have replaced the conventional 12-micron channel with a 13.3-micron CO2 absorption channel. The replacement of the 13.3-micron channel allows for the application of a CO2-slicing retrieval technique (Chahine et al. 1974; Smith and Platt 1978), which is one of the important passive satellite methods for remote sensing the altitudes of mid to high-level clouds. Using the CO2- slicing technique is more effective in detecting semitransparent cirrus clouds than using the conventional infrared-window method.

  11. Development and Validation of Improved Techniques for Cloud Property Retrieval from Environmental Satellites

    National Research Council Canada - National Science Library

    Gustafson, Gary

    2000-01-01

    ...) develop extensible cloud property retrieval algorithms suitable for expanding existing cloud analysis capabilities to utilize data from new and future environmental satellite sensing systems; (2...

  12. Defense Meteorological Satellite Program (DMSP)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Defense Meteorological Satellite Program (DMSP) satellites collect visible and infrared cloud imagery as well as monitoring the atmospheric, oceanographic,...

  13. Satellite remote sensing of aerosol and cloud properties over Eurasia

    Science.gov (United States)

    Sogacheva, Larisa; Kolmonen, Pekka; Saponaro, Giulia; Virtanen, Timo; Rodriguez, Edith; Sundström, Anu-Maija; Atlaskina, Ksenia; de Leeuw, Gerrit

    2015-04-01

    Satellite remote sensing provides the spatial distribution of aerosol and cloud properties over a wide area. In our studies large data sets are used for statistical studies on aerosol and cloud interaction in an area over Fennoscandia, the Baltic Sea and adjacent regions over the European mainland. This area spans several regimes with different influences on aerosol cloud interaction such as a the transition from relative clean air over Fennoscandia to more anthropogenically polluted air further south, and the influence maritime air over the Baltic and oceanic air advected from the North Atlantic. Anthropogenic pollution occurs in several parts of the study area, and in particular near densely populated areas and megacities, but also in industrialized areas and areas with dense traffic. The aerosol in such areas is quite different from that produced over the boreal forest and has different effects on air quality and climate. Studies have been made on the effects of aerosols on air quality and on the radiation balance in China. The aim of the study is to study the effect of these different regimes on aerosol-cloud interaction using a large aerosol and cloud data set retrieved with the (Advanced) Along Track Scanning Radiometer (A)ATSR Dual View algorithm (ADV) further developed at Finnish Meteorological Institute and aerosol and cloud data provided by MODIS. Retrieval algorithms for aerosol and clouds have been developed for the (A)ATSR, consisting of a series of instruments of which we use the second and third one: ATSR-2 which flew on the ERS-2 satellite (1995-2003) and AATSR which flew on the ENVISAT satellite (2002-2012) (both from the European Space Agency, ESA). The ADV algorithm provides aerosol data on a global scale with a default resolution of 10x10km2 (L2) and an aggregate product on 1x1 degree (L3). Optional, a 1x1 km2 retrieval products is available over smaller areas for specific studies. Since for the retrieval of AOD no prior knowledge is needed on

  14. Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

    Science.gov (United States)

    Su, X.

    2017-12-01

    A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

  15. Verifying Air Force Weather Passive Satellite Derived Cloud Analysis Products

    Science.gov (United States)

    Nobis, T. E.

    2017-12-01

    Air Force Weather (AFW) has developed an hourly World-Wide Merged Cloud Analysis (WWMCA) using imager data from 16 geostationary and polar-orbiting satellites. The analysis product contains information on cloud fraction, height, type and various optical properties including optical depth and integrated water path. All of these products are derived using a suite of algorithms which rely exclusively on passively sensed data from short, mid and long wave imager data. The system integrates satellites with a wide-range of capabilities, from the relatively simple two-channel OLS imager to the 16 channel ABI/AHI to create a seamless global analysis in real time. Over the last couple of years, AFW has started utilizing independent verification data from active sensed cloud measurements to better understand the performance limitations of the WWMCA. Sources utilized include space based lidars (CALIPSO, CATS) and radar (CloudSat) as well as ground based lidars from the Department of Energy ARM sites and several European cloud radars. This work will present findings from our efforts to compare active and passive sensed cloud information including comparison techniques/limitations as well as performance of the passive derived cloud information against the active.

  16. Estimation of daily global solar irradiation by coupling ground measurements of bright sunshine hours to satellite imagery

    International Nuclear Information System (INIS)

    Ener Rusen, Selmin; Hammer, Annette; Akinoglu, Bulent G.

    2013-01-01

    In this work, the current version of the satellite-based HELIOSAT method and ground-based linear Ångström–Prescott type relations are used in combination. The first approach is based on the use of a correlation between daily bright sunshine hours (s) and cloud index (n). In the second approach a new correlation is proposed between daily solar irradiation and daily data of s and n which is based on a physical parameterization. The performances of the proposed two combined models are tested against conventional methods. We test the use of obtained correlation coefficients for nearby locations. Our results show that the use of sunshine duration together with the cloud index is quite satisfactory in the estimation of daily horizontal global solar irradiation. We propose to use the new approaches to estimate daily global irradiation when the bright sunshine hours data is available for the location of interest, provided that some regression coefficients are determined using the data of a nearby station. In addition, if surface data for a close location does not exist then it is recommended to use satellite models like HELIOSAT or the new approaches instead the Ångström type models. - Highlights: • Satellite imagery together with surface measurements in solar radiation estimation. • The new coupled and conventional models (satellite and ground-based) are analyzed. • New models result in highly accurate estimation of daily global solar irradiation

  17. Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX radiation panel

    OpenAIRE

    Stubenrauch , C.J.; Rossow , W.B.; Kinne , S.; Ackerman , S.; Cesana , G.; Chepfer , H.; Di Girolamo , L.; Getzewich , B.; Guignard , A.; Heidinger , A.; Maddux , B.C.; Menzel , W.P.; Minnis , P.; Pearl , C.; Platnick , S.

    2013-01-01

    International audience; The Global Energy and Water Cycle Experiment (GEWEX) Radiation Panel initiated the GEWEX Cloud Assessment in 2005 to compare available, global, long-term cloud data products with the International Satellite Cloud Climatology Project (ISCCP). The GEWEX Cloud Assessment database included cloud properties retrieved from different satellite sensor measurements, taken at various local times and over various time periods. The relevant passive satellite sensors measured radia...

  18. High-resolution satellite imagery is an important yet underutilized resource in conservation biology.

    Science.gov (United States)

    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.

  19. ASSESSMENT OF ATMOSPHERIC CORRECTION METHODS FOR OPTIMIZING HAZY SATELLITE IMAGERIES

    Directory of Open Access Journals (Sweden)

    Umara Firman Rizidansyah

    2015-04-01

    Full Text Available The purpose of this research is to examine suitability of three types of haze correction methods toward distinctness of surface objects in land cover. Considering the formation of haze therefore the main research are divided into both region namely rural assumed as vegetation and urban assumed as non vegetation area. Region of interest for rural selected Balaraja and urban selected Penjaringan. Haze imagery reduction utilized techniques such as Dark Object Substration, Virtual Cloud Point and Histogram Match. By applying an equation of Haze Optimized Transformation HOT = DNbluesin(∂-DNredcos(∂, the main result of this research includes: in the case of AVNIR-Rural, VCP has good results on Band 1 while the HM has good results on band 2, 3 and 4, therefore in the case of Avnir-Rural can be applied to HM. in the case of AVNIR-Urban, DOS has good result on band 1, 2 and 3 meanwhile HM has good results on band 4, therefore in the case of AVNIR-Urban can be applied to DOS. In the case of Landsat-Rural, DOS has good result on band 1, 2 and 6 meanwhile VCP has good results on band 4 and 5 and the smallest average value of HOT is 106.547 by VCP, therefore in the case of Lansat-Rural can be applied to DOS and VCP. In the case of Landsat-Urban, DOS has good result on band 1, 2 and 6 meanwhile VCP has good results on band 3, 4 and 5, therefore in the case of Landsat-Urban can be applied to VCP.   Tujuan penelitian ini untuk menguji kesesuaian tiga jenis metode koreksi haze terhadap kejelasan obyek permukaan di wilayah tutupan vegetasi dan non vegetasi, berkenaan menghilangkan haze di wilayah citra satelit optis yang memiliki karakteristik tertentu dan diduga proses pembentukan partikel hazenya berbeda. Sehingga daerah penelitian dibagi menjadi wilayah rural yang diasumsikan sebagai daerah vegetasi dan urban sebagai non vegetasi. Pedesaan terpilih kecamatan Balaraja dan Perkotaan terpilih kecamatan Penjaringan. Tiap lokasi menggunakan Avnir-2 dan Landsat

  20. Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations

    Directory of Open Access Journals (Sweden)

    Ana F. Militino

    2018-03-01

    Full Text Available Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI and the day and night land surface temperature (LST of composite images from Navarre (Spain between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE, calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST, but reductions of up to 20% are achieved with the new proposal.

  1. CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    K.-Y. Lee

    2016-06-01

    Full Text Available Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012 uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate

  2. Cloud Detection of Optical Satellite Images Using Support Vector Machine

    Science.gov (United States)

    Lee, Kuan-Yi; Lin, Chao-Hung

    2016-06-01

    Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection

  3. Automated cloud screening of AVHRR imagery using split-and-merge clustering

    Science.gov (United States)

    Gallaudet, Timothy C.; Simpson, James J.

    1991-01-01

    Previous methods to segment clouds from ocean in AVHRR imagery have shown varying degrees of success, with nighttime approaches being the most limited. An improved method of automatic image segmentation, the principal component transformation split-and-merge clustering (PCTSMC) algorithm, is presented and applied to cloud screening of both nighttime and daytime AVHRR data. The method combines spectral differencing, the principal component transformation, and split-and-merge clustering to sample objectively the natural classes in the data. This segmentation method is then augmented by supervised classification techniques to screen clouds from the imagery. Comparisons with other nighttime methods demonstrate its improved capability in this application. The sensitivity of the method to clustering parameters is presented; the results show that the method is insensitive to the split-and-merge thresholds.

  4. Identification of High-Variation Fields based on Open Satellite Imagery

    DEFF Research Database (Denmark)

    Jeppesen, Jacob Høxbroe; Jacobsen, Rune Hylsberg; Nyholm Jørgensen, Rasmus

    2017-01-01

    . The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update......This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective...

  5. The Potential Uses of Commercial Satellite Imagery in the Middle East

    Energy Technology Data Exchange (ETDEWEB)

    Vannoni, M.G.

    1999-06-08

    It became clear during the workshop that the applicability of commercial satellite imagery to the verification of future regional arms control agreements is limited at this time. Non-traditional security topics such as environmental protection, natural resource management, and the development of infrastructure offer the more promising applications for commercial satellite imagery in the short-term. Many problems and opportunities in these topics are regional, or at least multilateral, in nature. A further advantage is that, unlike arms control and nonproliferation applications, cooperative use of imagery in these topics can be done independently of the formal Middle East Peace Process. The value of commercial satellite imagery to regional arms control and nonproliferation, however, will increase during the next three years as new, more capable satellite systems are launched. Aerial imagery, such as that used in the Open Skies Treaty, can also make significant contributions to both traditional and non-traditional security applications but has the disadvantage of requiring access to national airspace and potentially higher cost. There was general consensus that commercial satellite imagery is under-utilized in the Middle East and resources for remote sensing, both human and institutional, are limited. This relative scarcity, however, provides a natural motivation for collaboration in non-traditional security topics. Collaborations between scientists, businesses, universities, and non-governmental organizations can work at the grass-roots level and yield contributions to confidence building as well as scientific and economic results. Joint analysis projects would benefit the region as well as establish precedents for cooperation.

  6. The employment of weather satellite imagery in an effort to identify and locate the forest-tundra ecotone in Canada

    Science.gov (United States)

    Aldrich, S. A.; Aldrich, F. T.; Rudd, R. D.

    1969-01-01

    Weather satellite imagery provides the only routinely available orbital imagery depicting the high latitudes. Although resolution is low on this imagery, it is believed that a major natural feature, notably linear in expression, should be mappable on it. The transition zone from forest to tundra, the ecotone, is such a feature. Locational correlation is herein established between a linear signature on the imagery and several ground truth positions of the ecotone in Canada.

  7. Unveiling aerosol-cloud interactions - Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

    Science.gov (United States)

    Christensen, Matthew W.; Neubauer, David; Poulsen, Caroline A.; Thomas, Gareth E.; McGarragh, Gregory R.; Povey, Adam C.; Proud, Simon R.; Grainger, Roy G.

    2017-11-01

    Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud-aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (-0.28±0.26 W m-2) compared to those including pairs that are within 15 km of the nearest cloud (-0.49±0.18 W m-2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.

  8. "Data Day" and "Data Night" Definitions - Towards Producing Seamless Global Satellite Imagery

    Science.gov (United States)

    Schmaltz, J. E.

    2017-12-01

    For centuries, the art and science of cartography has struggled with the challenge of mapping the round earth on to a flat page, or a flat computer monitor. Earth observing satellites with continuous monitoring of our planet have added the additional complexity of the time dimension to this procedure. The most common current practice is to segment this data by 24-hour Coordinated Universal Time (UTC) day and then split the day into sun side "Data Day" and shadow side "Data Night" global imagery that spans from dateline to dateline. Due to the nature of satellite orbits, simply binning the data by UTC date produces significant discontinuities at the dateline for day images and at Greenwich for night images. Instead, imagery could be generated in a fashion that follows the spatial and temporal progression of the satellite which would produce seamless imagery everywhere on the globe for all times. This presentation will explore approaches to produce such imagery but will also address some of the practical and logistical difficulties in implementing such changes. Topics will include composites versus granule/orbit based imagery, day/night versus ascending/descending definitions, and polar versus global projections.

  9. Vegetation extraction from high-resolution satellite imagery using the Normalized Difference Vegetation Index (NDVI)

    Science.gov (United States)

    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

  10. Normalization and calibration of geostationary satellite radiances for the International Satellite Cloud Climatology Project

    Science.gov (United States)

    Desormeaux, Yves; Rossow, William B.; Brest, Christopher L.; Campbell, G. G.

    1993-01-01

    Procedures are described for normalizing the radiometric calibration of image radiances obtained from geostationary weather satellites that contributed data to the International Satellite Cloud Climatology Project. The key step is comparison of coincident and collocated measurements made by each satellite and the concurrent AVHRR on the 'afternoon' NOAA polar-orbiting weather satellite at the same viewing geometry. The results of this comparison allow transfer of the AVHRR absolute calibration, which has been established over the whole series, to the radiometers on the geostationary satellites. Results are given for Meteosat-2, 3, and 4, for GOES-5, 6, and 7, for GMS-2, 3, and 4 and for Insat-1B. The relative stability of the calibrations of these radiance data is estimated to be within +/- 3 percent; the uncertainty of the absolute calibrations is estimated to be less than 10 percent. The remaining uncertainties are at least two times smaller than for the original radiance data.

  11. NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 2+ Cloud and Moisture Imagery Products (CMIP)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Cloud and Moisture Imagery product contains one or more Earth-view images with pixel values identifying brightness values that are scaled to support visual...

  12. 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.

  13. Using satellite imagery to assess the influence of urban development on the impacts of extreme rainfall

    DEFF Research Database (Denmark)

    Kaspersen, Per Skougaard; Drews, Martin; Madsen, Henrik

    We investigate the applicability of medium resolution Landsat satellite imagery for mapping temporal changes in urban land cover for direct use in urban flood models. The overarching aim is to provide accurate and cost- and resource-efficient quantification of temporal changes in risk towards...

  14. Improved wetland classification using eight-band high-resolution satellite imagery and a hybrid approach

    Science.gov (United States)

    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...

  15. A data mining approach for sharpening satellite thermal imagery over land

    Science.gov (United States)

    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...

  16. Cloud-shadow removal for Unmanned Aerial System multispectral imagery based on tensor decomposition methods

    DEFF Research Database (Denmark)

    Baum, Andreas; Wang, Sheng; Garcia, Monica

    2017-01-01

    that are mosaicked into larger images to produce ortho-photomaps. Frequently, especially in northern latitudes, the images to be mosaicked have been acquired under varying irradiance conditions due to moving clouds that create artifacts in the detected signal unrelated to physical changes in vegetation properties......, this study succeeded to remove the cloud shadow effects and image noise in UAS imagery providing normalized reflectance. The comparison between the corrected and un-corrected images shows a significant improvement for reflectance estimation in the shadow areas. Further, analysis of vegetation indices e...

  17. Phase 2 Final Report. IAEA Safeguards: Implementation blueprint of commercial satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Andersson, Christer [SSC Satellitbild AB, Solna (Sweden)

    2000-01-01

    This document - IAEA Safeguards: Implementation Blueprint of Commercial Satellite Imagery - constitutes the second report from SSC Satellitbild giving a structured view and solid guidelines on how to proceed with a conceivable implementation of satellite imagery to support Safeguards activities of the Agency. This Phase 2 report presents a large number of concrete recommendations regarding suggested management issues, work organisation, imagery purchasing and team building. The study has also resulted in several lists of actions and preliminary project plans with GANT schedules concerning training, hardware and software, as well as for the initial pilot studies. In both the Phase 1 and Phase 2 studies it is confirmed that the proposed concept of a relatively small Imagery Unit using high-resolution data will be a sound and feasible undertaking. Such a unit capable of performing advanced image processing as a tool for various safeguard tasks will give the Agency an effective instrument for reference, monitoring, verification, and detection of declared and undeclared activities. The total cost for implementing commercial satellite imagery at the Department for Safeguards, as simulated in these studies, is approximately MUSD 1,5 per year. This cost is founded on an activity scenario with a staff of 4 experts working in an IAEA Imagery Unit with a workload of three dossiers or issues per week. The imagery unit is built around an advanced PC image processing system capable of handling several hundreds of pre-processed images per year. Alternatively a Reduced Scenario with a staff of 3 would need a budget of approximately MUSD 0,9 per year, whereas an Enhanced Imagery Unit including 5 experts and a considerably enlarged capacity would cost MUSD 1,7 per year. The Imagery Unit should be organised so it clearly reflects the objectives and role as set by the Member States and the management of the Agency. We recommend the Imagery Unit to be organised into four main work

  18. Phase 2 Final Report. IAEA Safeguards: Implementation blueprint of commercial satellite imagery

    International Nuclear Information System (INIS)

    Andersson, Christer

    2000-01-01

    This document - IAEA Safeguards: Implementation Blueprint of Commercial Satellite Imagery - constitutes the second report from SSC Satellitbild giving a structured view and solid guidelines on how to proceed with a conceivable implementation of satellite imagery to support Safeguards activities of the Agency. This Phase 2 report presents a large number of concrete recommendations regarding suggested management issues, work organisation, imagery purchasing and team building. The study has also resulted in several lists of actions and preliminary project plans with GANT schedules concerning training, hardware and software, as well as for the initial pilot studies. In both the Phase 1 and Phase 2 studies it is confirmed that the proposed concept of a relatively small Imagery Unit using high-resolution data will be a sound and feasible undertaking. Such a unit capable of performing advanced image processing as a tool for various safeguard tasks will give the Agency an effective instrument for reference, monitoring, verification, and detection of declared and undeclared activities. The total cost for implementing commercial satellite imagery at the Department for Safeguards, as simulated in these studies, is approximately MUSD 1,5 per year. This cost is founded on an activity scenario with a staff of 4 experts working in an IAEA Imagery Unit with a workload of three dossiers or issues per week. The imagery unit is built around an advanced PC image processing system capable of handling several hundreds of pre-processed images per year. Alternatively a Reduced Scenario with a staff of 3 would need a budget of approximately MUSD 0,9 per year, whereas an Enhanced Imagery Unit including 5 experts and a considerably enlarged capacity would cost MUSD 1,7 per year. The Imagery Unit should be organised so it clearly reflects the objectives and role as set by the Member States and the management of the Agency. We recommend the Imagery Unit to be organised into four main work

  19. Type-Dependent Responses of Ice Cloud Properties to Aerosols From Satellite Retrievals

    Science.gov (United States)

    Zhao, Bin; Gu, Yu; Liou, Kuo-Nan; Wang, Yuan; Liu, Xiaohong; Huang, Lei; Jiang, Jonathan H.; Su, Hui

    2018-04-01

    Aerosol-cloud interactions represent one of the largest uncertainties in external forcings on our climate system. Compared with liquid clouds, the observational evidence for the aerosol impact on ice clouds is much more limited and shows conflicting results, partly because the distinct features of different ice cloud and aerosol types were seldom considered. Using 9-year satellite retrievals, we find that, for convection-generated (anvil) ice clouds, cloud optical thickness, cloud thickness, and cloud fraction increase with small-to-moderate aerosol loadings (types provide valuable constraints on the modeling assessment of aerosol-ice cloud radiative forcing.

  20. An evaluation of the use of ERTS-1 satellite imagery for grizzly bear habitat analysis

    Science.gov (United States)

    Varney, J. R.; Craighead, J. J.; Sumner, J.

    1973-01-01

    Multispectral scanner images taken by the ERTS-1 satellite in August and October, 1972, were examined to determine if they would be useful in identifying and mapping favorable habitat for grizzly bears. It was possible to identify areas having a suitable mixture of alpine meadow and timber, and to eliminate those which did not meet the isolation requirements of grizzlies because of farming or grazing activity. High altitude timbered areas mapped from satellite imagery agreed reasonably well with the distribution of whitebark pine, an important food species. Analysis of satellite imagery appears to be a valuable supplement to present ground observation methods, since it allows the most important areas to be identified for intensive study and many others to be eliminated from consideration. A sampling plan can be developed from such data which will minimize field effort and overall program cost.

  1. Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Zhenfeng Shao

    2017-03-01

    Full Text Available Cloud detection of remote sensing imagery is quite challenging due to the influence of complicated underlying surfaces and the variety of cloud types. Currently, most of the methods mainly rely on prior knowledge to extract features artificially for cloud detection. However, these features may not be able to accurately represent the cloud characteristics under complex environment. In this paper, we adopt an innovative model named Fuzzy Autoencode Model (FAEM to integrate the feature learning ability of stacked autoencode networks and the detection ability of fuzzy function for highly accurate cloud detection on remote sensing imagery. Our proposed method begins by selecting and fusing spectral, texture, and structure information. Thereafter, the proposed technique established a FAEM to learn the deep discriminative features from a great deal of selected information. Finally, the learned features are mapped to the corresponding cloud density map with a fuzzy function. To demonstrate the effectiveness of the proposed method, 172 Landsat ETM+ images and 25 GF-1 images with different spatial resolutions are used in this paper. For the convenience of accuracy assessment, ground truth data are manually outlined. Results show that the average RER (ratio of right rate and error rate on Landsat images is greater than 29, while the average RER of Support Vector Machine (SVM is 21.8 and Random Forest (RF is 23. The results on GF-1 images exhibit similar performance as Landsat images with the average RER of 25.9, which is much higher than the results of SVM and RF. Compared to traditional methods, our technique has attained higher average cloud detection accuracy for either different spatial resolutions or various land surfaces.

  2. Modelling avian biodiversity using raw, unclassified satellite imagery.

    Science.gov (United States)

    St-Louis, Véronique; Pidgeon, Anna M; Kuemmerle, Tobias; Sonnenschein, Ruth; Radeloff, Volker C; Clayton, Murray K; Locke, Brian A; Bash, Dallas; Hostert, Patrick

    2014-01-01

    Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.

  3. Study of the Nevada Test Site using Landsat satellite imagery

    International Nuclear Information System (INIS)

    Zimmerman, P.D.

    1993-07-01

    In the period covered by the purchase order CSIS has obtained one Landsat image and determined that two images previously supplied to the principal investigator under a subcontract with George Washington University were inherently defective. We have negotiated with EOSAT over the reprocessing of those scenes and anticipate final delivery within the next few weeks. A critical early purchase during the subcontract period was of an EXABYTE tape drive, Adaptec SCSI interface, and the appropriate software with which to read Landsat images at CSIS. This gives us the capability of reading and manipulating imagery in house without reliance on outside services which have not proven satisfactory. In addition to obtaining imagery for the study, we have also performed considerable analytic work on the newly and previously purchased images. A technique developed under an earlier subcontract for identifying underground nuclear tests at Pahute Mesa has been significantly refined, and similar techniques were applied to the summit of Rainier Mesa and to the Yucca Flats area. An entirely new technique for enhancing the spectral signatures of different regions of NTS was recently developed, and appears to have great promise of success

  4. The Application of the Technology of 3D Satellite Cloud Imaging in Virtual Reality Simulation

    Directory of Open Access Journals (Sweden)

    Xiao-fang Xie

    2007-05-01

    Full Text Available Using satellite cloud images to simulate clouds is one of the new visual simulation technologies in Virtual Reality (VR. Taking the original data of satellite cloud images as the source, this paper depicts specifically the technology of 3D satellite cloud imaging through the transforming of coordinates and projection, creating a DEM (Digital Elevation Model of cloud imaging and 3D simulation. A Mercator projection was introduced to create a cloud image DEM, while solutions for geodetic problems were introduced to calculate distances, and the outer-trajectory science of rockets was introduced to obtain the elevation of clouds. For demonstration, we report on a computer program to simulate the 3D satellite cloud images.

  5. International Satellite Cloud Climatology Project (ISCCP) Climate Data Record, H-Series

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The International Satellite Cloud Climatology Project (ISCCP) focuses on the distribution and variation of cloud radiative properties to improve the understanding of...

  6. Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

    Science.gov (United States)

    Kit, Oleksandr; Lüdeke, Matthias

    2013-09-01

    This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

  7. Cloud and Thermodynamic Parameters Retrieved from Satellite Ultraspectral Infrared Measurements

    Science.gov (United States)

    Zhou, Daniel K.; Smith, William L.; Larar, Allen M.; Liu, Xu; Taylor, Jonathan P.; Schluessel, Peter; Strow, L. Larrabee; Mango, Stephen A.

    2008-01-01

    Atmospheric-thermodynamic parameters and surface properties are basic meteorological parameters for weather forecasting. A physical geophysical parameter retrieval scheme dealing with cloudy and cloud-free radiance observed with satellite ultraspectral infrared sounders has been developed and applied to the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric InfraRed Sounder (AIRS). The retrieved parameters presented herein are from radiance data gathered during the Joint Airborne IASI Validation Experiment (JAIVEx). JAIVEx provided intensive aircraft observations obtained from airborne Fourier Transform Spectrometer (FTS) systems, in-situ measurements, and dedicated dropsonde and radiosonde measurements for the validation of the IASI products. Here, IASI atmospheric profile retrievals are compared with those obtained from dedicated dropsondes, radiosondes, and the airborne FTS system. The IASI examples presented here demonstrate the ability to retrieve fine-scale horizontal features with high vertical resolution from satellite ultraspectral sounder radiance spectra.

  8. Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV Imagery

    Directory of Open Access Journals (Sweden)

    Arko Lucieer

    2012-05-01

    Full Text Available Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from 50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion can be monitored.

  9. Evidence of transport of hazy air masses from satellite imagery

    International Nuclear Information System (INIS)

    Lyons, W.A.

    1980-01-01

    Some observations of major aerosol events in the atmosphere by meteorological satellites are reviewed. The events included a massive plume of smoke from a Hawaiian volcanic eruption, dust plumes originating from the Sahara Desert and the central U.S., smoke from a small forest fire, and sulfate aerosol hazes. It is concluded that the routine detection and tracking of synoptic-scale pollution episodes, along with quantitative measurements of their intensity, are entirely feasible with existing spacecraft and data analysis systems

  10. Diurnal changes in ocean color sensed in satellite imagery

    Science.gov (United States)

    Arnone, Robert; Vandermuelen, Ryan; Soto, Inia; Ladner, Sherwin; Ondrusek, Michael; Yang, Haoping

    2017-07-01

    Measurements of diurnal changes in ocean color in turbid coastal regions in the Gulf of Mexico were characterized using above water spectral radiometry from a National Aeronautics and Space Administration (aerosol robotic network-WaveCIS CSI-06) site that can provide 8 to 10 observations per day. Satellite capability to detect diurnal changes in ocean color was characterized using hourly overlapping afternoon orbits of the visual infrared imaging radiometer suite (VIIRS) Suomi National Polar-orbiting Partnership ocean color sensor and validated with in situ observations. The monthly cycle of diurnal changes was investigated for different water masses using VIIRS overlaps. Results showed the capability of satellite observations to monitor hourly color changes in coastal regions that can be impacted by vertical movement of optical layers, in response to tides, resuspension, and river plume dispersion. The spatial variability of VIIRS diurnal changes showed the occurrence and displacement of phytoplankton blooming and decaying processes. The diurnal change in ocean color was above 20%, which represents a 30% change in chlorophyll-a. Seasonal changes in diurnal ocean color for different water masses suggest differences in summer and winter responses to surface processes. The diurnal changes observed using satellite ocean color can be used to define the following: surface processes associated with biological activity, vertical changes in optical depth, and advection of water masses.

  11. Modelling tick abundance using machine learning techniques and satellite imagery

    DEFF Research Database (Denmark)

    Kjær, Lene Jung; Korslund, L.; Kjelland, V.

    satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

  12. Compressing interpreted satellite imagery for geographic information systems applications over extensive regions

    Science.gov (United States)

    Miller, Stephan W.

    1981-01-01

    Image processing systems (IPS) and techniques effectively transform satellite imagery into data for input into a spatial database. Geographic information systems (GIS), consisting of graphic input and spatial database management subsystems, are capable of processing digital map and map overlay data to build and manipulate a spatial database. These systems can be successfully integrated to create a successful spatial data handling capability provided certain obstacle are understood and overcome.

  13. Geospatial Information from Satellite Imagery for Geovisualisation of Smart Cities in India

    Science.gov (United States)

    Mohan, M.

    2016-06-01

    In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

  14. GEOSPATIAL INFORMATION FROM SATELLITE IMAGERY FOR GEOVISUALISATION OF SMART CITIES IN INDIA

    Directory of Open Access Journals (Sweden)

    M. Mohan

    2016-06-01

    Full Text Available In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

  15. Polar bears from space: Assessing satellite imagery as a tool to track Arctic wildlife

    Science.gov (United States)

    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.

  16. Satellite Imagery and In-situ Data Overlay Approach for Fishery Zonation

    Directory of Open Access Journals (Sweden)

    Fardhi Adria

    2010-12-01

    Full Text Available Remote sensing technology can be used to better understand the earth’s characteristics. SeaWiFS (sea-viewing wide field-of-view sensor is one of remote sensors used to observe global ocean phenomena. Previous studies showed that the distribution of chlorophyll-a in the ocean indicates the presence of fish. However, only a few studies tried to directly relate the chlorophyll-a distribution obtained through interpretation of satellite imagery to in-situ data of fish distribution. This paper investigates the relation between chlorophyll-a distribution and fish-capturing points in Aceh Province sea waters using overlay image analysis. The results are then used to identify the potential fishing ground in Aceh. The profile of chlorophyll-a concentration is derived from SeaWIFS satellite imagery. Fish-capturing points data is obtained from the fisherman communities of Banda Aceh, starting from June to November 2008. The results showed that the chlorophyll-a profile derived from satellite imagery has a positive relationship to fish-capturing point data. The most potential fish-capturing zone in Aceh sea waters is identified at 5-8º north latitude (N and 96-99º east longitude (E.

  17. 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.

  18. Polar bears from space: assessing satellite imagery as a tool to track Arctic wildlife.

    Science.gov (United States)

    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.

  19. PLASTIC AND GLASS GREENHOUSES DETECTION AND DELINEATION FROM WORLDVIEW-2 SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    D. Koc-San

    2016-06-01

    Full Text Available Greenhouse detection using remote sensing technologies is an important research area for yield estimation, sustainable development, urban and rural planning and management. An approach was developed in this study for the detection and delineation of greenhouse areas from high resolution satellite imagery. Initially, the candidate greenhouse patches were detected using supervised classification techniques. For this purpose, Maximum Likelihood (ML, Random Forest (RF, and Support Vector Machines (SVM classification techniques were applied and compared. Then, sieve filter and morphological operations were performed for improving the classification results. Finally, the obtained candidate plastic and glass greenhouse areas were delineated using boundary tracing and Douglas Peucker line simplification algorithms. The proposed approach was implemented in the Kumluca district of Antalya, Turkey utilizing pan-sharpened WorldView-2 satellite imageries. Kumluca is the prominent district of Antalya with greenhouse cultivation and includes both plastic and glass greenhouses intensively. When the greenhouse classification results were analysed, it can be stated that the SVM classification provides most accurate results and RF classification follows this. The SVM classification overall accuracy was obtained as 90.28%. When the greenhouse boundary delineation results were considered, the plastic greenhouses were delineated with 92.11% accuracy, while glass greenhouses were delineated with 80.67% accuracy. The obtained results indicate that, generally plastic and glass greenhouses can be detected and delineated successfully from WorldView-2 satellite imagery.

  20. Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

    Directory of Open Access Journals (Sweden)

    M. C. Anderson

    2011-01-01

    Full Text Available Thermal infrared (TIR remote sensing of land-surface temperature (LST provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa

  1. Optical and Physical Methods for Mapping Flooding with Satellite Imagery

    Science.gov (United States)

    Fayne, Jessica Fayne; Bolten, John; Lakshmi, Venkat; Ahamed, Aakash

    2016-01-01

    Flood and surface water mapping is becoming increasingly necessary, as extreme flooding events worldwide can damage crop yields and contribute to billions of dollars economic damages as well as social effects including fatalities and destroyed communities (Xaio et al. 2004; Kwak et al. 2015; Mueller et al. 2016).Utilizing earth observing satellite data to map standing water from space is indispensable to flood mapping for disaster response, mitigation, prevention, and warning (McFeeters 1996; Brakenridge and Anderson 2006). Since the early 1970s(Landsat, USGS 2013), researchers have been able to remotely sense surface processes such as extreme flood events to help offset some of these problems. Researchers have demonstrated countless methods and modifications of those methods to help increase knowledge of areas at risk and areas that are flooded using remote sensing data from optical and radar systems, as well as free publically available and costly commercial datasets.

  2. Towards 3D Matching of Point Clouds Derived from Oblique and Nadir Airborne Imagery

    Science.gov (United States)

    Zhang, Ming

    Because of the low-expense high-efficient image collection process and the rich 3D and texture information presented in the images, a combined use of 2D airborne nadir and oblique images to reconstruct 3D geometric scene has a promising market for future commercial usage like urban planning or first responders. The methodology introduced in this thesis provides a feasible way towards fully automated 3D city modeling from oblique and nadir airborne imagery. In this thesis, the difficulty of matching 2D images with large disparity is avoided by grouping the images first and applying the 3D registration afterward. The procedure starts with the extraction of point clouds using a modified version of the RIT 3D Extraction Workflow. Then the point clouds are refined by noise removal and surface smoothing processes. Since the point clouds extracted from different image groups use independent coordinate systems, there are translation, rotation and scale differences existing. To figure out these differences, 3D keypoints and their features are extracted. For each pair of point clouds, an initial alignment and a more accurate registration are applied in succession. The final transform matrix presents the parameters describing the translation, rotation and scale requirements. The methodology presented in the thesis has been shown to behave well for test data. The robustness of this method is discussed by adding artificial noise to the test data. For Pictometry oblique aerial imagery, the initial alignment provides a rough alignment result, which contains a larger offset compared to that of test data because of the low quality of the point clouds themselves, but it can be further refined through the final optimization. The accuracy of the final registration result is evaluated by comparing it to the result obtained from manual selection of matched points. Using the method introduced, point clouds extracted from different image groups could be combined with each other to build a

  3. Polar clouds and radiation in satellite observations, reanalyses, and climate models

    NARCIS (Netherlands)

    Lenaerts, JTM; Van Tricht, Kristof; Lhermitte, S.L.M.; L'Ecuyer, T.S.

    2017-01-01

    Clouds play a pivotal role in the surface energy budget of the polar regions. Here we use two largely independent data sets of cloud and surface downwelling radiation observations derived by satellite remote sensing (2007–2010) to evaluate simulated clouds and radiation over both polar ice sheets

  4. 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

  5. Creating soil moisture maps based on radar satellite imagery

    Science.gov (United States)

    Hnatushenko, Volodymyr; Garkusha, Igor; Vasyliev, Volodymyr

    2017-10-01

    The presented work is related to a study of mapping soil moisture basing on radar data from Sentinel-1 and a test of adequacy of the models constructed on the basis of data obtained from alternative sources. Radar signals are reflected from the ground differently, depending on its properties. In radar images obtained, for example, in the C band of the electromagnetic spectrum, soils saturated with moisture usually appear in dark tones. Although, at first glance, the problem of constructing moisture maps basing on radar data seems intuitively clear, its implementation on the basis of the Sentinel-1 data on an industrial scale and in the public domain is not yet available. In the process of mapping, for verification of the results, measurements of soil moisture obtained from logs of the network of climate stations NOAA US Climate Reference Network (USCRN) were used. This network covers almost the entire territory of the United States. The passive microwave radiometers of Aqua and SMAP satellites data are used for comparing processing. In addition, other supplementary cartographic materials were used, such as maps of soil types and ready moisture maps. The paper presents a comparison of the effect of the use of certain methods of roughening the quality of radar data on the result of mapping moisture. Regression models were constructed showing dependence of backscatter coefficient values Sigma0 for calibrated radar data of different spatial resolution obtained at different times on soil moisture values. The obtained soil moisture maps of the territories of research, as well as the conceptual solutions about automation of operations of constructing such digital maps, are presented. The comparative assessment of the time required for processing a given set of radar scenes with the developed tools and with the ESA SNAP product was carried out.

  6. Novel method of drizzle formation observation at large horizontal scales using multi-wavelength satellite imagery simulation

    NARCIS (Netherlands)

    Stepanov, I.; Russchenberg, H.W.J.

    2014-01-01

    The observations of on-board satellite imaging radiometers are representative of a far-reaching two-dimensional cloud top properties, however with a cutback in the capacity of profiling the cloud vertically. A combination of simulated radiances calculated at the top of the cloud in the near-infrared

  7. Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery

    Science.gov (United States)

    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.

  8. Analysis on the Utility of Satellite Imagery for Detection of Agricultural Facility

    Science.gov (United States)

    Kang, J.-M.; Baek, S.-H.; Jung, K.-Y.

    2012-07-01

    Now that the agricultural facilities are being increase owing to development of technology and diversification of agriculture and the ratio of garden crops that are imported a lot and the crops cultivated in facilities are raised in Korea, the number of vinyl greenhouses is tending upward. So, it is important to grasp the distribution of vinyl greenhouses as much as that of rice fields, dry fields and orchards, but it is difficult to collect the information of wide areas economically and correctly. Remote sensing using satellite imagery is able to obtain data of wide area at the same time, quickly and cost-effectively collect, monitor and analyze information from every object on earth. In this study, in order to analyze the utilization of satellite imagery at detection of agricultural facility, image classification was performed about the agricultural facility, vinyl greenhouse using Formosat-2 satellite imagery. The training set of sea, vegetation, building, bare ground and vinyl greenhouse was set to monitor the agricultural facilities of the object area and the training set for the vinyl greenhouses that are main monitoring object was classified and set again into 3 types according the spectral characteristics. The image classification using 4 kinds of supervise classification methods applied by the same training set were carried out to grasp the image classification method which is effective for monitoring agricultural facilities. And, in order to minimize the misclassification appeared in the classification using the spectral information, the accuracy of classification was intended to be raised by adding texture information. The results of classification were analyzed regarding the accuracy comparing with that of naked-eyed detection. As the results of classification, the method of Mahalanobis distance was shown as more efficient than other methods and the accuracy of classification was higher when adding texture information. Hence the more effective

  9. ANALYSIS ON THE UTILITY OF SATELLITE IMAGERY FOR DETECTION OF AGRICULTURAL FACILITY

    Directory of Open Access Journals (Sweden)

    J.-M. Kang

    2012-07-01

    Full Text Available Now that the agricultural facilities are being increase owing to development of technology and diversification of agriculture and the ratio of garden crops that are imported a lot and the crops cultivated in facilities are raised in Korea, the number of vinyl greenhouses is tending upward. So, it is important to grasp the distribution of vinyl greenhouses as much as that of rice fields, dry fields and orchards, but it is difficult to collect the information of wide areas economically and correctly. Remote sensing using satellite imagery is able to obtain data of wide area at the same time, quickly and cost-effectively collect, monitor and analyze information from every object on earth. In this study, in order to analyze the utilization of satellite imagery at detection of agricultural facility, image classification was performed about the agricultural facility, vinyl greenhouse using Formosat-2 satellite imagery. The training set of sea, vegetation, building, bare ground and vinyl greenhouse was set to monitor the agricultural facilities of the object area and the training set for the vinyl greenhouses that are main monitoring object was classified and set again into 3 types according the spectral characteristics. The image classification using 4 kinds of supervise classification methods applied by the same training set were carried out to grasp the image classification method which is effective for monitoring agricultural facilities. And, in order to minimize the misclassification appeared in the classification using the spectral information, the accuracy of classification was intended to be raised by adding texture information. The results of classification were analyzed regarding the accuracy comparing with that of naked-eyed detection. As the results of classification, the method of Mahalanobis distance was shown as more efficient than other methods and the accuracy of classification was higher when adding texture information. Hence the more

  10. AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    P. Fischer

    2018-04-01

    Full Text Available This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

  11. Testing methods for using high-resolution satellite imagery to monitor polar bear abundance and distribution

    Science.gov (United States)

    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.

