Mapping of the Land Cover Spatiotemporal Characteristics in Northern Russia Caused by Climate Change

Science.gov (United States)

Panidi, E.; Tsepelev, V.; Torlopova, N.; Bobkov, A.

2016-06-01

The study is devoted to the investigation of regional climate change in Northern Russia. Due to sparseness of the meteorological observation network in northern regions, we investigate the application capabilities of remotely sensed vegetation cover as indicator of climate change at the regional scale. In previous studies, we identified statistically significant relationship between the increase of surface air temperature and increase of the shrub vegetation productivity. We verified this relationship using ground observation data collected at the meteorological stations and Normalised Difference Vegetation Index (NDVI) data produced from Terra/MODIS satellite imagery. Additionally, we designed the technique of growing seasons separation for detailed investigation of the land cover (shrub cover) dynamics. Growing seasons are the periods when the temperature exceeds +5°C and +10°C. These periods determine the vegetation productivity conditions (i.e., conditions that allow growth of the phytomass). We have discovered that the trend signs for the surface air temperature and NDVI coincide on planes and river floodplains. On the current stage of the study, we are working on the automated mapping technique, which allows to estimate the direction and magnitude of the climate change in Northern Russia. This technique will make it possible to extrapolate identified relationship between land cover and climate onto territories with sparse network of meteorological stations. We have produced the gridded maps of NDVI and NDWI for the test area in European part of Northern Russia covered with the shrub vegetation. Basing on these maps, we may determine the frames of growing seasons for each grid cell. It will help us to obtain gridded maps of the NDVI linear trend for growing seasons on cell-by-cell basis. The trend maps can be used as indicative maps for estimation of the climate change on the studied areas.

Forest cover of North America in the 1970s mapped using Landsat MSS data

Science.gov (United States)

Feng, M.; Sexton, J. O.; Channan, S.; Townshend, J. R.

2015-12-01

The distribution and changes in Earth's forests impact hydrological, biogeochemical, and energy fluxes, as well as ecosystems' capacity to support biodiversity and human economies. Long-term records of forest cover are needed across a broad range of investigation, including climate and carbon-cycle modeling, hydrological studies, habitat analyzes, biological conservation, and land-use planning. Satellite-based observations enable mapping and monitoring of forests at ecologically and economically relevant resolutions and continental or even global extents. Following early forest-mapping efforts using coarser resolution remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS), forests have been mapped regionally at developed by the Global Land Cover Facility (GLCF) as reference, we developed an automated approach to detect forests using MSS data by leveraging the multispectral and phenological characteristics of forests observed in MSS time-series. The forest-cover map is produced with layers representing the year of observation, detection of forest-cover change relative to 1990, and the uncertainty of forest-cover and -change layers. The approach has been implemented with open-source libraries to facilitate processing large volumes of Landsat MSS images on high-performance computing machines. As the first result of our global mapping effort, we present the forest cover for North America. More than 25,000 Landsat MSS scenes were processed to provide a 120-meter resolution forest cover for North America, which will be made publicly available on the GLCF website (http://www.landcover.org).

Fusion Approaches for Land Cover Map Production Using High Resolution Image Time Series without Reference Data of the Corresponding Period

Directory of Open Access Journals (Sweden)

Benjamin Tardy

2017-11-01

Full Text Available Optical sensor time series images allow one to produce land cover maps at a large scale. The supervised classification algorithms have been shown to be the best to produce maps automatically with good accuracy. The main drawback of these methods is the need for reference data, the collection of which can introduce important production delays. Therefore, the maps are often available too late for some applications. Domain adaptation methods seem to be efficient for using past data for land cover map production. According to this idea, the main goal of this study is to propose several simple past data fusion schemes to override the current land cover map production delays. A single classifier approach and three voting rules are considered to produce maps without reference data of the corresponding period. These four approaches reach an overall accuracy of around 80% with a 17-class nomenclature using Formosat-2 image time series. A study of the impact of the number of past periods used is also done. It shows that the overall accuracy increases with the number of periods used. The proposed methods require at least two or three previous years to be used.