  12. Testing methods for using high-resolution satellite imagery to monitor polar bear abundance and distribution

    Science.gov (United States)

    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.

  13. Satellite remote sensing of dust aerosol indirect effects on ice cloud formation.

    Science.gov (United States)

    Ou, Steve Szu-Cheng; Liou, Kuo-Nan; Wang, Xingjuan; Hansell, Richard; Lefevre, Randy; Cocks, Stephen

    2009-01-20

    We undertook a new approach to investigate the aerosol indirect effect of the first kind on ice cloud formation by using available data products from the Moderate-Resolution Imaging Spectrometer (MODIS) and obtained physical understanding about the interaction between aerosols and ice clouds. Our analysis focused on the examination of the variability in the correlation between ice cloud parameters (optical depth, effective particle size, cloud water path, and cloud particle number concentration) and aerosol optical depth and number concentration that were inferred from available satellite cloud and aerosol data products. Correlation results for a number of selected scenes containing dust and ice clouds are presented, and dust aerosol indirect effects on ice clouds are directly demonstrated from satellite observations.

  14. Assessing Field-Specific Risk of Soybean Sudden Death Syndrome Using Satellite Imagery in Iowa.

    Science.gov (United States)

    Yang, S; Li, X; Chen, C; Kyveryga, P; Yang, X B

    2016-08-01

    Moderate resolution imaging spectroradiometer (MODIS) satellite imagery from 2004 to 2013 were used to assess the field-specific risks of soybean sudden death syndrome (SDS) caused by Fusarium virguliforme in Iowa. Fields with a high frequency of significant decrease (>10%) of the normalized difference vegetation index (NDVI) observed in late July to middle August on historical imagery were hypothetically considered as high SDS risk. These high-risk fields had higher slopes and shorter distances to flowlines, e.g., creeks and drainages, particularly in the Des Moines lobe. Field data in 2014 showed a significantly higher SDS level in the high-risk fields than fields selected without considering NDVI information. On average, low-risk fields had 10 times lower F. virguliforme soil density, determined by quantitative polymerase chain reaction, compared with other surveyed fields. Ordinal logistic regression identified positive correlations between SDS and slope, June NDVI, and May maximum temperature, but high June maximum temperature hindered SDS. A modeled SDS risk map showed a clear trend of potential disease occurrences across Iowa. Landsat imagery was analyzed similarly, to discuss the ability to utilize higher spatial resolution data. The results demonstrated the great potential of both MODIS and Landsat imagery for SDS field-specific risk assessment.

  15. Method for validating cloud mask obtained from satellite measurements using ground-based sky camera.

    Science.gov (United States)

    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.

  16. Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel

    Science.gov (United States)

    Stubenrauch, C. J.; Rossow, W. B.; Kinne, S.; Ackerman, S.; Cesana, G.; Chepfer, H.; Getzewich, B.; Di Girolamo, L.; Guignard, A.; Heidinger, A.; hide

    2012-01-01

    Clouds cover about 70% of the Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the whole globe and across the wide range of spatial and temporal scales that comprise weather and climate variability. Satellite cloud data records now exceed more than 25 years in length. However, climatologies compiled from different satellite datasets can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors. The Global Energy and Water cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel, provided the first coordinated intercomparison of publically available, standard global cloud products (gridded, monthly statistics) retrieved from measurements of multi-spectral imagers (some with multiangle view and polarization capabilities), IR sounders and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature or altitude), cloud radiative properties (optical depth or emissivity), cloud thermodynamic phase and bulk microphysical properties (effective particle size and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. A monthly, gridded database, in common format, facilitates further assessments, climate studies and the evaluation of climate models.

  17. Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

    Directory of Open Access Journals (Sweden)

    Dimitris G. Stavrakoudis

    2014-07-01

    Full Text Available This study investigates the effectiveness of combining multispectral very high resolution (VHR and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM. The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements.

  18. On land-use modeling: A treatise of satellite imagery data and misclassification error

    Science.gov (United States)

    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.

  19. Wide area change detection with satellite imagery for locating underground nuclear testing

    International Nuclear Information System (INIS)

    Canty, M.J.; Jasani, B.; Schlittenhardt, J.

    2001-01-01

    With the advent of high resolution optical imagery from commercial earth observation satellites, the use of remote sensing data for verification of nuclear non-proliferation agreements is becoming increasingly attractive. Non-governmental organizations are routinely publishing high-quality imagery of sensitive nuclear installations round the world, and international verification authorities, such as the International Atomic Energy Agency (IAEA) or the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), will also want to make use, directly or indirectly, of this additional open source of information. Exact location of the sites of underground nuclear explosions is a task eminently suited to satellite imagery. Here both moderate resolutions for detecting signals in very large testing ranges as well as high resolution images for exact interpretation play important roles. We describe in our paper a particularly sensitive change detection procedure for bitemporal, multispectral satellite imagery which can be used to locate the spall zone of underground nuclear explosions with commercial satellite imagery. The method is based on the multivariate alteration detection (MAD) technique of Nielsen et al. Linear combinations of the spectral channels in two images of the same scene are chosen so as to minimize their positive correlation. This leads to a series of difference images - the so-called MAD components - which are mutually orthogonal (uncorrelated) and ordered according to decreasing variance in their pixel intensities. Since interesting changes in man-made structures may contribute minimally to the overall variance (as the latter may be dominated for instance by seasonal vegetation differences) it is often the case that such changes turn up in a higher order MAD component. This is because they will be uncorrelated with seasonal vegetation changes, stochastic image noise or other major contributions to the overall change signal. This in fact is one of the

  20. Evaluation of cloud properties in the NOAA/NCEP global forecast system using multiple satellite products

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Hyelim [University of Maryland, Department of Atmospheric and Oceanic Science, College Park, MD (United States); Li, Zhanqing [University of Maryland, Department of Atmospheric and Oceanic Science, College Park, MD (United States); Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, GCESS, Beijing (China)

    2012-12-15

    Knowledge of cloud properties and their vertical structure is important for meteorological studies due to their impact on both the Earth's radiation budget and adiabatic heating within the atmosphere. The objective of this study is to evaluate bulk cloud properties and vertical distribution simulated by the US National Oceanic and Atmospheric Administration National Centers for Environmental Prediction Global Forecast System (GFS) using three global satellite products. Cloud variables evaluated include the occurrence and fraction of clouds in up to three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active (CloudSat/CALIPSO) and passive sensors and are subsequently compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. Although the frequencies of global multi-layer clouds from observations are similar to those from the model, latitudinal variations show discrepancies in terms of structure and pattern. The modeled cloud optical depth over storm track region and subtropical region is less than that from the passive sensor and is overestimated for deep convective clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Discrepancies in LWP/IWP distributions between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds. (orig.)

  1. Monitoring Nuclear Facilities Using Satellite Imagery and Associated Remote Sensing Techniques

    International Nuclear Information System (INIS)

    Lafitte, Marc; Robin, Jean‑Philippe

    2015-01-01

    The mission of the European Union Satellite Centre (SatCen) is “to support the decision making and actions of the European Union in the field of the CFSP and in particular the CSDP, including European Union crisis management missions and operations, by providing, at the request of the Council or the European Union High Representative, products and services resulting from the exploitation of relevant space assets and collateral data, including satellite and aerial imagery, and related services”. The SatCen Non‑Proliferation Team, part of the SatCen Operations Division, is responsible for the analysis of installations that are involved, or could be involved, in the preparation or acquisition of capabilities intended to divert the production of nuclear material for military purposes and, in particular, regarding the spread of Weapons of Mass destruction and their means of delivery. For the last four decades, satellite imagery and associated remote sensing and geospatial techniques have increasingly expanded their capabilities. The unprecedented Very High Resolution (VHR) data currently available, the improved spectral capabilities, the increasing number of sensors and ever increasing computing capacity, has opened up a wide range of new perspectives for remote sensing applications. Concurrently, the availability of open source information (OSINF), has increased exponentially through the medium of the internet. This range of new capabilities for sensors and associated remote sensing techniques have strengthened the SatCen analysis capabilities for the monitoring of suspected proliferation installations for the detection of undeclared nuclear facilities, processes and activities. The combination of these remote sensing techniques, imagery analysis, open source investigation and their integration into Geographic Information Systems (GIS), undoubtedly improve the efficiency and comprehensive analysis capability provided by the SatCen to the EU stake‑holders. The

  2. Environmental waste site characterization utilizing aerial photographs and satellite imagery: Three sites in New Mexico, USA

    International Nuclear Information System (INIS)

    Van Eeckhout, E.; Pope, P.; Becker, N.; Wells, B.; Lewis, A.; David, N.

    1996-01-01

    The proper handling and characterization of past hazardous waste sites is becoming more and more important as world population extends into areas previously deemed undesirable. Historical photographs, past records, current aerial satellite imagery can play an important role in characterizing these sites. These data provide clear insight into defining problem areas which can be surface samples for further detail. Three such areas are discussed in this paper: (1) nuclear wastes buried in trenches at Los Alamos National Laboratory, (2) surface dumping at one site at Los Alamos National Laboratory, and (3) the historical development of a municipal landfill near Las Cruces, New Mexico

  3. Algorithmic Foundation of Spectral Rarefaction for Measuring Satellite Imagery Heterogeneity at Multiple Spatial Scales

    Science.gov (United States)

    Rocchini, Duccio

    2009-01-01

    Measuring heterogeneity in satellite imagery is an important task to deal with. Most measures of spectral diversity have been based on Shannon Information theory. However, this approach does not inherently address different scales, ranging from local (hereafter referred to alpha diversity) to global scales (gamma diversity). The aim of this paper is to propose a method for measuring spectral heterogeneity at multiple scales based on rarefaction curves. An algorithmic solution of rarefaction applied to image pixel values (Digital Numbers, DNs) is provided and discussed. PMID:22389600

  4. Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery

    Directory of Open Access Journals (Sweden)

    Nobuoto Nojima

    2010-09-01

    Full Text Available For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR loaded on the Advanced Land Observing Satellite (ALOS satellite, a model combining the usage of satellite synthetic aperture radar (SAR imagery and Japan Meteorological Agency (JMA-scale seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio, on the basis of dataset from Japanese Earth Resource Satellite-1 (JERS-1/SAR (L-band SAR images observed during the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images.

  5. Towards a Cloud Computing Environment: Near Real-time Cloud Product Processing and Distribution for Next Generation Satellites

    Science.gov (United States)

    Nguyen, L.; Chee, T.; Minnis, P.; Palikonda, R.; Smith, W. L., Jr.; Spangenberg, D.

    2016-12-01

    The NASA LaRC Satellite ClOud and Radiative Property retrieval System (SatCORPS) processes and derives near real-time (NRT) global cloud products from operational geostationary satellite imager datasets. These products are being used in NRT to improve forecast model, aircraft icing warnings, and support aircraft field campaigns. Next generation satellites, such as the Japanese Himawari-8 and the upcoming NOAA GOES-R, present challenges for NRT data processing and product dissemination due to the increase in temporal and spatial resolution. The volume of data is expected to increase to approximately 10 folds. This increase in data volume will require additional IT resources to keep up with the processing demands to satisfy NRT requirements. In addition, these resources are not readily available due to cost and other technical limitations. To anticipate and meet these computing resource requirements, we have employed a hybrid cloud computing environment to augment the generation of SatCORPS products. This paper will describe the workflow to ingest, process, and distribute SatCORPS products and the technologies used. Lessons learn from working on both AWS Clouds and GovCloud will be discussed: benefits, similarities, and differences that could impact decision to use cloud computing and storage. A detail cost analysis will be presented. In addition, future cloud utilization, parallelization, and architecture layout will be discussed for GOES-R.

  6. New Satellite Estimates of Mixed-Phase Cloud Properties: A Synergistic Approach for Application to Global Satellite Imager Data

    Science.gov (United States)

    Smith, W. L., Jr.; Spangenberg, D.; Fleeger, C.; Sun-Mack, S.; Chen, Y.; Minnis, P.

    2016-12-01

    Determining accurate cloud properties horizontally and vertically over a full range of time and space scales is currently next to impossible using data from either active or passive remote sensors or from modeling systems. Passive satellite imagers provide horizontal and temporal resolution of clouds, but little direct information on vertical structure. Active sensors provide vertical resolution but limited spatial and temporal coverage. Cloud models embedded in NWP can produce realistic clouds but often not at the right time or location. Thus, empirical techniques that integrate information from multiple observing and modeling systems are needed to more accurately characterize clouds and their impacts. Such a strategy is employed here in a new cloud water content profiling technique developed for application to satellite imager cloud retrievals based on VIS, IR and NIR radiances. Parameterizations are developed to relate imager retrievals of cloud top phase, optical depth, effective radius and temperature to ice and liquid water content profiles. The vertical structure information contained in the parameterizations is characterized climatologically from cloud model analyses, aircraft observations, ground-based remote sensing data, and from CloudSat and CALIPSO. Thus, realistic cloud-type dependent vertical structure information (including guidance on cloud phase partitioning) circumvents poor assumptions regarding vertical homogeneity that plague current passive satellite retrievals. This paper addresses mixed phase cloud conditions for clouds with glaciated tops including those associated with convection and mid-latitude storm systems. Novel outcomes of our approach include (1) simultaneous retrievals of ice and liquid water content and path, which are validated with active sensor, microwave and in-situ data, and yield improved global cloud climatologies, and (2) new estimates of super-cooled LWC, which are demonstrated in aviation safety applications and

  7. Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing

    Directory of Open Access Journals (Sweden)

    M. Joseph Hughes

    2014-05-01

    Full Text Available The use of Landsat data to answer ecological questions is greatly increased by the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, SPARCS: Spatial Procedures for Automated Removal of Cloud and Shadow. The method uses a neural network approach to determine cloud, cloud shadow, water, snow/ice and clear sky classification memberships of each pixel in a Landsat scene. It then applies a series of spatial procedures to resolve pixels with ambiguous membership by using information, such as the membership values of neighboring pixels and an estimate of cloud shadow locations from cloud and solar geometry. In a comparison with FMask, a high-quality cloud and cloud shadow classification algorithm currently available, SPARCS performs favorably, with substantially lower omission errors for cloud shadow (8.0% and 3.2%, only slightly higher omission errors for clouds (0.9% and 1.3%, respectively and fewer errors of commission (2.6% and 0.3%. Additionally, SPARCS provides a measure of uncertainty in its classification that can be exploited by other algorithms that require clear sky pixels. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of a method for vegetation change detection.

  8. Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

    Directory of Open Access Journals (Sweden)

    C. A. Poulsen

    2012-08-01

    Full Text Available Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase.

    The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick

  9. Global, Persistent, Real-time Multi-sensor Automated Satellite Image Analysis and Crop Forecasting in Commercial Cloud

    Science.gov (United States)

    Brumby, S. P.; Warren, M. S.; Keisler, R.; Chartrand, R.; Skillman, S.; Franco, E.; Kontgis, C.; Moody, D.; Kelton, T.; Mathis, M.

    2016-12-01

    Cloud computing, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of space and time granularity never before feasible. Multi-decadal Earth remote sensing datasets at the petabyte scale (8×10^15 bits) are now available in commercial cloud, and new satellite constellations will generate daily global coverage at a few meters per pixel. Public and commercial satellite observations now provide a wide range of sensor modalities, from traditional visible/infrared to dual-polarity synthetic aperture radar (SAR). This provides the opportunity to build a continuously updated map of the world supporting the academic community and decision-makers in government, finanace and industry. We report on work demonstrating country-scale agricultural forecasting, and global-scale land cover/land, use mapping using a range of public and commercial satellite imagery. We describe processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work combining this imagery with time-series SAR collected by ESA Sentinel 1. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields and detect threats to food security (e.g., flooding, drought). The software platform and analysis methodology also support monitoring water resources, forests and other general

  10. Sensitivities of simulated satellite views of clouds to subgrid-scale overlap and condensate heterogeneity

    Energy Technology Data Exchange (ETDEWEB)

    Hillman, Benjamin R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Marchand, Roger T. [Univ. of Washington, Seattle, WA (United States); Ackerman, Thomas P. [Univ. of Washington, Seattle, WA (United States)

    2017-08-01

    Satellite simulators are often used to account for limitations in satellite retrievals of cloud properties in comparisons between models and satellite observations. The purpose of the simulator framework is to enable more robust evaluation of model cloud properties, so that di erences between models and observations can more con dently be attributed to model errors. However, these simulators are subject to uncertainties themselves. A fundamental uncertainty exists in connecting the spatial scales at which cloud properties are retrieved with those at which clouds are simulated in global models. In this study, we create a series of sensitivity tests using 4 km global model output from the Multiscale Modeling Framework to evaluate the sensitivity of simulated satellite retrievals when applied to climate models whose grid spacing is many tens to hundreds of kilometers. In particular, we examine the impact of cloud and precipitation overlap and of condensate spatial variability. We find the simulated retrievals are sensitive to these assumptions. Specifically, using maximum-random overlap with homogeneous cloud and precipitation condensate, which is often used in global climate models, leads to large errors in MISR and ISCCP-simulated cloud cover and in CloudSat-simulated radar reflectivity. To correct for these errors, an improved treatment of unresolved clouds and precipitation is implemented for use with the simulator framework and is shown to substantially reduce the identified errors.

  11. Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

    Directory of Open Access Journals (Sweden)

    Jane Southworth

    2010-12-01

    Full Text Available Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub or grasslands. The methodology involved the identification and isolation of tree canopies within the imagery and the creation of tree polygon layers had an overall accuracy of 84%. In addition, the results were scaled up to a corresponding Landsat image of the same region, and the OBC results compared to corresponding pixel values of NDVI. The results were not compelling, indicating once more the problems of these traditional image analysis techniques for savanna ecosystems. Overall, the use of the OBC holds great promise for this ecosystem and could be utilized more frequently in studies of vegetation structure.

  12. Detection and Prediction of Hail Storms in Satellite Imagery using Deep Learning

    Science.gov (United States)

    Pullman, M.; Gurung, I.; Ramachandran, R.; Maskey, M.

    2017-12-01

    Natural hazards, such as damaging hail storms, dramatically disrupt both industry and agriculture, having significant socio-economic impacts in the United States. In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest 2016 weather phenomenon in the United States. The destructive nature and high cost of hail storms has driven research into the development of more accurate hail-prediction algorithms in an effort to mitigate societal impacts. Recently, weather forecasting efforts have turned to deep learning neural networks because neural networks can more effectively model complex, nonlinear, dynamical phenomenon that exist in large datasets through multiple stages of transformation and representation. In an effort to improve hail-prediction techniques, we propose a deep learning technique that leverages satellite imagery to detect and predict the occurrence of hail storms. The technique is applied to satellite imagery from 2006 to 2016 for the contiguous United States and incorporates hail reports obtained from the National Center for Environmental Information Storm Events Database for training and validation purposes. In this presentation, we describe a novel approach to predicting hail via a neural network model that creates a large labeled dataset of hail storms, the accuracy and results of the model, and its applications for improving hail forecasting.

  13. Ship detection in satellite imagery using rank-order greyscale hit-or-miss transforms

    Energy Technology Data Exchange (ETDEWEB)

    Harvey, Neal R [Los Alamos National Laboratory; Porter, Reid B [Los Alamos National Laboratory; Theiler, James [Los Alamos National Laboratory

    2010-01-01

    Ship detection from satellite imagery is something that has great utility in various communities. Knowing where ships are and their types provides useful intelligence information. However, detecting and recognizing ships is a difficult problem. Existing techniques suffer from too many false-alarms. We describe approaches we have taken in trying to build ship detection algorithms that have reduced false alarms. Our approach uses a version of the grayscale morphological Hit-or-Miss transform. While this is well known and used in its standard form, we use a version in which we use a rank-order selection for the dilation and erosion parts of the transform, instead of the standard maximum and minimum operators. This provides some slack in the fitting that the algorithm employs and provides a method for tuning the algorithm's performance for particular detection problems. We describe our algorithms, show the effect of the rank-order parameter on the algorithm's performance and illustrate the use of this approach for real ship detection problems with panchromatic satellite imagery.

  14. Assessment of spatial distribution of soil heavy metals using ANN-GA, MSLR and satellite imagery.

    Science.gov (United States)

    Naderi, Arman; Delavar, Mohammad Amir; Kaboudin, Babak; Askari, Mohammad Sadegh

    2017-05-01

    This study aims to assess and compare heavy metal distribution models developed using stepwise multiple linear regression (MSLR) and neural network-genetic algorithm model (ANN-GA) based on satellite imagery. The source identification of heavy metals was also explored using local Moran index. Soil samples (n = 300) were collected based on a grid and pH, organic matter, clay, iron oxide contents cadmium (Cd), lead (Pb) and zinc (Zn) concentrations were determined for each sample. Visible/near-infrared reflectance (VNIR) within the electromagnetic ranges of satellite imagery was applied to estimate heavy metal concentrations in the soil using MSLR and ANN-GA models. The models were evaluated and ANN-GA model demonstrated higher accuracy, and the autocorrelation results showed higher significant clusters of heavy metals around the industrial zone. The higher concentration of Cd, Pb and Zn was noted under industrial lands and irrigation farming in comparison to barren and dryland farming. Accumulation of industrial wastes in roads and streams was identified as main sources of pollution, and the concentration of soil heavy metals was reduced by increasing the distance from these sources. In comparison to MLSR, ANN-GA provided a more accurate indirect assessment of heavy metal concentrations in highly polluted soils. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.

  15. 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.

  16. Satellite imagery-based monitoring of archaeological site damage in the Syrian civil war.

    Science.gov (United States)

    Casana, Jesse; Laugier, Elise Jakoby

    2017-01-01

    Since the start of the Syrian civil war in 2011, the rich archaeological heritage of Syria and northern Iraq has faced severe threats, including looting, combat-related damage, and intentional demolition of monuments. However, the inaccessibility of the conflict zone to archaeologists or cultural heritage specialists has made it difficult to produce accurate damage assessments, impeding efforts to develop mitigation strategies and policies. This paper presents results of a project, undertaken in collaboration with the American Schools of Oriental Research (ASOR) and the US Department of State, to monitor damage to archaeological sites in Syria, northern Iraq, and southern Turkey using recent, high-resolution satellite imagery. Leveraging a large database of archaeological and heritage sites throughout the region, as well as access to continually updated satellite imagery from DigitalGlobe, this project has developed a flexible and efficient methodology to log observations of damage in a manner that facilitates spatial and temporal queries. With nearly 5000 sites carefully evaluated, analysis reveals unexpected patterns in the timing, severity, and location of damage, helping us to better understand the evolving cultural heritage crisis in Syria and Iraq. Results also offer a model for future remote sensing-based archaeological and heritage monitoring efforts in the Middle East and beyond.

  17. Satellite imagery-based monitoring of archaeological site damage in the Syrian civil war.

    Directory of Open Access Journals (Sweden)

    Jesse Casana

    Full Text Available Since the start of the Syrian civil war in 2011, the rich archaeological heritage of Syria and northern Iraq has faced severe threats, including looting, combat-related damage, and intentional demolition of monuments. However, the inaccessibility of the conflict zone to archaeologists or cultural heritage specialists has made it difficult to produce accurate damage assessments, impeding efforts to develop mitigation strategies and policies. This paper presents results of a project, undertaken in collaboration with the American Schools of Oriental Research (ASOR and the US Department of State, to monitor damage to archaeological sites in Syria, northern Iraq, and southern Turkey using recent, high-resolution satellite imagery. Leveraging a large database of archaeological and heritage sites throughout the region, as well as access to continually updated satellite imagery from DigitalGlobe, this project has developed a flexible and efficient methodology to log observations of damage in a manner that facilitates spatial and temporal queries. With nearly 5000 sites carefully evaluated, analysis reveals unexpected patterns in the timing, severity, and location of damage, helping us to better understand the evolving cultural heritage crisis in Syria and Iraq. Results also offer a model for future remote sensing-based archaeological and heritage monitoring efforts in the Middle East and beyond.

  18. Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations.

    Science.gov (United States)

    Checchi, Francesco; Stewart, Barclay T; Palmer, Jennifer J; Grundy, Chris

    2013-01-23

    Estimating the size of forcibly displaced populations is key to documenting their plight and allocating sufficient resources to their assistance, but is often not done, particularly during the acute phase of displacement, due to methodological challenges and inaccessibility. In this study, we explored the potential use of very high resolution satellite imagery to remotely estimate forcibly displaced populations. Our method consisted of multiplying (i) manual counts of assumed residential structures on a satellite image and (ii) estimates of the mean number of people per structure (structure occupancy) obtained from publicly available reports. We computed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons' camps and two urban neighbourhoods with a mixture of residents and displaced) ranging in population from 1,969 to 90,547, and compared these to "gold standard" reference population figures from census or other robust methods. Structure counts by independent analysts were reasonably consistent. Between one and 11 occupancy reports were available per site and most of these reported people per household rather than per structure. The imagery-based method had a precision relative to reference population figures of layout. For each site, estimates were produced in 2-5 working person-days. In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings. Based on these results, we discuss possible ways forward for the method's development.

  19. Using Online Citizen Science to Assess Giant Kelp Abundances Across the Globe with Satellite Imagery

    Science.gov (United States)

    Byrnes, J.; Cavanaugh, K. C.; Haupt, A. J.; Trouille, L.; Rosenthal, I.; Bell, T. W.; Rassweiler, A.; Pérez-Matus, A.; Assis, J.

    2017-12-01

    Global scale long-term data sets that document the patterns and variability of human impacts on marine ecosystems are rare. This lack is particularly glaring for underwater species - even moreso for ecologically important ones. Here we demonstrate how online Citizen Science combined with Landsat satellite imagery can help build a picture of change in the dynamics of giant kelp, an important coastal foundation species around the globe, from the 1984 to the present. Giant kelp canopy is visible from Landsat images, but these images defy easy machine classification. To get useful data, images must be processed by hand. While academic researchers have applied this method successfully at sub-regional scales, unlocking the value of the full global dataset has not been possible until given the massive effort required. Here we present Floating Forests (http://floatingforests.org), an international collaboration between kelp forest researchers and the citizen science organization Zooniverse. Floating Forests provides an interface that allows citizen scientists to identify canopy cover of giant kelp on Landsat images, enabling us to scale up the dataset to the globe. We discuss lessons learned from the initial version of the project launched in 2014, a prototype of an image processing pipeline to bring Landsat imagery to citizen science platforms, methods of assessing accuracy of citizen scientists, and preliminary data from our relaunch of the project. Through this project we have developed generalizable tools to facilitate citizen science-based analysis of Landsat and other satellite and aerial imagery. We hope that this create a powerful dataset to unlock our understanding of how global change has altered these critically important species in the sea.

  20. 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.

  1. Super-Resolution for "Jilin-1" Satellite Video Imagery via a Convolutional Network.

    Science.gov (United States)

    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.

  2. Multi-decadal record of ice dynamics on Daugaard Jensen Gletscher, East Greenland, from satellite imagery and terrestrial measurements

    DEFF Research Database (Denmark)

    Stearns, L.A.; Hamilton, G.S.; Reeh, Niels

    2005-01-01

    The history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained in approxima......The history of ice velocity and calving front position of Daugaard Jensen Gletscher, a large outlet glacier in East Greenland, is reconstructed from field measurements, aerial photography and satellite imagery for the period 1950-2001. The calving terminus of the glacier has remained...

  3. Application of satellite imagery to monitoring human rights abuse of vulnerable communities, with minimal risk to relief staff

    Energy Technology Data Exchange (ETDEWEB)

    Lavers, C; Bishop, C; Hawkins, O; Grealey, E; Cox, C; Thomas, D; Trimel, S, E-mail: brnc-radarcomms1@nrta.mod.u [Sensors Team, Plymouth University at Britannia Royal Naval College, Dartmouth (United Kingdom); DMC International Imaging, Tycho House, Surrey Research Park, Guildford (United Kingdom); Qinetiq, Cody Technology Park, Cody Building, Ively Road, Farnborough (United Kingdom); Humanitarian Aid Relief Trust (HART), 3 Arnellan House, Kingsbury, London (United Kingdom); Amnesty International USA, 5 Penn Plaza, New York (United States)

    2009-07-01

    Space imagery offers remote surveillance of ethnic people groups at risk of human rights abuse. We highlight work in alleged violations in Burma and Sudan, using satellite imagery for verification with Amnesty International. We consider how imaging may effectively support small to medium-sized Non Governmental Organisations and charities, e.g. HART, working in dangerous zones on the ground. Satellite based sensing applications are now at a sufficiently mature stage for moderate Governmental funding levels to help prevent human rights abuse, rather than the greater cost of rebuilding communities and healing sectarian divisions after abuse has taken place.

  4. Application of satellite imagery to monitoring human rights abuse of vulnerable communities, with minimal risk to relief staff

    International Nuclear Information System (INIS)

    Lavers, C; Bishop, C; Hawkins, O; Grealey, E; Cox, C; Thomas, D; Trimel, S

    2009-01-01

    Space imagery offers remote surveillance of ethnic people groups at risk of human rights abuse. We highlight work in alleged violations in Burma and Sudan, using satellite imagery for verification with Amnesty International. We consider how imaging may effectively support small to medium-sized Non Governmental Organisations and charities, e.g. HART, working in dangerous zones on the ground. Satellite based sensing applications are now at a sufficiently mature stage for moderate Governmental funding levels to help prevent human rights abuse, rather than the greater cost of rebuilding communities and healing sectarian divisions after abuse has taken place.

  5. DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

    Science.gov (United States)

    Kalia, S.; Li, S.; Ganguly, S.; Nemani, R. R.

    2017-12-01

    Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remotesensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud/shadow mask from geostationary satellite data iscritical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds, which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classifycloud/shadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoder-decoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multi-spectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

  6. 3D Modeling of Building Indoor Spaces and Closed Doors from Imagery and Point Clouds

    Directory of Open Access Journals (Sweden)

    Lucía Díaz-Vilariño

    2015-02-01

    Full Text Available 3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.

  7. The Role of Satellite Imagery to Improve Pastureland Estimates in South America

    Science.gov (United States)

    Graesser, J.

    2015-12-01

    Agriculture has changed substantially across the globe over the past half century. While much work has been done to improve spatial-temporal estimates of agricultural changes, we still know more about the extent of row-crop agriculture than livestock-grazed land. The gap between cropland and pastureland estimates exists largely because it is challenging to characterize natural versus grazed grasslands from a remote sensing perspective. However, the impasse of pastureland estimates is set to break, with an increasing number of spaceborne sensors and freely available satellite data. The Landsat satellite archive in particular provides researchers with immense amounts of data to improve pastureland information. Here we focus on South America, where pastureland expansion has been scrutinized for the past few decades. We explore the challenges of estimating pastureland using temporal Landsat imagery and focus on key agricultural countries, regions, and ecosystems. We focus on the suggested shift of pastureland from the Argentine Pampas to northern Argentina, and the mixing of small-scale and large-scale ranching in eastern Paraguay and how it could impact the Chaco forest to the west. Further, the Beni Savannahs of northern Bolivia and the Colombian Llanos—both grassland and savannah regions historically used for livestock grazing—have been hinted at as future areas for cropland expansion. There are certainly environmental concerns with pastureland expansion into forests; but what are the environmental implications when well-managed pasture systems are converted to intensive soybean or palm oil plantation? Tropical, grazed grasslands are important habitats for biodiversity, and pasturelands can mitigate soil erosion when well managed. Thus, we must improve estimates of grazed land before we can make informed policy and conservation decisions. This talk presents insights into pastureland estimates in South America and discusses the feasibility to improve current

  8. Using Information From Prior Satellite Scans to Improve Cloud Detection Near the Day-Night Terminator

    Science.gov (United States)

    Yost, Christopher R.; Minnis, Patrick; Trepte, Qing Z.; Palikonda, Rabindra; Ayers, Jeffrey K.; Spangenberg, Doulas A.

    2012-01-01

    With geostationary satellite data it is possible to have a continuous record of diurnal cycles of cloud properties for a large portion of the globe. Daytime cloud property retrieval algorithms are typically superior to nighttime algorithms because daytime methods utilize measurements of reflected solar radiation. However, reflected solar radiation is difficult to accurately model for high solar zenith angles where the amount of incident radiation is small. Clear and cloudy scenes can exhibit very small differences in reflected radiation and threshold-based cloud detection methods have more difficulty setting the proper thresholds for accurate cloud detection. Because top-of-atmosphere radiances are typically more accurately modeled outside the terminator region, information from previous scans can help guide cloud detection near the terminator. This paper presents an algorithm that uses cloud fraction and clear and cloudy infrared brightness temperatures from previous satellite scan times to improve the performance of a threshold-based cloud mask near the terminator. Comparisons of daytime, nighttime, and terminator cloud fraction derived from Geostationary Operational Environmental Satellite (GOES) radiance measurements show that the algorithm greatly reduces the number of false cloud detections and smoothes the transition from the daytime to the nighttime clod detection algorithm. Comparisons with the Geoscience Laser Altimeter System (GLAS) data show that using this algorithm decreases the number of false detections by approximately 20 percentage points.

  9. Overview of Boundary Layer Clouds Using Satellite and Ground-Based Measurements

    Science.gov (United States)

    Xi, B.; Dong, X.; Wu, P.; Qiu, S.

    2017-12-01

    A comprehensive summary of boundary layer clouds properties based on our few recently studies will be presented. The analyses include the global cloud fractions and cloud macro/micro- physical properties based on satellite measurements using both CERES-MODIS and CloudSat/Caliposo data products,; the annual/seasonal/diurnal variations of stratocumulus clouds over different climate regions (mid-latitude land, mid-latitude ocean, and Arctic region) using DOE ARM ground-based measurements over Southern great plain (SGP), Azores (GRW), and North slope of Alaska (NSA) sites; the impact of environmental conditions to the formation and dissipation process of marine boundary layer clouds over Azores site; characterizing Arctice mixed-phase cloud structure and favorable environmental conditions for the formation/maintainess of mixed-phase clouds over NSA site. Though the presentation has widely spread topics, we will focus on the representation of the ground-based measurements over different climate regions; evaluation of satellite retrieved cloud properties using these ground-based measurements, and understanding the uncertainties of both satellite and ground-based retrievals and measurements.

  10. Quantifying the Climate-Scale Accuracy of Satellite Cloud Retrievals

    Science.gov (United States)

    Roberts, Y.; Wielicki, B. A.; Sun-Mack, S.; Minnis, P.; Liang, L.; Di Girolamo, L.

    2014-12-01

    Instrument calibration and cloud retrieval algorithms have been developed to minimize retrieval errors on small scales. However, measurement uncertainties and assumptions within retrieval algorithms at the pixel level may alias into decadal-scale trends of cloud properties. We first, therefore, quantify how instrument calibration changes could alias into cloud property trends. For a perfect observing system the climate trend accuracy is limited only by the natural variability of the climate variable. Alternatively, for an actual observing system, the climate trend accuracy is additionally limited by the measurement uncertainty. Drifts in calibration over time may therefore be disguised as a true climate trend. We impose absolute calibration changes to MODIS spectral reflectance used as input to the CERES Cloud Property Retrieval System (CPRS) and run the modified MODIS reflectance through the CPRS to determine the sensitivity of cloud properties to calibration changes. We then use these changes to determine the impact of instrument calibration changes on trend uncertainty in reflected solar cloud properties. Secondly, we quantify how much cloud retrieval algorithm assumptions alias into cloud optical retrieval trends by starting with the largest of these biases: the plane-parallel assumption in cloud optical thickness (τC) retrievals. First, we collect liquid water cloud fields obtained from Multi-angle Imaging Spectroradiometer (MISR) measurements to construct realistic probability distribution functions (PDFs) of 3D cloud anisotropy (a measure of the degree to which clouds depart from plane-parallel) for different ISCCP cloud types. Next, we will conduct a theoretical study with dynamically simulated cloud fields and a 3D radiative transfer model to determine the relationship between 3D cloud anisotropy and 3D τC bias for each cloud type. Combining these results provides distributions of 3D τC bias by cloud type. Finally, we will estimate the change in

  11. Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method

    Science.gov (United States)

    Brovelli, Maria Antonia; Crespi, Mattia; Fratarcangeli, Francesca; Giannone, Francesca; Realini, Eugenio

    Interest in high-resolution satellite imagery (HRSI) is spreading in several application fields, at both scientific and commercial levels. Fundamental and critical goals for the geometric use of this kind of imagery are their orientation and orthorectification, processes able to georeference the imagery and correct the geometric deformations they undergo during acquisition. In order to exploit the actual potentialities of orthorectified imagery in Geomatics applications, the definition of a methodology to assess the spatial accuracy achievable from oriented imagery is a crucial topic. In this paper we want to propose a new method for accuracy assessment based on the Leave-One-Out Cross-Validation (LOOCV), a model validation method already applied in different fields such as machine learning, bioinformatics and generally in any other field requiring an evaluation of the performance of a learning algorithm (e.g. in geostatistics), but never applied to HRSI orientation accuracy assessment. The proposed method exhibits interesting features which are able to overcome the most remarkable drawbacks involved by the commonly used method (Hold-Out Validation — HOV), based on the partitioning of the known ground points in two sets: the first is used in the orientation-orthorectification model (GCPs — Ground Control Points) and the second is used to validate the model itself (CPs — Check Points). In fact the HOV is generally not reliable and it is not applicable when a low number of ground points is available. To test the proposed method we implemented a new routine that performs the LOOCV in the software SISAR, developed by the Geodesy and Geomatics Team at the Sapienza University of Rome to perform the rigorous orientation of HRSI; this routine was tested on some EROS-A and QuickBird images. Moreover, these images were also oriented using the world recognized commercial software OrthoEngine v. 10 (included in the Geomatica suite by PCI), manually performing the LOOCV

  12. The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities

    Directory of Open Access Journals (Sweden)

    G. Broquet

    2018-02-01

    Full Text Available This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA. This assessment is based on observing system simulation experiments (OSSEs with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ∼ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system

  13. The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities

    Science.gov (United States)

    Broquet, Grégoire; Bréon, François-Marie; Renault, Emmanuel; Buchwitz, Michael; Reuter, Maximilian; Bovensmann, Heinrich; Chevallier, Frédéric; Wu, Lin; Ciais, Philippe

    2018-02-01

    This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ˜ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the

  14. Evaluation of Satellite-Based Upper Troposphere Cloud Top Height Retrievals in Multilayer Cloud Conditions During TC4

    Science.gov (United States)

    Chang, Fu-Lung; Minnis, Patrick; Ayers, J. Kirk; McGill, Matthew J.; Palikonda, Rabindra; Spangenberg, Douglas A.; Smith, William L., Jr.; Yost, Christopher R.

    2010-01-01

    Upper troposphere cloud top heights (CTHs), restricted to cloud top pressures (CTPs) less than 500 hPa, inferred using four satellite retrieval methods applied to Twelfth Geostationary Operational Environmental Satellite (GOES-12) data are evaluated using measurements during the July August 2007 Tropical Composition, Cloud and Climate Coupling Experiment (TC4). The four methods are the single-layer CO2-absorption technique (SCO2AT), a modified CO2-absorption technique (MCO2AT) developed for improving both single-layered and multilayered cloud retrievals, a standard version of the Visible Infrared Solar-infrared Split-window Technique (old VISST), and a new version of VISST (new VISST) recently developed to improve cloud property retrievals. They are evaluated by comparing with ER-2 aircraft-based Cloud Physics Lidar (CPL) data taken during 9 days having extensive upper troposphere cirrus, anvil, and convective clouds. Compared to the 89% coverage by upper tropospheric clouds detected by the CPL, the SCO2AT, MCO2AT, old VISST, and new VISST retrieved CTPs less than 500 hPa in 76, 76, 69, and 74% of the matched pixels, respectively. Most of the differences are due to subvisible and optically thin cirrus clouds occurring near the tropopause that were detected only by the CPL. The mean upper tropospheric CTHs for the 9 days are 14.2 (+/- 2.1) km from the CPL and 10.7 (+/- 2.1), 12.1 (+/- 1.6), 9.7 (+/- 2.9), and 11.4 (+/- 2.8) km from the SCO2AT, MCO2AT, old VISST, and new VISST, respectively. Compared to the CPL, the MCO2AT CTHs had the smallest mean biases for semitransparent high clouds in both single-layered and multilayered situations whereas the new VISST CTHs had the smallest mean biases when upper clouds were opaque and optically thick. The biases for all techniques increased with increasing numbers of cloud layers. The transparency of the upper layer clouds tends to increase with the numbers of cloud layers.

  15. Comparison of cloud top heights derived from FY-2 meteorological satellites with heights derived from ground-based millimeter wavelength cloud radar

    Science.gov (United States)

    Wang, Zhe; Wang, Zhenhui; Cao, Xiaozhong; Tao, Fa

    2018-01-01

    Clouds are currently observed by both ground-based and satellite remote sensing techniques. Each technique has its own strengths and weaknesses depending on the observation method, instrument performance and the methods used for retrieval. It is important to study synergistic cloud measurements to improve the reliability of the observations and to verify the different techniques. The FY-2 geostationary orbiting meteorological satellites continuously observe the sky over China. Their cloud top temperature product can be processed to retrieve the cloud top height (CTH). The ground-based millimeter wavelength cloud radar can acquire information about the vertical structure of clouds-such as the cloud base height (CBH), CTH and the cloud thickness-and can continuously monitor changes in the vertical profiles of clouds. The CTHs were retrieved using both cloud top temperature data from the FY-2 satellites and the cloud radar reflectivity data for the same time period (June 2015 to May 2016) and the resulting datasets were compared in order to evaluate the accuracy of CTH retrievals using FY-2 satellites. The results show that the concordance rate of cloud detection between the two datasets was 78.1%. Higher consistencies were obtained for thicker clouds with larger echo intensity and for more continuous clouds. The average difference in the CTH between the two techniques was 1.46 km. The difference in CTH between low- and mid-level clouds was less than that for high-level clouds. An attenuation threshold of the cloud radar for rainfall was 0.2 mm/min; a rainfall intensity below this threshold had no effect on the CTH. The satellite CTH can be used to compensate for the attenuation error in the cloud radar data.

  16. [Extraction of buildings three-dimensional information from high-resolution satellite imagery based on Barista software].

    Science.gov (United States)

    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.

  17. A fast radiative transfer method for the simulation of visible satellite imagery

    Science.gov (United States)

    Scheck, Leonhard; Frèrebeau, Pascal; Buras-Schnell, Robert; Mayer, Bernhard

    2016-05-01

    A computationally efficient radiative transfer method for the simulation of visible satellite images is presented. The top of atmosphere reflectance is approximated by a function depending on vertically integrated optical depths and effective particle sizes for water and ice clouds, the surface albedo, the sun and satellite zenith angles and the scattering angle. A look-up table (LUT) for this reflectance function is generated by means of the discrete ordinate method (DISORT). For a constant scattering angle the reflectance is a relatively smooth and symmetric function of the two zenith angles, which can be well approximated by the lowest-order terms of a 2D Fourier series. By storing only the lowest Fourier coefficients and adopting a non-equidistant grid for the scattering angle, the LUT is reduced to a size of 21 MB per satellite channel. The computation of the top of atmosphere reflectance requires only the calculation of the cloud parameters from the model state and the evaluation and interpolation of the reflectance function using the compressed LUT and is thus orders of magnitude faster than DISORT. The accuracy of the method is tested by generating synthetic satellite images for the 0.6 μm and 0.8 μm channels of the SEVIRI instrument for operational COSMO-DE model forecasts from the German Weather Service (DWD) and comparing them to DISORT results. For a test period in June the root mean squared absolute reflectance error is about 10-2 and the mean relative reflectance error is less than 2% for both channels. For scattering angles larger than 170 ° the rapid variation of reflectance with the particle size related to the backscatter glory reduces the accuracy and the errors increase by a factor of 3-4. Speed and accuracy of the new method are sufficient for operational data assimilation and high-resolution model verification applications.

  18. Assessing the population coverage of a health demographic surveillance system using satellite imagery and crowd-sourcing

    NARCIS (Netherlands)

    Pasquale, Di Aurelio; Mc Cann, Robert; Maire, Nicolas

    2017-01-01

    Remotely sensed data can serve as an independent source of information about the location of residential structures in areas under demographic and health surveillance. We report on results obtained combining satellite imagery, imported from Bing, with location data routinely collected using the

  19. Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields

    NARCIS (Netherlands)

    Hamzeh, Saied; Naseri, Abd Ali; Alavipanah, Seyed Kazem; Bartholomeus, Harm; Herold, Martin

    2016-01-01

    This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image

  20. Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Hasituya

    2017-03-01

    Full Text Available In recent years, the area of plastic-mulched farmland (PMF has undergone rapid growth and raised remarkable environmental problems. Therefore, mapping the PMF plays a crucial role in agricultural production, environmental protection and resource management. However, appropriate data selection criteria are currently lacking. Thus, this study was carried out in two main plastic-mulching practice regions, Jizhou and Guyuan, to look for an appropriate spatial scale for mapping PMF with remote sensing. The average local variance (ALV function was used to obtain the appropriate spatial scale for mapping PMF based on the GaoFen-1 (GF-1 satellite imagery. Afterwards, in order to validate the effectiveness of the selected method and to interpret the relationship between the appropriate spatial scale derived from the ALV and the spatial scale with the highest classification accuracy, we classified the imagery with varying spatial resolution by the Support Vector Machine (SVM algorithm using the spectral features, textural features and the combined spectral and textural features respectively. The results indicated that the appropriate spatial scales from the ALV lie between 8 m and 20 m for mapping the PMF both in Jizhou and Guyuan. However, there is a proportional relation: the spatial scale with the highest classification accuracy is at the 1/2 location of the appropriate spatial scale generated from the ALV in Jizhou and at the 2/3 location of the appropriate spatial scale generated from the ALV in Guyuan. Therefore, the ALV method for quantitatively selecting the appropriate spatial scale for mapping PMF with remote sensing imagery has theoretical and practical significance.

  1. Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992

    Science.gov (United States)

    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).

  2. Evaluation of the MiKlip decadal prediction system using satellite based cloud products

    Directory of Open Access Journals (Sweden)

    Thomas Spangehl

    2016-12-01

    Full Text Available The decadal hindcast simulations performed for the Mittelfristige Klimaprognosen (MiKlip project are evaluated using satellite-retrieved cloud parameters from the CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data (CLARA-A1 provided by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF and from the International Satellite Cloud Climatology Project (ISCCP. The forecast quality of two sets of hindcasts, Baseline-1-LR and Baseline-0, which use differing initialisations, is assessed. Basic evaluation focuses on multi-year ensemble mean fields and cloud-type histograms utilizing satellite simulator output. Additionally, ensemble evaluation employing analysis of variance (ANOVA, analysis rank histograms (ARH and a deterministic correlation score is performed. Satellite simulator output is available for a subset of the full hindcast ensembles only. Therefore, the raw model cloud cover is complementary used. The new Baseline-1-LR hindcasts are closer to satellite data with respect to the simulated tropical/subtropical mean cloud cover pattern than the reference hindcasts (Baseline-0 emphasizing improvements of the new MiKlip initialisation procedure. A slightly overestimated occurrence rate of optically thick cloud-types is analysed for different experiments including hindcasts and simulations using realistic sea surface boundaries according to the Atmospheric Model Intercomparison Project (AMIP. By contrast, the evaluation of cirrus and cirrostratus clouds is complicated by observational based uncertainties. Time series of the 3-year mean total cloud cover averaged over the tropical warm pool (TWP region show some correlation with the CLARA-A1 cloud fractional cover. Moreover, ensemble evaluation of the Baseline-1-LR hindcasts reveals potential predictability of the 2–5 lead year averaged total cloud cover for a large part of this region when regarding the full observational period. However, the hindcasts show only

  3. Identifying clouds over the Pierre Auger Observatory using infrared satellite data

    Energy Technology Data Exchange (ETDEWEB)

    Abreu, Pedro; et al.,

    2013-12-01

    We describe a new method of identifying night-time clouds over the Pierre Auger Observatory using infrared data from the Imager instruments on the GOES-12 and GOES-13 satellites. We compare cloud identifications resulting from our method to those obtained by the Central Laser Facility of the Auger Observatory. Using our new method we can now develop cloud probability maps for the 3000 km^2 of the Pierre Auger Observatory twice per hour with a spatial resolution of ~2.4 km by ~5.5 km. Our method could also be applied to monitor cloud cover for other ground-based observatories and for space-based observatories.

  4. Wind class sampling of satellite SAR imagery for offshore wind resource mapping

    DEFF Research Database (Denmark)

    Badger, Merete; Badger, Jake; Nielsen, Morten

    2010-01-01

    developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter......High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical......-dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally...

  5. Estimation Of The Spatial Distribution Of Crop Coefficient (Kc) From Landsat Satellite Imagery

    International Nuclear Information System (INIS)

    Abou EI-Magd, I.H.

    2009-01-01

    Single crop coefficient factor (K c ) is an essential component for crop water allocation for efficient irrigation scheduling and irrigation water management. Kc is basically defined as the ratio of actual evapotranspiration and grass/alfalfa reference evapotranspiration and always measured by lysimeter in localized area in the field, which then generalized on the whole irrigated land. The lack of precise information about the crop coefficient particularly in our country together with both small sized fields and heterogeneity of agricultural crops calls for developing a new methodology for computing a real time crop coefficient from remotely sensed data. This paper discusses the methodology developed for obtaining a real time single crop coefficient from Landsat Satellite ETM + 7 imageries. The methodology was applied and optimized on one irrigation field with two different dates and crop cover in the northern Delta of Egypt

  6. Coastal erosion and accretion in Pak Phanang, Thailand by GIS analysis of maps and satellite imagery

    Directory of Open Access Journals (Sweden)

    Sayedur Rahman Chowdhury

    2013-12-01

    Full Text Available Coastal erosion and accretion in Pak Phanang of southern Thailand between 1973 and 2003 was measured using multi-temporal topographic maps and Landsat satellite imageries. Within a GIS environment landward and seaward movements of shoreline was estimated by a transect-based analysis, and amounts of land accretion and erosion were estimated by a parcel-based geoprocessing. The whole longitudinal extent of the 58 kilometer coast was classified based on the erosion and accretion trends during this period using agglomerative hierarchical clustering approach. Erosion and accretion were found variable over time and space, and periodic reversal of status was also noticed in many places. Estimates of erosion were evaluated against field-survey based data, and found reasonably accurate where the rates were relatively great. Smoothing of shoreline datasets was found desirable as its impacts on the estimates remained within tolerable limits.

  7. Improvements to Lunar BRDF-Corrected Nighttime Satellite Imagery: Uses and Applications

    Science.gov (United States)

    Cole, Tony A.; Molthan, Andrew L.; Schultz, Lori A.; Roman, Miguel O.; Wanik, David W.

    2016-01-01

    Observations made by the VIIRS day/night band (DNB) provide daily, nighttime measurements to monitor Earth surface processes.However, these observations are impacted by variations in reflected solar radiation on the moon's surface. As the moon transitions from new to full phase, increasing radiance is reflected to the Earth's surface and contributes additional reflected moonlight from clouds and land surface, in addition to emissions from other light sources observed by the DNB. The introduction of a bi-directional reflectance distribution function (BRDF) algorithm serves to remove these lunar variations and normalize observed radiances. Provided by the Terrestrial Information Systems Laboratory at Goddard Space Flight Center, a 1 km gridded lunar BRDF-corrected DNB product and VIIRS cloud mask can be used for a multitude of nighttime applications without influence from the moon. Such applications include the detection of power outages following severe weather events using pre-and post-event DNB imagery, as well as the identification of boat features to curtail illegal fishing practices. This presentation will provide context on the importance of the lunar BRDF correction algorithm and explore the aforementioned uses of this improved DNB product for applied science applications.

  8. Improvements to Lunar BRDF-Corrected Nighttime Satellite Imagery: Uses and Applications

    Science.gov (United States)

    Cole, T.; Molthan, A.; Schultz, L. A.; Roman, M. O.; Wanik, D. W.

    2016-12-01

    Observations made by the VIIRS day/night band (DNB) provide daily, nighttime measurements to monitor Earth surface processes. However, these observations are impacted by variations in reflected solar radiation on the moon's surface. As the moon transitions from new to full phase, increasing radiance is reflected to the Earth's surface and contributes additional reflected moonlight from clouds and land surface, in addition to emissions from other light sources observed by the DNB. The introduction of a bi-directional reflectance distribution function (BRDF) algorithm serves to remove these lunar variations and normalize observed radiances. Provided by the Terrestrial Information Systems Laboratory at Goddard Space Flight Center, a 1 km gridded lunar BRDF-corrected DNB product and VIIRS cloud mask can be used for a multitude of nighttime applications without influence from the moon. Such applications include the detection of power outages following severe weather events using pre- and post-event DNB imagery, as well as the identification of boat features to curtail illegal fishing practices. This presentation will provide context on the importance of the lunar BRDF correction algorithm and explore the aforementioned uses of this improved DNB product for applied science applications.

  9. Monitoring Termite-Mediated Ecosystem Processes Using Moderate and High Resolution Satellite Imagery

    Science.gov (United States)

    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.

  10. Monitoring of oil pollution in the Arabian Gulf based on medium resolution satellite imagery

    Science.gov (United States)

    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

  11. Using Satellite Imagery to Identify Tornado Damage Tracks and Recovery from the April 27, 2011 Severe Weather Outbreak

    Science.gov (United States)

    Cole, Tony A.; Molthan, Andrew L.; Bell, Jordan R.

    2014-01-01

    Emergency response to natural disasters requires coordination between multiple local, state, and federal agencies. Single, relatively weak tornado events may require comparatively simple response efforts; but larger "outbreak" events with multiple strong, long-track tornadoes can benefit from additional tools to help expedite these efforts. Meteorologists from NOAA's National Weather Service conduct field surveys to map tornado tracks, assess damage, and determine the tornado intensity following each event. Moderate and high resolution satellite imagery can support these surveys by providing a high-level view of the affected areas. Satellite imagery could then be used to target areas for immediate survey or to corroborate the results of the survey after it is completed. In this study, the feasibility of using satellite imagery to identify tornado damage tracks was determined by comparing the characteristics of tracks observed from low-earth orbit to tracks assessed during the official NWS storm survey process. Of the 68 NWS confirmed centerlines, 24 tracks (35.3%) could be distinguished from other surface features using satellite imagery. Within each EF category, 0% of EF-0, 3% of EF-1, 50% of EF-2, 77.7% of EF-3, 87.5% of EF-4 and 100% of EF-5 tornadoes were detected. It was shown that satellite data can be used to identify tornado damage tracks in MODIS and ASTER NDVI imagery, where damage to vegetation creates a sharp drop in values though the minimum EF-category which can be detected is dependent upon the type of sensor used and underlying vegetation. Near-real time data from moderate resolution sensors compare favorably to field surveys after the event and suggest that the data can provide some value in the assessment process.

  12. A cloud-ozone data product from Aura OMI and MLS satellite measurements

    Directory of Open Access Journals (Sweden)

    J. R. Ziemke

    2017-11-01

    Full Text Available Ozone within deep convective clouds is controlled by several factors involving photochemical reactions and transport. Gas-phase photochemical reactions and heterogeneous surface chemical reactions involving ice, water particles, and aerosols inside the clouds all contribute to the distribution and net production and loss of ozone. Ozone in clouds is also dependent on convective transport that carries low-troposphere/boundary-layer ozone and ozone precursors upward into the clouds. Characterizing ozone in thick clouds is an important step for quantifying relationships of ozone with tropospheric H2O, OH production, and cloud microphysics/transport properties. Although measuring ozone in deep convective clouds from either aircraft or balloon ozonesondes is largely impossible due to extreme meteorological conditions associated with these clouds, it is possible to estimate ozone in thick clouds using backscattered solar UV radiation measured by satellite instruments. Our study combines Aura Ozone Monitoring Instrument (OMI and Microwave Limb Sounder (MLS satellite measurements to generate a new research product of monthly-mean ozone concentrations in deep convective clouds between 30° S and 30° N for October 2004–April 2016. These measurements represent mean ozone concentration primarily in the upper levels of thick clouds and reveal key features of cloud ozone including: persistent low ozone concentrations in the tropical Pacific of  ∼ 10 ppbv or less; concentrations of up to 60 pphv or greater over landmass regions of South America, southern Africa, Australia, and India/east Asia; connections with tropical ENSO events; and intraseasonal/Madden–Julian oscillation variability. Analysis of OMI aerosol measurements suggests a cause and effect relation between boundary-layer pollution and elevated ozone inside thick clouds over landmass regions including southern Africa and India/east Asia.

  13. Detection and retrieval of multi-layered cloud properties using satellite data

    Science.gov (United States)

    Minnis, Patrick; Sun-Mack, Sunny; Chen, Yan; Yi, Helen; Huang, Jianping; Nguyen, Louis; Khaiyer, Mandana M.

    2005-10-01

    Four techniques for detecting multilayered clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. A new technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other methods examined here use atmospheric sounding data (CO2-slicing, CO2), BTD, or microwave data. The CO2 and BTD methods are limited to optically thin cirrus over low clouds, while the MWR methods are limited to ocean areas only. This paper explores the use of the BTD and CO2 methods as applied to Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data taken from the Aqua satellite over ocean surfaces. Cloud properties derived from MODIS data for the Clouds and the Earth's Radiant Energy System (CERES) Project are used to classify cloud phase and optical properties. The preliminary results focus on a MODIS image taken off the Uruguayan coast. The combined MW visible infrared (MVI) method is assumed to be the reference for detecting multilayered ice-over-water clouds. The BTD and CO2 techniques accurately match the MVI classifications in only 51 and 41% of the cases, respectively. Much additional study is need to determine the uncertainties in the MVI method and to analyze many more overlapped cloud scenes.

  14. Using satellites and global models to investigate aerosol-cloud interactions

    Science.gov (United States)

    Gryspeerdt, E.; Quaas, J.; Goren, T.; Sourdeval, O.; Mülmenstädt, J.

    2017-12-01

    Aerosols are known to impact liquid cloud properties, through both microphysical and radiative processes. Increasing the number concentration of aerosol particles can increase the cloud droplet number concentration (CDNC). Through impacts on precipitation processes, this increase in CDNC may also be able to impact the cloud fraction (CF) and the cloud liquid water path (LWP). Several studies have looked into the effect of aerosols on the CDNC, but as the albedo of a cloudy scene depends much more strongly on LWP and CF, an aerosol influence on these properties could generate a significant radiative forcing. While the impact of aerosols on cloud properties can be seen in case studies involving shiptracks and volcanoes, producing a global estimate of these effects remains challenging due to the confounding effect of local meteorology. For example, relative humidity significantly impacts the aerosol optical depth (AOD), a common satellite proxy for CCN, as well as being a strong control on cloud properties. This can generate relationships between AOD and cloud properties, even when there is no impact of aerosol-cloud interactions. In this work, we look at how aerosol-cloud interactions can be distinguished from the effect of local meteorology in satellite studies. With a combination global climate models and multiple sources of satellite data, we show that the choice of appropriate mediating variables and case studies can be used to develop constraints on the aerosol impact on CF and LWP. This will lead to improved representations of clouds in global climate models and help to reduce the uncertainty in the global impact of anthropogenic aerosols on cloud properties.

  15. Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992

    Science.gov (United States)

    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.

  16. Validating Satellite-Retrieved Cloud Properties for Weather and Climate Applications

    Science.gov (United States)

    Minnis, P.; Bedka, K. M.; Smith, W., Jr.; Yost, C. R.; Bedka, S. T.; Palikonda, R.; Spangenberg, D.; Sun-Mack, S.; Trepte, Q.; Dong, X.; Xi, B.

    2014-12-01

    Cloud properties determined from satellite imager radiances are increasingly used in weather and climate applications, particularly in nowcasting, model assimilation and validation, trend monitoring, and precipitation and radiation analyses. The value of using the satellite-derived cloud parameters is determined by the accuracy of the particular parameter for a given set of conditions, such as viewing and illumination angles, surface background, and cloud type and structure. Because of the great variety of those conditions and of the sensors used to monitor clouds, determining the accuracy or uncertainties in the retrieved cloud parameters is a daunting task. Sensitivity studies of the retrieved parameters to the various inputs for a particular cloud type are helpful for understanding the errors associated with the retrieval algorithm relative to the plane-parallel world assumed in most of the model clouds that serve as the basis for the retrievals. Real world clouds, however, rarely fit the plane-parallel mold and generate radiances that likely produce much greater errors in the retrieved parameter than can be inferred from sensitivity analyses. Thus, independent, empirical methods are used to provide a more reliable uncertainty analysis. At NASA Langley, cloud properties are being retrieved from both geostationary (GEO) and low-earth orbiting (LEO) satellite imagers for climate monitoring and model validation as part of the NASA CERES project since 2000 and from AVHRR data since 1978 as part of the NOAA CDR program. Cloud properties are also being retrieved in near-real time globally from both GEO and LEO satellites for weather model assimilation and nowcasting for hazards such as aircraft icing. This paper discusses the various independent datasets and approaches that are used to assessing the imager-based satellite cloud retrievals. These include, but are not limited to data from ARM sites, CloudSat, and CALIPSO. This paper discusses the use of the various

  17. Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

    Energy Technology Data Exchange (ETDEWEB)

    Clark, B.

    2010-07-01

    The Taita Hills in southeastern Kenya form the northernmost part of Africa's Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor (rho{sub s}). Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable (rho{sub s}) throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular (rho{sub s}) field measurements were taken and where horizontal visibility meteorological data concurrent with image

  18. Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors

    Science.gov (United States)

    Winker, David; Chepfer, Helene; Noel, Vincent; Cai, Xia

    2017-11-01

    Cloud profiling from active lidar and radar in the A-train satellite constellation has significantly advanced our understanding of clouds and their role in the climate system. Nevertheless, the response of clouds to a warming climate remains one of the largest uncertainties in predicting climate change and for the development of adaptions to change. Both observation of long-term changes and observational constraints on the processes responsible for those changes are necessary. We review recent progress in our understanding of the cloud feedback problem. Capabilities and advantages of active sensors for observing clouds are discussed, along with the importance of active sensors for deriving constraints on cloud feedbacks as an essential component of a global climate observing system.

  19. NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE): Changing patterns in the use of NRT satellite imagery

    Science.gov (United States)

    Davies, D.; Michael, K.; Schmaltz, J. E.; Harrison, S.; Ding, F.; Durbin, P. B.; Boller, R. A.; Cechini, M. F.; Rinsland, P. L.; Ye, G.; Mauoka, E.

    2015-12-01

    NASA's Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE) provides data and imagery approximately 3 hours from satellite observation, to monitor natural events globally and to meet the needs of the near real-time (NRT) applications community. This article describes LANCE, and how the use of NRT data and imagery has evolved. Since 2010 there has been a four-fold increase in both the volume of data and the number of files downloaded. Over the last year there has been a marked shift in the way in which users are accessing NRT imagery; users are gravitating towards Worldview and the Global Imagery Browse Services (GIBS) and away from MODIS Rapid Response, in part due to the increased exposure through social media. In turn this is leading to a broader range of users viewing NASA NRT imagery. This article also describes new, and planned, product enhancements to LANCE. Over the last year, LANCE has expanded to support NRT products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Multi-angle Imaging SpectroRadiometer (MISR). LANCE elements are also planning to ingest and process NRT data from the Visible Infrared Imager Radiometer Suite (VIIRS), and the advanced Ozone Mapping and Profiler Suite (OMPS) instruments onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite in the near future.

  20. Effective Detection of Sub-Surface Archeological Features from Laser Scanning Point Clouds and Imagery Data

    Science.gov (United States)

    Fryskowska, A.; Kedzierski, M.; Walczykowski, P.; Wierzbicki, D.; Delis, P.; Lada, A.

    2017-08-01

    The archaeological heritage is non-renewable, and any invasive research or other actions leading to the intervention of mechanical or chemical into the ground lead to the destruction of the archaeological site in whole or in part. For this reason, modern archeology is looking for alternative methods of non-destructive and non-invasive methods of new objects identification. The concept of aerial archeology is relation between the presence of the archaeological site in the particular localization, and the phenomena that in the same place can be observed on the terrain surface form airborne platform. One of the most appreciated, moreover, extremely precise, methods of such measurements is airborne laser scanning. In research airborne laser scanning point cloud with a density of 5 points/sq. m was used. Additionally unmanned aerial vehicle imagery data was acquired. Test area is located in central Europe. The preliminary verification of potentially microstructures localization was the creation of digital terrain and surface models. These models gave an information about the differences in elevation, as well as regular shapes and sizes that can be related to the former settlement/sub-surface feature. The paper presents the results of the detection of potentially sub-surface microstructure fields in the forestry area.

  1. EFFECTIVE DETECTION OF SUB-SURFACE ARCHEOLOGICAL FEATURES FROM LASER SCANNING POINT CLOUDS AND IMAGERY DATA

    Directory of Open Access Journals (Sweden)

    A. Fryskowska

    2017-08-01

    Full Text Available The archaeological heritage is non-renewable, and any invasive research or other actions leading to the intervention of mechanical or chemical into the ground lead to the destruction of the archaeological site in whole or in part. For this reason, modern archeology is looking for alternative methods of non-destructive and non-invasive methods of new objects identification. The concept of aerial archeology is relation between the presence of the archaeological site in the particular localization, and the phenomena that in the same place can be observed on the terrain surface form airborne platform. One of the most appreciated, moreover, extremely precise, methods of such measurements is airborne laser scanning. In research airborne laser scanning point cloud with a density of 5 points/sq. m was used. Additionally unmanned aerial vehicle imagery data was acquired. Test area is located in central Europe. The preliminary verification of potentially microstructures localization was the creation of digital terrain and surface models. These models gave an information about the differences in elevation, as well as regular shapes and sizes that can be related to the former settlement/sub-surface feature. The paper presents the results of the detection of potentially sub-surface microstructure fields in the forestry area.

  2. Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

    Directory of Open Access Journals (Sweden)

    Niangang Jiao

    2018-05-01

    Full Text Available With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth’s environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs. Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method.

  3. Does the Data Resolution/origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant Invasions

    Science.gov (United States)

    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.

  4. Sherlock Holmes' or Don Quixote`s certainty? Interpretations of cropmarks on satellite imageries in archaeological investigation

    Science.gov (United States)

    Wilgocka, Aleksandra; RÄ czkowski, Włodzimierz; Kostyrko, Mikołaj; Ruciński, Dominik

    2016-08-01

    Years of experience in air-photo interpretations provide us to conclusion that we know what we are looking at, we know why we can see cropmarks, we even can estimate, when are the best opportunities to observe them. But even today cropmarks may be a subject of misinterpretation or wishful thinking. The same problems appear when working with aerial photographs, satellite imageries, ALS, geophysics, etc. In the paper we present several case studies based on data acquired for and within ArchEO - archaeological applications of Earth Observation techniques project to discuss complexity and consequences of archaeological interpretations. While testing usefulness of satellite imagery in Poland on various types of sites, cropmarks were the most frequent indicators of past landscapes as well as archaeological and natural features. Hence, new archaeological sites have been discovered mainly thanks to cropmarks. This situation has given us an opportunity to test not only satellite imageries as a source of data but also confront them with results of other non-invasive methods of data acquisition. When working with variety of data we have met several issues which raised problems of interpretation. Consequently, questions related to the cognitive value of remote sensing data appear and should be discussed. What do the data represent? To what extent the imageries, cropmarks or other visualizations represent the past? How should we deal with ambiguity of data? What can we learn from pitfalls in the interpretation of cropmarks, soilmarks etc. to share more Sherlock's methodology rather than run around Don Quixote's delusions?

  5. Nonlinear bias compensation of ZiYuan-3 satellite imagery with cubic splines

    Science.gov (United States)

    Cao, Jinshan; Fu, Jianhong; Yuan, Xiuxiao; Gong, Jianya

    2017-11-01

    Like many high-resolution satellites such as the ALOS, MOMS-2P, QuickBird, and ZiYuan1-02C satellites, the ZiYuan-3 satellite suffers from different levels of attitude oscillations. As a result of such oscillations, the rational polynomial coefficients (RPCs) obtained using a terrain-independent scenario often have nonlinear biases. In the sensor orientation of ZiYuan-3 imagery based on a rational function model (RFM), these nonlinear biases cannot be effectively compensated by an affine transformation. The sensor orientation accuracy is thereby worse than expected. In order to eliminate the influence of attitude oscillations on the RFM-based sensor orientation, a feasible nonlinear bias compensation approach for ZiYuan-3 imagery with cubic splines is proposed. In this approach, no actual ground control points (GCPs) are required to determine the cubic splines. First, the RPCs are calculated using a three-dimensional virtual control grid generated based on a physical sensor model. Second, one cubic spline is used to model the residual errors of the virtual control points in the row direction and another cubic spline is used to model the residual errors in the column direction. Then, the estimated cubic splines are used to compensate the nonlinear biases in the RPCs. Finally, the affine transformation parameters are used to compensate the residual biases in the RPCs. Three ZiYuan-3 images were tested. The experimental results showed that before the nonlinear bias compensation, the residual errors of the independent check points were nonlinearly biased. Even if the number of GCPs used to determine the affine transformation parameters was increased from 4 to 16, these nonlinear biases could not be effectively compensated. After the nonlinear bias compensation with the estimated cubic splines, the influence of the attitude oscillations could be eliminated. The RFM-based sensor orientation accuracies of the three ZiYuan-3 images reached 0.981 pixels, 0.890 pixels, and 1

  6. gProcess and ESIP Platforms for Satellite Imagery Processing over the Grid

    Science.gov (United States)

    Bacu, Victor; Gorgan, Dorian; Rodila, Denisa; Pop, Florin; Neagu, Gabriel; Petcu, Dana

    2010-05-01

    The Environment oriented Satellite Data Processing Platform (ESIP) is developed through the SEE-GRID-SCI (SEE-GRID eInfrastructure for regional eScience) co-funded by the European Commission through FP7 [1]. The gProcess Platform [2] is a set of tools and services supporting the development and the execution over the Grid of the workflow based processing, and particularly the satelite imagery processing. The ESIP [3], [4] is build on top of the gProcess platform by adding a set of satellite image processing software modules and meteorological algorithms. The satellite images can reveal and supply important information on earth surface parameters, climate data, pollution level, weather conditions that can be used in different research areas. Generally, the processing algorithms of the satellite images can be decomposed in a set of modules that forms a graph representation of the processing workflow. Two types of workflows can be defined in the gProcess platform: abstract workflow (PDG - Process Description Graph), in which the user defines conceptually the algorithm, and instantiated workflow (iPDG - instantiated PDG), which is the mapping of the PDG pattern on particular satellite image and meteorological data [5]. The gProcess platform allows the definition of complex workflows by combining data resources, operators, services and sub-graphs. The gProcess platform is developed for the gLite middleware that is available in EGEE and SEE-GRID infrastructures [6]. gProcess exposes the specific functionality through web services [7]. The Editor Web Service retrieves information on available resources that are used to develop complex workflows (available operators, sub-graphs, services, supported resources, etc.). The Manager Web Service deals with resources management (uploading new resources such as workflows, operators, services, data, etc.) and in addition retrieves information on workflows. The Executor Web Service manages the execution of the instantiated workflows

  7. Seeing is believing I: The use of thermal sensing from satellite imagery to predict crop yield

    International Nuclear Information System (INIS)

    Potgieter A B; Rodriguez D; Power B; Mclean J; Davis P

    2014-01-01

    Volatility in crop production has been part of the Australian environment since cropping began with the arrival of the first European settlers. Climate variability is the main factor affecting crop production at national, state and local scales. At field level spatial patterns on yield production are also determined by spatially changing soil properties in interaction with seasonal climate conditions and weather patterns at critical stages in the crop development. Here we used a combination of field level weather records, canopy characteristics, and satellite information to determine the spatial performance of a large field of wheat. The main objective of this research is to determine the ability of remote sensing technologies to capture yield losses due to water stress at the canopy level. The yield, canopy characteristics (i.e. canopy temperature and ground cover) and seasonal conditions of a field of wheat (∼1400ha) (-29.402° South and 149.508°, New South Wales, Australia) were continuously monitored during the winter of 2011. Weather and crop variables were continuously monitored by installing three automatic weather stations in a transect covering different positions and soils in the landscape. Weather variables included rainfall, minimum and maximum temperatures and relative humidity, and crop characteristics included ground cover and canopy temperature. Satellite imagery Landsat TM 5 and 7 was collected at five different stages in the crop cycle. Weather variables and crop characteristics were used to calculate a crop stress index (CSI) at point and field scale (39 fields). Field data was used to validate a spatial satellite image derived index. Spatial yield data was downloaded from the harvester at the different locations in the field. We used the thermal band (land surface temperature, LST) and enhanced vegetation index (EVI) bands from the MODIS (250 m for visible bands and 1km for thermal band) and a derived EVI from Landsat TM 7 (25 m for visible

  8. Long-term Satellite Observations of Cloud and Aerosol Radiative Effects Using the (A)ATSR Satellite Data Record

    Science.gov (United States)

    Christensen, M.; McGarragh, G.; Thomas, G.; Povey, A.; Proud, S.; Poulsen, C. A.; Grainger, R. G.

    2016-12-01

    Radiative forcing by clouds, aerosols, and their interactions constitute some of the largest sources of uncertainties in the climate system (Chapter 7 IPCC, 2013). It is essential to understand the past through examination of long-term satellite observation records to provide insight into the uncertainty characteristics of these radiative forcers. As part of the ESA CCI (Climate Change Initiative) we have recently implemented a broadband radiative flux algorithm (known as BUGSrad) into the Optimal Retrieval for Aerosol and Cloud (ORAC) scheme. ORAC achieves radiative consistency of its aerosol and cloud products through an optimal estimation scheme and is highly versatile, enabling retrievals for numerous satellite sensors: ATSR, MODIS, VIIRS, AVHRR, SLSTR, SEVIRI, and AHI. An analysis of the 17-year well-calibrated Along Track Scanning Radiometer (ATSR) data is used to quantify trends in cloud and aerosol radiative effects over a wide range of spatiotemporal scales. The El Niño Southern Oscillation stands out as the largest contributing mode of variability to the radiative energy balance (long wave and shortwave fluxes) at the top of the atmosphere. Furthermore, trends in planetary albedo show substantial decreases across the Arctic Ocean (likely due to the melting of sea ice and snow) and modest increases in regions dominated by stratocumulus (e.g., off the coast of California) through notable increases in cloud fraction and liquid water path. Finally, changes in volcanic activity and biomass burning aerosol over this period show sizeable radiative forcing impacts at local-scales. We will demonstrate that radiative forcing from aerosols and clouds have played a significant role in the identified key climate processes using 17 years of satellite observational data.

  9. Visual attention based detection of signs of anthropogenic activities in satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Skurikhin, Alexei N [Los Alamos National Laboratory

    2010-10-13

    With increasing deployment of satellite imaging systems, only a small fraction of collected data can be subject to expert scrutiny. We present and evaluate a two-tier approach to broad area search for signs of anthropogenic activities in high-resolution commercial satellite imagery. The method filters image information using semantically oriented interest points by combining Harris corner detection and spatial pyramid matching. The idea is that anthropogenic structures, such as rooftop outlines, fence corners, road junctions, are locally arranged in specific angular relations to each other. They are often oriented at approximately right angles to each other (which is known as rectilinearity relation). Detecting the rectilinearity provides an opportunity to highlight regions most likely to contain anthropogenic activity. This is followed by supervised classification of regions surrounding the detected corner points as man-made vs. natural scenes. We consider, in particular, a search for anthropogenic activities in uncluttered areas. In this paper, we proposed and evaluated a two-tier approach to broad area search for signs of anthropogenic activities. Results from experiments on high-resolution ({approx}0.6m) commercial satellite image data showed the potential applicability of this approach and its ability of achieving both high precision and recall rates. The main advantage of combining corner-based cueing with general object recognition is that the incorporation of domain specific knowledge even in its more general form, such as presence of comers, provides a useful cue to narrow the focus of search for signs of anthropogenic activities. Combination of comer based cueing with spatial pyramid matching addressed the issue of comer categorization. An important practical issue for further research is optimizing the balance between false positive and false negative rates. While the results presented in the paper are encouraging, the problem of an automated broad area

  10. Limitations and potential of satellite imagery to monitor environmental response to coastal flooding

    Science.gov (United States)

    Ramsey, Elijah W.; Werle, Dirk; Suzuoki, Yukihiro; Rangoonwala, Amina; Lu, Zhong

    2012-01-01

    Storm-surge flooding and marsh response throughout the coastal wetlands of Louisiana were mapped using several types of remote sensing data collected before and after Hurricanes Gustav and Ike in 2008. These included synthetic aperture radar (SAR) data obtained from the (1) C-band advance SAR (ASAR) aboard the Environmental Satellite, (2) phased-array type L-band SAR (PALSAR) aboard the Advanced Land Observing Satellite, and (3) optical data obtained from Thematic Mapper (TM) sensor aboard the Land Satellite (Landsat). In estuarine marshes, L-band SAR and C-band ASAR provided accurate flood extent information when depths averaged at least 80 cm, but only L-band SAR provided consistent subcanopy detection when depths averaged 50 cm or less. Low performance of inundation mapping based on C-band ASAR was attributed to an apparent inundation detection limit (>30 cm deep) in tall Spartina alterniflora marshes, a possible canopy collapse of shoreline fresh marsh exposed to repeated storm-surge inundations, wind-roughened water surfaces where water levels reached marsh canopy heights, and relatively high backscatter in the near-range portion of the SAR imagery. A TM-based vegetation index of live biomass indicated that the severity of marsh dieback was linked to differences in dominant species. The severest impacts were not necessarily caused by longer inundation but rather could be caused by repeated exposure of the palustrine marsh to elevated salinity floodwaters. Differential impacts occurred in estuarine marshes. The more brackish marshes on average suffered higher impacts than the more saline marshes, particularly the nearshore coastal marshes occupied by S. alterniflora.

  11. Using Satellite Imagery to Quantify Water Quality Impacts and Recovery from Hurricane Harvey

    Science.gov (United States)

    Sobel, R. S.; Kiaghadi, A.; Rifai, H. S.

    2017-12-01

    Record rainfall during Hurricane Harvey in the Houston-Galveston region generated record flows containing suspended sediment that was likely contaminated. Conventional water quality monitoring requires resource intensive field campaigns, and produces sparse datasets. In this study, satellite data were used to quantify suspended sediment (TSS) concentrations and mass within the region's estuary system and to estimate sediment deposition and transport. A conservative two band, red-green empirical regression was developed from the Sentinel 2 satellite to calculate TSS concentrations and masses. The regression was calibrated with an R2 = 0.73 (n=28) and validated with an R2 = 0.75 (n=12) using 2016 & 2017 imagery. TSS concentrations four days, 14 days, and 44 days post-storm were compared with a reference condition three days before storm arrival. Results indicated that TSS concentrations were an average of 100% higher four days post-storm, and 150% higher after 14 days, however, the average concentration on day 144 was only seven percent higher than the reference condition, suggesting the estuary system is approaching recovery to pre-storm conditions. Sediment masses were determined from the regressed concentrations and water volumes estimated from a bottom elevation grid combined with water surface elevations observed coincidently with the satellite image. While water volumes were only 13% higher on both day four and day 14 post-storm; sediment masses were 195% and 227% higher than the reference condition, respectively. By day 44, estuary sediment mass returned to just 2.9% above the reference load. From a mechanistic standpoint, the elevated TSS concentrations on day four indicated an advection-based regime due to stormwater runoff draining through the estuarine system. Sometime, however, between days 14 and 44, transport conditions switched from advection-dominated to deposition-driven as indicated by the near normal TSS concentrations on day 44.

  12. Galactic cosmic ray and El Nino Southern Oscillation trends in International Satellite Cloud Climatology Project D2 low-cloud properties

    DEFF Research Database (Denmark)

    Marsh, N.; Svensmark, Henrik

    2003-01-01

    [1] The recently reported correlation between clouds and galactic cosmic rays (GCR) implies the existence of a previously unknown process linking solar variability and climate. An analysis of the interannual variability of International Satellite Cloud Climatology Project D2 (ISCCP-D2) low-cloud...... a strong correlation with GCR, which suggests that low-cloud properties observed in these regions are less likely to be contaminated from overlying cloud. The GCR-low cloud correlation cannot easily be explained by internal climate processes, changes in direct solar forcing, or UV-ozone interactions...... properties over the period July 1983 to August 1994 suggests that low clouds are statistically related to two processes, (1) GCR and (2) El Nino-Southern Oscillation (ENSO), with GCR explaining a greater percentage of the total variance. Areas where satellites have an unobstructed view of low cloud possess...

  13. Monitoring the effect of restoration measures in Indonesian peatlands by radar satellite imagery.

    Science.gov (United States)

    Jaenicke, J; Englhart, S; Siegert, F

    2011-03-01

    In the context of the ongoing climate change discussions the importance of peatlands as carbon stores is increasingly recognised in the public. Drainage, deforestation and peat fires are the main reasons for the release of huge amounts of carbon from peatlands. Successful restoration of degraded tropical peatlands is of high interest due to their huge carbon store and sequestration potential. The blocking of drainage canals by dam building has become one of the most important measures to restore the hydrology and the ecological function of the peat domes. This study investigates the capability of using multitemporal radar remote sensing imagery for monitoring the hydrological effects of these measures. The study area is the former Mega Rice Project area in Central Kalimantan, Indonesia, where peat drainage and forest degradation is especially intense. Restoration measures started in July 2004 by building 30 large dams until June 2008. We applied change detection analysis with more than 80 ENVISAT ASAR and ALOS PALSAR images, acquired between 2004 and 2009. Radar signal increases of up to 1.36 dB show that high frequency multitemporal radar satellite imagery can be used to detect an increase in peat soil moisture after dam construction, especially in deforested areas with a high density of dams. Furthermore, a strong correlation between cross-polarised radar backscatter coefficients and groundwater levels above -50 cm was found. Monitoring peatland rewetting and quantifying groundwater level variations is important information for vegetation re-establishment, fire hazard warning and making carbon emission mitigation tradable under the voluntary carbon market or REDD (Reducing Emissions from Deforestation and Degradation) mechanism. Copyright © 2010 Elsevier Ltd. All rights reserved.