Open land cover from OpenStreetMap and remote sensing

Science.gov (United States)

Schultz, Michael; Voss, Janek; Auer, Michael; Carter, Sarah; Zipf, Alexander

2017-12-01

OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at osmlanduse.org. Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011-2016, and 46% of the area was representative of 2016-2017.

An automated approach for mapping persistent ice and snow cover over high latitude regions

Science.gov (United States)

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly

Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

Science.gov (United States)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.

Land-cover mapping using multitemporal, dual-frequency polarimetric SAR data

DEFF Research Database (Denmark)

Skriver, Henning; Schou, Jesper; Dierking, Wolfgang

2000-01-01

during the growing season acquired a lot of data over a Danish agricultural site. The data acquisitions were co-ordinated with ground surveys to obtain a detailed land cover map. The test area contains a large number of different land cover classes, such as more than 10 different crop types, deciduous......The Danish Center for Remote Sensing (DCRS) is, in collaboration with the Danish mapping agency, conducting a study on topographic mapping using SAR data, and land cover mapping results are presented. The Danish EMISAR system (an L- and C-band, fully polarimetric, airborne SAR) have in 1994 to 1999...

Error and Uncertainty in the Accuracy Assessment of Land Cover Maps

Science.gov (United States)

Sarmento, Pedro Alexandre Reis

Traditionally the accuracy assessment of land cover maps is performed through the comparison of these maps with a reference database, which is intended to represent the "real" land cover, being this comparison reported with the thematic accuracy measures through confusion matrixes. Although, these reference databases are also a representation of reality, containing errors due to the human uncertainty in the assignment of the land cover class that best characterizes a certain area, causing bias in the thematic accuracy measures that are reported to the end users of these maps. The main goal of this dissertation is to develop a methodology that allows the integration of human uncertainty present in reference databases in the accuracy assessment of land cover maps, and analyse the impacts that uncertainty may have in the thematic accuracy measures reported to the end users of land cover maps. The utility of the inclusion of human uncertainty in the accuracy assessment of land cover maps is investigated. Specifically we studied the utility of fuzzy sets theory, more precisely of fuzzy arithmetic, for a better understanding of human uncertainty associated to the elaboration of reference databases, and their impacts in the thematic accuracy measures that are derived from confusion matrixes. For this purpose linguistic values transformed in fuzzy intervals that address the uncertainty in the elaboration of reference databases were used to compute fuzzy confusion matrixes. The proposed methodology is illustrated using a case study in which the accuracy assessment of a land cover map for Continental Portugal derived from Medium Resolution Imaging Spectrometer (MERIS) is made. The obtained results demonstrate that the inclusion of human uncertainty in reference databases provides much more information about the quality of land cover maps, when compared with the traditional approach of accuracy assessment of land cover maps. None

A procedure for merging land cover/use data from Landsat, aerial photography, and map sources - Compatibility, accuracy and cost

Science.gov (United States)

Enslin, W. R.; Tilmann, S. E.; Hill-Rowley, R.; Rogers, R. H.

1977-01-01

A method is developed to merge land cover/use data from Landsat, aerial photography and map sources into a grid-based geographic information system. The method basically involves computer-assisted categorization of Landsat data to provide certain user-specified land cover categories; manual interpretation of aerial photography to identify other selected land cover/use categories that cannot be obtained from Landsat data; identification of special features from aerial photography or map sources; merging of the interpreted data from all the sources into a computer compatible file under a standardized coding structure; and the production of land cover/use maps, thematic maps, and tabular data. The specific tasks accomplished in producing the merged land cover/use data file and subsequent output products are identified and discussed. It is shown that effective implementation of the merging method is critically dependent on selecting the 'best' data source for each user-specified category in terms of accuracy and time/cost tradeoffs.

A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model

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Apisit Eiumnoh

2013-10-01

Full Text Available Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF model to simultaneously align two or more images and obtain a land cover map (LCM of the scene. The expectation maximization (EM algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.

Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

NARCIS (Netherlands)

Ali, A.; de Bie, C.A.J.M.; Skidmore, A.K.; Scarrott, R.G.; Hamad, A.A.; Venus, V.; Lymberakis, P.

2013-01-01

The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but

Nielsen number of a covering map

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Jezierski Jerzy

2006-01-01

Full Text Available We consider a finite regular covering over a compact polyhedron and a map admitting a lift . We show some formulae expressing the Nielsen number as a linear combination of the Nielsen numbers of its lifts.

The Regional Land Cover Monitoring System: Building regional capacity through innovative land cover mapping approaches

Science.gov (United States)

Saah, D.; Tenneson, K.; Hanh, Q. N.; Aekakkararungroj, A.; Aung, K. S.; Goldstein, J.; Cutter, P. G.; Maus, P.; Markert, K. N.; Anderson, E.; Ellenburg, W. L.; Ate, P.; Flores Cordova, A. I.; Vadrevu, K.; Potapov, P.; Phongsapan, K.; Chishtie, F.; Clinton, N.; Ganz, D.

2017-12-01

Earth observation and Geographic Information System (GIS) tools, products, and services are vital to support the environmental decision making by governmental institutions, non-governmental agencies, and the general public. At the heart of environmental decision making is the monitoring land cover and land use change (LCLUC) for land resource planning and for ecosystem services, including biodiversity conservation and resilience to climate change. A major challenge for monitoring LCLUC in developing regions, such as Southeast Asia, is inconsistent data products at inconsistent intervals that have different typologies across the region and are typically made in without stakeholder engagement or input. Here we present the Regional Land Cover Monitoring System (RLCMS), a novel land cover mapping effort for Southeast Asia, implemented by SERVIR-Mekong, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries. The RLCMS focuses on mapping biophysical variables (e.g. canopy cover, tree height, or percent surface water) at an annual interval and in turn using those biophysical variables to develop land cover maps based on stakeholder definitions of land cover classes. This allows for flexible and consistent land cover classifications that can meet the needs of different institutions across the region. Another component of the RLCMS production is the stake-holder engagement through co-development. Institutions that directly benefit from this system have helped drive the development for regional needs leading to services for their specific uses. Examples of services for regional stakeholders include using the RLCMS to develop maps using the IPCC classification scheme for GHG emission reporting and developing custom annual maps as an input to hydrologic modeling/flood forecasting systems. In addition to the implementation of this system and the service stemming from the RLCMS in Southeast Asia, it is

High resolution population maps for low income nations: combining land cover and census in East Africa.

Directory of Open Access Journals (Sweden)

Andrew J Tatem

2007-12-01

Full Text Available Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas.We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps.We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km(2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk and are freely available.

High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa

Science.gov (United States)

Tatem, Andrew J.; Noor, Abdisalan M.; von Hagen, Craig; Di Gregorio, Antonio; Hay, Simon I.

2007-01-01

Background Between 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas. Methodology/Principal Findings We examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps. Conclusions We find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available. PMID:18074022

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

Science.gov (United States)

Selkowitz, David J.; Forster, Richard R.

2016-01-01

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.

The stepwise discriminant algorithm for snow cover mapping based on FY-3/MERSI data

Science.gov (United States)