  14. Multi-Temporal Satellite Imagery for Urban Expansion Assessment at Sharjah City /UAE

    International Nuclear Information System (INIS)

    Al-Ruzouq, R; Shanableh, A

    2014-01-01

    Change detection is the process of identifying differences in land cover over time. As human and natural forces continue to alter the landscape, it is important to develop monitoring methods to assess and quantify these changes. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, are allowing for efficient identification of change patterns and the prediction of areas of growth. Sharjah is the third largest and most populous city in the United Arab Emirates (UAE). It is located along the northern coast of the Persian Gulf on the Arabian Peninsula. After the discovery of oil and its export in the last four decades at UAE, it has experienced very rapid growth in industry, economy and population. The main purpose of this study is to detect urban development in Sharjah city by detecting and registering linear features in multi-temporal Landsat images. This paper used linear features for image registration that were chosen since they can be reliably extracted from imagery with significantly different geometric and radiometric properties. Derived edges from the registered images are used as the basis for change detection. Image registration and pixel-pixel subtraction has been implement using multi- temporal Landsat images for Sharjah City. Straight-line segments have been used for accurate co-registration as well as main element for a reliable change detection procedure. Results illustrate that highest range of growth that represented by linear features (building and roads) have been accrued during 1976 – 1987 and stand for 36.24% of the total urban features inside Sharjah city. Moreover, result shows that since 1976 to 2010, the cumulative urban expansion inside Sharjah city is 71.9%

  15. Atmospheric Correction Performance of Hyperspectral Airborne Imagery over a Small Eutrophic Lake under Changing Cloud Cover

    Directory of Open Access Journals (Sweden)

    Lauri Markelin

    2016-12-01

    Full Text Available Atmospheric correction of remotely sensed imagery of inland water bodies is essential to interpret water-leaving radiance signals and for the accurate retrieval of water quality variables. Atmospheric correction is particularly challenging over inhomogeneous water bodies surrounded by comparatively bright land surface. We present results of AisaFENIX airborne hyperspectral imagery collected over a small inland water body under changing cloud cover, presenting challenging but common conditions for atmospheric correction. This is the first evaluation of the performance of the FENIX sensor over water bodies. ATCOR4, which is not specifically designed for atmospheric correction over water and does not make any assumptions on water type, was used to obtain atmospherically corrected reflectance values, which were compared to in situ water-leaving reflectance collected at six stations. Three different atmospheric correction strategies in ATCOR4 was tested. The strategy using fully image-derived and spatially varying atmospheric parameters produced a reflectance accuracy of ±0.002, i.e., a difference of less than 15% compared to the in situ reference reflectance. Amplitude and shape of the remotely sensed reflectance spectra were in general accordance with the in situ data. The spectral angle was better than 4.1° for the best cases, in the spectral range of 450–750 nm. The retrieval of chlorophyll-a (Chl-a concentration using a popular semi-analytical band ratio algorithm for turbid inland waters gave an accuracy of ~16% or 4.4 mg/m3 compared to retrieval of Chl-a from reflectance measured in situ. Using fixed ATCOR4 processing parameters for whole images improved Chl-a retrieval results from ~6 mg/m3 difference to reference to approximately 2 mg/m3. We conclude that the AisaFENIX sensor, in combination with ATCOR4 in image-driven parametrization, can be successfully used for inland water quality observations. This implies that the need for in situ

  16. The Dependence of Cloud Property Trend Detection on Absolute Calibration Accuracy of Passive Satellite Sensors

    Science.gov (United States)

    Shea, Y.; Wielicki, B. A.; Sun-Mack, S.; Minnis, P.; Zelinka, M. D.

    2016-12-01

    Detecting trends in climate variables on global, decadal scales requires highly accurate, stable measurements and retrieval algorithms. Trend uncertainty depends on its magnitude, natural variability, and instrument and retrieval algorithm accuracy and stability. We applied a climate accuracy framework to quantify the impact of absolute calibration on cloud property trend uncertainty. The cloud properties studied were cloud fraction, effective temperature, optical thickness, and effective radius retrieved using the Clouds and the Earth's Radiant Energy System (CERES) Cloud Property Retrieval System, which uses Moderate-resolution Imaging Spectroradiometer measurements (MODIS). Modeling experiments from the fifth phase of the Climate Model Intercomparison Project (CMIP5) agree that net cloud feedback is likely positive but disagree regarding its magnitude, mainly due to uncertainty in shortwave cloud feedback. With the climate accuracy framework we determined the time to detect trends for instruments with various calibration accuracies. We estimated a relationship between cloud property trend uncertainty, cloud feedback, and Equilibrium Climate Sensitivity and also between effective radius trend uncertainty and aerosol indirect effect trends. The direct relationship between instrument accuracy requirements and climate model output provides the level of instrument absolute accuracy needed to reduce climate model projection uncertainty. Different cloud types have varied radiative impacts on the climate system depending on several attributes, such as their thermodynamic phase, altitude, and optical thickness. Therefore, we also conducted these studies by cloud types for a clearer understanding of instrument accuracy requirements needed to detect changes in their cloud properties. Combining this information with the radiative impact of different cloud types helps to prioritize among requirements for future satellite sensors and understanding the climate detection

  17. A first map of tropical Africa's above-ground biomass derived from satellite imagery

    International Nuclear Information System (INIS)

    Baccini, A; Laporte, N; Goetz, S J; Sun, M; Dong, H

    2008-01-01

    Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000-2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha -1 for a range of biomass between 0 and 454 Mg ha -1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate that the model successfully captured the regional distribution of AGB. The results showed a strong positive correlation (R 2 = 0.90) between the GLAS height metrics and predicted AGB.

  18. Cirrus cloud-temperature interactions over a tropical station, Gadanki from lidar and satellite observations

    International Nuclear Information System (INIS)

    S, Motty G; Satyanarayana, M.; Krishnakumar, V.; Dhaman, Reji k.

    2014-01-01

    The cirrus clouds play an important role in the radiation budget of the earth's atmospheric system and are important to characterize their vertical structure and optical properties. LIDAR measurements are obtained from the tropical station Gadanki (13.5 0 N, 79.2 0 E), India, and meteorological indicators derived from Radiosonde data. Most of the cirrus clouds are observed near to the tropopause, which substantiates the strength of the tropical convective processes. The height and temperature dependencies of cloud height, optical depth, and depolarization ratio were investigated. Cirrus observations made using CALIPSO satellite are compared with lidar data for systematic statistical study of cirrus climatology

  19. Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate

    International Nuclear Information System (INIS)

    Hashim, M; Pour, A B; Onn, C H

    2014-01-01

    Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction of Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution of cloud contaminations, are always a critical task in the tropical climate. This paper suggests a fast and alternative approach to remove cloud and shadow contaminations for Landsat Enhanced Thematic Mapper + (ETM + ) multi temporal datasets. Band 3 and Band 4 from all the Landsat ETM + dataset are two main spectral bands that are very crucial in this study for cloud removal technique. The Normalise difference vegetation index (NDVI) and the normalised difference soil index (NDSI) are two main derivatives derived from the datasets. Change vector analysis is used in this study to seek the vegetation dynamics. The approach developed in this study for cloud optimizing can be broadly applicable for optical remote sensing satellite data, which are seriously obscured with heavy cloud contamination in the tropical climate

  20. 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.)

  1. Monitoring vegetation change in Abu Dhabi Emirate from 1996 to 2000 and 2004 using Landsat Satellite Imagery

    International Nuclear Information System (INIS)

    Starbuck, M.J.; Tamayo, J.

    2007-01-01

    In the fall of 2001, a study was initiated to investigate vegetation changes in the Abu Dhabi Emirates. The vast majority of vegetation present in the region is irrigated and analysis of vegetation change will support groundwater investigations in the region by indicating areas of increased water use. Satellite-based imaging systems provide a good source of data for such an analysis. The recent analysis was completed between February and November 2002 using Landsat 5 Thematic Mapper satellite imagery acquired in 1996 and Landsat 7 Enhanced Thematic Mapper Plus imagery acquired in 2000. These assessments were augmented in 2004with the study of Landsat 7 imagery acquired in early 2004. The total area of vegetation for each of seven study areas was calculated using the Normalized Difference Vegetation Index (NDVI) technique. Multiband image classification was used to differentiate general vegetation types. Change analysis consisted of simple NDVI image differencing and post-classification change matrices. Measurements of total vegetation are for the Abu Dhabi Emirate indicate an increase from 77,200 hectares in 1996 to 162,700 hectares in 2000 (110% increase). Based on comparison with manual interpretation of satellite imagery, the amount of under-reporting of irrigated land is estimated at about 15% of the actual area. From the assessment of 2004 Landset imagery, it was found that the growth of irrigated vegetation in most areas of Emirate had stabilized and had actually slightly decreased in some cases. The decreases are probably due to variability in the measurement technique and not due to actual decreases in area of vegetation. (author)

  2. Progress in Near Real-Time Volcanic Cloud Observations Using Satellite UV Instruments

    Science.gov (United States)

    Krotkov, N. A.; Yang, K.; Vicente, G.; Hughes, E. J.; Carn, S. A.; Krueger, A. J.

    2011-12-01

    Volcanic clouds from explosive eruptions can wreak havoc in many parts of the world, as exemplified by the 2010 eruption at the Eyjafjöll volcano in Iceland, which caused widespread disruption to air traffic and resulted in economic impacts across the globe. A suite of satellite-based systems offer the most effective means to monitor active volcanoes and to track the movement of volcanic clouds globally, providing critical information for aviation hazard mitigation. Satellite UV sensors, as part of this suite, have a long history of making unique near-real time (NRT) measurements of sulfur dioxide (SO2) and ash (aerosol Index) in volcanic clouds to supplement operational volcanic ash monitoring. Recently a NASA application project has shown that the use of near real-time (NRT,i.e., not older than 3 h) Aura/OMI satellite data produces a marked improvement in volcanic cloud detection using SO2 combined with Aerosol Index (AI) as a marker for ash. An operational online NRT OMI AI and SO2 image and data product distribution system was developed in collaboration with the NOAA Office of Satellite Data Processing and Distribution. Automated volcanic eruption alarms, and the production of volcanic cloud subsets for multiple regions are provided through the NOAA website. The data provide valuable information in support of the U.S. Federal Aviation Administration goal of a safe and efficient National Air Space. In this presentation, we will highlight the advantages of UV techniques and describe the advances in volcanic SO2 plume height estimation and enhanced volcanic ash detection using hyper-spectral UV measurements, illustrated with Aura/OMI observations of recent eruptions. We will share our plan to provide near-real-time volcanic cloud monitoring service using the Ozone Mapping and Profiler Suite (OMPS) on the Joint Polar Satellite System (JPSS).

  3. Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

    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.

  4. Using Satellite Imagery to Monitor the Major Lakes; Case Study Lake Hamun

    Science.gov (United States)

    Norouzi, H.; Islam, R.; Bah, A.; AghaKouchak, A.

    2015-12-01

    Proper lakes function can ease the impact of floods and drought especially in arid and semi-arid regions. They are important environmentally and can directly affect human lives. Better understanding of the effect of climate change and human-driven changes on lakes would provide invaluable information for policy-makers and local people. As part of a comprehensive study, we aim to monitor the land-cover/ land-use changes in the world's major lakes using satellite observations. As a case study, Hamun Lake which is a pluvial Lake, also known as shallow Lake, located on the south-east of Iran and adjacent to Afghanistan, and Pakistan borders is investigated. The Lake is the main source of resources (agriculture, fishing and hunting) for the people around it and politically important in the region since it is shared among three different countries. The purpose of the research is to find the Lake's area from 1972 to 2015 and to see if any drought or water resources management has affected the lake. Analyzing satellites imagery from Landsat shows that the area of the Lake changes seasonally and intra-annually. Significant seasonal effects are found in 1975,1977, 1987, 1993, 1996, 1998, 2000, 2009 and 2011, as well as, substantial amount of shallow water is found throughout the years. The precipitation records as well as drought historical records are studied for the lake's basin. Meteorological studies suggest that the drought, decrease of rainfalls in the province and the improper management of the Lake have caused environmental, economic and geographical consequences. The results reveal that lake has experienced at least two prolong dryings since 1972 which drought cannot solely be blamed as main forcing factor.Proper lakes function can ease the impact of floods and drought especially in arid and semi-arid regions. They are important environmentally and can directly affect human lives. Better understanding of the effect of climate change and human-driven changes on lakes

  5. Detailed Maps Depicting the Shallow-Water Benthic Habitats of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery

    Data.gov (United States)

    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...

  6. Detailed Maps Depicting the Shallow-Water Benthic Habitats of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

    Data.gov (United States)

    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...

  7. Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery.

    Science.gov (United States)

    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.

  8. Monitoring of Conservation Tillage and Tillage Intensity by Ground and Satellite Imagery

    Directory of Open Access Journals (Sweden)

    M.A Rostami

    2014-09-01

    Full Text Available Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. The present study was devoted to investigate accurate methods for monitoring residue management and tillage practices. The satellite imagery technique was used as a rapid and spatially explicit method for delineating crop residue coverage and as an estimator of conservation tillage adoption and intensity. The potential of multispectral high-spatial resolution WorldView-2 local data was evaluated using the total of eleven satellite spectral indices and Linear Spectral Unmixing Analysis (LSUA. The total of ninety locations was selected for this study and for each location the residue coverage was measured by the image processing method and recorded as ground control. The output of indices and LSUA method were individually correlated to the control and the relevant R2 was calculated. Results indicated that crop residue cover was related to IPVI, RVI1, RVI2 and GNDVI spectral indices and satisfactory correlations were established (0.74 - 0.81. The crop residue coverage estimated from the LSUA approach was found to be correlated with the ground residue data (0.75. Two effective indices named as Infrared Percentage Vegetation Index (IPVI and Ratio Vegetation Index (RVI with maximum R2 were considered for classification of tillage intensity. Results indicated that the classification accuracy with IPVI and RVI indices in different conditions varied from 78-100 percent and therefore in good agreement with ground measurement, observations and field records.

  9. Calculating Viewing Angles Pixel by Pixel in Optical Remote Sensing Satellite Imagery Using the Rational Function Model

    OpenAIRE

    Kai Xu; Guo Zhang; Qingjun Zhang; Deren Li

    2018-01-01

    In studies involving the extraction of surface physical parameters using optical remote sensing satellite imagery, sun-sensor geometry must be known, especially for sensor viewing angles. However, while pixel-by-pixel acquisitions of sensor viewing angles are of critical importance to many studies, currently available algorithms for calculating sensor-viewing angles focus only on the center-point pixel or are complicated and are not well known. Thus, this study aims to provide a simple and ge...

  10. Seasonally-managed wetland footprint delineation using Landsat ETM+ satellite imagery

    Energy Technology Data Exchange (ETDEWEB)

    Quinn, Nigel W. T. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Epshtein, Olga [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Arizona State Univ., Tempe, AZ (United States). School of Sustainable Engineering and the Built Environment

    2014-01-09

    One major challenge in water resource management is the estimation of evapotranspiration losses from seasonally managed wetlands. Quantifying these losses is complicated by the dynamic nature of the wetlands' areal footprint during the periods of flood-up and drawdown. In this paper, we present a data-lean solution to this problem using an example application in the San Joaquin Basin, California. Through analysis of high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) satellite imagery, we develop a metric to better capture the extent of total flooded wetland area. The procedure is validated using year-long, continuously-logged field datasets for two wetlands within the study area. The proposed classification which uses a Landsat ETM + Band 5 (mid-IR wavelength) to Band 2 (visible green wavelength) ratio improves estimates by 30–50% relative to previous wetland delineation studies. Finally, requiring modest ancillary data, the study results provide a practical and efficient option for wetland management in data-sparse regions or un-gauged watersheds.

  11. FULLY AUTOMATED GENERATION OF ACCURATE DIGITAL SURFACE MODELS WITH SUB-METER RESOLUTION FROM SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    J. Wohlfeil

    2012-07-01

    Full Text Available Modern pixel-wise image matching algorithms like Semi-Global Matching (SGM are able to compute high resolution digital surface models from airborne and spaceborne stereo imagery. Although image matching itself can be performed automatically, there are prerequisites, like high geometric accuracy, which are essential for ensuring the high quality of resulting surface models. Especially for line cameras, these prerequisites currently require laborious manual interaction using standard tools, which is a growing problem due to continually increasing demand for such surface models. The tedious work includes partly or fully manual selection of tie- and/or ground control points for ensuring the required accuracy of the relative orientation of images for stereo matching. It also includes masking of large water areas that seriously reduce the quality of the results. Furthermore, a good estimate of the depth range is required, since accurate estimates can seriously reduce the processing time for stereo matching. In this paper an approach is presented that allows performing all these steps fully automated. It includes very robust and precise tie point selection, enabling the accurate calculation of the images’ relative orientation via bundle adjustment. It is also shown how water masking and elevation range estimation can be performed automatically on the base of freely available SRTM data. Extensive tests with a large number of different satellite images from QuickBird and WorldView are presented as proof of the robustness and reliability of the proposed method.

  12. Urban thermal environment and its biophysical parameters derived from satellite remote sensing imagery

    Science.gov (United States)

    Zoran, Maria A.; Savastru, Roxana S.; Savastru, Dan M.; Tautan, Marina N.; Baschir, Laurentiu V.

    2013-10-01

    In frame of global warming, the field of urbanization and urban thermal environment are important issues among scientists all over the world. This paper investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Bucharest metropolitan area of Romania based on satellite remote sensing imagery Landsat TM/ETM+, time series MODIS Terra/Aqua data and IKONOS acquired during 1990 - 2012 period. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also retrieved from thermal infrared band of Landsat TM/ETM+, from MODIS Terra/Aqua datasets. Based on these parameters, the urban growth, urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results indicated that the metropolitan area ratio of impervious surface in Bucharest increased significantly during two decades investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

  13. A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects

    Directory of Open Access Journals (Sweden)

    Ronald Scheirer

    2018-04-01

    Full Text Available Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images derived from visible channels appear often too bright and with reduced contrast. This hampers the use of RGB imagery otherwise useful in ocean color applications and in forecasting or operational disaster monitoring, for example forest fires. In order to correct for the dominant source of atmospheric noise, a simple, fast and flexible algorithm has been developed. The algorithm is implemented in Python and freely available in PySpectral which is part of the PyTroll family of open source packages, allowing easy access to powerful real-time image-processing tools. Pre-calculated look-up tables of top of atmosphere reflectance are derived by off-line calculations with RTM DISORT as part of the LibRadtran package. The approach is independent of platform and sensor bands, and allows it to be applied to any band in the visible spectral range. Due to the use of standard atmospheric profiles and standard aerosol loads, it is possible just to reduce the background disturbance. Thus signals from excess aerosols become more discernible. Examples of uncorrected and corrected satellite images demonstrate that this flexible real-time algorithm is a useful tool for atmospheric correction.

  14. Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

    Science.gov (United States)

    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.

  15. Effective System for Automatic Bundle Block Adjustment and Ortho Image Generation from Multi Sensor Satellite Imagery

    Science.gov (United States)

    Akilan, A.; Nagasubramanian, V.; Chaudhry, A.; Reddy, D. Rajesh; Sudheer Reddy, D.; Usha Devi, R.; Tirupati, T.; Radhadevi, P. V.; Varadan, G.

    2014-11-01

    Block Adjustment is a technique for large area mapping for images obtained from different remote sensingsatellites.The challenge in this process is to handle huge number of satellite imageries from different sources with different resolution and accuracies at the system level. This paper explains a system with various tools and techniques to effectively handle the end-to-end chain in large area mapping and production with good level of automation and the provisions for intuitive analysis of final results in 3D and 2D environment. In addition, the interface for using open source ortho and DEM references viz., ETM, SRTM etc. and displaying ESRI shapes for the image foot-prints are explained. Rigorous theory, mathematical modelling, workflow automation and sophisticated software engineering tools are included to ensure high photogrammetric accuracy and productivity. Major building blocks like Georeferencing, Geo-capturing and Geo-Modelling tools included in the block adjustment solution are explained in this paper. To provide optimal bundle block adjustment solution with high precision results, the system has been optimized in many stages to exploit the full utilization of hardware resources. The robustness of the system is ensured by handling failure in automatic procedure and saving the process state in every stage for subsequent restoration from the point of interruption. The results obtained from various stages of the system are presented in the paper.

  16. A FUZZY AUTOMATIC CAR DETECTION METHOD BASED ON HIGH RESOLUTION SATELLITE IMAGERY AND GEODESIC MORPHOLOGY

    Directory of Open Access Journals (Sweden)

    N. Zarrinpanjeh

    2017-09-01

    Full Text Available Automatic car detection and recognition from aerial and satellite images is mostly practiced for the purpose of easy and fast traffic monitoring in cities and rural areas where direct approaches are proved to be costly and inefficient. Towards the goal of automatic car detection and in parallel with many other published solutions, in this paper, morphological operators and specifically Geodesic dilation are studied and applied on GeoEye-1 images to extract car items in accordance with available vector maps. The results of Geodesic dilation are then segmented and labeled to generate primitive car items to be introduced to a fuzzy decision making system, to be verified. The verification is performed inspecting major and minor axes of each region and the orientations of the cars with respect to the road direction. The proposed method is implemented and tested using GeoEye-1 pansharpen imagery. Generating the results it is observed that the proposed method is successful according to overall accuracy of 83%. It is also concluded that the results are sensitive to the quality of available vector map and to overcome the shortcomings of this method, it is recommended to consider spectral information in the process of hypothesis verification.

  17. a Fuzzy Automatic CAR Detection Method Based on High Resolution Satellite Imagery and Geodesic Morphology

    Science.gov (United States)

    Zarrinpanjeh, N.; Dadrassjavan, F.

    2017-09-01

    Automatic car detection and recognition from aerial and satellite images is mostly practiced for the purpose of easy and fast traffic monitoring in cities and rural areas where direct approaches are proved to be costly and inefficient. Towards the goal of automatic car detection and in parallel with many other published solutions, in this paper, morphological operators and specifically Geodesic dilation are studied and applied on GeoEye-1 images to extract car items in accordance with available vector maps. The results of Geodesic dilation are then segmented and labeled to generate primitive car items to be introduced to a fuzzy decision making system, to be verified. The verification is performed inspecting major and minor axes of each region and the orientations of the cars with respect to the road direction. The proposed method is implemented and tested using GeoEye-1 pansharpen imagery. Generating the results it is observed that the proposed method is successful according to overall accuracy of 83%. It is also concluded that the results are sensitive to the quality of available vector map and to overcome the shortcomings of this method, it is recommended to consider spectral information in the process of hypothesis verification.

  18. Assessment of Mining Extent and Expansion in Myanmar Based on Freely-Available Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Katherine J. LaJeunesse Connette

    2016-11-01

    Full Text Available Using freely-available data and open-source software, we developed a remote sensing methodology to identify mining areas and assess recent mining expansion in Myanmar. Our country-wide analysis used Landsat 8 satellite data from a select number of mining areas to create a raster layer of potential mining areas. We used this layer to guide a systematic scan of freely-available fine-resolution imagery, such as Google Earth, in order to digitize likely mining areas. During this process, each mining area was assigned a ranking indicating our certainty in correct identification of the mining land use. Finally, we identified areas of recent mining expansion based on the change in albedo, or brightness, between Landsat images from 2002 and 2015. We identified 90,041 ha of potential mining areas in Myanmar, of which 58% (52,312 ha was assigned high certainty, 29% (26,251 ha medium certainty, and 13% (11,478 ha low certainty. Of the high-certainty mining areas, 62% of bare ground was disturbed (had a large increase in albedo since 2002. This four-month project provides the first publicly-available database of mining areas in Myanmar, and it demonstrates an approach for large-scale assessment of mining extent and expansion based on freely-available data.

  19. Coastline changes in North Bengkalis Island, Indonesia: satellite imagery analysis and observation

    Directory of Open Access Journals (Sweden)

    M Mubarak

    2018-01-01

    Full Text Available Coastal area activity on human exploitation greatly affected aquatic ecosystems. Land changes disturbed the level of soil stability, soil will be easily eroded by the flow of water, the surface tide ran off to the sea. North waters of the island of Bengkalis is a place boiling down to several rivers, including the river Jangkang and river Liung. The rivers have affected the concentration of total suspended solid (TSS in the strait waters of North Bengkalis Island. This research demonstrated water sampling by using sampling point determined by purposive sampling method mixing the layer of water depth ratio. The results based on satellite imagery data showed that TSS was quite high in the West season period until the transition period I (West to East with a large concentration value of 200 mg / L. For the lowest TSS concentration occurred in the East season i.e., between 0 - 200 mg/L. TSS concentrations that dominated in the East season ranged from 51 to 75 mg/L This value was higher than the TSS concentration of field data analysis, i.e., between 23 - 39 mg/L. Changes of coastal coastline of North Bengkalis during the last 20 years continue to change the size of the land area, with a land area of 131 ha lost.

  20. Rigorous Line-Based Transformation Model Using the Generalized Point Strategy for the Rectification of High Resolution Satellite Imagery

    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.

  1. The retrieval of cloud microphysical properties using satellite measurements and an in situ database

    Directory of Open Access Journals (Sweden)

    C. Poix

    1996-01-01

    Full Text Available By combining AVHRR data from the NOAA satellites with information from a database of in situ measurements, large-scale maps can be generated of the microphysical parameters most immediately significant for the modelling of global circulation and climate. From the satellite data, the clouds can be classified into cumuliform, stratiform and cirrus classes and then into further sub-classes by cloud top temperature. At the same time a database of in situ measurements made by research aircraft is classified into the same sub-classes and a statistical analysis is used to derive relationships between the sub-classes and the cloud microphysical properties. These two analyses are then linked to give estimates of the microphysical properties of the satellite observed clouds. Examples are given of the application of this technique to derive maps of the probability of occurrence of precipitating clouds and of precipitating water content derived from a case study within the International Cirrus Experiment (ICE held in 1989 over the North Sea.

  2. The retrieval of cloud microphysical properties using satellite measurements and an in situ database

    Directory of Open Access Journals (Sweden)

    Christophe Poix

    Full Text Available By combining AVHRR data from the NOAA satellites with information from a database of in situ measurements, large-scale maps can be generated of the microphysical parameters most immediately significant for the modelling of global circulation and climate. From the satellite data, the clouds can be classified into cumuliform, stratiform and cirrus classes and then into further sub-classes by cloud top temperature. At the same time a database of in situ measurements made by research aircraft is classified into the same sub-classes and a statistical analysis is used to derive relationships between the sub-classes and the cloud microphysical properties. These two analyses are then linked to give estimates of the microphysical properties of the satellite observed clouds. Examples are given of the application of this technique to derive maps of the probability of occurrence of precipitating clouds and of precipitating water content derived from a case study within the International Cirrus Experiment (ICE held in 1989 over the North Sea.

  3. The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics

    Directory of Open Access Journals (Sweden)

    Jędrzej S. Bojanowski

    2014-12-01

    Full Text Available Cloud property data sets derived from passive sensors onboard the polar orbiting satellites (such as the NOAA’s Advanced Very High Resolution Radiometer have global coverage and now span a climatological time period. Synoptic surface observations (SYNOP are often used to characterize the accuracy of satellite-based cloud cover. Infrequent overpasses of polar orbiting satellites combined with the 3- or 6-h SYNOP frequency lead to collocation time differences of up to 3 h. The associated collocation error degrades the cloud cover performance statistics such as the Hanssen-Kuiper’s discriminant (HK by up to 45%. Limiting the time difference to 10 min, on the other hand, introduces a sampling error due to a lower number of corresponding satellite and SYNOP observations. This error depends on both the length of the validated time series and the SYNOP frequency. The trade-off between collocation and sampling error call for an optimum collocation time difference. It however depends on cloud cover characteristics and SYNOP frequency, and cannot be generalized. Instead, a method is presented to reconstruct the unbiased (true HK from HK affected by the collocation differences, which significantly (t-test p < 0.01 improves the validation results.

  4. Observations of X-ray sources in the Large Magellanic cloud by the OSO-7 satellite

    International Nuclear Information System (INIS)

    Markert, T.H.; Clark, G.W.

    1975-01-01

    Observations of the Large Magellanic Cloud with the 1-40 keV X-ray detectors on the OSO-7 satellite are reported. Results include the discovery of a previously unreported source LMC X-5, measurements of the spectral characteristics of four sources, and observations of their variability on time scales of months

  5. Detailed maps of tropical forest types are within reach: forest tree communities for Trinidad and Tobago mapped with multiseason Landsat and multiseason fine-resolution imagery

    Science.gov (United States)

    Eileen H. Helmer; Thomas S. Ruzycki; Jay Benner; Shannon M. Voggesser; Barbara P. Scobie; Courtenay Park; David W. Fanning; Seepersad. Ramnarine

    2012-01-01

    Tropical forest managers need detailed maps of forest types for REDD+, but spectral similarity among forest types; cloud and scan-line gaps; and scarce vegetation ground plots make producing such maps with satellite imagery difficult. How can managers map tropical forest tree communities with satellite imagery given these challenges? Here we describe a case study of...

  6. A Satellite Imagery Approach to Monitor Turbidity and Total Suspended Sediments in Green Bay, WI

    Science.gov (United States)

    Khazaei, B.; Hamidi, S.; Hosseiny, S. M. H.; Ekhtari, N.

    2017-12-01

    Fox River is a major source of land-based pollutants, nutrients, and sediment that flows into the southern Green Bay (GB). GB supplies one-third of the total nutrient loading to Lake Michigan. This can play a significant role in the biological functioning of the Bay and development of managerial scenarios. To name a few, it can degrade the quality of the aquatic life, add to the costs for treatment processes, and reduce coastal quality. Water quality evaluation is a time consuming and costly process. Spaceborne imagery data provides a cheap and valuable source of information as an alternative for field monitoring of the water resources. Sediment is an optically active variable; hence; remote sensing techniques can be utilized to estimate Total Suspended Sediments (TSS) and Turbidity (TU) of water. In this study, we developed relationships between remote sensing imagery data with daily in situ measurements of TSS and TU in the summers of 2011 to 2014. Surface reflectance (SR) values obtained from Band 1 of MYD09GQ dataset-a level 2 product of MODerate Resolution Imaging Spectroradiometer (MODIS). This band covers SR between 620 and 670nm, in which, the wavelength is sensitive to mineral suspended matters most. After elimination of days with cloud contamination, 118 pairs of data remained for analysis. Several possible functions were tested and exponential function was the best estimator of the SR-TSS and SR-TU relationships with R2 values of 0.8269 and 0.8688, respectively. We then used 2014 data to validate the proposed functions. The model was able to estimate TSS and TU with NRMSE values of 0.36 and 0.30. It indicates that the model can be well-applied to predict TSS and TU within a reasonable margin of error. Then, equations were used to map the spatiotemporal dynamics of sediment in GB. Area of the plume ranges between 12 to 180 km2 while 50% of the time the area of the turbid plume is more than 106 km2. Expectedly, the concentration of sediment is much higher

  7. Evaluating Cloud and Precipitation Processes in Numerical Models using Current and Potential Future Satellite Missions

    Science.gov (United States)

    van den Heever, S. C.; Tao, W. K.; Skofronick Jackson, G.; Tanelli, S.; L'Ecuyer, T. S.; Petersen, W. A.; Kummerow, C. D.

    2015-12-01

    Cloud, aerosol and precipitation processes play a fundamental role in the water and energy cycle. It is critical to accurately represent these microphysical processes in numerical models if we are to better predict cloud and precipitation properties on weather through climate timescales. Much has been learned about cloud properties and precipitation characteristics from NASA satellite missions such as TRMM, CloudSat, and more recently GPM. Furthermore, data from these missions have been successfully utilized in evaluating the microphysical schemes in cloud-resolving models (CRMs) and global models. However, there are still many uncertainties associated with these microphysics schemes. These uncertainties can be attributed, at least in part, to the fact that microphysical processes cannot be directly observed or measured, but instead have to be inferred from those cloud properties that can be measured. Evaluation of microphysical parameterizations are becoming increasingly important as enhanced computational capabilities are facilitating the use of more sophisticated schemes in CRMs, and as future global models are being run on what has traditionally been regarded as cloud-resolving scales using CRM microphysical schemes. In this talk we will demonstrate how TRMM, CloudSat and GPM data have been used to evaluate different aspects of current CRM microphysical schemes, providing examples of where these approaches have been successful. We will also highlight CRM microphysical processes that have not been well evaluated and suggest approaches for addressing such issues. Finally, we will introduce a potential NASA satellite mission, the Cloud and Precipitation Processes Mission (CAPPM), which would facilitate the development and evaluation of different microphysical-dynamical feedbacks in numerical models.

  8. Correcting bias in the rational polynomial coefficients of satellite imagery using thin-plate smoothing splines

    Science.gov (United States)

    Shen, Xiang; Liu, Bin; Li, Qing-Quan

    2017-03-01

    The Rational Function Model (RFM) has proven to be a viable alternative to the rigorous sensor models used for geo-processing of high-resolution satellite imagery. Because of various errors in the satellite ephemeris and instrument calibration, the Rational Polynomial Coefficients (RPCs) supplied by image vendors are often not sufficiently accurate, and there is therefore a clear need to correct the systematic biases in order to meet the requirements of high-precision topographic mapping. In this paper, we propose a new RPC bias-correction method using the thin-plate spline modeling technique. Benefiting from its excellent performance and high flexibility in data fitting, the thin-plate spline model has the potential to remove complex distortions in vendor-provided RPCs, such as the errors caused by short-period orbital perturbations. The performance of the new method was evaluated by using Ziyuan-3 satellite images and was compared against the recently developed least-squares collocation approach, as well as the classical affine-transformation and quadratic-polynomial based methods. The results show that the accuracies of the thin-plate spline and the least-squares collocation approaches were better than the other two methods, which indicates that strong non-rigid deformations exist in the test data because they cannot be adequately modeled by simple polynomial-based methods. The performance of the thin-plate spline method was close to that of the least-squares collocation approach when only a few Ground Control Points (GCPs) were used, and it improved more rapidly with an increase in the number of redundant observations. In the test scenario using 21 GCPs (some of them located at the four corners of the scene), the correction residuals of the thin-plate spline method were about 36%, 37%, and 19% smaller than those of the affine transformation method, the quadratic polynomial method, and the least-squares collocation algorithm, respectively, which demonstrates

  9. New insight of Arctic cloud parameterization from regional climate model simulations, satellite-based, and drifting station data

    Science.gov (United States)

    Klaus, D.; Dethloff, K.; Dorn, W.; Rinke, A.; Wu, D. L.

    2016-05-01

    Cloud observations from the CloudSat and CALIPSO satellites helped to explain the reduced total cloud cover (Ctot) in the atmospheric regional climate model HIRHAM5 with modified cloud physics. Arctic climate conditions are found to be better reproduced with (1) a more efficient Bergeron-Findeisen process and (2) a more generalized subgrid-scale variability of total water content. As a result, the annual cycle of Ctot is improved over sea ice, associated with an almost 14% smaller area average than in the control simulation. The modified cloud scheme reduces the Ctot bias with respect to the satellite observations. Except for autumn, the cloud reduction over sea ice improves low-level temperature profiles compared to drifting station data. The HIRHAM5 sensitivity study highlights the need for improving accuracy of low-level (<700 m) cloud observations, as these clouds exert a strong impact on the near-surface climate.

  10. Improved cloud parameterization for Arctic climate simulations based on satellite data

    Science.gov (United States)

    Klaus, Daniel; Dethloff, Klaus; Dorn, Wolfgang; Rinke, Annette

    2015-04-01

    The defective representation of Arctic cloud processes and properties remains a crucial problem in climate modelling and in reanalysis products. Satellite-based cloud observations (MODIS and CPR/CALIOP) and single-column model simulations (HIRHAM5-SCM) were exploited to evaluate and improve the simulated Arctic cloud cover of the atmospheric regional climate model HIRHAM5. The ECMWF reanalysis dataset 'ERA-Interim' (ERAint) was used for the model initialization, the lateral boundary forcing as well as the dynamical relaxation inside the pan-Arctic domain. HIRHAM5 has a horizontal resolution of 0.25° and uses 40 pressure-based and terrain-following vertical levels. In comparison with the satellite observations, the HIRHAM5 control run (HH5ctrl) systematically overestimates total cloud cover, but to a lesser extent than ERAint. The underestimation of high- and mid-level clouds is strongly outweighed by the overestimation of low-level clouds. Numerous sensitivity studies with HIRHAM5-SCM suggest (1) the parameter tuning, enabling a more efficient Bergeron-Findeisen process, combined with (2) an extension of the prognostic-statistical (PS) cloud scheme, enabling the use of negatively skewed beta distributions. This improved model setup was then used in a corresponding HIRHAM5 sensitivity run (HH5sens). While the simulated high- and mid-level cloud cover is improved only to a limited extent, the large overestimation of low-level clouds can be systematically and significantly reduced, especially over sea ice. Consequently, the multi-year annual mean area average of total cloud cover with respect to sea ice is almost 14% lower than in HH5ctrl. Overall, HH5sens slightly underestimates the observed total cloud cover but shows a halved multi-year annual mean bias of 2.2% relative to CPR/CALIOP at all latitudes north of 60° N. Importantly, HH5sens produces a more realistic ratio between the cloud water and ice content. The considerably improved cloud simulation manifests in

  11. Quantifying the Value of Satellite Imagery in Agriculture and other Sectors

    Science.gov (United States)

    Brown, M. E.; Abbott, P. C.; Escobar, V. M.

    2013-12-01

    This study focused on quantifying the commercial value of satellite remote sensing for agriculture. Commercial value from satellite imagery arises when improved information leads to better economic decisions. We identified five areas of application of remote sensing to agriculture where there is this potential: crop management (precision agriculture), insurance, real estate assessment, crop forecasting, and environmental monitoring. These applications can be divided between public information (crop forecasting) and those that may generate private commercial value (crop management), with both public and private information dimensions in some categories. Public information applications of remote sensing have been more successful in the past, and are likely to generate more economic value in the future. It was found that several issues have limited realization of the potential to generate private value from remote sensing in agriculture. The scale of use is small to the high cost of acquiring and interpreting large images has limited the cost effectiveness to individual farmers. Insurance, environmental monitoring, and crop management services by cooperatives or consultants may be cases overcoming this limitation. The greatest opportunities for potential commercial value from agriculture are probably in the crop forecasting area, especially where agricultural statistics services are not as well developed, since public market information benefits a broad range of economic actors, not limited to countries where forecasts are made. We estimate here the value from components of USDA's World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, indicating potential value increasing in the range of 60 to 240 million if improved satellite based information enhances those forecasts. The research was conducted by agricultural economists at Purdue University, and will be the basis for further evaluation of the use of satellite data within the NASA Carbon

  12. Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

    Science.gov (United States)

    Kukunda, Collins B.; Duque-Lazo, Joaquín; González-Ferreiro, Eduardo; Thaden, Hauke; Kleinn, Christoph

    2018-03-01

    Distinguishing tree species is relevant in many contexts of remote sensing assisted forest inventory. Accurate tree species maps support management and conservation planning, pest and disease control and biomass estimation. This study evaluated the performance of applying ensemble techniques with the goal of automatically distinguishing Pinus sylvestris L. and Pinus uncinata Mill. Ex Mirb within a 1.3 km2 mountainous area in Barcelonnette (France). Three modelling schemes were examined, based on: (1) high-density LiDAR data (160 returns m-2), (2) Worldview-2 multispectral imagery, and (3) Worldview-2 and LiDAR in combination. Variables related to the crown structure and height of individual trees were extracted from the normalized LiDAR point cloud at individual-tree level, after performing individual tree crown (ITC) delineation. Vegetation indices and the Haralick texture indices were derived from Worldview-2 images and served as independent spectral variables. Selection of the best predictor subset was done after a comparison of three variable selection procedures: (1) Random Forests with cross validation (AUCRFcv), (2) Akaike Information Criterion (AIC) and (3) Bayesian Information Criterion (BIC). To classify the species, 9 regression techniques were combined using ensemble models. Predictions were evaluated using cross validation and an independent dataset. Integration of datasets and models improved individual tree species classification (True Skills Statistic, TSS; from 0.67 to 0.81) over individual techniques and maintained strong predictive power (Relative Operating Characteristic, ROC = 0.91). Assemblage of regression models and integration of the datasets provided more reliable species distribution maps and associated tree-scale mapping uncertainties. Our study highlights the potential of model and data assemblage at improving species classifications needed in present-day forest planning and management.

  13. Absorbing Aerosols Above Cloud: Detection, Quantitative Retrieval, and Radiative Forcing from Satellite-based Passive Sensors

    Science.gov (United States)

    Jethva, H.; Torres, O.; Remer, L. A.; Bhartia, P. K.

    2012-12-01

    Light absorbing particles such as carbonaceous aerosols generated from biomass burning activities and windblown dust particles can exert a net warming effect on climate; the strength of which depends on the absorption capacity of the particles and brightness of the underlying reflecting background. When advected over low-level bright clouds, these aerosols absorb the cloud reflected radiation from ultra-violet (UV) to shortwave-IR (SWIR) and makes cloud scene darker-a phenomenon commonly known as "cloud darkening". The apparent "darkening" effect can be seen by eyes in satellite images as well as quantitatively in the spectral reflectance measurements made by space borne sensors over regions where light absorbing carbonaceous and dust aerosols overlay low-level cloud decks. Theoretical radiative transfer simulations support the observational evidence, and further reveal that the strength of the cloud darkening and its spectral signature (or color ratio) between measurements at two wavelengths are a bi-function of aerosol and cloud optical thickness (AOT and COT); both are measures of the total amount of light extinction caused by aerosols and cloud, respectively. Here, we developed a retrieval technique, named as the "color ratio method" that uses the satellite measurements at two channels, one at shorter wavelength in the visible and one at longer wavelength in the shortwave-IR for the simultaneous retrieval of AOT and COT. The present technique requires assumptions on the aerosol single-scattering albedo and aerosol-cloud separation which are supplemented by the Aerosol Robotic Network (AERONET) and space borne CALIOP lidar measurements. The retrieval technique has been tested making use of the near-UV and visible reflectance observations made by the Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) for distinct above-cloud smoke and dust aerosol events observed seasonally over the southeast and tropical Atlantic Ocean

  14. Ozone mixing ratios inside tropical deep convective clouds from OMI satellite measurements

    Directory of Open Access Journals (Sweden)

    J. R. Ziemke

    2009-01-01

    Full Text Available We have developed a new technique for estimating ozone mixing ratio inside deep convective clouds. The technique uses the concept of an optical centroid cloud pressure that is indicative of the photon path inside clouds. Radiative transfer calculations based on realistic cloud vertical structure as provided by CloudSat radar data show that because deep convective clouds are optically thin near the top, photons can penetrate significantly inside the cloud. This photon penetration coupled with in-cloud scattering produces optical centroid pressures that are hundreds of hPa inside the cloud. We combine measured column ozone and the optical centroid cloud pressure derived using the effects of rotational-Raman scattering to estimate O3 mixing ratio in the upper regions of deep convective clouds. The data are obtained from the Ozone Monitoring Instrument (OMI onboard NASA's Aura satellite. Our results show that low O3 concentrations in these clouds are a common occurrence throughout much of the tropical Pacific. Ozonesonde measurements in the tropics following convective activity also show very low concentrations of O3 in the upper troposphere. These low amounts are attributed to vertical injection of ozone poor oceanic boundary layer air during convection into the upper troposphere followed by convective outflow. Over South America and Africa, O3 mixing ratios inside deep convective clouds often exceed 50 ppbv which are comparable to mean background (cloud-free amounts and are consistent with higher concentrations of injected boundary layer/lower tropospheric O3 relative to the remote Pacific. The Atlantic region in general also consists of higher amounts of O3 precursors due to both biomass burning and lightning. Assuming that O3 is well mixed (i.e., constant mixing ratio with height up to the tropopause, we can estimate the stratospheric column O3 over

  15. Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level.

    Science.gov (United States)

    Taylor, Patrick C; Kato, Seiji; Xu, Kuan-Man; Cai, Ming

    2015-12-27

    Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and midtropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500 m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

  16. Covariance Between Arctic Sea Ice and Clouds Within Atmospheric State Regimes at the Satellite Footprint Level

    Science.gov (United States)

    Taylor, Patrick C.; Kato, Seiji; Xu, Kuan-Man; Cai, Ming

    2015-01-01

    Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and mid-tropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

  17. Satellite Cloud and Radiative Property Processing and Distribution System on the NASA Langley ASDC OpenStack and OpenShift Cloud Platform

    Science.gov (United States)

    Nguyen, L.; Chee, T.; Palikonda, R.; Smith, W. L., Jr.; Bedka, K. M.; Spangenberg, D.; Vakhnin, A.; Lutz, N. E.; Walter, J.; Kusterer, J.

    2017-12-01

    Cloud Computing offers new opportunities for large-scale scientific data producers to utilize Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) IT resources to process and deliver data products in an operational environment where timely delivery, reliability, and availability are critical. The NASA Langley Research Center Atmospheric Science Data Center (ASDC) is building and testing a private and public facing cloud for users in the Science Directorate to utilize as an everyday production environment. The NASA SatCORPS (Satellite ClOud and Radiation Property Retrieval System) team processes and derives near real-time (NRT) global cloud products from operational geostationary (GEO) satellite imager datasets. To deliver these products, we will utilize the public facing cloud and OpenShift to deploy a load-balanced webserver for data storage, access, and dissemination. The OpenStack private cloud will host data ingest and computational capabilities for SatCORPS processing. This paper will discuss the SatCORPS migration towards, and usage of, the ASDC Cloud Services in an operational environment. Detailed lessons learned from use of prior cloud providers, specifically the Amazon Web Services (AWS) GovCloud and the Government Cloud administered by the Langley Managed Cloud Environment (LMCE) will also be discussed.

  18. Extracting Urban Morphology for Atmospheric Modeling from Multispectral and SAR Satellite Imagery

    Science.gov (United States)

    Wittke, S.; Karila, K.; Puttonen, E.; Hellsten, A.; Auvinen, M.; Karjalainen, M.

    2017-05-01

    This paper presents an approach designed to derive an urban morphology map from satellite data while aiming to minimize the cost of data and user interference. The approach will help to provide updates to the current morphological databases around the world. The proposed urban morphology maps consist of two layers: 1) Digital Elevation Model (DEM) and 2) land cover map. Sentinel-2 data was used to create a land cover map, which was realized through image classification using optical range indices calculated from image data. For the purpose of atmospheric modeling, the most important classes are water and vegetation areas. The rest of the area includes bare soil and built-up areas among others, and they were merged into one class in the end. The classification result was validated with ground truth data collected both from field measurements and aerial imagery. The overall classification accuracy for the three classes is 91 %. TanDEM-X data was processed into two DEMs with different grid sizes using interferometric SAR processing. The resulting DEM has a RMSE of 3.2 meters compared to a high resolution DEM, which was estimated through 20 control points in flat areas. Comparing the derived DEM with the ground truth DEM from airborne LIDAR data, it can be seen that the street canyons, that are of high importance for urban atmospheric modeling are not detectable in the TanDEM-X DEM. However, the derived DEM is suitable for a class of urban atmospheric models. Based on the numerical modeling needs for regional atmospheric pollutant dispersion studies, the generated files enable the extraction of relevant parametrizations, such as Urban Canopy Parameters (UCP).

  19. Improved Wetland Classification Using Eight-Band High Resolution Satellite Imagery and a Hybrid Approach

    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.

  20. EXTRACTING URBAN MORPHOLOGY FOR ATMOSPHERIC MODELING FROM MULTISPECTRAL AND SAR SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    S. Wittke

    2017-05-01

    Full Text Available This paper presents an approach designed to derive an urban morphology map from satellite data while aiming to minimize the cost of data and user interference. The approach will help to provide updates to the current morphological databases around the world. The proposed urban morphology maps consist of two layers: 1 Digital Elevation Model (DEM and 2 land cover map. Sentinel-2 data was used to create a land cover map, which was realized through image classification using optical range indices calculated from image data. For the purpose of atmospheric modeling, the most important classes are water and vegetation areas. The rest of the area includes bare soil and built-up areas among others, and they were merged into one class in the end. The classification result was validated with ground truth data collected both from field measurements and aerial imagery. The overall classification accuracy for the three classes is 91 %. TanDEM-X data was processed into two DEMs with different grid sizes using interferometric SAR processing. The resulting DEM has a RMSE of 3.2 meters compared to a high resolution DEM, which was estimated through 20 control points in flat areas. Comparing the derived DEM with the ground truth DEM from airborne LIDAR data, it can be seen that the street canyons, that are of high importance for urban atmospheric modeling are not detectable in the TanDEM-X DEM. However, the derived DEM is suitable for a class of urban atmospheric models. Based on the numerical modeling needs for regional atmospheric pollutant dispersion studies, the generated files enable the extraction of relevant parametrizations, such as Urban Canopy Parameters (UCP.

  1. Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

    Science.gov (United States)

    Williams, Jack G.; Rosser, Nick J.; Kincey, Mark E.; Benjamin, Jessica; Oven, Katie J.; Densmore, Alexander L.; Milledge, David G.; Robinson, Tom R.; Jordan, Colm A.; Dijkstra, Tom A.

    2018-01-01

    Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.

  2. Cloud GIS Based Watershed Management

    Science.gov (United States)

    Bediroğlu, G.; Colak, H. E.

    2017-11-01

    In this study, we generated a Cloud GIS based watershed management system with using Cloud Computing architecture. Cloud GIS is used as SAAS (Software as a Service) and DAAS (Data as a Service). We applied GIS analysis on cloud in terms of testing SAAS and deployed GIS datasets on cloud in terms of DAAS. We used Hybrid cloud computing model in manner of using ready web based mapping services hosted on cloud (World Topology, Satellite Imageries). We uploaded to system after creating geodatabases including Hydrology (Rivers, Lakes), Soil Maps, Climate Maps, Rain Maps, Geology and Land Use. Watershed of study area has been determined on cloud using ready-hosted topology maps. After uploading all the datasets to systems, we have applied various GIS analysis and queries. Results shown that Cloud GIS technology brings velocity and efficiency for watershed management studies. Besides this, system can be easily implemented for similar land analysis and management studies.

  3. Satellite Observations of Volcanic Clouds from the Eruption of Redoubt Volcano, Alaska, 2009

    Science.gov (United States)

    Dean, K. G.; Ekstrand, A. L.; Webley, P.; Dehn, J.

    2009-12-01

    Redoubt Volcano began erupting on 23 March 2009 (UTC) and consisted of 19 events over a 14 day period. The volcano is located on the Alaska Peninsula, 175 km southwest of Anchorage, Alaska. The previous eruption was in 1989/1990 and seriously disrupted air traffic in the region, including the near catastrophic engine failure of a passenger airliner. Plumes and ash clouds from the recent eruption were observed on a variety of satellite data (AVHRR, MODIS and GOES). The eruption produced volcanic clouds up to 19 km which are some of the highest detected in recent times in the North Pacific region. The ash clouds primarily drifted north and east of the volcano, had a weak ash signal in the split window data and resulted in light ash falls in the Cook Inlet basin and northward into Alaska’s Interior. Volcanic cloud heights were measured using ground-based radar, and plume temperature and wind shear methods but each of the techniques resulted in significant variations in the estimates. Even though radar showed the greatest heights, satellite data and wind shears suggest that the largest concentrations of ash may be at lower altitudes in some cases. Sulfur dioxide clouds were also observed on satellite data (OMI, AIRS and Calipso) and they primarily drifted to the east and were detected at several locations across North America, thousands of kilometers from the volcano. Here, we show time series data collected by the Alaska Volcano Observatory, illustrating the different eruptive events and ash clouds that developed over the subsequent days.

  4. Validation of satellite-retrieved MBL cloud properties using DOE ARM AMF measurements at the Azores

    Science.gov (United States)

    Xi, B.; Dong, X.; Minnis, P.; Sun-Mack, S.

    2013-05-01

    Marine Boundary Layer (MBL) cloud properties derived for the Clouds and the Earth's Radiant Energy System (CERES) Project using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with observations taken at the Atmospheric Radiation Measurement (ARM) AMF AZORES site from June 2009 through December 2010. Retrievals from ARM surface-based data were averaged over a 1-hour interval centered at the time of each satellite overpass, and the CERES-MODIS Ed4 cloud properties were averaged within a 30-km x 30-km box centered on the ARM AZORES site. Two datasets were analyzed: all of the single-layered unbroken decks (SL) and those cases without temperature inversions. The CERES-MODIS cloud top/base heights were determined from cloud top/base temperature by using a lapse rate method normalized to the 24-h mean surface air temperature. The preliminary results show: for all SL MBL at daytime, they are, on average, 0.148 km (cloud top) and 0.087 km (cloud base) higher than the ARM radar-lidar observed cloud top and base, respectively. At nighttime, they are 0.446 km (cloud top) and 0.334 km (cloud base). For those cases without temperature inversions, the comparisons are close to their SL counterparts. For cloud temperatures, the MODIS-derived cloud-top and -base temperatures are 1.6 K lower and 0.4 K higher than the surface values with correlations of 0.92 during daytime. At nighttime, the differences are slightly larger and correlations are lower than daytime comparisons. Variations in the height difference are mainly caused by uncertainties in the surface air temperatures and lapse rates. Based on a total of 61 daytime and 87 nighttime samples (ALL SL cases), the temperature inversion layers occur about 72% during daytime and 83% during nighttime. The difference of surface-observed lapse rate and the satellite derived lapse rate can be 1.6 K/km for daytime and 3.3K/km for nighttime. From these lapse rates, we can further analyze the surface

  5. Algorithm and Application of Gcp-Independent Block Adjustment for Super Large-Scale Domestic High Resolution Optical Satellite Imagery

    Science.gov (United States)

    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.

  6. Combining structure-from-motion derived point clouds from satellites and unmanned aircraft systems images with ground-truth data to create high-resolution digital elevation models

    Science.gov (United States)

    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.

  7. Evaluation of the shortwave cloud radiative effect over the ocean by use of ship and satellite observations

    Directory of Open Access Journals (Sweden)

    T. Hanschmann

    2012-12-01

    Full Text Available In this study the shortwave cloud radiative effect (SWCRE over ocean calculated by the ECHAM 5 climate model is evaluated for the cloud property input derived from ship based measurements and satellite based estimates and compared to ship based radiation measurements. The ship observations yield cloud fraction, liquid water path from a microwave radiometer, cloud bottom height as well as temperature and humidity profiles from radiosonde ascents. Level-2 products of the Satellite Application Facility on Climate Monitoring (CM~SAF from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI have been used to characterize clouds. Within a closure study six different experiments have been defined to find the optimal set of measurements to calculate downward shortwave radiation (DSR and the SWCRE from the model, and their results have been evaluated under seven different synoptic situations. Four of these experiments are defined to investigate the advantage of including the satellite-based cloud droplet effective radius as additional cloud property. The modeled SWCRE based on satellite retrieved cloud properties has a comparable accuracy to the modeled SWCRE based on ship data. For several cases, an improvement through introducing the satellite-based estimate of effective radius as additional information to the ship based data was found. Due to their different measuring characteristics, however, each dataset shows best results for different atmospheric conditions.

  8. Is ozone model bias driven by errors in cloud predictions? A quantitative assessment using satellite cloud retrievals in WRF-Chem

    Science.gov (United States)

    Ryu, Y. H.; Hodzic, A.; Barré, J.; Descombes, G.; Minnis, P.

    2017-12-01

    Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much of the bias in O3 predictions is caused by inaccurate cloud predictions. This study quantifies the errors in surface O3 predictions associated with clouds in summertime over CONUS using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Cloud fields used for photochemistry are corrected based on satellite cloud retrievals in sensitivity simulations. It is found that the WRF-Chem model is able to detect about 60% of clouds in the right locations and generally underpredicts cloud optical depths. The errors in hourly O3 due to the errors in cloud predictions can be up to 60 ppb. On average in summertime over CONUS, the errors in 8-h average O3 of 1-6 ppb are found to be attributable to those in cloud predictions under cloudy sky conditions. The contribution of changes in photolysis rates due to clouds is found to be larger ( 80 % on average) than that of light-dependent BVOC emissions. The effects of cloud corrections on O­3 are about 2 times larger in VOC-limited than NOx-limited regimes, suggesting that the benefits of accurate cloud predictions would be greater in VOC-limited than NOx-limited regimes.

  9. Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery

    Science.gov (United States)

    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

  10. Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

    Science.gov (United States)

    Champion, Nicolas

    2016-06-01

    Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pl

  11. AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

    Directory of Open Access Journals (Sweden)

    N. Champion

    2016-06-01

    Full Text Available Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8

  12. An estimation model of population in China using time series DMSP night-time satellite imagery from 2002-2010

    Science.gov (United States)

    Zhang, Xiaoyong; Zhang, Zhijie; Chang, Yuguang; Chen, Zhengchao

    2015-12-01

    Accurate data on the spatial distribution and potential growth estimation of human population are playing pivotal role in addressing and mitigating heavy lose caused by earthquake. Traditional demographic data is limited in its spatial resolution and is extremely hard to update. With the accessibility of massive DMSP/OLS night time imagery, it is possible to model population distribution at the county level across China. In order to compare and improve the continuity and consistency of time-series DMSP night-time satellite imagery obtained by different satellites in same year or different years by the same satellite from 2002-2010, normalized method was deployed for the inter-correction among imageries. And we referred to the reference F162007 Jixi city, whose social-economic has been relatively stable. Through binomial model, with average R2 0.90, then derived the correction factor of each year. The normalization obviously improved consistency comparing to previous data, which enhanced the correspondent accuracy of model. Then conducted the model of population density between average night-time light intensity in eight-economic districts. According to the two parameters variation law of consecutive years, established the prediction model of next following years with R2of slope and constant typically 0.85 to 0.95 in different regions. To validate the model, taking the year of 2005 as example, retrieved quantitatively population distribution in per square kilometer based on the model, then compared the results to the statistical data based on census, the difference of the result is acceptable. In summary, the estimation model facilitates the quick estimation and prediction in relieving the damage to people, which is significant in decision-making.

  13. The International Satellite Cloud Climatology Project H-Series climate data record product

    Science.gov (United States)

    Young, Alisa H.; Knapp, Kenneth R.; Inamdar, Anand; Hankins, William; Rossow, William B.

    2018-03-01

    This paper describes the new global long-term International Satellite Cloud Climatology Project (ISCCP) H-series climate data record (CDR). The H-series data contain a suite of level 2 and 3 products for monitoring the distribution and variation of cloud and surface properties to better understand the effects of clouds on climate, the radiation budget, and the global hydrologic cycle. This product is currently available for public use and is derived from both geostationary and polar-orbiting satellite imaging radiometers with common visible and infrared (IR) channels. The H-series data currently span July 1983 to December 2009 with plans for continued production to extend the record to the present with regular updates. The H-series data are the longest combined geostationary and polar orbiter satellite-based CDR of cloud properties. Access to the data is provided in network common data form (netCDF) and archived by NOAA's National Centers for Environmental Information (NCEI) under the satellite Climate Data Record Program (https://doi.org/10.7289/V5QZ281S" target="_blank">https://doi.org/10.7289/V5QZ281S). The basic characteristics, history, and evolution of the dataset are presented herein with particular emphasis on and discussion of product changes between the H-series and the widely used predecessor D-series product which also spans from July 1983 through December 2009. Key refinements included in the ISCCP H-series CDR are based on improved quality control measures, modified ancillary inputs, higher spatial resolution input and output products, calibration refinements, and updated documentation and metadata to bring the H-series product into compliance with existing standards for climate data records.

  14. Advanced Analysis of the Influence of Clouds, Precipiation and Surface Emissivity on DMSP/NPOESS Satellite Microwave Channels

    National Research Council Canada - National Science Library

    Isaacs, R

    2002-01-01

    ...: development of databases of brightness temperatures from various satellite sensors; development databases of conventional analysis to verify the presence and amount of clouds and precipitation and for verification of retrieval results...

  15. CHASER: An Innovative Satellite Mission Concept to Measure the Effects of Aerosols on Clouds and Climate

    Science.gov (United States)

    Renno, N.; Williams, E.; Rosenfeld, D.; Fischer, D.; Fischer, J.; Kremic, T.; Agrawal, A.; Andreae, M.; Bierbaum, R.; Blakeslee, R.; Boerner, A.; Bowles, N.; Christian, H.; Dunion, J.; Horvath, A.; Huang, X.; Khain, A.; Kinne, S.; Lemos, M.-C.; Penner, J.

    2012-04-01

    The formation of cloud droplets on aerosol particles, technically known as the activation of cloud condensation nuclei (CCN), is the fundamental process driving the interactions of aerosols with clouds and precipitation. Knowledge of these interactions is foundational to our understanding of weather and climate. The Intergovernmental Panel on Climate Change (IPCC) and the Decadal Survey (NRC 2007) indicate that the uncertainty in how clouds adjust to aerosol perturbations dominates the uncertainty in the overall quantification of the radiative forcing attributable to human activities. The Clouds, Hazards, and Aerosols Survey for Earth Researchers (CHASER) mission concept responds to the IPCC and Decadal Survey concerns by studying the activation of CCN and their interactions with clouds and storms. CHASER proposes to revolutionize our understanding of the interactions of aerosols with clouds by making the first global measurements of the fundamental physical entity linking them: activated cloud condensation nuclei. The CHASER mission was conceptualized to measure all quantities necessary for determining the interactions of aerosols with clouds and storms. Measurements by current satellites allow the determination of crude profiles of cloud particle size but not of the activated CCN that seed them. CHASER uses a new technique (Freud et al. 2011; Rosenfeld et al. 2012) and high-heritage instruments to produce the first global maps of activated CCN and the properties of the clouds associated with them. CHASER measures the CCN concentration and cloud thermodynamic forcing simultaneously, allowing their effects to be distinguished. Changes in the behavior of a group of weather systems in which only one of the quantities varies (a partial derivative of the intensity with the desirable quantity) allow the determination of each effect statistically. The high uncertainties of current climate predictions limit their much-needed use in decision-making. CHASER mitigates this

  16. CLASSIFIER FUSION OF HIGH-RESOLUTION OPTICAL AND SYNTHETIC APERTURE RADAR (SAR SATELLITE IMAGERY FOR CLASSIFICATION IN URBAN AREA

    Directory of Open Access Journals (Sweden)

    T. Alipour Fard

    2014-10-01

    Full Text Available This study concerned with fusion of synthetic aperture radar and optical satellite imagery. Due to the difference in the underlying sensor technology, data from synthetic aperture radar (SAR and optical sensors refer to different properties of the observed scene and it is believed that when they are fused together, they complement each other to improve the performance of a particular application. In this paper, two category of features are generate and six classifier fusion operators implemented and evaluated. Implementation results show significant improvement in the classification accuracy.

  17. Point Cloud Based Relative Pose Estimation of a Satellite in Close Range

    Directory of Open Access Journals (Sweden)

    Lujiang Liu

    2016-06-01

    Full Text Available Determination of the relative pose of satellites is essential in space rendezvous operations and on-orbit servicing missions. The key problems are the adoption of suitable sensor on board of a chaser and efficient techniques for pose estimation. This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor. A novel model based pose estimation method is proposed; it includes a fast and reliable pose initial acquisition method based on global optimal searching by processing the dense point cloud data directly, and a pose tracking method based on Iterative Closest Point algorithm. Also, a simulation system is presented in this paper in order to evaluate the performance of the sensor and generate simulated sensor point cloud data. It also provides truth pose of the test target so that the pose estimation error can be quantified. To investigate the effectiveness of the proposed approach and achievable pose accuracy, numerical simulation experiments are performed; results demonstrate algorithm capability of operating with point cloud directly and large pose variations. Also, a field testing experiment is conducted and results show that the proposed method is effective.

  18. Satellite-based trends of solar radiation and cloud parameters in Europe

    Science.gov (United States)

    Pfeifroth, Uwe; Bojanowski, Jedrzej S.; Clerbaux, Nicolas; Manara, Veronica; Sanchez-Lorenzo, Arturo; Trentmann, Jörg; Walawender, Jakub P.; Hollmann, Rainer

    2018-04-01

    Solar radiation is the main driver of the Earth's climate. Measuring solar radiation and analysing its interaction with clouds are essential for the understanding of the climate system. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates satellite-based, high-quality climate data records, with a focus on the energy balance and water cycle. Here, multiple of these data records are analyzed in a common framework to assess the consistency in trends and spatio-temporal variability of surface solar radiation, top-of-atmosphere reflected solar radiation and cloud fraction. This multi-parameter analysis focuses on Europe and covers the time period from 1992 to 2015. A high correlation between these three variables has been found over Europe. An overall consistency of the climate data records reveals an increase of surface solar radiation and a decrease in top-of-atmosphere reflected radiation. In addition, those trends are confirmed by negative trends in cloud cover. This consistency documents the high quality and stability of the CM SAF climate data records, which are mostly derived independently from each other. The results of this study indicate that one of the main reasons for the positive trend in surface solar radiation since the 1990's is a decrease in cloud coverage even if an aerosol contribution cannot be completely ruled out.

  19. A Lagrangian Analysis of Cold Cloud Clusters and Their Life Cycles With Satellite Observations

    Science.gov (United States)

    Esmaili, Rebekah Bradley; Tian, Yudong; Vila, Daniel Alejandro; Kim, Kyu-Myong

    2016-01-01

    Cloud movement and evolution signify the complex water and energy transport in the atmosphere-ocean-land system. Detecting, clustering, and tracking clouds as semi coherent cluster objects enables study of their evolution which can complement climate model simulations and enhance satellite retrieval algorithms, where there are large gaps between overpasses. Using an area-overlap cluster tracking algorithm, in this study we examine the trajectories, horizontal extent, and brightness temperature variations of millions of individual cloud clusters over their lifespan, from infrared satellite observations at 30-minute, 4-km resolution, for a period of 11 years. We found that the majority of cold clouds were both small and short-lived and that their frequency and location are influenced by El Nino. More importantly, this large sample of individually tracked clouds shows their horizontal size and temperature evolution. Longer lived clusters tended to achieve their temperature and size maturity milestones at different times, while these stages often occurred simultaneously in shorter lived clusters. On average, clusters with this lag also exhibited a greater rainfall contribution than those where minimum temperature and maximum size stages occurred simultaneously. Furthermore, by examining the diurnal cycle of cluster development over Africa and the Indian subcontinent, we observed differences in the local timing of the maximum occurrence at different life cycle stages. Over land there was a strong diurnal peak in the afternoon while over the ocean there was a semi-diurnal peak composed of longer-lived clusters in the early morning hours and shorter-lived clusters in the afternoon. Building on regional specific work, this study provides a long-term, high-resolution, and global survey of object-based cloud characteristics.

  20. Vectorized Shoreline of Guam, Derived from IKONOS Satellite Imagery, 2000 through 2003

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping...

  1. Semi-automatic building extraction in informal settlements from high-resolution satellite imagery

    Science.gov (United States)

    Mayunga, Selassie David

    The extraction of man-made features from digital remotely sensed images is considered as an important step underpinning management of human settlements in any country. Man-made features and buildings in particular are required for varieties of applications such as urban planning, creation of geographical information systems (GIS) databases and Urban City models. The traditional man-made feature extraction methods are very expensive in terms of equipment, labour intensive, need well-trained personnel and cannot cope with changing environments, particularly in dense urban settlement areas. This research presents an approach for extracting buildings in dense informal settlement areas using high-resolution satellite imagery. The proposed system uses a novel strategy of extracting building by measuring a single point at the approximate centre of the building. The fine measurement of the building outlines is then effected using a modified snake model. The original snake model on which this framework is based, incorporates an external constraint energy term which is tailored to preserving the convergence properties of the snake model; its use to unstructured objects will negatively affect their actual shapes. The external constrained energy term was removed from the original snake model formulation, thereby, giving ability to cope with high variability of building shapes in informal settlement areas. The proposed building extraction system was tested on two areas, which have different situations. The first area was Tungi in Dar Es Salaam, Tanzania where three sites were tested. This area is characterized by informal settlements, which are illegally formulated within the city boundaries. The second area was Oromocto in New Brunswick, Canada where two sites were tested. Oromocto area is mostly flat and the buildings are constructed using similar materials. Qualitative and quantitative measures were employed to evaluate the accuracy of the results as well as the performance

  2. A radiation closure study of Arctic stratus cloud microphysical properties using the collocated satellite-surface data and Fu-Liou radiative transfer model

    Science.gov (United States)

    Dong, Xiquan; Xi, Baike; Qiu, Shaoyue; Minnis, Patrick; Sun-Mack, Sunny; Rose, Fred

    2016-09-01

    Retrievals of cloud microphysical properties based on passive satellite imagery are especially difficult over snow-covered surfaces because of the bright and cold surface. To help quantify their uncertainties, single-layered overcast liquid-phase Arctic stratus cloud microphysical properties retrieved by using the Clouds and the Earth's Radiant Energy System Edition 2 and Edition 4 (CERES Ed2 and Ed4) algorithms are compared with ground-based retrievals at the Atmospheric Radiation Measurement North Slope of Alaska (ARM NSA) site at Barrow, AK, during the period from March 2000 to December 2006. A total of 206 and 140 snow-free cases (Rsfc ≤ 0.3), and 108 and 106 snow cases (Rsfc > 0.3), respectively, were selected from Terra and Aqua satellite passes over the ARM NSA site. The CERES Ed4 and Ed2 optical depth (τ) and liquid water path (LWP) retrievals from both Terra and Aqua are almost identical and have excellent agreement with ARM retrievals under snow-free and snow conditions. In order to reach a radiation closure study for both the surface and top of atmosphere (TOA) radiation budgets, the ARM precision spectral pyranometer-measured surface albedos were adjusted (63.6% and 80% of the ARM surface albedos for snow-free and snow cases, respectively) to account for the water and land components of the domain of 30 km × 30 km. Most of the radiative transfer model calculated SW↓sfc and SW↑TOA fluxes by using ARM and CERES cloud retrievals and the domain mean albedos as input agree with the ARM and CERES flux observations within 10 W m-2 for both snow-free and snow conditions. Sensitivity studies show that the ARM LWP and re retrievals are less dependent on solar zenith angle (SZA), but all retrieved optical depths increase with SZA.

  3. Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012

    Directory of Open Access Journals (Sweden)

    Jihye Ahn

    2014-06-01

    Full Text Available Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. Our method may also be worth consideration when forecasting future sea ice concentration by using the climate data provided by general circulation models (GCM.

  4. 3D high resolution tracking of ice flow using mutli-temporal stereo satellite imagery, Franz Josef Glacier, New Zealand

    Science.gov (United States)

    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.

  5. Employing high resolution satellite imagery to document a rapid glacier surge in the Karakoram - risks and opportunities for hazard assessment

    Science.gov (United States)

    Steiner, J. F.; Kraaijenbrink, P. D. A.; Jiduc, S. G.; Immerzeel, W. W.

    2017-12-01

    Glacier surges occur regularly in the Karakoram but their driving mechanisms, recurrence and its relation to climatic change remain unclear. Since many glacier tongues in the region reach to very low elevations, local populations are often exposed to glacial hazards. While the scientific interpretation of hazard is one challenge, adequately communicating results to possibly affected stakeholders poses a different set of hurdles. Using DEMs as well as Landsat imagery in combination with high-resolution Planet imagery we quantify surface elevation changes and flow velocities to document a glacier surge of the Khurdopin glacier, located in a remote valley in Pakistan, in the first half of 2017. Results reveal that an accumulation of ice mass leads to a rapid surge in peaking with velocities above 5000 m a-1 or 0.5 m h-1 during a few days. Velocities increase steadily during a four-year build-up phase prior to the actual surge, while the remaining 15 years of the recurring cycle the glacier is quiescent. It is hypothesized that the surge is mainly initiated as a result of increased pressure melting caused by ice accumulation. However, surface observations show increased crevassing and disappearance of supra glacial ponds, which could have led to increased lubrication of the glacier bed. As a consequence of the surging tongue blocking the main valley a lake has formed and grown continuously in size over two months at a rate of up to 3000 m2 per day. Using satellite imagery with a frequent overpass rate we are able to (a) characterize the nature of glacier surges in the region with greater detail and (b) monitor the surge as well as the formation of the lake as it develops. Having developed a connection to local stakeholders we were able to provide rapid hazard assessments to affected communities, which can be employed to define possible actions. We show the potential of satellite imagery - freely available Landsat in combination with commercial Planet imagery -, which

  6. A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.

    Science.gov (United States)

    Meng, Fan; Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

    2017-09-15

    Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

  7. Multimodel evaluation of cloud phase transition using satellite and reanalysis data

    Science.gov (United States)

    Cesana, G.; Waliser, D. E.; Jiang, X.; Li, J.-L. F.

    2015-08-01

    We take advantage of climate simulations from two multimodel experiments to characterize and evaluate the cloud phase partitioning in 16 general circulation models (GCMs), specifically the vertical structure of the transition between liquid and ice in clouds. We base our analysis on the ratio of ice condensates to the total condensates (phase ratio, PR). Its transition at 90% (PR90) and its links with other relevant variables are evaluated using the GCM-Oriented Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Cloud Product climatology, reanalysis data, and other satellite observations. In 13 of 16 models, the PR90 transition height occurs too low (6 km to 8.4 km) and at temperatures too warm (-13.9°C to -32.5°C) compared to observations (8.6 km, -33.7°C); features consistent with a lack of supercooled liquid with respect to ice above 6.5 km. However, this bias would be slightly reduced by using the lidar simulator. In convective regimes (more humid air and precipitation), the observed cloud phase transition occurs at a warmer temperature than for subsidence regimes (less humid air and precipitation). Only few models manage to roughly replicate the observed correlations with humidity (5/16), vertical velocity (5/16), and precipitation (4/16); 3/16 perform well for all these parameters (MPI-ESM, NCAR-CAM5, and NCHU). Using an observation-based Clausius-Clapeyron phase diagram, we illustrate that the Bergeron-Findeisen process is a necessary condition for models to represent the observed features. Finally, the best models are those that include more complex microphysics.

  8. Detection of Coccolithophore Blooms in Ocean Color Satellite Imagery: a Generalized Approach for Use with Multiple Sensors

    Science.gov (United States)

    Moore, Timothy; Dowell, Mark; Franz, Bryan A.

    2012-01-01

    A generalized coccolithophore bloom classifier has been developed for use with ocean color imagery. The bloom classifier was developed using extracted satellite reflectance data from SeaWiFS images screened by the default bloom detection mask. In the current application, we extend the optical water type (OWT) classification scheme by adding a new coccolithophore bloom class formed from these extracted reflectances. Based on an in situ coccolithophore data set from the North Atlantic, the detection levels with the new scheme were between 1,500 and 1,800 coccolithophore cellsmL and 43,000 and 78,000 lithsmL. The detected bloom area using the OWT method was an average of 1.75 times greater than the default bloom detector based on a collection of SeaWiFS 1 km imagery. The versatility of the scheme is shown with SeaWiFS, MODIS Aqua, CZCS and MERIS imagery at the 1 km scale. The OWT scheme was applied to the daily global SeaWiFS imagery mission data set (years 19972010). Based on our results, average annual coccolithophore bloom area was more than two times greater in the southern hemisphere compared to the northern hemi- sphere with values of 2.00 106 km2 and 0.75 106 km2, respectively. The new algorithm detects larger bloom areas in the Southern Ocean compared to the default algorithm, and our revised global annual average of 2.75106 km2 is dominated by contributions from the Southern Ocean.

  9. Improved land use classification from Landsat and Seasat satellite imagery registered to a common map base

    Science.gov (United States)

    Clark, J.

    1981-01-01

    In the case of Landsat Multispectral Scanner System (MSS) data, ambiguities in spectral signature can arise in urban areas. A study was initiated in the belief that Seasat digital SAR could help provide the spectral separability needed for a more accurate urban land use classification. A description is presented of the results of land use classifications performed on Landsat and preprocessed Seasat imagery that were registered to a common map base. The process of registering imagery and training site boundary coordinates to a common map has been reported by Clark (1980). It is found that preprocessed Seasat imagery provides signatures for urban land uses which are spectrally separable from Landsat signatures. This development appears to significantly improve land use classifications in an urban setting for class 12 (Commercial and Services), class 13 (Industrial), and class 14 (Transportation, Communications, and Utilities).

  10. Cloud Masking and Surface Temperature Distribution in the Polar Regions Using AVHRR and other Satellite Data

    Science.gov (United States)

    Comiso, Joey C.

    1995-01-01

    Surface temperature is one of the key variables associated with weather and climate. Accurate measurements of surface air temperatures are routinely made in meteorological stations around the world. Also, satellite data have been used to produce synoptic global temperature distributions. However, not much attention has been paid on temperature distributions in the polar regions. In the polar regions, the number of stations is very sparse. Because of adverse weather conditions and general inaccessibility, surface field measurements are also limited. Furthermore, accurate retrievals from satellite data in the region have been difficult to make because of persistent cloudiness and ambiguities in the discrimination of clouds from snow or ice. Surface temperature observations are required in the polar regions for air-sea-ice interaction studies, especially in the calculation of heat, salinity, and humidity fluxes. They are also useful in identifying areas of melt or meltponding within the sea ice pack and the ice sheets and in the calculation of emissivities of these surfaces. Moreover, the polar regions are unique in that they are the sites of temperature extremes, the location of which is difficult to identify without a global monitoring system. Furthermore, the regions may provide an early signal to a potential climate change because such signal is expected to be amplified in the region due to feedback effects. In cloud free areas, the thermal channels from infrared systems provide surface temperatures at relatively good accuracies. Previous capabilities include the use of the Temperature Humidity Infrared Radiometer (THIR) onboard the Nimbus-7 satellite which was launched in 1978. Current capabilities include the use of the Advance Very High Resolution Radiometer (AVHRR) aboard NOAA satellites. Together, these two systems cover a span of 16 years of thermal infrared data. Techniques for retrieving surface temperatures with these sensors in the polar regions have

  11. Mapping and Visualization of The Deepwater Horizon Oil Spill Using Satellite Imagery

    Science.gov (United States)

    Ferreira Pichardo, E.

    2017-12-01

    Satellites are man-made objects hovering around the Earth's orbit and are essential for Earth observation, i.e. the monitoring and gathering of data about the Earth's vital systems. Environmental Satellites are used for atmospheric research, weather forecasting, and warning as well as monitoring extreme weather events. These satellites are categorized into Geosynchronous and Low Earth (Polar) orbiting satellites. Visualizing satellite data is critical to understand the Earth's systems and changes to our environment. The objective of this research is to examine satellite-based remotely sensed data that needs to be processed and rendered in the form of maps or other forms of visualization to understand and interpret the satellites' observations to monitor the status, changes and evolution of the mega-disaster Deepwater Horizon Spill that occurred on April 20, 2010 in the Gulf of Mexico. In this project, we will use an array of tools and programs such as Python, CSPP and Linux. Also, we will use data from the National Oceanic and Atmospheric Administration (NOAA): Polar-Orbiting Satellites Terra Earth Observing System AM-1 (EOS AM-1), and Aqua EOS PM-1 to investigate the mega-disaster. Each of these satellites carry a variety of instruments, and we will use the data obtained from the remote sensor Moderate-Resolution Imaging Spectroradiometer (MODIS). Ultimately, this study shows the importance of mapping and visualizing data such as satellite data (MODIS) to understand the extents of environmental impacts disasters such as the Deepwater Horizon Oil spill.

  12. Efficient Photometry In-Frame Calibration (EPIC) Gaussian Corrections for Automated Background Normalization of Rate-Tracked Satellite Imagery

    Science.gov (United States)

    Griesbach, J.; Wetterer, C.; Sydney, P.; Gerber, J.

    calculation along the deck dimension for each image pixel. Because the images are rate-tracked, moving objects (such as background stars) are quickly eliminated. Stationary RSO signatures are removed from the resultant median combined image using a local area median filter that smoothes over the RSO responses. The result is an estimate of the background noise bias. The calculated bias estimate image is subtracted from each deck image, which effectively removes noise bias. A variance correction is applied to address spatially varying noise by dividing each pixel by the square-root of its measured variance. This technique essentially uses the sky background noise (assumed to be uniform over the telescope aperture) to normalize the CCD quantum efficiency/optical path vignetting effects. Poisson distributed random variables have, by definition, equal mean and variance, which eliminates the need to estimate the variance explicitly. Simply dividing each pixel by the square-root of the measured bias effectively normalizes the imagery. The result after the bias and variance corrections are applied is a statistically stationary noise background appropriate for CFAR detection. Additional techniques to address time-varying noise backgrounds (caused by atmospheric turbulence, clouds, and stray light) and hot-pixel / cosmic-ray mitigation are also explored.

  13. Revisiting the iris effect of tropical cirrus clouds with TRMM and A-Train satellite data

    Science.gov (United States)

    Choi, Yong-Sang; Kim, WonMoo; Yeh, Sang-Wook; Masunaga, Hirohiko; Kwon, Min-Jae; Jo, Hyun-Su; Huang, Lei

    2017-06-01

    Just as the iris of human eye controls the light influx (iris effect), tropical anvil cirrus clouds may regulate the Earth's surface warming by controlling outgoing longwave radiation. This study examines this possible effect with monthly satellite observations such as Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer cirrus fraction, and Clouds and the Earth's Radiant Energy System top-of-the-atmosphere radiative fluxes averaged over different tropical domains from March 2000 to October 2014. To confirm that high-level cirrus is relevant to this study, Cloud-Aerosol Lidar with Orthogonal Polarization high cloud observations were also analyzed from June 2006 to December 2015. Our analysis revealed that the increase in sea surface temperature in the tropical western Pacific tends to concentrate convective cloud systems. This concentration effect very likely induces the significant reduction of both stratiform rain rate and cirrus fraction, without appreciable change in the convective rain rate. This reduction of stratiform rain rate and cirrus fraction cannot be found over its subregion or the tropical eastern Pacific, where the concentration effect of anvil cirrus is weak. Consistently, over the tropical western Pacific, the higher ratio of convective rain rate to total rain rate (i.e., precipitation efficiency) significantly correlates with warmer sea surface temperature and lower cirrus fraction. The reduced cirrus eventually increased outgoing longwave radiation to a greater degree than absorbed solar radiation. Finally, the negative relationship between precipitation efficiency and cirrus fraction tends to correspond to a low global equilibrium climate sensitivity in the models in the Coupled Model Intercomparison Project Phase 5. This suggests that tropical anvil cirrus clouds exert a negative climate feedback in strong association with precipitation efficiency.