Han, Tao; Wang, Dawei; Jiang, Youyan; Wang, Xiaowei

2013-10-01

Medium Resolution Spectral Imager (MERSI) on board China's new generation polar orbit meteorological satellite FY- 3A provides a new data source for snow monitoring in large area. As a case study, the typical snow cover of Qilian Mountains in northwest China was selected in this paper to develop the algorithm to map snow cover using FY- 3A/MERSI. By analyzing the spectral response characteristics of snow and other surface elements, as well as each channel image quality on FY-3A/MERSI, the widely used Normalized Difference Snow Index (NDSI) was defined to be computed from channel 2 and channel 7 for this satellite data. Basing on NDSI, a tree-structure prototype version of snow identification model was proposed, including five newly-built multi-spectral indexes to remove those pixels such as forest, cloud shadow, water, lake ice, sand (salty land), or cloud that are usually confused with snow step by step, especially, a snow/cloud discrimination index was proposed to eliminate cloud, apart from use of cloud mask product in advance. Furthermore, land cover land use (LULC) image has been adopted as auxiliary dataset to adjust the corresponding LULC NDSI threshold constraints for snow final determination and optimization. This model is composed as the core of FY-3A/MERSI snow cover mapping flowchart, to produce daily snow map at 250m spatial resolution, and statistics can be generated on the extent and persistence of snow cover in each pixel for time series maps. Preliminary validation activities of our snow identification model have been undertaken. Comparisons of the 104 FY- 3A/MERSI snow cover maps in 2010-2011 snow season with snow depth records from 16 meteorological stations in Qilian Mountains region, the sunny snow cover had an absolute accuracy of 92.8%. Results of the comparison with the snow cover identified from 6 Terra/MODIS scenes showed that they had consistent pixels about 85%. When the two satellite resultant snow cover maps compared with the 6

Design, Development and Testing of Web Services for Multi-Sensor Snow Cover Mapping

Science.gov (United States)

Kadlec, Jiri

to combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the MODIS snow cover product. The method is demonstrated by producing a continuous daily time step snow presence probability map dataset for the Czech Republic region. The ability of the presented methodology to reconstruct MODIS snow cover under cloud is validated by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. Using crowdsourcing data (volunteer snow reports and ski tracks) improves the map accuracy by 0.7--1.2%. The output snow probability map data sets are published online using web applications and web services. Keywords: crowdsourcing, image analysis, interpolation, MODIS, R statistical software, snow cover, snowpack probability, Tethys platform, time series, WaterML, web services, winter sports.

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

Science.gov (United States)

Riggs, George A.; Hall, Dorothy K.

2010-01-01

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

Land-Use and Land-Cover Mapping Using a Gradable Classification Method

Directory of Open Access Journals (Sweden)

Keigo Kitada

2012-05-01

Full Text Available Conventional spectral-based classification methods have significant limitations in the digital classification of urban land-use and land-cover classes from high-resolution remotely sensed data because of the lack of consideration given to the spatial properties of images. To recognize the complex distribution of urban features in high-resolution image data, texture information consisting of a group of pixels should be considered. Lacunarity is an index used to characterize different texture appearances. It is often reported that the land-use and land-cover in urban areas can be effectively classified using the lacunarity index with high-resolution images. However, the applicability of the maximum-likelihood approach for hybrid analysis has not been reported. A more effective approach that employs the original spectral data and lacunarity index can be expected to improve the accuracy of the classification. A new classification procedure referred to as “gradable classification method” is proposed in this study. This method improves the classification accuracy in incremental steps. The proposed classification approach integrates several classification maps created from original images and lacunarity maps, which consist of lacnarity values, to create a new classification map. The results of this study confirm the suitability of the gradable classification approach, which produced a higher overall accuracy (68% and kappa coefficient (0.64 than those (65% and 0.60, respectively obtained with the maximum-likelihood approach.

Aggregated Habitat Cover 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 — Shallow-water, aggregated cover maps were produced by combining as many as four or more detailed habitat types into general cover categories. The original detailed...

Shallow-water Benthic Habitats of the Northwestern Hawaiian Islands from Aggregated Habitat Cover Maps Derived from High Resolution IKONOS Satellite Imagery (Draft)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Shallow-water, aggregated cover maps were produced by combining as many as four or more detailed habitat types into general cover categories. The original detailed...

Historical satellite data used to map Pan-Amazon forest cover

Science.gov (United States)

Kalluri, Satya; Desch, Arthur; Curry, Troy; Altstatt, Alice; Devers, Didier; Townshend, John; Tucker, Compton

Deforestation in the Brazilian Amazon is well documented and the contributions of Brazilian deforestation to global change have been extensively discussed in both scientific and popular literature [e.g., Skole and Tucker, 1993]. However, deforestation within the non-Brazilian tropics of South America has received much less attention. The Pan-Amazon region covering Venezuela, Colombia, Ecuador, Peru, and Bolivia comprises ˜2 million km2 of tropical forest that is under increasing pressure from logging and development. Wall-to-wall high-resolution forest cover maps are needed to properly document the complex distribution patterns of deforestation in the Pan-Amazon [Tucker and Townshend, 2000]. The Deforestation Mapping Group at the University of Marylands Global Land Cover Facility is using Landsat data to generate tropical forest cover maps in this region (Figure l). The study shows that while rates of forest loss are generally lower than those in Brazil, there are hot spots where deforestation rates run as high as 2,200 km2 yr1.