  14. Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns.

    Science.gov (United States)

    Edward J. Laurent; Haijin Shi; Demetrios Gatziolis; Joseph P. LeBouton; Michael B. Walters; Jianguo Liu

    2005-01-01

    We investigated the potential of using unclassified spectral data for predicting the distribution of three bird species over a -400,000 ha region of Michigan's Upper Peninsula using Landsat ETM+ imagery and 433 locations sampled for birds through point count surveys. These species, Black-throated Green Warbler, Nashville Warbler, and Ovenbird. were known to be...

  15. Building damage assessment after the earthquake in Haiti using two postevent satellite stereo imagery and DSMs

    DEFF Research Database (Denmark)

    Tian, Jiaojiao; Nielsen, Allan Aasbjerg; Reinartz, Peter

    2015-01-01

    In this article, a novel after-disaster building damage monitoring method is presented. This method combines the multispectral imagery and digital surface models (DSMs) from stereo matching of two dates to obtain three kinds of changes: collapsed buildings, newly built buildings and temporary she...... changes after the 2010 Haiti earthquake, and the obtained results are further evaluated both visually and numerically....

  16. Automatic Radiometric Normalization of Multitemporal Satellite Imagery with the Iteratively Re-weighted MAD Transformation

    DEFF Research Database (Denmark)

    Canty, Morton John; Nielsen, Allan Aasbjerg

    2008-01-01

    A recently proposed method for automatic radiometric normalization of multi- and hyper-spectral imagery based on the invariance property of the Multivariate Alteration Detection (MAD) transformation and orthogonal linear regression is extended by using an iterative re-weighting scheme involving no...

  17. H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

    Science.gov (United States)

    Kalia, Subodh; Ganguly, Sangram; Li, Shuang; Nemani, Ramakrishna R.

    2017-01-01

    Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

  18. [Application of single-band brightness variance ratio to the interference dissociation of cloud for satellite data].

    Science.gov (United States)

    Qu, Wei-ping; Liu, Wen-qing; Liu, Jian-guo; Lu, Yi-huai; Zhu, Jun; Qin, Min; Liu, Cheng

    2006-11-01

    In satellite remote-sensing detection, cloud as an interference plays a negative role in data retrieval. How to discern the cloud fields with high fidelity thus comes as a need to the following research. A new method rooting in atmospheric radiation characteristics of cloud layer, in the present paper, presents a sort of solution where single-band brightness variance ratio is used to detect the relative intensity of cloud clutter so as to delineate cloud field rapidly and exactly, and the formulae of brightness variance ratio of satellite image, image reflectance variance ratio, and brightness temperature variance ratio of thermal infrared image are also given to enable cloud elimination to produce data free from cloud interference. According to the variance of the penetrating capability for different spectra bands, an objective evaluation is done on cloud penetration of them with the factors that influence penetration effect. Finally, a multi-band data fusion task is completed using the image data of infrared penetration from cirrus nothus. Image data reconstruction is of good quality and exactitude to show the real data of visible band covered by cloud fields. Statistics indicates the consistency of waveband relativity with image data after the data fusion.

  19. An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV Imagery, Based on Structure from Motion (SfM Point Clouds

    Directory of Open Access Journals (Sweden)

    Christopher Watson

    2012-05-01

    Full Text Available Unmanned Aerial Vehicles (UAVs are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.

  20. Statistical evaluation of the feasibility of satellite-retrieved cloud parameters as indicators of PM2.5 levels.

    Science.gov (United States)

    Yu, Chao; Di Girolamo, Larry; Chen, Liangfu; Zhang, Xueying; Liu, Yang

    2015-01-01

    The spatial and temporal characteristics of fine particulate matter (PM2.5, particulate matter research has been conducted on the association between cloud properties and PM2.5 levels. In this study, we analyzed the relationships between ground PM2.5 concentrations and two satellite-retrieved cloud parameters using data from the Southeastern Aerosol Research and Characterization (SEARCH) Network during 2000-2010. We found that both satellite-retrieved cloud fraction (CF) and cloud optical thickness (COT) are negatively associated with PM2.5 levels. PM2.5 speciation and meteorological analysis suggested that the main reason for these negative relationships might be the decreased secondary particle generation. Stratified analyses by season, land use type, and site location showed that seasonal impacts on this relationship are significant. These associations do not vary substantially between urban and rural sites or inland and coastal sites. The statistically significant negative associations of PM2.5 mass concentrations with CF and COT suggest that satellite-retrieved cloud parameters have the potential to serve as predictors to fill the data gap left by satellite aerosol optical depth in satellite-driven PM2.5 models.

  1. Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

    Energy Technology Data Exchange (ETDEWEB)

    Stenz, Ronald; Dong, Xiquan; Xi, Baike; Feng, Zhe; Kuligowski, Robert J.

    2016-02-01

    To address significant gaps in ground-based radar coverage and rain gauge networks in the U.S., geostationary satellite quantitative precipitation estimates (QPEs) such as the Self-Calibrating Multivariate Precipitation Retrievals (SCaMPR) can be used to fill in both the spatial and temporal gaps of ground-based measurements. Additionally, with the launch of GOES-R, the temporal resolution of satellite QPEs may be comparable to that of Weather Service Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every five minutes. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations particularly during convective events. Deep Convective Systems (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little/no precipitation) cannot be distinguished from rain-cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth (τ) has been found to reduce overestimates of precipitation in anvil regions (Stenz et al. 2014). A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. The performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. (2012) classification algorithm. SCaMPR estimates with the new rain mask applied benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.

  2. The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States-Executive report

    Science.gov (United States)

    Miller, Holly M.; Sexton, Natalie R.; Koontz, Lynne; Loomis, John; Koontz, Stephen R.; Hermans, Caroline

    2011-01-01

    Moderate-resolution imagery (MRI), such as that provided by the Landsat satellites, provides unique spatial information for use by many people both within and outside of the United States (U.S.). However, exactly who these users are, how they use the imagery, and the value and benefits derived from the information are, to a large extent, unknown. To explore these issues, social scientists at the USGS Fort Collins Science Center conducted a study of U.S.-based MRI users from 2008 through 2010 in two parts: 1) a user identification and 2) a user survey. The objectives for this study were to: 1) identify and classify U.S.-based users of this imagery; 2) better understand how and why MRI, and specifically Landsat, is being used; and 3) qualitatively and quantitatively measure the value and societal benefits of MRI (focusing on Landsat specifically). The results of the survey revealed that respondents from multiple sectors use Landsat imagery in many different ways, as demonstrated by the breadth of project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance placed on the imagery, the numerous benefits received from projects using Landsat imagery, the negative impacts if Landsat imagery was no longer available, and the substantial willingness to pay for replacement imagery in the event of a data gap. The survey collected information from users who are both part of and apart from the known user community. The diversity of the sample delivered results that provide a baseline of knowledge about the users, uses, and value of Landsat imagery. While the results supply a wealth of information on their own, they can also be built upon through further research to generate a more complete picture of the population of Landsat users as a whole.

  3. Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana: A Case Study in Bundaberg, Australia

    Directory of Open Access Journals (Sweden)

    Andrew Robson

    2017-11-01

    Full Text Available Accurate pre-harvest estimation of avocado (Persea americana cv. Haas yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. This study evaluated the accuracies of high resolution WorldView (WV 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of total fruit weight (kg·tree−1 and average fruit size (g and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI derived from the WV images and sampled for total fruit weight (kg·tree−1 and average fruit size (g per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs derived. Principal component analysis (PCA and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017 a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1 to both total fruit weight (kg·tree−1 (R2 = 0.45, 0.28, and 0.29 respectively and average fruit size (g (R2 = 0.56, 0.37, and 0.29 respectively across all orchard blocks. Separate analysis of each orchard block produced higher R2 values as well as identifying different

  4. USING OF THE MULTITEMPORAL THERMAL INFRARED SATELLITE IMAGERY FOR NATURAL AREAS MAPPING (CASE OF MENDELEEV VOLCANO

    Directory of Open Access Journals (Sweden)

    M. Y. Grishchenko

    2014-01-01

    Full Text Available In the paper authors examine the mountain group of Mendeleev volcano situated on the Kunashir island, Kuril archipelago, Russia. Ground observations were led to examine the vegetation cover of the area as well as its typical landscapes. The other type of used data is Landsat imagery. Images were combined into multitemporal thermal infrared and multispectral pictures, which were classified to reveal the heterogeneity of the study area. Ground observations and comparison of the classification results with landscape map derive that the multitemporal thermal infrared image classification result describes better the vegetation cover structure of the area and particularity of its typical landscapes distribution. It leads to the proposition that miltitemporal thermal infrared imagery can be used to refine landscape and vegetation cover contours. 

  5. An evaluation of the use of ERTS-1 satellite imagery for grizzly bear habitat analysis. [Montana

    Science.gov (United States)

    Varney, J. R.; Craighead, J. J.; Sumner, J. S.

    1974-01-01

    Improved classification and mapping of grizzly habitat will permit better estimates of population density and distribution, and allow accurate evaluation of the potential effects of changes in land use, hunting regulation, and management policies on existing populations. Methods of identifying favorable habitat from ERTS-1 multispectral scanner imagery were investigated and described. This technique could reduce the time and effort required to classify large wilderness areas in the Western United States.

  6. Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations

    Science.gov (United States)

    We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop si...

  7. Stormwater Runoff Plumes in Southern California Detected with Satellite SAR and MODIS Imagery - Areas of Increased Contamination Risk

    Science.gov (United States)

    Trinh, R. C.; Holt, B.; Gierach, M.

    2016-12-01

    Coastal pollution poses both a major health and environmental hazard, not only for beachgoers and coastal communities, but for marine organisms as well. Stormwater runoff is the largest source of pollution in the coastal waters of the Southern California Bight (SCB). The SCB is the final destination of four major urban watersheds and associated rivers, Ballona Creek, the Los Angeles River, the San Gabriel River, and the Santa Ana River, which act as channels for runoff and pollution during and after episodic rainstorms. Previous studies of SCB water quality have made use of both fine resolution Synthetic Aperture Radar (SAR) imagery and wide-swath medium resolution optical "ocean color" imagery from SeaWiFS and MODIS. In this study, we expand on previous SAR efforts, compiling a more extensive collection of multi-sensor SAR data, spanning from 1992 to 2014, analyzing the surface slick component of stormwater plumes. We demonstrate the use of SAR data in early detection of coastal stormwater plumes, relating plume extent to cumulative river discharge, and shoreline fecal bacteria loads. Intensity maps of the primary extent and direction of plumes were created, identifying coastal areas that may be subject to the greatest risk of environmental contamination. Additionally, we illustrate the differences in the detection of SAR surface plumes with the sediment-related discharge plumes derived from MODIS ocean color imagery. Finally, we provide a concept for satellite monitoring of stormwater plumes, combining both optical and radar sensors, to be used to guide the collection of in situ water quality data and enhance the assessment of related beach closures.

  8. 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....

  9. Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

    Science.gov (United States)

    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.

  10. Tracking an Oil Tanker Collision and Spilled Oils in the East China Sea Using Multisensor Day and Night Satellite Imagery

    Science.gov (United States)

    Sun, Shaojie; Lu, Yingcheng; Liu, Yongxue; Wang, Mengqiu; Hu, Chuanmin

    2018-04-01

    Satellite remote sensing is well known to play a critical role in monitoring marine accidents such as oil spills, yet the recent SANCHI oil tanker collision event in January 2018 in the East China Sea indicates that traditional techniques using synthetic aperture radar or daytime optical imagery could not provide timely and adequate coverage. In this study, we show the unprecedented value of Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and Day/Night Band data in tracking the oil tanker's drifting pathway and locations when all other means are not as effective for the same purpose. Such pathway and locations can also be reproduced with a numerical model, with root-mean-square error of days of the tanker's sinking reveals much larger oil spill area (>350 km2) than previous reports, the impact of the spilled condensate oil on the marine environment requires further research.

  11. Evaluation of the Cloud Fields in the UK Met Office HadGEM3-UKCA Model Using the CCCM Satellite Data Product to Advance Our Understanding of the Influence of Clouds on Tropospheric Composition and Chemistry

    Science.gov (United States)

    Varma, Sunil; Voulgarakis, Apostolos; Liu, Hongyu; Crawford, James H.; White, James

    2016-01-01

    To determine the role of clouds in driving inter-annual and inter-seasonal variability of trace gases in the troposphere and lower stratosphere with a particular focus on the importance of cloud modification of photolysis. To evaluate the cloud fields and their vertical distribution in the HadGEM3 model utilizing CCCM, a unique 3-D cloud data product merged from multiple A-Train satellites (CERES, CloudSat, CALIPSO, and MODIS) developed at the NASA Langley Research Center.

  12. Automatic urban debris zone extraction from post-hurricane very high-resolution satellite and aerial imagery

    Directory of Open Access Journals (Sweden)

    Shasha Jiang

    2016-05-01

    Full Text Available Automated remote sensing methods have not gained widespread usage for damage assessment after hurricane events, especially for low-rise buildings, such as individual houses and small businesses. Hurricane wind, storm surge with waves, and inland flooding have unique damage signatures, further complicating the development of robust automated assessment methodologies. As a step toward realizing automated damage assessment for multi-hazard hurricane events, this paper presents a mono-temporal image classification methodology that quickly and accurately differentiates urban debris from non-debris areas using post-event images. Three classification approaches are presented: spectral, textural, and combined spectral–textural. The methodology is demonstrated for Gulfport, Mississippi, using IKONOS panchromatic satellite and NOAA aerial colour imagery collected after 2005 Hurricane Katrina. The results show that multivariate texture information significantly improves debris class detection performance by decreasing the confusion between debris and other land cover types, and the extracted debris zone accurately captures debris distribution. Additionally, the extracted debris boundary is approximately equivalent regardless of imagery type, demonstrating the flexibility and robustness of the debris mapping methodology. While the test case presents results for hurricane hazards, the proposed methodology is generally developed and expected to be effective in delineating debris zones for other natural hazards, including tsunamis, tornadoes, and earthquakes.

  13. Use of multispectral satellite imagery and hyperspectral endmember libraries for urban land cover mapping at the metropolitan scale

    Science.gov (United States)

    Priem, Frederik; Okujeni, Akpona; van der Linden, Sebastian; Canters, Frank

    2016-10-01

    The value of characteristic reflectance features for mapping urban materials has been demonstrated in many experiments with airborne imaging spectrometry. Analysis of larger areas requires satellite-based multispectral imagery, which typically lacks the spatial and spectral detail of airborne data. Consequently the need arises to develop mapping methods that exploit the complementary strengths of both data sources. In this paper a workflow for sub-pixel quantification of Vegetation-Impervious-Soil urban land cover is presented, using medium resolution multispectral satellite imagery, hyperspectral endmember libraries and Support Vector Regression. A Landsat 8 Operational Land Imager surface reflectance image covering the greater metropolitan area of Brussels is selected for mapping. Two spectral libraries developed for the cities of Brussels and Berlin based on airborne hyperspectral APEX and HyMap data are used. First the combined endmember library is resampled to match the spectral response of the Landsat sensor. The library is then optimized to avoid spectral redundancy and confusion. Subsequently the spectra of the endmember library are synthetically mixed to produce training data for unmixing. Mapping is carried out using Support Vector Regression models trained with spectra selected through stratified sampling of the mixed library. Validation on building block level (mean size = 46.8 Landsat pixels) yields an overall good fit between reference data and estimation with Mean Absolute Errors of 0.06, 0.06 and 0.08 for vegetation, impervious and soil respectively. Findings of this work may contribute to the use of universal spectral libraries for regional scale land cover fraction mapping using regression approaches.

  14. Image Fusion Applied to Satellite Imagery for the Improved Mapping and Monitoring of Coral Reefs: a Proposal

    Science.gov (United States)

    Gholoum, M.; Bruce, D.; Hazeam, S. Al

    2012-07-01

    A coral reef ecosystem, one of the most complex marine environmental systems on the planet, is defined as biologically diverse and immense. It plays an important role in maintaining a vast biological diversity for future generations and functions as an essential spawning, nursery, breeding and feeding ground for many kinds of marine species. In addition, coral reef ecosystems provide valuable benefits such as fisheries, ecological goods and services and recreational activities to many communities. However, this valuable resource is highly threatened by a number of environmental changes and anthropogenic impacts that can lead to reduced coral growth and production, mass coral mortality and loss of coral diversity. With the growth of these threats on coral reef ecosystems, there is a strong management need for mapping and monitoring of coral reef ecosystems. Remote sensing technology can be a valuable tool for mapping and monitoring of these ecosystems. However, the diversity and complexity of coral reef ecosystems, the resolution capabilities of satellite sensors and the low reflectivity of shallow water increases the difficulties to identify and classify its features. This paper reviews the methods used in mapping and monitoring coral reef ecosystems. In addition, this paper proposes improved methods for mapping and monitoring coral reef ecosystems based on image fusion techniques. This image fusion techniques will be applied to satellite images exhibiting high spatial and low to medium spectral resolution with images exhibiting low spatial and high spectral resolution. Furthermore, a new method will be developed to fuse hyperspectral imagery with multispectral imagery. The fused image will have a large number of spectral bands and it will have all pairs of corresponding spatial objects. This will potentially help to accurately classify the image data. Accuracy assessment use ground truth will be performed for the selected methods to determine the quality of the

  15. IMAGE FUSION APPLIED TO SATELLITE IMAGERY FOR THE IMPROVED MAPPING AND MONITORING OF CORAL REEFS: A PROPOSAL

    Directory of Open Access Journals (Sweden)

    M. Gholoum

    2012-07-01

    Full Text Available A coral reef ecosystem, one of the most complex marine environmental systems on the planet, is defined as biologically diverse and immense. It plays an important role in maintaining a vast biological diversity for future generations and functions as an essential spawning, nursery, breeding and feeding ground for many kinds of marine species. In addition, coral reef ecosystems provide valuable benefits such as fisheries, ecological goods and services and recreational activities to many communities. However, this valuable resource is highly threatened by a number of environmental changes and anthropogenic impacts that can lead to reduced coral growth and production, mass coral mortality and loss of coral diversity. With the growth of these threats on coral reef ecosystems, there is a strong management need for mapping and monitoring of coral reef ecosystems. Remote sensing technology can be a valuable tool for mapping and monitoring of these ecosystems. However, the diversity and complexity of coral reef ecosystems, the resolution capabilities of satellite sensors and the low reflectivity of shallow water increases the difficulties to identify and classify its features. This paper reviews the methods used in mapping and monitoring coral reef ecosystems. In addition, this paper proposes improved methods for mapping and monitoring coral reef ecosystems based on image fusion techniques. This image fusion techniques will be applied to satellite images exhibiting high spatial and low to medium spectral resolution with images exhibiting low spatial and high spectral resolution. Furthermore, a new method will be developed to fuse hyperspectral imagery with multispectral imagery. The fused image will have a large number of spectral bands and it will have all pairs of corresponding spatial objects. This will potentially help to accurately classify the image data. Accuracy assessment use ground truth will be performed for the selected methods to determine

  16. An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Xiao Ling

    2016-08-01

    Full Text Available This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC, which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3, Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.

  17. Assessing the population coverage of a health demographic surveillance system using satellite imagery and crowd-sourcing.

    Directory of Open Access Journals (Sweden)

    Aurelio Di Pasquale

    Full Text Available Remotely sensed data can serve as an independent source of information about the location of residential structures in areas under demographic and health surveillance. We report on results obtained combining satellite imagery, imported from Bing, with location data routinely collected using the built-in GPS sensors of tablet computers, to assess completeness of population coverage in a Health and Demographic Surveillance System in Malawi. The Majete Malaria Project Health and Demographic Surveillance System, in Malawi, started in 2014 to support a project with the aim of studying the reduction of malaria using an integrated control approach by rolling out insecticide treated nets and improved case management supplemented with house improvement and larval source management. In order to support the monitoring of the trial a Health and Demographic Surveillance System was established in the area that surrounds the Majete Wildlife Reserve (1600 km2, using the OpenHDS data system. We compared house locations obtained using GPS recordings on mobile devices during the demographic surveillance census round with those acquired from satellite imagery. Volunteers were recruited through the crowdcrafting.org platform to identify building structures on the images, which enabled the compilation of a database with coordinates of potential residences. For every building identified on these satellite images by the volunteers (11,046 buildings identified of which 3424 (ca. 30% were part of the censused area, we calculated the distance to the nearest house enumerated on the ground by fieldworkers during the census round of the HDSS. A random sample of buildings (85 structures identified on satellite images without a nearby location enrolled in the census were visited by a fieldworker to determine how many were missed during the baseline census survey, if any were missed. The findings from this ground-truthing effort suggest that a high population coverage was

  18. Aerial Photography and Imagery, Ortho-Corrected, This data set contains imagery from the National Agriculture Imagery Program (NAIP). NAIP acquires digital ortho imagery during the agricultural growing seasons in the continental U.S. NAIP imagery may contain as much as 10% cloud cover per tile. This fil, Published in 2005, 1:63360 (1in=1mile) scale, University of Georgia.

    Data.gov (United States)

    NSGIC Education | GIS Inventory — Aerial Photography and Imagery, Ortho-Corrected dataset current as of 2005. This data set contains imagery from the National Agriculture Imagery Program (NAIP). NAIP...

  19. Using limnological and optical knowledge to detect discharges from nuclear facilities - Potential application of satellite imagery for international safeguards

    International Nuclear Information System (INIS)

    Borstad, G.; Truong, Q.S. Bob; Keeffe, R.; Baines, P.; Staenz, K.; Neville, R.

    2001-01-01

    Previous work carried out under the Canadian Safeguards Support Program, has shown that thermal imagery from the American Landsat satellites could be used to detect the cooling water discharges, and could therefore be used to verify the operational status of nuclear facilities. In some images, thermal plumes could be easily detected in single band imagery with no mathematical manipulation and little image enhancement because there was a very strong thermal contrast between the effluent and the receiving water. However, for certain situations such as discharges into well mixed conditions (cold water and violent tides) the thermal plume may be more subtle. We show here that the visible bands of Landsat and IKONOS images often contain additional information, and that the thermal signature of a discharge from a nuclear facility is not the only signal available to describe its operation. This paper introduces some important hydrological phenomena that govern the biological and physical organization of water bodies, and discusses some basic concepts of marine and aquatic optics that are relevant to the safeguards problem. Using image analysis techniques that have been used widely in ocean optics work and in applications in the mapping and monitoring of water quality, we have re-analyzed data that were obtained under a joint project between various Canadian government departments. We present a preliminary examination of imagery from both satellite multispectral and aircraft hyperspectral sensors, and discuss methods to extract information that could be useful in the detection and verification of declared or undeclared nuclear activities. In one example of an IKONOS image of the Canadian Bruce Nuclear Generating Facility, simple enhancement techniques failed to find any plume other than a small jet visible in the surface wave field. With knowledge of limnology, oceanography and aquatic optics, we have been able to separate and remove the surface reflection, and detect a

  20. Developing national on-line services to annotate and analyse underwater imagery in a research cloud

    Science.gov (United States)

    Proctor, R.; Langlois, T.; Friedman, A.; Davey, B.

    2017-12-01

    Fish image annotation data is currently collected by various research, management and academic institutions globally (+100,000's hours of deployments) with varying degrees of standardisation and limited formal collaboration or data synthesis. We present a case study of how national on-line services, developed within a domain-oriented research cloud, have been used to annotate habitat images and synthesise fish annotation data sets collected using Autonomous Underwater Vehicles (AUVs) and baited remote underwater stereo-video (stereo-BRUV). Two developing software tools have been brought together in the marine science cloud to provide marine biologists with a powerful service for image annotation. SQUIDLE+ is an online platform designed for exploration, management and annotation of georeferenced images & video data. It provides a flexible annotation framework allowing users to work with their preferred annotation schemes. We have used SQUIDLE+ to sample the habitat composition and complexity of images of the benthos collected using stereo-BRUV. GlobalArchive is designed to be a centralised repository of aquatic ecological survey data with design principles including ease of use, secure user access, flexible data import, and the collection of any sampling and image analysis information. To easily share and synthesise data we have implemented data sharing protocols, including Open Data and synthesis Collaborations, and a spatial map to explore global datasets and filter to create a synthesis. These tools in the science cloud, together with a virtual desktop analysis suite offering python and R environments offer an unprecedented capability to deliver marine biodiversity information of value to marine managers and scientists alike.

  1. Digital processing of satellite imagery application to jungle areas of Peru

    Science.gov (United States)

    Pomalaza, J. C. (Principal Investigator); Pomalaza, C. A.; Espinoza, J.

    1976-01-01

    The author has identified the following significant results. The use of clustering methods permits the development of relatively fast classification algorithms that could be implemented in an inexpensive computer system with limited amount of memory. Analysis of CCTs using these techniques can provide a great deal of detail permitting the use of the maximum resolution of LANDSAT imagery. Potential cases were detected in which the use of other techniques for classification using a Gaussian approximation for the distribution functions can be used with advantage. For jungle areas, channels 5 and 7 can provide enough information to delineate drainage patterns, swamp and wet areas, and make a reasonable broad classification of forest types.

  2. Predicting lake trophic state by relating Secchi-disk transparency measurements to Landsat-satellite imagery for Michigan inland lakes, 2003-05 and 2007-08

    Science.gov (United States)

    Fuller, L.M.; Jodoin, R.S.; Minnerick, R.J.

    2011-01-01

    Inland lakes are an important economic and environmental resource for Michigan. The U.S. Geological Survey and the Michigan Department of Natural Resources and Environment have been cooperatively monitoring the quality of selected lakes in Michigan through the Lake Water Quality Assessment program. Sampling for this program began in 2001; by 2010, 730 of Michigan’s 11,000 inland lakes are expected to have been sampled once. Volunteers coordinated by the Michigan Department of Natural Resources and Environment began sampling lakes in 1974 and continue to sample (in 2010) approximately 250 inland lakes each year through the Michigan Cooperative Lakes Monitoring Program. Despite these sampling efforts, it still is impossible to physically collect measurements for all Michigan inland lakes; however, Landsat-satellite imagery has been used successfully in Minnesota, Wisconsin, Michigan, and elsewhere to predict the trophic state of unsampled inland lakes greater than 20 acres by producing regression equations relating in-place Secchi-disk measurements to Landsat bands. This study tested three alternatives to methods previously used in Michigan to improve results for predicted statewide Trophic State Index (TSI) computed from Secchi-disk transparency (TSI (SDT)). The alternative methods were used on 14 Landsat-satellite scenes with statewide TSI (SDT) for two time periods (2003– 05 and 2007–08). Specifically, the methods were (1) satellitedata processing techniques to remove areas affected by clouds, cloud shadows, haze, shoreline, and dense vegetation for inland lakes greater than 20 acres in Michigan; (2) comparison of the previous method for producing a single open-water predicted TSI (SDT) value (which was based on an area of interest (AOI) and lake-average approach) to an alternative Gethist method for identifying open-water areas in inland lakes (which follows the initial satellite-data processing and targets the darkest pixels, representing the deepest water

  3. Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

    Science.gov (United States)

    Minnis, P.; Smith, W., Jr.; Bedka, K. M.; Nguyen, L.; Palikonda, R.; Hong, G.; Trepte, Q.; Chee, T.; Scarino, B. R.; Spangenberg, D.; Sun-Mack, S.; Fleeger, C.; Ayers, J. K.; Chang, F. L.; Heck, P. W.

    2014-12-01

    Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-real time globally from both geostationary (GEO) and low-earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

  4. Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

    Science.gov (United States)

    Minnis, Patrick; Smith, William L., Jr.; Bedka, Kristopher M.; Nguyen, Louis; Palikonda, Rabindra; Hong, Gang; Trepte, Qing Z.; Chee, Thad; Scarino, Benjamin; Spangenberg, Douglas A.; hide

    2014-01-01

    Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-­-real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-­-real time globally from both geostationary (GEO) and low-­-earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

  5. The UNOSAT-GRID Project: Access to Satellite Imagery through the Grid Environment

    CERN Document Server

    Méndez-Lorenzo, P; Lamanna, M; Meyer, X; Lazeyras, M; Bjorgo, E; Retiere, A; Falzone, A; Venuti, N; Maccarone, S; Ugolotti, B

    2007-01-01

    UNOSAT is a United Nations activity to provide access to satellite images and geographic system services for humanitarian operations for rescue or aid activities. UNOSAT is implemented by the UN Institute for Training and Research (UNITAR) and managed by the UN Office for Project Services (UNOPS). In addition, partners from different organizations constitute the UNOSAT consortium. Among these partners, CERN participates actively providing the required computational and storage resources. The critical part of the UNOSAT activity is the storage and processing of large quantities of satellite images. The fast and secure access to these images from any part of the world is mandatory during these activities. Based on two successful CERN-GRID/UNOSAT pilot projects (data storage/compression/download and image access through mobile phone), the GRIDUNOSAT project has consolidated the considerable work undertaken so far in the present activity. The main use case already demonstrated is the delivery of satellite images ...

  6. Land surface temperature distribution and development for green open space in Medan city using imagery-based satellite Landsat 8

    Science.gov (United States)

    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.

  7. Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery

    Science.gov (United States)

    Iabchoon, Sanwit; Wongsai, Sangdao; Chankon, Kanoksuk

    2017-10-01

    Land use and land cover (LULC) data are important to monitor and assess environmental change. LULC classification using satellite images is a method widely used on a global and local scale. Especially, urban areas that have various LULC types are important components of the urban landscape and ecosystem. This study aims to classify urban LULC using WorldView-3 (WV-3) very high-spatial resolution satellite imagery and the object-based image analysis method. A decision rules set was applied to classify the WV-3 images in Kathu subdistrict, Phuket province, Thailand. The main steps were as follows: (1) the image was ortho-rectified with ground control points and using the digital elevation model, (2) multiscale image segmentation was applied to divide the image pixel level into image object level, (3) development of the decision ruleset for LULC classification using spectral bands, spectral indices, spatial and contextual information, and (4) accuracy assessment was computed using testing data, which sampled by statistical random sampling. The results show that seven LULC classes (water, vegetation, open space, road, residential, building, and bare soil) were successfully classified with overall classification accuracy of 94.14% and a kappa coefficient of 92.91%.

  8. Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling

    Science.gov (United States)

    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

  9. Using satellite imagery for qualitative evaluation of plume transport in modeling the effects of the Kuwait oil fire smoke plumes

    International Nuclear Information System (INIS)

    Bass, A.; Janota, P.

    1992-01-01

    To forecast the behavior of the Kuwait oil fire smoke plumes and their possible acute or chronic health effects over the Arabian Gulf region, TASC created a comprehensive health and environmental impacts modeling system. A specially-adapted Lagrangian puff transport model was used to create (a) short-term (multiday) forecasts of plume transport and ground-level concentrations of soot and SO 2 ; and (b) long-term (seasonal and longer) estimates of average surface concentrations and depositions. EPA-approved algorithms were used to transform exposures to SO 2 and soot (as PAH/BaP) into morbidity, mortality and crop damage risks. Absent any ground truth, satellite imagery from the NOAA Polar Orbiter and the ESA Geostationary Meteosat offered the only opportunity for timely qualitative evaluation of the long-range plume transport and diffusion predictions. This paper shows the use of actual satellite images (including animated loops of hourly Meteosat images) to evaluate plume forecasts in near-real-time, and to sanity-check the meso- and long-range plume transport projections for the long-term estimates. Example modeled concentrations, depositions and health effects are shown

  10. High Resolution Topography of Polar Regions from Commercial Satellite Imagery, Petascale Computing and Open Source Software

    Science.gov (United States)

    Morin, Paul; Porter, Claire; Cloutier, Michael; Howat, Ian; Noh, Myoung-Jong; Willis, Michael; Kramer, WIlliam; Bauer, Greg; Bates, Brian; Williamson, Cathleen

    2017-04-01

    Surface topography is among the most fundamental data sets for geosciences, essential for disciplines ranging from glaciology to geodynamics. Two new projects are using sub-meter, commercial imagery licensed by the National Geospatial-Intelligence Agency and open source photogrammetry software to produce a time-tagged 2m posting elevation model of the Arctic and an 8m posting reference elevation model for the Antarctic. When complete, this publically available data will be at higher resolution than any elevation models that cover the entirety of the Western United States. These two polar projects are made possible due to three equally important factors: 1) open-source photogrammetry software, 2) petascale computing, and 3) sub-meter imagery licensed to the United States Government. Our talk will detail the technical challenges of using automated photogrammetry software; the rapid workflow evolution to allow DEM production; the task of deploying the workflow on one of the world's largest supercomputers; the trials of moving massive amounts of data, and the management strategies the team needed to solve in order to meet deadlines. Finally, we will discuss the implications of this type of collaboration for future multi-team use of leadership-class systems such as Blue Waters, and for further elevation mapping.

  11. Advances In very high resolution satellite imagery analysis for Monitoring human settlements

    Energy Technology Data Exchange (ETDEWEB)

    Vatsavai, Raju [ORNL; Cheriyadat, Anil M [ORNL; Bhaduri, Budhendra L [ORNL

    2014-01-01

    The high rate of urbanization, political conflicts and ensuing internal displacement of population, and increased poverty in the 20th century has resulted in rapid increase of informal settlements. These unplanned, unauthorized, and/or unstructured homes, known as informal settlements, shantytowns, barrios, or slums, pose several challenges to the nations, as these settlements are often located in most hazardous regions and lack basic services. Though several World Bank and United Nations sponsored studies stress the importance of poverty maps in designing better policies and interventions, mapping slums of the world is a daunting and challenging task. In this paper, we summarize our ongoing research on settlement mapping through the utilization of Very high resolution (VHR) remote sensing imagery. Most existing approaches used to classify VHR images are single instance (or pixel-based) learning algorithms, which are inadequate for analyzing VHR imagery, as single pixels do not contain sufficient contextual information (see Figure 1). However, much needed spatial contextual information can be captured via feature extraction and/or through newer machine learning algorithms in order to extract complex spatial patterns that distinguish informal settlements from formal ones. In recent years, we made significant progress in advancing the state of art in both directions. This paper summarizes these results.

  12. Insights on the Feasibility, Modeling and Field Testing of Cirrus Cloud Thinning from Satellite Remote Sensing

    Science.gov (United States)

    Mitchell, D. L.; Garnier, A.; Mejia, J.; Avery, M. A.; Erfani, E.

    2016-12-01

    To date, it is not clear whether the climate intervention method known as cirrus cloud thinning (CCT) can be viable since it requires cirrus clouds to form through homogeneous ice nucleation (henceforth hom) and some recent GCM studies predict cirrus are formed primarily through heterogeneous ice nucleation (henceforth het). A new CALIPSO infrared retrieval method has been developed for single-layer cirrus cloud that measures the temperature dependence of their layer-averaged number concentration N, effective diameter De and ice water content for optical depths (OD) between 0.3 and 3.0. Based on N, the prevailing ice nucleation mechanism (hom or het) can be estimated as a function of temperature, season, latitude and surface type. These satellite results indicate that seeding cirrus clouds at high latitudes during winter may produce significant global surface cooling. This is because hom often appears to dominate over land during winter north of 30°N latitude while the same appears true for most of the Southern Hemisphere (south of 30°S) during all seasons. Moreover, the sampled cirrus cloud frequency of occurrence in the Arctic is at least twice as large during winter relative to other seasons, while frequency of occurrence in the Antarctic peaks in the spring and is second-highest during winter. During Arctic winter, a combination of frequent hom cirrus, maximum cirrus coverage and an extreme or absent sun angle produces the maximum seasonal cirrus net radiative forcing (warming). Thus a reduction in OD and coverage (via CCT) for these cirrus clouds could yield a significant net cooling effect. From these CALIPSO retrievals, De-T relationships are generated as a function of season, latitude and surface type (land vs. ocean). These will be used in CAM5 to estimate De and the ice fall speed, from which the cirrus radiative forcing will be estimated during winter north of 30°latitude, where hom cirrus are common. Another CAM5 simulation will replace the hom

  13. Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

    Science.gov (United States)

    DeWitt, Jessica D.; Warner, Timothy A.; Chirico, Peter G.; Bergstresser, Sarah E.

    2017-01-01

    For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.

  14. Estimating the top altitude of optically thick ice clouds from thermal infrared satellite observations using CALIPSO data

    Science.gov (United States)

    Minnis, Patrick; Yost, Chris R.; Sun-Mack, Sunny; Chen, Yan

    2008-06-01

    The difference between cloud-top altitude Z top and infrared effective radiating height Z eff for optically thick ice clouds is examined using April 2007 data taken by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and the Moderate-Resolution Imaging Spectroradiometer (MODIS). For even days, the difference ΔZ between CALIPSO Z top and MODIS Z eff is 1.58 +/- 1.26 km. The linear fit between Z top and Z eff , applied to odd-day data, yields a difference of 0.03 +/- 1.21 km and can be used to estimate Z top from any infrared-based Z eff for thick ice clouds. Random errors appear to be due primarily to variations in cloud ice-water content (IWC). Radiative transfer calculations show that ΔZ corresponds to an optical depth of ~1, which based on observed ice-particle sizes yields an average cloud-top IWC of ~0.015 gm-3, a value consistent with in situ measurements. The analysis indicates potential for deriving cloud-top IWC using dual-satellite data.

  15. Using high-resolution satellite imagery and double sampling as a ...

    African Journals Online (AJOL)

    QuickBird satellite images were used to extract auxiliary variables (image data), such as photogrammetric crown diameter and number of stems, using visual interpretation and measuring tools offered by Erdas 8.7 geographic imaging software. Field inventory data (terrestric data) collected in 2002 were used to obtain the ...

  16. Harmonizing estimates of forest land area from national-level forest inventory and satellite imagery

    Science.gov (United States)

    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...

  17. Identifying and Allocating Geodetic Systems to historical oil gas wells by using high-resolution satellite imagery

    Science.gov (United States)

    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.

  18. Geographic object-based delineation of neighborhoods of Accra, Ghana using QuickBird satellite imagery.

    Science.gov (United States)

    Stow, Douglas A; Lippitt, Christopher D; Weeks, John R

    2010-08-01

    The objective was to test GEographic Object-based Image Analysis (GEOBIA) techniques for delineating neighborhoods of Accra, Ghana using QuickBird multispectral imagery. Two approaches to aggregating census enumeration areas (EAs) based on image-derived measures of vegetation objects were tested: (1) merging adjacent EAs according to vegetation measures and (2) image segmentation. Both approaches exploit readily available functions within commercial GEOBIA software. Image-derived neighborhood maps were compared to a reference map derived by spatial clustering of slum index values (from census data), to provide a relative assessment of potential map utility. A size-constrained iterative segmentation approach to aggregation was more successful than standard image segmentation or feature merge techniques. The segmentation approaches account for size and shape characteristics, enabling more realistic neighborhood boundaries to be delineated. The percentage of vegetation patches within each EA yielded more realistic delineation of potential neighborhoods than mean vegetation patch size per EA.

  19. The theory precision analyse of RFM localization of satellite remote sensing imagery

    Science.gov (United States)

    Zhang, Jianqing; Xv, Biao

    2009-11-01

    The tradition method of detecting precision of Rational Function Model(RFM) is to make use of a great deal check points, and it calculates mean square error through comparing calculational coordinate with known coordinate. This method is from theory of probability, through a large number of samples to statistic estimate value of mean square error, we can think its estimate value approaches in its true when samples are well enough. This paper is from angle of survey adjustment, take law of propagation of error as the theory basis, and it calculates theory precision of RFM localization. Then take the SPOT5 three array imagery as experiment data, and the result of traditional method and narrated method in the paper are compared, while has confirmed tradition method feasible, and answered its theory precision question from the angle of survey adjustment.

  20. Satellite retrieval of the liquid water fraction in tropical clouds between −20 and −38 °C

    Directory of Open Access Journals (Sweden)

    D. L. Mitchell

    2012-07-01

    Full Text Available This study describes a satellite remote sensing method for directly retrieving the liquid water fraction in mixed phase clouds, and appears unique in this respect. The method uses MODIS split-window channels for retrieving the liquid fraction from cold clouds where the liquid water fraction is less than 50% of the total condensate. This makes use of the observation that clouds only containing ice exhibit effective 12-to-11 μm absorption optical thickness ratios (βeff that are quasi-constant with retrieved cloud temperature T. This observation was made possible by using two CO2 channels to retrieve T and then using the 12 and 11 μm channels to retrieve emissivities and βeff. Thus for T < −40 °C, βeff is constant, but for T > −40 °C, βeff slowly increases due to the presence of liquid water, revealing mean liquid fractions of ~ 10% around −22 °C from tropical clouds identified as cirrus by the cloud mask. However, the uncertainties for these retrievals are large, and extensive in situ measurements are needed to refine and validate these retrievals. Such liquid levels are shown to reduce the cloud effective diameter De such that cloud optical thickness will increase by more than 50% for a given water path, relative to De corresponding to pure ice clouds. Such retrieval information is needed for validation of the cloud microphysics in climate models. Since low levels of liquid water can dominate cloud optical properties, tropical clouds between −25 and −20 °C may be susceptible to the first aerosol indirect effect.

  1. The Radiative Properties of Small Clouds: Multi-Scale Observations and Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Feingold, Graham [NOAA ESRL; McComiskey, Allison [CIRES, University of Colorado

    2013-09-25

    Warm, liquid clouds and their representation in climate models continue to represent one of the most significant unknowns in climate sensitivity and climate change. Our project combines ARM observations, LES modeling, and satellite imagery to characterize shallow clouds and the role of aerosol in modifying their radiative effects.

  2. UNOSAT at CERN – 15 years of satellite imagery support to the humanitarian and development community

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Abstract: UNOSAT is part of the United Nations Institute for Training and Research (UNITAR) and has been hosted at CERN since 2001. This partnership allows UNOSAT to benefit from CERN's IT infrastructure whenever the situation requires, allowing the UN to be at the forefront of satellite-analysis technology. Specialists in geographic information systems (GIS) and in the analysis of satellite data, supported by IT engineers and policy experts, ensure a dedicated service to the international humanitarian and development communities 24 hours a day, seven days a week. The presentation will give an overview of the variety of activities carried out by UNOSAT over the last 15 years including support to humanitarian assistance and protection of cultural heritage, sustainable water management in Chad and training & capacity development in East Africa and Asia. The talk will be followed at 12:00 by the inauguration of the UNOSAT exhibition, in front of the Users' office. Speaker: Einar Bjor...

  3. Becoming Bombs: 3D Animated Satellite Imagery and the Weaponization of the Civic Eye

    Directory of Open Access Journals (Sweden)

    Roger Stahl

    2010-02-01

    Full Text Available This essay traces the recent history of 3D satellite animation from its military origins to its visibility in the civic sphere. Specifically, technologies unveiled in 2004 as Google Earth first received widespread public visibility in the television coverage of the 2003 U.S. invasion of Iraq. The essay first maps the political economy of the “military-media-geotech” complex, focusing mainly on the coverage of the Iraq War as an nexus of interests. Second, the essay analyzes the aesthetic uses of 3D satellite animation on the news during this period, including how these imaging practices meshed with existing discourses such as the clean war, the weaponization of the civic gaze, and others. The essay concludes with thoughts regarding what these practices mean for the efficacy of the deliberative citizen, public life, and the meaning of war.

  4. Use of satellite imagery to assess the trophic state of Miyun Reservoir, Beijing, China

    International Nuclear Information System (INIS)

    Wang Zhengjun; Hong Jianming; Du Guisen

    2008-01-01

    The objective of this research is to explore an appropriate way of monitoring and assessing water quality by satellite remote sensing techniques in the Miyun reservoir of Beijing, China. Two scene Thematic Mapper images in May and October of 2003 were acquired and simultaneous in situ measurements, sampling and analysis were conducted. Statistical analysis indicates that satellite-based normalized ratio vegetation index (NRVI) and in situ measured water chlorophyll a (Chl-a) concentration have very high correlation. Two linear regression models with high determination coefficients were constructed for NRVI and Chl-a of sample points. According to the modified trophic state index map, water quality in the western section of Miyun reservoir was consistently higher than in the eastern section during the two months tested. The trophic grade of the eastern reservoir remained mesotrophic with a tendency for eutrophication. - Remote sensing techniques can effectively monitor the change of water quality with time and space

  5. Land use change detection based on multi-date imagery from different satellite sensor systems

    Science.gov (United States)

    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.

  6. Computational Research on Mobile Pastoralism Using Agent-Based Modeling and Satellite Imagery.

    Directory of Open Access Journals (Sweden)

    Takuto Sakamoto

    Full Text Available Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.

  7. OVERVIEW OF MODERN RESEARCH OF LANDSLIDES ACCORDING TO AERIAL AND SATELLITE IMAGERY

    Directory of Open Access Journals (Sweden)

    K. M. Lyapishev

    2015-01-01

    Full Text Available This article is an overview of researches of landslides using remote sensing methods such as aerial photography, satellite images, radar interferometry, and their combination with the use of GIS technology. Modern methods of investigation of landslides are very diverse. The authors propose different approaches to the identification, classification and monitoring of landslides. Data analysis techniques can help in creating more sophisticated approach to the analysis of landslides.

  8. Comparing airborne and satellite retrievals of cloud optical thickness and particle effective radius using a spectral radiance ratio technique: two case studies for cirrus and deep convective clouds

    Science.gov (United States)

    Krisna, Trismono C.; Wendisch, Manfred; Ehrlich, André; Jäkel, Evelyn; Werner, Frank; Weigel, Ralf; Borrmann, Stephan; Mahnke, Christoph; Pöschl, Ulrich; Andreae, Meinrat O.; Voigt, Christiane; Machado, Luiz A. T.

    2018-04-01

    Solar radiation reflected by cirrus and deep convective clouds (DCCs) was measured by the Spectral Modular Airborne Radiation Measurement System (SMART) installed on the German High Altitude and Long Range Research Aircraft (HALO) during the Mid-Latitude Cirrus (ML-CIRRUS) and the Aerosol, Cloud, Precipitation, and Radiation Interaction and Dynamic of Convective Clouds System - Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modelling and to the Global Precipitation Measurement (ACRIDICON-CHUVA) campaigns. On particular flights, HALO performed measurements closely collocated with overpasses of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. A cirrus cloud located above liquid water clouds and a DCC topped by an anvil cirrus are analyzed in this paper. Based on the nadir spectral upward radiance measured above the two clouds, the optical thickness τ and particle effective radius reff of the cirrus and DCC are retrieved using a radiance ratio technique, which considers the cloud thermodynamic phase, the vertical profile of cloud microphysical properties, the presence of multilayer clouds, and the heterogeneity of the surface albedo. For the cirrus case, the comparison of τ and reff retrieved on the basis of SMART and MODIS measurements yields a normalized mean absolute deviation of up to 1.2 % for τ and 2.1 % for reff. For the DCC case, deviations of up to 3.6 % for τ and 6.2 % for reff are obtained. The larger deviations in the DCC case are mainly attributed to the fast cloud evolution and three-dimensional (3-D) radiative effects. Measurements of spectral upward radiance at near-infrared wavelengths are employed to investigate the vertical profile of reff in the cirrus. The retrieved values of reff are compared with corresponding in situ measurements using a vertical weighting method. Compared to the MODIS observations, measurements of SMART provide more information on the

  9. Instantaneous Shoreline Extraction Utilizing Integrated Spectrum and Shadow Analysis From LiDAR Data and High-resolution Satellite Imagery

    Science.gov (United States)

    Lee, I.-Chieh

    Shoreline delineation and shoreline change detection are expensive processes in data source acquisition and manual shoreline delineation. These costs confine the frequency and interval of shoreline mapping periods. In this dissertation, a new shoreline delineation approach was developed targeting on lowering the data source cost and reducing human labor. To lower the cost of data sources, we used the public domain LiDAR data sets and satellite images to delineate shorelines without the requirement of data sets being acquired simultaneously, which is a new concept in this field. To reduce the labor cost, we made improvements in classifying LiDAR points and satellite images. Analyzing shadow relations with topography to improve the satellite image classification performance is also a brand-new concept. The extracted shoreline of the proposed approach could achieve an accuracy of 1.495 m RMSE, or 4.452m at the 95% confidence level. Consequently, the proposed approach could successfully lower the cost and shorten the processing time, in other words, to increase the shoreline mapping frequency with a reasonable accuracy. However, the extracted shoreline may not compete with the shoreline extracted by aerial photogrammetric procedures in the aspect of accuracy. Hence, this is a trade-off between cost and accuracy. This approach consists of three phases, first, a shoreline extraction procedure based mainly on LiDAR point cloud data with multispectral information from satellite images. Second, an object oriented shoreline extraction procedure to delineate shoreline solely from satellite images; in this case WorldView-2 images were used. Third, a shoreline integration procedure combining these two shorelines based on actual shoreline changes and physical terrain properties. The actual data source cost would only be from the acquisition of satellite images. On the other hand, only two processes needed human attention. First, the shoreline within harbor areas needed to be

  10. What do satellite backscatter ultraviolet and visible spectrometers see over snow and ice? A study of clouds and ozone using the A-train

    Directory of Open Access Journals (Sweden)

    A. P. Vasilkov

    2010-05-01

    Full Text Available In this paper, we examine how clouds over snow and ice affect ozone absorption and how these effects may be accounted for in satellite retrieval algorithms. Over snow and ice, the Aura Ozone Monitoring Instrument (OMI Raman cloud pressure algorithm derives an effective scene pressure. When this scene pressure differs appreciably from the surface pressure, the difference is assumed to be caused by a cloud that is shielding atmospheric absorption and scattering below cloud-top from satellite view. A pressure difference of 100 hPa is used as a crude threshold for the detection of clouds that significantly shield tropospheric ozone absorption. Combining the OMI effective scene pressure and the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS cloud top pressure, we can distinguish between shielding and non-shielding clouds.

    To evaluate this approach, we performed radiative transfer simulations under various observing conditions. Using cloud vertical extinction profiles from the CloudSat Cloud Profiling Radar (CPR, we find that clouds over a bright surface can produce significant shielding (i.e., a reduction in the sensitivity of the top-of-the-atmosphere radiance to ozone absorption below the clouds. The amount of shielding provided by clouds depends upon the geometry (solar and satellite zenith angles and the surface albedo as well as cloud optical thickness. We also use CloudSat observations to qualitatively evaluate our approach. The CloudSat, Aqua, and Aura satellites fly in an afternoon polar orbit constellation with ground overpass times within 15 min of each other.

    The current Total Ozone Mapping Spectrometer (TOMS total column ozone algorithm (that has also been applied to the OMI assumes no clouds over snow and ice. This assumption leads to errors in the retrieved ozone column. We show that the use of OMI effective scene pressures over snow and ice reduces these errors and leads to a more homogeneous spatial

  11. The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

    Science.gov (United States)

    Belle, Jessica H.; Chang, Howard H.; Wang, Yujie; Hu, Xuefei; Lyapustin, Alexei; Liu, Yang

    2017-01-01

    Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, approximately 70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

  12. The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

    Directory of Open Access Journals (Sweden)

    Jessica H. Belle

    2017-10-01

    Full Text Available Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5 concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, ~70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

  13. The effects of clouds on the detection of light signals from near-ground nuclear bursts at satellite

    International Nuclear Information System (INIS)

    Zhang Zhongshan; Zhang Enshan; Zhao Wenli; Gao Chunxia

    2002-01-01

    The effects of clouds on the detection of light signals from near-ground nuclear bursts are analysed quantitatively. The results indicate: the degree of the effect increasing with the growth of clouds optical thickness and satellite look angle; clouds lead really harmful effect in detectable signal intensity and precision of optical location, but degree of the effect is not great too. The enhancement of the photon optical paths by multiple scattering within the cloud will cause both a delay and a time-broadening of an impulsive light signal, and get 'lower and fat'; upward optical transmission of light through clouds is essentially the same as if there were no cloud present at all, when a point source is above the geometrical mid-plane of the cloud. And if the point source is below the mid-plane, then upward optical transmission of light through clods will be related closely to the distance of the source below the mid-plane. Given also some charts which evaluate conveniently degree of the effect due to clouds for the purpose of reference and use of a person of the same trade or occupation are given also

  14. A comparison of ground and satellite observations of cloud cover to saturation pressure differences during a cold air outbreak

    Energy Technology Data Exchange (ETDEWEB)

    Alliss, R.J.; Raman, S. [North Carolina State Univ., Raleigh, NC (United States)

    1996-04-01

    The role of clouds in the atmospheric general circulation and the global climate is twofold. First, clouds owe their origin to large-scale dynamical forcing, radiative cooling in the atmosphere, and turbulent transfer at the surface. In addition, they provide one of the most important mechanisms for the vertical redistribution of momentum and sensible and latent heat for the large scale, and they influence the coupling between the atmosphere and the surface as well as the radiative and dynamical-hydrological balance. In existing diagnostic cloudiness parameterization schemes, relative humidity is the most frequently used variable for estimating total cloud amount or stratiform cloud amount. However, the prediction of relative humidity in general circulation models (GCMs) is usually poor. Even for the most comprehensive GCMs, the predicted relative humidity may deviate greatly from that observed, as far as the frequency distribution of relative humidity is concerned. Recently, there has been an increased effort to improve the representation of clouds and cloud-radiation feedback in GCMs, but the verification of cloudiness parameterization schemes remains a severe problem because of the lack of observational data sets. In this study, saturation pressure differences (as opposed to relative humidity) and satellite-derived cloud heights and amounts are compared with ground determinations of cloud cover over the Gulf Stream Locale (GSL) during a cold air outbreak.

  15. Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data.

    Science.gov (United States)

    Shang, Huazhe; Letu, Husi; Nakajima, Takashi Y; Wang, Ziming; Ma, Run; Wang, Tianxing; Lei, Yonghui; Ji, Dabin; Li, Shenshen; Shi, Jiancheng

    2018-01-18

    Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements. For AHI and MODIS, the false alarm rate of AHI and MODIS in cloud identification over the TP was 7.51% and 1.94%, respectively, and the cloud hit rate was 73.55% and 80.15%, respectively. Using hourly cloud-cover data from the Himawari-8 satellites, we found that at the monthly scale, the diurnal cycle in cloud cover over the TP tends to increase throughout the day, with the minimum and maximum cloud fractions occurring at 10:00 a.m. and 18:00 p.m. local time. Due to the limited time resolution of polar-orbiting satellites, the underestimation of MODIS daytime average cloud cover is approximately 4.00% at the annual scale, with larger biases during the spring (5.40%) and winter (5.90%).

  16. Satellite retrieved cloud optical thickness sensitive to surface wind speed in the subarctic marine boundary layer

    International Nuclear Information System (INIS)

    Glantz, Paul

    2010-01-01

    The optical and microphysical properties of low level marine clouds, presented over the Norwegian Sea and Barents Sea, have been investigated for the period 2000-2006. The air masses were transported for more or less seven days over the warmer North Atlantic before they arrived at the area investigated. The main focus in this study is on investigating the relationship between cloud optical thickness (COT) and surface wind speed (U 10m ) using satellite retrievals in combination with operational meteorological data. A relatively strong correlation (R 2 = 0.97) is obtained for wind speeds up to 12 m s -1 , in air masses that were probably to a major degree influenced by wind shears and to a minor degree by buoyancy. The relationship (U 2.5 ) is also in between those most commonly found in the literature for water vapor (∼U 1 ) and sea salt (∼U 3.4 ). The present results highlight the magnitude of marine sea-spray influence on COT and their global climatic importance.

  17. Use of open source information and commercial satellite imagery for nuclear nonproliferation regime compliance verification by a community of academics

    Science.gov (United States)

    Solodov, Alexander

    The proliferation of nuclear weapons is a great threat to world peace and stability. The question of strengthening the nonproliferation regime has been open for a long period of time. In 1997 the International Atomic Energy Agency (IAEA) Board of Governors (BOG) adopted the Additional Safeguards Protocol. The purpose of the protocol is to enhance the IAEA's ability to detect undeclared production of fissile materials in member states. However, the IAEA does not always have sufficient human and financial resources to accomplish this task. Developed here is a concept for making use of human and technical resources available in academia that could be used to enhance the IAEA's mission. The objective of this research was to study the feasibility of an academic community using commercially or publicly available sources of information and products for the purpose of detecting covert facilities and activities intended for the unlawful acquisition of fissile materials or production of nuclear weapons. In this study, the availability and use of commercial satellite imagery systems, commercial computer codes for satellite imagery analysis, Comprehensive Test Ban Treaty (CTBT) verification International Monitoring System (IMS), publicly available information sources such as watchdog groups and press reports, and Customs Services information were explored. A system for integrating these data sources to form conclusions was also developed. The results proved that publicly and commercially available sources of information and data analysis can be a powerful tool in tracking violations in the international nuclear nonproliferation regime and a framework for implementing these tools in academic community was developed. As a result of this study a formation of an International Nonproliferation Monitoring Academic Community (INMAC) is proposed. This would be an independent organization consisting of academics (faculty, staff and students) from both nuclear weapon states (NWS) and

  18. FOREST TREE SPECIES DISTRIBUTION MAPPING USING LANDSAT SATELLITE IMAGERY AND TOPOGRAPHIC VARIABLES WITH THE MAXIMUM ENTROPY METHOD IN MONGOLIA

    Directory of Open Access Journals (Sweden)

    S. H. Chiang

    2016-06-01

    Full Text Available Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered. In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface

  19. A general framework of TOPSIS method for integration of airborne geophysics, satellite imagery, geochemical and geological data

    Science.gov (United States)

    Abedi, Maysam; Norouzi, Gholam-Hossain

    2016-04-01

    This work presents the promising application of three variants of TOPSIS method (namely the conventional, adjusted and modified versions) as a straightforward knowledge-driven technique in multi criteria decision making processes for data fusion of a broad exploratory geo-dataset in mineral potential/prospectivity mapping. The method is implemented to airborne geophysical data (e.g. potassium radiometry, aeromagnetic and frequency domain electromagnetic data), surface geological layers (fault and host rock zones), extracted alteration layers from remote sensing satellite imagery data, and five evidential attributes from stream sediment geochemical data. The central Iranian volcanic-sedimentary belt in Kerman province at the SE of Iran that is embedded in the Urumieh-Dokhtar Magmatic Assemblage arc (UDMA) is chosen to integrate broad evidential layers in the region of prospect. The studied area has high potential of ore mineral occurrences especially porphyry copper/molybdenum and the generated mineral potential maps aim to outline new prospect zones for further investigation in future. Two evidential layers of the downward continued aeromagnetic data and its analytic signal filter are prepared to be incorporated in fusion process as geophysical plausible footprints of the porphyry type mineralization. The low values of the apparent resistivity layer calculated from the airborne frequency domain electromagnetic data are also used as an electrical criterion in this investigation. Four remote sensing evidential layers of argillic, phyllic, propylitic and hydroxyl alterations were extracted from ASTER images in order to map the altered areas associated with porphyry type deposits, whilst the ETM+ satellite imagery data were used as well to map iron oxide layer. Since potassium alteration is generally the mainstay of porphyry ore mineralization, the airborne potassium radiometry data was used. The geochemical layers of Cu/B/Pb/Zn elements and the first component of PCA

  20. Forest Tree Species Distribution Mapping Using Landsat Satellite Imagery and Topographic Variables with the Maximum Entropy Method in Mongolia

    Science.gov (United States)

    Hao Chiang, Shou; Valdez, Miguel; Chen, Chi-Farn

    2016-06-01

    Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered. In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM) were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface reflectance coupled

  1. Assessment of the Impact of Reservoirs in the Upper Mekong River Using Satellite Radar Altimetry and Remote Sensing Imageries

    Directory of Open Access Journals (Sweden)

    Kuan-Ting Liu

    2016-04-01

    Full Text Available Water level (WL and water volume (WV of surface-water bodies are among the most crucial variables used in water-resources assessment and management. They fluctuate as a result of climatic forcing, and they are considered as indicators of climatic impacts on water resources. Quantifying riverine WL and WV, however, usually requires the availability of timely and continuous in situ data, which could be a challenge for rivers in remote regions, including the Mekong River basin. As one of the most developed rivers in the world, with more than 20 dams built or under construction, Mekong River is in need of a monitoring system that could facilitate basin-scale management of water resources facing future climate change. This study used spaceborne sensors to investigate two dams in the upper Mekong River, Xiaowan and Jinghong Dams within China, to examine river flow dynamics after these dams became operational. We integrated multi-mission satellite radar altimetry (RA, Envisat and Jason-2 and Landsat-5/-7/-8 Thematic Mapper (TM/Enhanced Thematic Mapper plus (ETM+/Operational  Land Imager (OLI optical remote sensing (RS imageries to construct composite WL time series with enhanced spatial resolutions and substantially extended WL data records. An empirical relationship between WL variation and water extent was first established for each dam, and then the combined long-term WL time series from Landsat images are reconstructed for the dams. The R2 between altimetry WL and Landsat water area measurements is >0.95. Next, the Tropical Rainfall Measuring Mission (TRMM data were used to diagnose and determine water variation caused by the precipitation anomaly within the basin. Finally, the impact of hydrologic dynamics caused by the impoundment of the dams is assessed. The discrepancy between satellite-derived WL and available in situ gauge data, in term of root-mean-square error (RMSE is at 2–5 m level. The estimated WV variations derived from combined RA

  2. FEASIBILITY COMPARISON OF AIRBORNE LASER SCANNING DATA AND 3D-POINT CLOUDS FORMED FROM UNMANNED AERIAL VEHICLE (UAV-BASED IMAGERY USED FOR 3D PROJECTING

    Directory of Open Access Journals (Sweden)

    I. I. Rilskiy

    2017-01-01

    Full Text Available New, innovative methods of aerial surveys have changed the approaches to information provision of projecting dramatically for the last 15 years. Nowadays there are at least two methods that claim to be the most efficient way for collecting geospatial data intended for projecting – the airborne laser scanning (LIDAR data and photogrammetrically processed unmanned aerial vehicle (UAV-based aerial imagery, forming 3D point clouds. But these materials are not identical to each other neither in precision, nor in completeness.Airborne laser scanning (LIDAR is normally being performed using manned aircrafts. LIDAR data are very precise, they allow us to achieve data about relief even overgrown with vegetation, or to collect laser reflections from wires, metal constructions and poles. UAV surveys are normally being performed using frame digital cameras (lightweight, full-frame, or mid-size. These cameras form images that are being processed using 3D photogrammetric software in automatic mode that allows one to generate 3D point cloud, which is used for building digital elevation models, surfaces, orthomosaics, etc.All these materials are traditionally being used for making maps and GIS data. LIDAR data have been popular in design work. Also there have been some attempts to use for the same purpose 3D-point clouds, formed by photogrammetric software from images acquired from UAVs.After comparison of the datasets from these two different types of surveying (surveys were made simultaneously on the same territory, it became possible to define some specific, typical for LIDAR or imagery-based 3D data. It can be mentioned that imagery-based 3D data (3D point clouds, formed in automatic mode using photogrammetry, are much worse than LIDAR data – both in terms of precision and completeness.The article highlights these differences and makes attempts at explaining the origin of these differences. 

  3. Satellite infrared imagery for thermal plume contamination monitoring in coastal ecosystem of Cernavoda NPP

    Science.gov (United States)

    Zoran, M. A.; Zoran, Liviu Florin V.; Dida, Adrian I.

    2017-10-01

    Satellite remote sensing is an important tool for spatio-temporal analysis and surveillance of NPP environment, thermal heat waste of waters being a major concern in many coastal ecosystems involving nuclear power plants. As a test case the adopted methodology was applied for 700x2 MW Cernavoda nuclear power plant (NPP) located in the South-Eastern part of Romania, which discharges warm water affecting coastal ecology. The thermal plume signatures in the NPP hydrological system have been investigated based on TIR (Thermal Infrared) spectral bands of NOAA AVHRR, Landsat TM/ETM+/OLI, and MODIS Terra/Aqua time series satellite data during 1990-2016 period. If NOAA AVHRR data proved the general pattern and extension of the thermal plume signature in Danube river and Black Sea coastal areas, Landsat TM/ETM and MODIS data used for WST (Water Surface Temperature) change detection, mapping and monitoring provided enhanced information about the plume shape, dimension and direction of dispersion in these waters. Thermal discharge from two nuclear reactors cooling is dissipated as waste heat in Danube-Black -Sea Channel and Danube River. From time-series analysis of satellite data during period 1990-2016 was found that during the winter season thermal plume was localized to an area of a few km of NPP, and the mean temperature difference between the plume and non-plume areas was about 1.7 oC. During summer and fall, derived mean temperature difference between the plume and non-plume areas was of about 1.3°C and thermal plume area was extended up to 5- 10 km far along Danube Black Sea Channel.

  4. Monitoring Powdery Mildew of Winter Wheat by Using Moderate Resolution Multi-Temporal Satellite Imagery

    Science.gov (United States)

    Zhang, Jingcheng; Pu, Ruiliang; Yuan, Lin; Wang, Jihua; Huang, Wenjiang; Yang, Guijun

    2014-01-01

    Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale. PMID:24691435

  5. Estimating seasonal variations in cloud droplet number concentration over the boreal forest from satellite observations

    NARCIS (Netherlands)

    Janssen, R.; Ganzeveld, L.N.; Kabat, P.; Kulmala, M.; Nieminen, T.; Roebeling, R.A.

    2011-01-01

    Seasonal variations in cloud droplet number concentration (NCD) in low-level stratiform clouds over the boreal forest are estimated from MODIS observations of cloud optical and microphysical properties, using a sub-adiabatic cloud model to interpret vertical profiles of cloud properties. An

  6. Influence of turbidity and clouds on satellite total ozone data over Madrid (Spain)

    Energy Technology Data Exchange (ETDEWEB)

    Camacho, J.L. [Agencia Estatal de Meteorologia (AEMET), Madrid (Spain); Anton, M. [Granada Univ. (Spain). Dept. de Fisica Aplicada; Loyola, D. [German Aerospace Center (DLR), Wessling (DE). Remote Sensing Technology Inst. (IMF); Hernandez, E. [Madrid Univ. Complutense (Spain). Dept. Fisica de la Tierra II

    2010-07-01

    This article focuses on the comparison of the total ozone column data from three satellite instruments; Total Ozone Mapping Spectrometers (TOMS) on board the Earth Probe (EP), Ozone Monitoring Instrument (OMI) on board AURA and Global Ozone Monitoring Experiment (GOME) on board ERS/2, with ground-based measurement recorded by a well calibrated Brewer spectrophotometer located in Madrid during the period 1996-2008. A cluster classification based on solar radiation (global, direct and diffuse), cloudiness and aerosol index allow selecting hazy, cloudy, very cloudy and clear days. Thus, the differences between Brewer and satellite total ozone data for each cluster have been analyzed. The accuracy of EP-TOMS total ozone data is affected by moderate cloudiness, showing a mean absolute bias error (MABE) of 2.0%. In addition, the turbidity also has a significant influence on EP-TOMS total ozone data with a MABE {proportional_to}1.6%. Those data are in contrast with clear days with MABE {proportional_to}1.2%. The total ozone data derived from the OMI instrument show clear bias at clear and hazy days with small uncertainties ({proportional_to}0.8%). Finally, the total ozone observations obtained with the GOME instrument show a very smooth dependence with respect to clouds and turbidity, showing a robust retrieval algorithm over these conditions. (orig.)

  7. A Subpixel Classification of Multispectral Satellite Imagery for Interpetation of Tundra-Taiga Ecotone Vegetation (Case Study on Tuliok River Valley, Khibiny, Russia)

    Science.gov (United States)

    Mikheeva, A. I.; Tutubalina, O. V.; Zimin, M. V.; Golubeva, E. I.

    2017-12-01

    The tundra-taiga ecotone plays significant role in northern ecosystems. Due to global climatic changes, the vegetation of the ecotone is the key object of many remote-sensing studies. The interpretation of vegetation and nonvegetation objects of the tundra-taiga ecotone on satellite imageries of a moderate resolution is complicated by the difficulty of extracting these objects from the spectral and spatial mixtures within a pixel. This article describes a method for the subpixel classification of Terra ASTER satellite image for vegetation mapping of the tundra-taiga ecotone in the Tuliok River, Khibiny Mountains, Russia. It was demonstrated that this method allows to determine the position of the boundaries of ecotone objects and their abundance on the basis of quantitative criteria, which provides a more accurate characteristic of ecotone vegetation when compared to the per-pixel approach of automatic imagery interpretation.

  8. Preliminary hard and soft bottom seafloor substrate map derived from an supervised classification of bathymetry derived from multispectral World View-2 satellite imagery of Ni'ihau Island, Territory of Main Hawaiian Islands, USA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Preliminary hard and soft seafloor substrate map derived from a supervised classification from multispectral World View-2 satellite imagery of Ni'ihau Island,...

  9. Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study.

    Science.gov (United States)

    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.

  10. Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011

    Directory of Open Access Journals (Sweden)

    Timothy Shields

    2016-05-01

    Full Text Available Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase. Comparison of the images indicated that 971 (25.4% structures were added and 536 (14.0% removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

  11. Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Timothy G. Whiteside

    2015-09-01

    Full Text Available Vegetation plays a key role in the environmental function of wetlands. The Ramsar-listed wetlands of the Magela Creek floodplain in Northern Australia are identified as being at risk from weeds, fire and climate change. In addition, the floodplain is a downstream receiving environment for the Ranger Uranium Mine. Accurate methods for mapping wetland vegetation are required to provide contemporary baselines of annual vegetation dynamics on the floodplain to assist with analysing any potential change during and after minesite rehabilitation. The aim of this study was to develop and test the applicability of geographic object-based image analysis including decision tree classification to classify WorldView-2 imagery and LiDAR-derived ancillary data to map the aquatic vegetation communities of the Magela Creek floodplain. Results of the decision tree classification were compared against a Random Forests classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010. The decision tree classification method provided an overall accuracy of 78% which was significantly higher than the overall accuracy of the Random Forests classification (67%. Most of the error in both classifications was associated with confusion between spectrally similar classes dominated by grasses, such as Hymenachne and Pseudoraphis. In addition, the extent of the sedge Eleocharis was under-estimated in both cases. This suggests the method could be useful for mapping wetlands where statistical-based supervised classifications have achieved less than satisfactory results. Based upon the results, the decision tree method will form part of an ongoing operational monitoring program.

  12. Winter Crop Mapping for Improving Crop Production Estimates in Argentina Using Moderation Resolution Satellite Imagery

    Science.gov (United States)

    Humber, M. L.; Copati, E.; Sanchez, A.; Sahajpal, R.; Puricelli, E.; Becker-Reshef, I.

    2017-12-01

    Accurate crop production data is fundamental for reducing uncertainly and volatility in the domestic and international agricultural markets. The Agricultural Estimates Department of the Buenos Aires Grain Exchange has worked since 2000 on the estimation of different crop production data. With this information, the Grain Exchange helps different actors of the agricultural chain, such as producers, traders, seed companies, market analyst, policy makers, into their day to day decision making. Since 2015/16 season, the Grain Exchange has worked on the development of a new earth observations-based method to identify winter crop planted area at a regional scale with the aim of improving crop production estimates. The objective of this new methodology is to create a reliable winter crop mask at moderate spatial resolution using Landsat-8 imagery by exploiting bi-temporal differences in the phenological stages of winter crops as compared to other landcover types. In collaboration with the University of Maryland, the map has been validated by photointerpretation of a stratified statistically random sample of independent ground truth data in the four largest producing provinces of Argentina: Buenos Aires, Cordoba, La Pampa, and Santa Fe. In situ measurements were also used to further investigate conditions in the Buenos Aires province. Preliminary results indicate that while there are some avenues for improvement, overall the classification accuracy of the cropland and non-cropland classes are sufficient to improve downstream production estimates. Continuing research will focus on improving the methodology for winter crop mapping exercises on a yearly basis as well as improving the sampling methodology to optimize collection of validation data in the future.

  13. Identification of the potential gap areas for the developing green infrastructure in the Urban area using High resolution satellite Imagery

    Science.gov (United States)

    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.

  14. Poverty assessment using DMSP/OLS night-time light satellite imagery at a provincial scale in China

    Science.gov (United States)

    Wang, Wen; Cheng, Hui; Zhang, Li

    2012-04-01

    All countries around the world and many international bodies, including the United Nations Development Program (UNDP), United Nations Food and Agricultural Organization (FAO), the International Fund for Agricultural Development (IFAD) and the International Labor Organization (ILO), have to eliminate rural poverty. Estimation of regional poverty level is a key issue for making strategies to eradicate poverty. Most of previous studies on regional poverty evaluations are based on statistics collected typically in administrative units. This paper has discussed the deficiencies of traditional studies, and attempted to research regional poverty evaluation issues using 3-year DMSP/OLS night-time light satellite imagery. In this study, we adopted 17 socio-economic indexes to establish an integrated poverty index (IPI) using principal component analysis (PCA), which was proven to provide a good descriptor of poverty levels in 31 regions at a provincial scale in China. We also explored the relationship between DMSP/OLS night-time average light index and the poverty index using regression analysis in SPSS and a good positive linear correlation was modelled, with R2 equal to 0.854. We then looked at provincial poverty problems in China based on this correlation. The research results indicated that the DMSP/OLS night-time light data can assist analysing provincial poverty evaluation issues.

  15. Automatic Classification of High Resolution Satellite Imagery - a Case Study for Urban Areas in the Kingdom of Saudi Arabia

    Science.gov (United States)

    Maas, A.; Alrajhi, M.; Alobeid, A.; Heipke, C.

    2017-05-01

    Updating topographic geospatial databases is often performed based on current remotely sensed images. To automatically extract the object information (labels) from the images, supervised classifiers are being employed. Decisions to be taken in this process concern the definition of the classes which should be recognised, the features to describe each class and the training data necessary in the learning part of classification. With a view to large scale topographic databases for fast developing urban areas in the Kingdom of Saudi Arabia we conducted a case study, which investigated the following two questions: (a) which set of features is best suitable for the classification?; (b) what is the added value of height information, e.g. derived from stereo imagery? Using stereoscopic GeoEye and Ikonos satellite data we investigate these two questions based on our research on label tolerant classification using logistic regression and partly incorrect training data. We show that in between five and ten features can be recommended to obtain a stable solution, that height information consistently yields an improved overall classification accuracy of about 5%, and that label noise can be successfully modelled and thus only marginally influences the classification results.

  16. Mapping invasive Phragmites australis in the coastal Great Lakes with ALOS PALSAR satellite imagery for decision support

    Science.gov (United States)

    Bourgeau-Chavez, Laura L.; Kowalski, Kurt P.; Carlson Mazur, Martha L.; Scarbrough, Kirk A.; Powell, Richard B.; Brooks, Colin N.; Huberty, Brian; Jenkins, Liza K.; Banda, Elizabeth C.; Galbraith, David M.; Laubach, Zachary M.; Riordan, Kevin

    2013-01-01

    The invasive variety of Phragmites australis (common reed) forms dense stands that can cause negative impacts on coastal Great Lakes wetlands including habitat degradation and reduced biological diversity. Early treatment is key to controlling Phragmites, therefore a map of the current distribution is needed. ALOS PALSAR imagery was used to produce the first basin-wide distribution map showing the extent of large, dense invasive Phragmites-dominated habitats in wetlands and other coastal ecosystems along the U.S. shore of the Great Lakes. PALSAR is a satellite imaging radar sensor that is sensitive to differences in plant biomass and inundation patterns, allowing for the detection and delineation of these tall (up to 5 m), high density, high biomass invasive Phragmites stands. Classification was based on multi-season ALOS PALSAR L-band (23 cm wavelength) HH and HV polarization data. Seasonal (spring, summer, and fall) datasets were used to improve discrimination of Phragmites by taking advantage of phenological changes in vegetation and inundation patterns over the seasons. Extensive field collections of training and randomly selected validation data were conducted in 2010–2011 to aid in mapping and for accuracy assessments. Overall basin-wide map accuracy was 87%, with 86% producer's accuracy and 43% user's accuracy for invasive Phragmites. The invasive Phragmites maps are being used to identify major environmental drivers of this invader's distribution, to assess areas vulnerable to new invasion, and to provide information to regional stakeholders through a decision support tool.

  17. Application of an optimization algorithm to satellite ocean color imagery: A case study in Southwest Florida coastal waters

    Science.gov (United States)

    Hu, Chuanmin; Lee, Zhongping; Muller-Karger, Frank E.; Carder, Kendall L.

    2003-05-01

    A spectra-matching optimization algorithm, designed for hyperspectral sensors, has been implemented to process SeaWiFS-derived multi-spectral water-leaving radiance data. The algorithm has been tested over Southwest Florida coastal waters. The total spectral absorption and backscattering coefficients can be well partitioned with the inversion algorithm, resulting in RMS errors generally less than 5% in the modeled spectra. For extremely turbid waters that come from either river runoff or sediment resuspension, the RMS error is in the range of 5-15%. The bio-optical parameters derived in this optically complex environment agree well with those obtained in situ. Further, the ability to separate backscattering (a proxy for turbidity) from the satellite signal makes it possible to trace water movement patterns, as indicated by the total absorption imagery. The derived patterns agree with those from concurrent surface drifters. For waters where CDOM overwhelmingly dominates the optical signal, however, the procedure tends to regard CDOM as the sole source of absorption, implying the need for better atmospheric correction and for adjustment of some model coefficients for this particular region.

  18. Phenology-based Spartina alterniflora mapping in coastal wetland of the Yangtze Estuary using time series of GaoFen satellite no. 1 wide field of view imagery

    Science.gov (United States)

    Ai, Jinquan; Gao, Wei; Gao, Zhiqiang; Shi, Runhe; Zhang, Chao

    2017-04-01

    Spartina alterniflora is an aggressive invasive plant species that replaces native species, changes the structure and function of the ecosystem across coastal wetlands in China, and is thus a major conservation concern. Mapping the spread of its invasion is a necessary first step for the implementation of effective ecological management strategies. The performance of a phenology-based approach for S. alterniflora mapping is explored in the coastal wetland of the Yangtze Estuary using a time series of GaoFen satellite no. 1 wide field of view camera (GF-1 WFV) imagery. First, a time series of the normalized difference vegetation index (NDVI) was constructed to evaluate the phenology of S. alterniflora. Two phenological stages (the senescence stage from November to mid-December and the green-up stage from late April to May) were determined as important for S. alterniflora detection in the study area based on NDVI temporal profiles, spectral reflectance curves of S. alterniflora and its coexistent species, and field surveys. Three phenology feature sets representing three major phenology-based detection strategies were then compared to map S. alterniflora: (1) the single-date imagery acquired within the optimal phenological window, (2) the multitemporal imagery, including four images from the two important phenological windows, and (3) the monthly NDVI time series imagery. Support vector machines and maximum likelihood classifiers were applied on each phenology feature set at different training sample sizes. For all phenology feature sets, the overall results were produced consistently with high mapping accuracies under sufficient training samples sizes, although significantly improved classification accuracies (10%) were obtained when the monthly NDVI time series imagery was employed. The optimal single-date imagery had the lowest accuracies of all detection strategies. The multitemporal analysis demonstrated little reduction in the overall accuracy compared with the

  19. Automatic Blocked Roads Assessment after Earthquake Using High Resolution Satellite Imagery

    Science.gov (United States)

    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.

  20. Automated quantification of surface water inundation in wetlands using optical satellite imagery

    Science.gov (United States)

    DeVries, Ben; Huang, Chengquan; Lang, Megan W.; Jones, John W.; Huang, Wenli; Creed, Irena F.; Carroll, Mark L.

    2017-01-01

    We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.

  1. Effects of 3-D clouds on atmospheric transmission of solar radiation: Cloud type dependencies inferred from A-train satellite data

    Science.gov (United States)

    Ham, Seung-Hee; Kato, Seiji; Barker, Howard W.; Rose, Fred G.; Sun-Mack, Sunny

    2014-01-01

    Three-dimensional (3-D) effects on broadband shortwave top of atmosphere (TOA) nadir radiance, atmospheric absorption, and surface irradiance are examined using 3-D cloud fields obtained from one hour's worth of A-train satellite observations and one-dimensional (1-D) independent column approximation (ICA) and full 3-D radiative transfer simulations. The 3-D minus ICA differences in TOA nadir radiance multiplied by π, atmospheric absorption, and surface downwelling irradiance, denoted as πΔI, ΔA, and ΔT, respectively, are analyzed by cloud type. At the 1 km pixel scale, πΔI, ΔA, and ΔT exhibit poor spatial correlation. Once averaged with a moving window, however, better linear relationships among πΔI, ΔA, and ΔT emerge, especially for moving windows larger than 5 km and large θ0. While cloud properties and solar geometry are shown to influence the relationships amongst πΔI, ΔA, and ΔT, once they are separated by cloud type, their linear relationships become much stronger. This suggests that ICA biases in surface irradiance and atmospheric absorption can be approximated based on ICA biases in nadir radiance as a function of cloud type.