A Tool for Creating Regionally Calibrated High-Resolution Land Cover Data Sets for the West African Sahel: Using Machine Learning to Scale Up Hand-Classified Maps in a Data-Sparse Environment

Science.gov (United States)

Van Gordon, M.; Van Gordon, S.; Min, A.; Sullivan, J.; Weiner, Z.; Tappan, G. G.

2017-12-01

Using support vector machine (SVM) learning and high-accuracy hand-classified maps, we have developed a publicly available land cover classification tool for the West African Sahel. Our classifier produces high-resolution and regionally calibrated land cover maps for the Sahel, representing a significant contribution to the data available for this region. Global land cover products are unreliable for the Sahel, and accurate land cover data for the region are sparse. To address this gap, the U.S. Geological Survey and the Regional Center for Agriculture, Hydrology and Meteorology (AGRHYMET) in Niger produced high-quality land cover maps for the region via hand-classification of Landsat images. This method produces highly accurate maps, but the time and labor required constrain the spatial and temporal resolution of the data products. By using these hand-classified maps alongside SVM techniques, we successfully increase the resolution of the land cover maps by 1-2 orders of magnitude, from 2km-decadal resolution to 30m-annual resolution. These high-resolution regionally calibrated land cover datasets, along with the classifier we developed to produce them, lay the foundation for major advances in studies of land surface processes in the region. These datasets will provide more accurate inputs for food security modeling, hydrologic modeling, analyses of land cover change and climate change adaptation efforts. The land cover classification tool we have developed will be publicly available for use in creating additional West Africa land cover datasets with future remote sensing data and can be adapted for use in other parts of the world.

Covering Materials for Anaerobic Digesters Producing Biogas

International Nuclear Information System (INIS)

Itodo, I. N.; Philips, T. K.

2002-01-01

The suitability of foam, concrete and clay soil as covering material on anaerobic digesters producing biogas was investigated using four batch-type digesters of 20 litres volume. The methane yield from the digesters was of the order: foam >control> concrete > clay soil. The digester covered with foam had the highest methane yield, best temperature control and most favourable pH conditions. It is most suitable as cover material on anaerobic digesters

Regional Quantitative Cover Mapping of Tundra Plant Functional Types in Arctic Alaska

Directory of Open Access Journals (Sweden)

Matthew J. Macander

2017-10-01

Full Text Available Ecosystem maps are foundational tools that support multi-disciplinary study design and applications including wildlife habitat assessment, monitoring and Earth-system modeling. Here, we present continuous-field cover maps for tundra plant functional types (PFTs across ~125,000 km2 of Alaska’s North Slope at 30-m resolution. To develop maps, we collected a field-based training dataset using a point-intercept sampling method at 225 plots spanning bioclimatic and geomorphic gradients. We stratified vegetation by nine PFTs (e.g., low deciduous shrub, dwarf evergreen shrub, sedge, lichen and summarized measurements of the PFTs, open water, bare ground and litter using the cover metrics total cover (areal cover including the understory and top cover (uppermost canopy or ground cover. We then developed 73 spectral predictors derived from Landsat satellite observations (surface reflectance composites for ~15-day periods from May–August and five gridded environmental predictors (e.g., summer temperature, climatological snow-free date to model cover of PFTs using the random forest data-mining algorithm. Model performance tended to be best for canopy-forming PFTs, particularly deciduous shrubs. Our assessment of predictor importance indicated that models for low-statured PFTs were improved through the use of seasonal composites from early and late in the growing season, particularly when similar PFTs were aggregated together (e.g., total deciduous shrub, herbaceous. Continuous-field maps have many advantages over traditional thematic maps, and the methods described here are well-suited to support periodic map updates in tandem with future field and Landsat observations.