  2. Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery

    Science.gov (United States)

    García, Mariano; Saatchi, Sassan; Ustin, Susan; Balzter, Heiko

    2018-04-01

    Spatially-explicit information on forest structure is paramount to estimating aboveground carbon stocks for designing sustainable forest management strategies and mitigating greenhouse gas emissions from deforestation and forest degradation. LiDAR measurements provide samples of forest structure that must be integrated with satellite imagery to predict and to map landscape scale variations of forest structure. Here we evaluate the capability of existing satellite synthetic aperture radar (SAR) with multispectral data to estimate forest canopy height over five study sites across two biomes in North America, namely temperate broadleaf and mixed forests and temperate coniferous forests. Pixel size affected the modelling results, with an improvement in model performance as pixel resolution coarsened from 25 m to 100 m. Likewise, the sample size was an important factor in the uncertainty of height prediction using the Support Vector Machine modelling approach. Larger sample size yielded better results but the improvement stabilised when the sample size reached approximately 10% of the study area. We also evaluated the impact of surface moisture (soil and vegetation moisture) on the modelling approach. Whereas the impact of surface moisture had a moderate effect on the proportion of the variance explained by the model (up to 14%), its impact was more evident in the bias of the models with bias reaching values up to 4 m. Averaging the incidence angle corrected radar backscatter coefficient (γ°) reduced the impact of surface moisture on the models and improved their performance at all study sites, with R2 ranging between 0.61 and 0.82, RMSE between 2.02 and 5.64 and bias between 0.02 and -0.06, respectively, at 100 m spatial resolution. An evaluation of the relative importance of the variables in the model performance showed that for the study sites located within the temperate broadleaf and mixed forests biome ALOS-PALSAR HV polarised backscatter was the most important

  3. Validation of Cloud Parameters Derived from Geostationary Satellites, AVHRR, MODIS, and VIIRS Using SatCORPS Algorithms

    Science.gov (United States)

    Minnis, P.; Sun-Mack, S.; Bedka, K. M.; Yost, C. R.; Trepte, Q. Z.; Smith, W. L., Jr.; Painemal, D.; Chen, Y.; Palikonda, R.; Dong, X.; hide

    2016-01-01

    Validation is a key component of remote sensing that can take many different forms. The NASA LaRC Satellite ClOud and Radiative Property retrieval System (SatCORPS) is applied to many different imager datasets including those from the geostationary satellites, Meteosat, Himiwari-8, INSAT-3D, GOES, and MTSAT, as well as from the low-Earth orbiting satellite imagers, MODIS, AVHRR, and VIIRS. While each of these imagers have similar sets of channels with wavelengths near 0.65, 3.7, 11, and 12 micrometers, many differences among them can lead to discrepancies in the retrievals. These differences include spatial resolution, spectral response functions, viewing conditions, and calibrations, among others. Even when analyzed with nearly identical algorithms, it is necessary, because of those discrepancies, to validate the results from each imager separately in order to assess the uncertainties in the individual parameters. This paper presents comparisons of various SatCORPS-retrieved cloud parameters with independent measurements and retrievals from a variety of instruments. These include surface and space-based lidar and radar data from CALIPSO and CloudSat, respectively, to assess the cloud fraction, height, base, optical depth, and ice water path; satellite and surface microwave radiometers to evaluate cloud liquid water path; surface-based radiometers to evaluate optical depth and effective particle size; and airborne in-situ data to evaluate ice water content, effective particle size, and other parameters. The results of comparisons are compared and contrasted and the factors influencing the differences are discussed.

  4. Several thoughts for using new satellite remote sensing and global modeling for aerosol and cloud climate studies

    Science.gov (United States)

    Nakajima, Teruyuki; Hashimoto, Makiko; Takenaka, Hideaki; Goto, Daisuke; Oikawa, Eiji; Suzuki, Kentaroh; Uchida, Junya; Dai, Tie; Shi, Chong

    2017-04-01

    The rapid growth of satellite remote sensing technologies in the last two decades widened the utility of satellite data for understanding climate impacts of aerosols and clouds. The climate modeling community also has received the benefit of the earth observation and nowadays closed-collaboration of the two communities make us possible to challenge various applications for societal problems, such as for global warming and global-scale air pollution and others. I like to give several thoughts of new algorithm developments, model use of satellite data for climate impact studies and societal applications related with aerosols and clouds. Important issues are 1) Better aerosol detection and solar energy application using expanded observation ability of the third generation geostationary satellites, i.e. Himawari-8, GOES-R and future MTG, 2) Various observation functions by directional, polarimetric, and high resolution near-UV band by MISR, POLDER&PARASOL, GOSAT/CAI and future GOSAT2/CAI2, 3) Various applications of general purpose-imagers, MODIS, VIIRS and future GCOM-C/SGLI, and 4) Climate studies of aerosol and cloud stratification and convection with active and passive sensors, especially climate impact of BC aerosols using CLOUDSAT&CALIPSO and future Earth Explorer/EarthCARE.

  5. Validation of quasi-invariant ice cloud radiative quantities with MODIS satellite-based cloud property retrievals

    International Nuclear Information System (INIS)

    Ding, Jiachen; Yang, Ping; Kattawar, George W.; King, Michael D.; Platnick, Steven; Meyer, Kerry G.

    2017-01-01

    Similarity relations applied to ice cloud radiance calculations are theoretically analyzed and numerically validated. If τ(1–ϖ) and τ(1–ϖg) are conserved where τ is optical thickness, ϖ the single-scattering albedo, and g the asymmetry factor, it is possible that substantially different phase functions may give rise to similar radiances in both conservative and non-conservative scattering cases, particularly in the case of large optical thicknesses. In addition to theoretical analysis, this study uses operational ice cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Collection 5 (C5) and Collection 6 (C6) cloud property products to verify radiative similarity relations. It is found that, if the MODIS C5 and C6 ice cloud optical thickness values are multiplied by their respective (1–ϖg) factors, the resultant products referred to as the effective optical thicknesses become similar with their ratio values around unity. Furthermore, the ratios of the C5 and C6 ice cloud effective optical thicknesses display an angular variation pattern similar to that of the corresponding ice cloud phase function ratios. The MODIS C5 and C6 values of ice cloud similarity parameter, defined as [(1–ϖ)/(1–ϖg)]"1"/"2, also tend to be similar. - Highlights: • Similarity relations are theoretically analyzed and validated. • Similarity relations are verified with the MODIS Level 2 Collection 5 and 6 ice cloud property products. • The product of ice cloud optical thickness and (1–ϖg) is approximately invariant. • The similarity parameter derived from the MODIS ice cloud effective radius retrieval tends to be invariant.

  6. AN ACCURACY ASSESSMENT OF GEOREFERENCED POINT CLOUDS PRODUCED VIA MULTI-VIEW STEREO TECHNIQUES APPLIED TO IMAGERY ACQUIRED VIA UNMANNED AERIAL VEHICLE

    Directory of Open Access Journals (Sweden)

    S. Harwin

    2012-08-01

    Full Text Available Low-cost Unmanned Aerial Vehicles (UAVs are becoming viable environmental remote sensing tools. Sensor and battery technology is expanding the data capture opportunities. The UAV, as a close range remote sensing platform, can capture high resolution photography on-demand. This imagery can be used to produce dense point clouds using multi-view stereopsis techniques (MVS combining computer vision and photogrammetry. This study examines point clouds produced using MVS techniques applied to UAV and terrestrial photography. A multi-rotor micro UAV acquired aerial imagery from a altitude of approximately 30–40 m. The point clouds produced are extremely dense (<1–3 cm point spacing and provide a detailed record of the surface in the study area, a 70 m section of sheltered coastline in southeast Tasmania. Areas with little surface texture were not well captured, similarly, areas with complex geometry such as grass tussocks and woody scrub were not well mapped. The process fails to penetrate vegetation, but extracts very detailed terrain in unvegetated areas. Initially the point clouds are in an arbitrary coordinate system and need to be georeferenced. A Helmert transformation is applied based on matching ground control points (GCPs identified in the point clouds to GCPs surveying with differential GPS. These point clouds can be used, alongside laser scanning and more traditional techniques, to provide very detailed and precise representations of a range of landscapes at key moments. There are many potential applications for the UAV-MVS technique, including coastal erosion and accretion monitoring, mine surveying and other environmental monitoring applications. For the generated point clouds to be used in spatial applications they need to be converted to surface models that reduce dataset size without loosing too much detail. Triangulated meshes are one option, another is Poisson Surface Reconstruction. This latter option makes use of point normal

  7. Revisiting Past Earthquakes and Seismo-Volcanic Crises Using Declassified Optical Satellite Imagery (Invited)

    Science.gov (United States)

    Hollingsworth, J.; Leprince, S.; Ayoub, F.; Avouac, J.

    2009-12-01

    In this study we demonstrate that the recently declassified Corona KH-9 images can be used to measure ground deformation due to seismotectonic and volcanic events from optical sub-pixel correlation. We use high resolution (6-9 m) satellite images, available from the USGS for a relatively small cost ($30 per image, swath measuring 250 x 125 km). The images are processed with the user-friendly software package COSI-Corr, which allows for automatic and precise ortho-rectification, co-registration, and sub-pixel correlation of pushbroom satellite and aerial images. Knowledge of the camera calibration information is required to determine the interior and exterior orientation parameters of the camera, which are in turn needed to successfully orthorectify and co-register the images using COSI-Corr. Because the camera information still remains classified, we follow the approach of Surazakov, et al., (2009), who conclude the Hexagon KH9 camera system is similar to the NASA Large Format Camera (LFC) system. We successfully tested the approach on the 1999 Hector Mine, USA (Ms 7.4) and 1992 Landers, USA (Ms 7.5) earthquakes and then moved on to analyze a number of other large events. We have in particular been able to measure the surface deformation induced by the 1975-1984 Krafla rifting crisis in NE Iceland, by correlating a Hexagon image from 15th September 1977 with a SPOT5 image from 2002. During the period 1977-2002 we find an average E-W extension of 3±0.5 m across the rift, which extends NNE from Lake Myvatn in the south to Ásbyrgi canyon near the coast to the north (a distance of over 40 km) and were able to determine which faults were activated. We have also co-registered a number of Hexagon images to both SPOT and ASTER images (orthorectified using either SRTMv2 or ASTER GDEM topographic data) to determine the co-seismic rupture location and amount of displacement in various significant intraplate earthquakes for which InSAR or GPS data is unavailable: 1976

  8. Mid-term fire danger index based on satellite imagery and ancillary geographic data

    Science.gov (United States)

    Stefanidou, A.; Dragozi, E.; Tompoulidou, M.; Stepanidou, L.; Grigoriadis, D.; Katagis, T.; Stavrakoudis, D.; Gitas, I.

    2017-09-01

    Fire danger forecast constitutes one of the most important components of integrated fire management since it provides crucial information for efficient pre-fire planning, alertness and timely response to a possible fire event. The aim of this work is to develop an index that has the capability of predicting accurately fire danger on a mid-term basis. The methodology that is currently under development is based on an innovative approach that employs dry fuel spatial connectivity as well as biophysical and topological variables for the reliable prediction of fire danger. More specifically, the estimation of the dry fuel connectivity is based on a previously proposed automated procedure implemented in R software that uses Moderate Resolution Imaging Spectrometer (MODIS) time series data. Dry fuel connectivity estimates are then combined with other ancillary data such as fuel type and proximity to roads in order to result in the generation of the proposed mid-term fire danger index. The innovation of the proposed index—which will be evaluated by comparison to historical fire data—lies in the fact that its calculation is almost solely affected by the availability of satellite data. Finally, it should be noted that the index is developed within the framework of the National Observatory of Forest Fires (NOFFi) project.

  9. Forest mapping and change analysis, using satellite imagery in Zagros mountain Iran, Islamic Republic o

    International Nuclear Information System (INIS)

    Torahi, A.A.

    2013-01-01

    A methodology to map and monitor land cover change using multi temporal Landsat Thematic Mapper (TM) and ASTER data in Zagros mountains of Iran for 1990, 1998, and 2006 was developed. Land- use/cover mapping is achieved through interpretation of Landsat TM satellite images of 1990, 1998 and TERRA-ASTER image of 2006 using ENVI 4.3. Basedon the Anderson land-use/cover classification system, land-use and land-covers are classified as forest land, range land, water bodies, agricultural land and residential land.The unsupervised image classification method was carried out prior to field visit, in order to determine strata for ground truth. Fieldwork was carried out to collect data for training and validating land use/cover interpretation from satellite image of 2006, and for qualitative description of the characteristics of each land use/cover class. The land - use/cover maps of 1990,1998 and 2006 were produced by using supervised image classification technique based on the Maximum Likelihood Classifier (MLC) and 132 training samples. Error matrices as cross-tabulations of the mapped class vs. the reference class were used to assess classification accuracy. Overall accuracy, users and produce accuracies, and the Kappa statistic were then derived from the error matrices. A multi-date post-classification comparison change detection algorithm was used to determine changes in land cover in three intervals, 1990,1998, 1998, 2006 and 1990, 2006.To evaluate the maps change for the 1990 to 2006 interval, areas classified as change and no-change were randomly sampled and checked whether they were correctly classified. The maps showed that between 1990 and 2006 the amount of forest land decreased from 67% to 38.5% of the total area, while rangelands, agriculture, settlement and surface water increased from 30.8% to 45%, 1.2% to.0%, 0.3% to 7.5% and 0.6% to 1.8%, respectively.In 1990,1998 and 2006, the area was dominated by dense forest (35.9%, 28.9%, 29.3%), open forest and

  10. Online Access to Weather Satellite Imagery Through the World Wide Web

    Science.gov (United States)

    Emery, W.; Baldwin, D.

    1998-01-01

    Both global area coverage (GAC) and high-resolution picture transmission (HRTP) data from the Advanced Very High Resolution Radiometer (AVHRR) are made available to laternet users through an online data access system. Older GOES-7 data am also available. Created as a "testbed" data system for NASA's future Earth Observing System Data and Information System (EOSDIS), this testbed provides an opportunity to test both the technical requirements of an onune'd;ta system and the different ways in which the -general user, community would employ such a system. Initiated in December 1991, the basic data system experienced five major evolutionary changes In response to user requests and requirements. Features added with these changes were the addition of online browse, user subsetting, dynamic image Processing/navigation, a stand-alone data storage system, and movement,from an X-windows graphical user Interface (GUI) to a World Wide Web (WWW) interface. Over Its lifetime, the system has had as many as 2500 registered users. The system on the WWW has had over 2500 hits since October 1995. Many of these hits are by casual users that only take the GIF images directly from the interface screens and do not specifically order digital data. Still, there b a consistent stream of users ordering the navigated image data and related products (maps and so forth). We have recently added a real-time, seven- day, northwestern United States normalized difference vegetation index (NDVI) composite that has generated considerable Interest. Index Terms-Data system, earth science, online access, satellite data.

  11. Drought resistance across California ecosystems: Evaluating changes in carbon dynamics using satellite imagery

    Science.gov (United States)

    Malone, Sparkle; Tulbure, Mirela; Pérez-Luque, Antonio J.; Assal, Timothy J.; Bremer, Leah; Drucker, Debora; Hillis, Vicken; Varela, Sara; Goulden, Michael

    2016-01-01

    Drought is a global issue that is exacerbated by climate change and increasing anthropogenic water demands. The recent occurrence of drought in California provides an important opportunity to examine drought response across ecosystem classes (forests, shrublands, grasslands, and wetlands), which is essential to understand how climate influences ecosystem structure and function. We quantified ecosystem resistance to drought by comparing changes in satellite-derived estimates of water-use efficiency (WUE = net primary productivity [NPP]/evapotranspiration [ET]) under normal (i.e., baseline) and drought conditions (ΔWUE = WUE2014 − baseline WUE). With this method, areas with increasing WUE under drought conditions are considered more resilient than systems with declining WUE. Baseline WUE varied across California (0.08 to 3.85 g C/mm H2O) and WUE generally increased under severe drought conditions in 2014. Strong correlations between ΔWUE, precipitation, and leaf area index (LAI) indicate that ecosystems with a lower average LAI (i.e., grasslands) also had greater C-uptake rates when water was limiting and higher rates of carbon-uptake efficiency (CUE = NPP/LAI) under drought conditions. We also found that systems with a baseline WUE ≤ 0.4 exhibited a decline in WUE under drought conditions, suggesting that a baseline WUE ≤ 0.4 might be indicative of low drought resistance. Drought severity, precipitation, and WUE were identified as important drivers of shifts in ecosystem classes over the study period. These findings have important implications for understanding climate change effects on primary productivity and C sequestration across ecosystems and how this may influence ecosystem resistance in the future.

  12. Spatial and Temporal Analysis of Sea Surface Salinity Using Satellite Imagery in Gulf of Mexico

    Science.gov (United States)

    Rajabi, S.; Hasanlou, M.; Safari, A. R.

    2017-09-01

    The recent development of satellite sea surface salinity (SSS) observations has enabled us to analyse SSS variations with high spatiotemporal resolution. In this regards, The Level3-version4 data observed by Aquarius are used to examine the variability of SSS in Gulf of Mexico for the 2012-2014 time periods. The highest SSS value occurred in April 2013 with the value of 36.72 psu while the lowest value (35.91 psu) was observed in July 2014. Based on the monthly distribution maps which will be demonstrated in the literature, it was observed that east part of the region has lower salinity values than the west part for all months mainly because of the currents which originate from low saline waters of the Caribbean Sea and furthermore the eastward currents like loop current. Also the minimum amounts of salinity occur in coastal waters where the river runoffs make fresh the high saline waters. Our next goal here is to study the patterns of sea surface temperature (SST), chlorophyll-a (CHLa) and fresh water flux (FWF) and examine the contributions of them to SSS variations. So by computing correlation coefficients, the values obtained for SST, FWF and CHLa are 0.7, 0.22 and 0.01 respectively which indicated high correlation of SST on SSS variations. Also by considering the spatial distribution based on the annual means, it found that there is a relationship between the SSS, SST, CHLa and the latitude in the study region which can be interpreted by developing a mathematical model.

  13. The 2006 July 17 Java (Indonesia) tsunami from satellite imagery and numerical modelling: a single or complex source?

    Science.gov (United States)

    Hébert, H.; Burg, P.-E.; Binet, R.; Lavigne, F.; Allgeyer, S.; Schindelé, F.

    2012-12-01

    The Mw 7.8 2006 July 17 earthquake off the southern coast of Java, Indonesia, has been responsible for a very large tsunami causing more than 700 casualties. The tsunami has been observed on at least 200 km of coastline in the region of Pangandaran (West Java), with run-up heights from 5 to more than 20 m. Such a large tsunami, with respect to the source magnitude, has been attributed to the slow character of the seismic rupture, defining the event as a so-called tsunami earthquake, but it has also been suggested that the largest run-up heights are actually the result of a second local landslide source. Here we test whether a single slow earthquake source can explain the tsunami run-up, using a combination of new detailed data in the region of the largest run-ups and comparison with modelled run-ups for a range of plausible earthquake source models. Using high-resolution satellite imagery (SPOT 5 and Quickbird), the coastal impact of the tsunami is refined in the surroundings of the high-security Permisan prison on Nusa Kambangan island, where 20 m run-up had been recorded directly after the event. These data confirm the extreme inundation lengths close to the prison, and extend the area of maximum impact further along the Nusa Kambangan island (about 20 km of shoreline), where inundation lengths reach several hundreds of metres, suggesting run-up as high as 10-15 m. Tsunami modelling has been conducted in detail for the high run-up Permisan area (Nusa Kambangan) and the PLTU power plant about 25 km eastwards, where run-up reached only 4-6 m and a video recording of the tsunami arrival is available. For the Permisan prison a high-resolution DEM was built from stereoscopic satellite imagery. The regular basin of the PLTU plant was designed using photographs and direct observations. For the earthquake's mechanism, both static (infinite) and finite (kinematic) ruptures are investigated using two published source models. The models account rather well for the sea level

  14. Detecting the changes in rural communities in Taiwan by applying multiphase segmentation on FORMOSA-2 satellite imagery

    Science.gov (United States)

    Huang, Yishuo

    2015-09-01

    regions containing roads, buildings, and other manmade construction works and the class with high values of NDVI indicates that those regions contain vegetation in good health. In order to verify the processed results, the regional boundaries were extracted and laid down on the given images to check whether the extracted boundaries were laid down on buildings, roads, or other artificial constructions. In addition to the proposed approach, another approach called statistical region merging was employed by grouping sets of pixels with homogeneous properties such that those sets are iteratively grown by combining smaller regions or pixels. In doing so, the segmented NDVI map can be generated. By comparing the areas of the merged classes in different years, the changes occurring in the rural communities of Taiwan can be detected. The satellite imagery of FORMOSA-2 with 2-m ground resolution is employed to evaluate the performance of the proposed approach. The satellite imagery of two rural communities (Jhumen and Taomi communities) is chosen to evaluate environmental changes between 2005 and 2010. The change maps of 2005-2010 show that a high density of green on a patch of land is increased by 19.62 ha in Jhumen community and conversely a similar patch of land is significantly decreased by 236.59 ha in Taomi community. Furthermore, the change maps created by another image segmentation method called statistical region merging generate similar processed results to multiphase segmentation.

  15. Multiscale assessment of progress of electrification in Indonesia based on brightness level derived from nighttime satellite imagery.

    Science.gov (United States)

    Ramdani, Fatwa; Setiani, Putri

    2017-06-01

    Availability of electricity can be used as an indicator to proximate parameters related to human well-being. Overall, the electrification process in Indonesia has been accelerating in the past two decades. Unfortunately, monitoring the country's progress on its effort to provide wider access to electricity poses challenges due to inconsistency of data provided by each national bureau, and limited availability of information. This study attempts to provide a reliable measure by employing nighttime satellite imagery to observe and to map the progress of electrification within a duration of 20 years, from 1993 to 2013. Brightness of 67,021 settlement-size points in 1993, 2003, and 2013 was assessed using data from DMSP/OLS instruments to study the electrification progress in the three service regions (Sumatera, Java-Bali, and East Indonesia) of the country's public electricity company, PLN. Observation of all service areas shows that the increase in brightness, which correspond with higher electricity development and consumption, has positive correlation with both population density (R 2  = 0.70) and urban change (R 2  = 0.79). Moreover, urban change has a stronger correlation with brightness, which is probably due to the high energy consumption in urban area per capita. This study also found that the brightness in Java-Bali region is very dominant, while the brightness in other areas has been lagging during the period of analysis. The slow development of electricity infrastructure, particularly in major parts of East Indonesia region, affects the low economic growth in some areas and formed vicious cycle.

  16. Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning

    Science.gov (United States)

    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

  17. Mapping Urban Tree Canopy Coverage and Structure using Data Fusion of High Resolution Satellite Imagery and Aerial Lidar

    Science.gov (United States)

    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.

  18. Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery

    Science.gov (United States)

    Shupe, Scott Marshall

    2000-10-01

    Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers

  19. Monitoring and modeling land-use change in the Pearl River Delta, China, using satellite imagery and socioeconomic data

    Science.gov (United States)

    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

  20. Inferring species richness and turnover by statistical multiresolution texture analysis of satellite imagery.

    Directory of Open Access Journals (Sweden)

    Matteo Convertino

    Full Text Available BACKGROUND: The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover. METHODOLOGY/PRINCIPAL FINDINGS: We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL. Species turnover, or [Formula: see text] diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species

  1. Satellites

    International Nuclear Information System (INIS)

    Burns, J.A.; Matthews, M.S.

    1986-01-01

    The present work is based on a conference: Natural Satellites, Colloquium 77 of the IAU, held at Cornell University from July 5 to 9, 1983. Attention is given to the background and origins of satellites, protosatellite swarms, the tectonics of icy satellites, the physical characteristics of satellite surfaces, and the interactions of planetary magnetospheres with icy satellite surfaces. Other topics include the surface composition of natural satellites, the cratering of planetary satellites, the moon, Io, and Europa. Consideration is also given to Ganymede and Callisto, the satellites of Saturn, small satellites, satellites of Uranus and Neptune, and the Pluto-Charon system

  2. 75 FR 39701 - Revision of a Currently Approved Collection: Users, Uses, and Benefits of Landsat Satellite Imagery

    Science.gov (United States)

    2010-07-12

    ... information from this collection to understand if they are currently meeting the needs of their user community... (1028-0091) provided up-to-date information about the current users and uses of Landsat imagery, as well... provided general information from a broader population of moderate resolution imagery users. This revised...

  3. Cloud Detection by Fusing Multi-Scale Convolutional Features

    Science.gov (United States)

    Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

    2018-04-01

    Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

  4. Remote sensing of contrails and aircraft altered cirrus clouds

    Energy Technology Data Exchange (ETDEWEB)

    Palikonda, R.; Nguyen, L.; Garber, D.P.; Smith, W.L. Jr [Analytical Services and Materials, Inc., Hampton, VA (United States); Minnis, P.; Young, D.F. [National Aeronautics and Space Administration, Hampton, VA (United States). Langley Research Center

    1997-12-31

    Analyses of satellite imagery are used to show that contrails can develop into fully extended cirrus cloud systems. Contrails can be advective on great distances, but would appear to observers as natural cirrus clouds. The conversion of simple contrails into cirrus may help explain the apparent increase of cloudiness over populated areas since the beginning of commercial jet air travel. Statistics describing the typical growth, advection, and lifetime of contrail cirrus is needed to evaluate their effects on climate. (author) 4 refs.

  5. Remote sensing of contrails and aircraft altered cirrus clouds

    Energy Technology Data Exchange (ETDEWEB)

    Palikonda, R; Nguyen, L; Garber, D P; Smith, Jr, W L [Analytical Services and Materials, Inc., Hampton, VA (United States); Minnis, P; Young, D F [National Aeronautics and Space Administration, Hampton, VA (United States). Langley Research Center

    1998-12-31

    Analyses of satellite imagery are used to show that contrails can develop into fully extended cirrus cloud systems. Contrails can be advective on great distances, but would appear to observers as natural cirrus clouds. The conversion of simple contrails into cirrus may help explain the apparent increase of cloudiness over populated areas since the beginning of commercial jet air travel. Statistics describing the typical growth, advection, and lifetime of contrail cirrus is needed to evaluate their effects on climate. (author) 4 refs.

  6. Using satellite-derived optical thickness to assess the influence of clouds on terrestrial carbon uptake

    Science.gov (United States)

    S.J. Cheng; A.L. Steiner; D.Y. Hollinger; G. Bohrer; K.J. Nadelhoffer

    2016-01-01

    Clouds scatter direct solar radiation, generating diffuse radiation and altering the ratio of direct to diffuse light. If diffuse light increases plant canopy CO2 uptake, clouds may indirectly influence climate by altering the terrestrial carbon cycle. However, past research primarily uses proxies or qualitative categories of clouds to connect...

  7. Potential for a biogenic influence on cloud microphysics over the ocean: a correlation study with satellite-derived data

    Directory of Open Access Journals (Sweden)

    A. Lana

    2012-09-01

    Full Text Available Aerosols have a large potential to influence climate through their effects on the microphysics and optical properties of clouds and, hence, on the Earth's radiation budget. Aerosol–cloud interactions have been intensively studied in polluted air, but the possibility that the marine biosphere plays an important role in regulating cloud brightness in the pristine oceanic atmosphere remains largely unexplored. We used 9 yr of global satellite data and ocean climatologies to derive parameterizations of the temporal variability of (a production fluxes of sulfur aerosols formed by the oxidation of the biogenic gas dimethylsulfide emitted from the sea surface; (b production fluxes of secondary organic aerosols from biogenic organic volatiles; (c emission fluxes of biogenic primary organic aerosols ejected by wind action on sea surface; and (d emission fluxes of sea salt also lifted by the wind upon bubble bursting. Series of global monthly estimates of these fluxes were correlated to series of potential cloud condensation nuclei (CCN numbers derived from satellite (MODIS. More detailed comparisons among weekly series of estimated fluxes and satellite-derived cloud droplet effective radius (re data were conducted at locations spread among polluted and clean regions of the oceanic atmosphere. The outcome of the statistical analysis was that positive correlation to CCN numbers and negative correlation to re were common at mid and high latitude for sulfur and organic secondary aerosols, indicating both might be important in seeding cloud droplet activation. Conversely, primary aerosols (organic and sea salt showed widespread positive correlations to CCN only at low latitudes. Correlations to re were more variable, non-significant or positive, suggesting that, despite contributing to large shares of the marine aerosol mass, primary aerosols are not widespread major drivers of the variability of cloud

  8. An Evaluation of Marine Boundary Layer Cloud Property Simulations in the Community Atmosphere Model Using Satellite Observations: Conventional Subgrid Parameterization versus CLUBB

    Energy Technology Data Exchange (ETDEWEB)

    Song, Hua [Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland; Zhang, Zhibo [Joint Center for Earth Systems Technology, and Physics Department, University of Maryland, Baltimore County, Baltimore, Maryland; Ma, Po-Lun [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington; Ghan, Steven J. [Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington; Wang, Minghuai [Institute for Climate and Global Change Research, and School of Atmospheric Sciences, Nanjing University, Nanjing, China

    2018-03-01

    This paper presents a two-step evaluation of the marine boundary layer (MBL) cloud properties from two Community Atmospheric Model (version 5.3, CAM5) simulations, one based on the CAM5 standard parameterization schemes (CAM5-Base), and the other on the Cloud Layers Unified By Binormals (CLUBB) scheme (CAM5-CLUBB). In the first step, we compare the cloud properties directly from model outputs between the two simulations. We find that the CAM5-CLUBB run produces more MBL clouds in the tropical and subtropical large-scale descending regions. Moreover, the stratocumulus (Sc) to cumulus (Cu) cloud regime transition is much smoother in CAM5-CLUBB than in CAM5-Base. In addition, in CAM5-Base we find some grid cells with very small low cloud fraction (<20%) to have very high in-cloud water content (mixing ratio up to 400mg/kg). We find no such grid cells in the CAM5-CLUBB run. However, we also note that both simulations, especially CAM5-CLUBB, produce a significant amount of “empty” low cloud cells with significant cloud fraction (up to 70%) and near-zero in-cloud water content. In the second step, we use satellite observations from CERES, MODIS and CloudSat to evaluate the simulated MBL cloud properties by employing the COSP satellite simulators. We note that a feature of the COSP-MODIS simulator to mimic the minimum detection threshold of MODIS cloud masking removes much more low clouds from CAM5-CLUBB than it does from CAM5-Base. This leads to a surprising result — in the large-scale descending regions CAM5-CLUBB has a smaller COSP-MODIS cloud fraction and weaker shortwave cloud radiative forcing than CAM5-Base. A sensitivity study suggests that this is because CAM5-CLUBB suffers more from the above-mentioned “empty” clouds issue than CAM5-Base. The COSP-MODIS cloud droplet effective radius in CAM5-CLUBB shows a spatial increase from coastal St toward Cu, which is in qualitative agreement with MODIS observations. In contrast, COSP-MODIS cloud droplet

  9. Estimation of time-series properties of gourd observed solar irradiance data using cloud properties derived from satellite observations

    Science.gov (United States)

    Watanabe, T.; Nohara, D.

    2017-12-01

    The shorter temporal scale variation in the downward solar irradiance at the ground level (DSI) is not understood well because researches in the shorter-scale variation in the DSI is based on the ground observation and ground observation stations are located coarsely. Use of dataset derived from satellite observation will overcome such defect. DSI data and MODIS cloud properties product are analyzed simultaneously. Three metrics: mean, standard deviation and sample entropy, are used to evaluate time-series properties of the DSI. Three metrics are computed from two-hours time-series centered at the observation time of MODIS over the ground observation stations. We apply the regression methods to design prediction models of each three metrics from cloud properties. The validation of the model accuracy show that mean and standard deviation are predicted with a higher degree of accuracy and that the accuracy of prediction of sample entropy, which represents the complexity of time-series, is not high. One of causes of lower prediction skill of sample entropy is the resolution of the MODIS cloud properties. Higher sample entropy is corresponding to the rapid fluctuation, which is caused by the small and unordered cloud. It seems that such clouds isn't retrieved well.

  10. Intercomparison between CMIP5 model and MODIS satellite-retrieved data of aerosol optical depth, cloud fraction, and cloud-aerosol interactions

    Science.gov (United States)

    Sockol, Alyssa; Small Griswold, Jennifer D.

    2017-08-01

    Aerosols are a critical component of the Earth's atmosphere and can affect the climate of the Earth through their interactions with solar radiation and clouds. Cloud fraction (CF) and aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used with analogous cloud and aerosol properties from Historical Phase 5 of the Coupled Model Intercomparison Project (CMIP5) model runs that explicitly include anthropogenic aerosols and parameterized cloud-aerosol interactions. The models underestimate AOD by approximately 15% and underestimate CF by approximately 10% overall on a global scale. A regional analysis is then used to evaluate model performance in two regions with known biomass burning activity and absorbing aerosol (South America (SAM) and South Africa (SAF)). In SAM, the models overestimate AOD by 4.8% and underestimate CF by 14%. In SAF, the models underestimate AOD by 35% and overestimate CF by 13.4%. Average annual cycles show that the monthly timing of AOD peaks closely match satellite data in both SAM and SAF for all except the Community Atmosphere Model 5 and Geophysical Fluid Dynamics Laboratory (GFDL) models. Monthly timing of CF peaks closely match for all models (except GFDL) for SAM and SAF. Sorting monthly averaged 2° × 2.5° model or MODIS CF as a function of AOD does not result in the previously observed "boomerang"-shaped CF versus AOD relationship characteristic of regions with absorbing aerosols from biomass burning. Cloud-aerosol interactions, as observed using daily (or higher) temporal resolution data, are not reproducible at the spatial or temporal resolution provided by the CMIP5 models.

  11. Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery

    Science.gov (United States)

    Eva, Hugh; Carboni, Silvia; Achard, Frédéric; Stach, Nicolas; Durieux, Laurent; Faure, Jean-François; Mollicone, Danilo

    A global systematic sampling scheme has been developed by the UN FAO and the EC TREES project to estimate rates of deforestation at global or continental levels at intervals of 5 to 10 years. This global scheme can be intensified to produce results at the national level. In this paper, using surrogate observations, we compare the deforestation estimates derived from these two levels of sampling intensities (one, the global, for the Brazilian Amazon the other, national, for French Guiana) to estimates derived from the official inventories. We also report the precisions that are achieved due to sampling errors and, in the case of French Guiana, compare such precision with the official inventory precision. We extract nine sample data sets from the official wall-to-wall deforestation map derived from satellite interpretations produced for the Brazilian Amazon for the year 2002 to 2003. This global sampling scheme estimate gives 2.81 million ha of deforestation (mean from nine simulated replicates) with a standard error of 0.10 million ha. This compares with the full population estimate from the wall-to-wall interpretations of 2.73 million ha deforested, which is within one standard error of our sampling test estimate. The relative difference between the mean estimate from sampling approach and the full population estimate is 3.1%, and the standard error represents 4.0% of the full population estimate. This global sampling is then intensified to a territorial level with a case study over French Guiana to estimate deforestation between the years 1990 and 2006. For the historical reference period, 1990, Landsat-5 Thematic Mapper data were used. A coverage of SPOT-HRV imagery at 20 m × 20 m resolution acquired at the Cayenne receiving station in French Guiana was used for year 2006. Our estimates from the intensified global sampling scheme over French Guiana are compared with those produced by the national authority to report on deforestation rates under the Kyoto

  12. Use of satellite imagery to identify vegetation cover changes following the Waldo Canyon Fire event, Colorado, 2012-2013

    Science.gov (United States)

    Cole, Christopher J.; Friesen, Beverly A.; Wilson, Earl M.

    2014-01-01

    The Waldo Canyon Fire of 2012 was one of the most destructive wildfire events in Colorado history. The fire burned a total of 18,247 acres, claimed 2 lives, and destroyed 347 homes. The Waldo Canyon Fire continues to pose challenges to nearby communities. In a preliminary emergency assessment conducted in 2012, the U.S. Geological Survey (USGS) concluded that drainage basins within and near the area affected by the Waldo Canyon Fire pose a risk for future debris flow events. Rainfall over burned, formerly vegetated surfaces resulted in multiple flood and debris flow events that affected the cities of Colorado Springs and Manitou Springs in 2013. One fatality resulted from a mudslide near Manitou Springs in August 2013. Federal, State, and local governments continue to monitor these hazards and other post-fire effects, along with the region’s ecological recovery. At the request of the Colorado Springs Office of Emergency Management, the USGS Special Applications Science Center developed a geospatial product to identify vegetation cover changes following the 2012 Waldo Canyon Fire event. Vegetation cover was derived from July 2012 WorldView-2 and September 2013 QuickBird multispectral imagery at a spatial resolution of two meters. The 2012 image was collected after the fire had reached its maximum extent. Per-pixel increases and decreases in vegetation cover were identified by measuring spectral changes that occurred between the 2012 and 2013 image dates. A Normalized Difference Vegetation Index (NDVI), and Green-Near Infrared Index (GRNIR) were computed from each image. These spectral indices are commonly used to characterize vegetation cover and health condition, due to their sensitivity to detect foliar chlorophyll content. Vector polygons identifying surface-cover feature boundaries were derived from the 2013 imagery using image segmentation software. This geographic software groups similar image pixels into vector objects based upon their spatial and spectral

  13. A Neutral-Network-Fusion Architecture for Automatic Extraction of Oceanographic Features from Satellite Remote Sensing Imagery

    National Research Council Canada - National Science Library

    Askari, Farid

    1999-01-01

    This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of evidence...

  14. Recent Trends of the Tropical Hydrological Cycle Inferred from Global Precipitation Climatology Project and International Satellite Cloud Climatology Project data

    Science.gov (United States)

    Zhou, Y. P.; Xu, Kuan-Man; Sud, Y. C.; Betts, A. K.

    2011-01-01

    Scores of modeling studies have shown that increasing greenhouse gases in the atmosphere impact the global hydrologic cycle; however, disagreements on regional scales are large, and thus the simulated trends of such impacts, even for regions as large as the tropics, remain uncertain. The present investigation attempts to examine such trends in the observations using satellite data products comprising Global Precipitation Climatology Project precipitation and International Satellite Cloud Climatology Project cloud and radiation. Specifically, evolving trends of the tropical hydrological cycle over the last 20-30 years were identified and analyzed. The results show (1) intensification of tropical precipitation in the rising regions of the Walker and Hadley circulations and weakening over the sinking regions of the associated overturning circulation; (2) poleward shift of the subtropical dry zones (up to 2deg/decade in June-July-August (JJA) in the Northern Hemisphere and 0.3-0.7deg/decade in June-July-August and September-October-November in the Southern Hemisphere) consistent with an overall broadening of the Hadley circulation; and (3) significant poleward migration (0.9-1.7deg/decade) of cloud boundaries of Hadley cell and plausible narrowing of the high cloudiness in the Intertropical Convergence Zone region in some seasons. These results support findings of some of the previous studies that showed strengthening of the tropical hydrological cycle and expansion of the Hadley cell that are potentially related to the recent global warming trends.

  15. An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

    Science.gov (United States)

    Greenwald, Thomas J.; Bennartz, Ralf; Lebsock, Matthew; Teixeira, João.

    2018-04-01

    The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9-year record of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E): clear-sky bias, cloud-rain partition (CRP) bias, cloud-fraction-dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR-E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud-Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud-fraction-dependent bias was found to be a combination of the CRP bias, an in-cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in-cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias-corrected AMSR-E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible-infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations.

  16. Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

    Directory of Open Access Journals (Sweden)

    Claire E. Bulgin

    2018-04-01

    Full Text Available Clouds remain one of the largest sources of uncertainty in remote sensing of surface temperature in the infrared, but this uncertainty has not generally been quantified. We present a new approach to do so, applied here to the Advanced Along-Track Scanning Radiometer (AATSR. We use an ensemble of cloud masks based on independent methodologies to investigate the magnitude of cloud detection uncertainties in area-average Land Surface Temperature (LST retrieval. We find that at a grid resolution of 625 km 2 (commensurate with a 0.25 ∘ grid size at the tropics, cloud detection uncertainties are positively correlated with cloud-cover fraction in the cell and are larger during the day than at night. Daytime cloud detection uncertainties range between 2.5 K for clear-sky fractions of 10–20% and 1.03 K for clear-sky fractions of 90–100%. Corresponding night-time uncertainties are 1.6 K and 0.38 K, respectively. Cloud detection uncertainty shows a weaker positive correlation with the number of biomes present within a grid cell, used as a measure of heterogeneity in the background against which the cloud detection must operate (e.g., surface temperature, emissivity and reflectance. Uncertainty due to cloud detection errors is strongly dependent on the dominant land cover classification. We find cloud detection uncertainties of a magnitude of 1.95 K over permanent snow and ice, 1.2 K over open forest, 0.9–1 K over bare soils and 0.09 K over mosaic cropland, for a standardised clear-sky fraction of 74.2%. As the uncertainties arising from cloud detection errors are of a significant magnitude for many surface types and spatially heterogeneous where land classification varies rapidly, LST data producers are encouraged to quantify cloud-related uncertainties in gridded products.

  17. Monitoring volcanic ash cloud top height through simultaneous retrieval of optical data from polar orbiting and geostationary satellites

    Directory of Open Access Journals (Sweden)

    K. Zakšek

    2013-03-01

    Full Text Available Volcanic ash cloud-top height (ACTH can be monitored on the global level using satellite remote sensing. Here we propose a photogrammetric method based on the parallax between data retrieved from geostationary and polar orbiting satellites to overcome some limitations of the existing methods of ACTH retrieval. SEVIRI HRV band and MODIS band 1 are a good choice because of their high resolution. The procedure works well if the data from both satellites are retrieved nearly simultaneously. MODIS does not retrieve the data at exactly the same time as SEVIRI. To compensate for advection we use two sequential SEVIRI images (one before and one after the MODIS retrieval and interpolate the cloud position from SEVIRI data to the time of MODIS retrieval. The proposed method was tested for the case of the Eyjafjallajökull eruption in April 2010. The parallax between MODIS and SEVIRI data can reach 30 km, which implies an ACTH of approximately 12 km at the beginning of the eruption. At the end of April eruption an ACTH of 3–4 km is observed. The accuracy of ACTH was estimated to be 0.6 km.

  18. Built-Up Area and Land Cover Extraction Using High Resolution Pleiades Satellite Imagery for Midrand, in Gauteng Province, South Africa

    Science.gov (United States)

    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.

  19. BUILT-UP AREA AND LAND COVER EXTRACTION USING HIGH RESOLUTION PLEIADES SATELLITE IMAGERY FOR MIDRAND, IN GAUTENG PROVINCE, SOUTH AFRICA

    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.