A globally complete map of supraglacial debris cover and a new toolkit for debris cover research

Science.gov (United States)

Herreid, Sam; Pellicciotti, Francesca

2017-04-01

A growing canon of literature is focused on resolving the processes and implications of debris cover on glaciers. However, this work is often confined to a handful of glaciers that were likely selected based on criteria optimizing their suitability to test a specific hypothesis or logistical ease. The role of debris cover in a glacier system is likely to not go overlooked in forthcoming research, yet the magnitude of this role at a global scale has not yet been fully described. Here, we present a map of debris cover for all glacierized regions on Earth including the Greenland Ice Sheet using 30 m Landsat data. This dataset will begin to open a wider context to the high quality, localized findings from the debris-covered glacier research community and help inform large-scale modeling efforts. A global map of debris cover also facilitates analysis attempting to isolate first order geomorphological and climate controls of supraglacial debris production. Furthering the objective of expanding the inclusion of debris cover in forthcoming research, we also present an under development suite of open-source, Python based tools. Requiring minimal and often freely available input data, we have automated the mapping of: i) debris cover, ii) ice cliffs, iii) debris cover evolution over the Landsat era and iv) glacier flow instabilities from altered debris structures. At the present time, debris extent is the only globally complete quantity but with the expanding repository of high quality global datasets and further tool development minimizing manual tasks and computational cost, we foresee all of these tools being applied globally in the near future.

Land Use Cover Mapping of Water Melon and Cereals in Southern Italy

Directory of Open Access Journals (Sweden)

Costanza Fiorentino

2010-06-01

Full Text Available The new high-resolution images from the satellites as IKONOS, SPOT5, Quickbird2 give us the opportunity to map ground features, which were not detectable in the past, by using medium resolution remote sensed data (LANDSAT. More accurate and reliable maps of land cover can then be produced. However, classification procedure with these images is more complex than with the medium resolution remote sensing data for two main reasons: firstly, because of their exiguous number of spectral bands, secondly, owing to high spatial resolution, the assumption of pixel independence does not generally hold. It is then necessary to have a multi-temporal series of images or to use classifiers taking into account also proximal information. The data in this study were (i a remote sensing image taken by SPOT5 satellite in July 2007 and used to discriminate the water melon cover class and, (ii three multi-temporal remote sensing images taken by SPOT5 satellite in May, June and July 2008 used to discriminate water melon and cereal crop cover classes. For water melon recognition, providing a single image in 2007, an object-oriented technique was applied instead of a traditional, per pixel technique obtaining an increase of overall accuracy of 15%. In 2008, since it was available a multi-temporal data set, a traditional ‘Maximum Likelihood’ technique was applied for both water melon and cereal crop cover class. The overall accuracy is greater than 95%.

Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

Directory of Open Access Journals (Sweden)

Courage Kamusoko

2014-06-01

Full Text Available Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT, random forests (RF, and support vector machines (SVM in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran’s I variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem.

Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Science.gov (United States)

Dirk Pflugmacher; Olga N. Krankina; Warren B. Cohen; Mark A. Friedl; Damien Sulla-Menashe; Robert E. Kennedy; Peder Nelson; Tatiana V. Loboda; Tobias Kuemmerle; Egor Dyukarev; Vladimir Elsadov; Viacheslav I. Kharuk

2011-01-01

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse...

Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

Directory of Open Access Journals (Sweden)

Konrad J. Wessels

2016-10-01

Full Text Available The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%–67% using 22 detailed classes and 72%–74% using 12 aggregated national classes. “Water”, “Plantations”, “Plantations—clearfelled”, “Orchards—trees”, “Sugarcane”, “Built-up/dense settlement”, “Cultivation—Irrigated” and “Forest (indigenous” had user’s accuracies above 70%. Other detailed classes (e.g., “Low density settlements”, “Mines and Quarries”, and “Cultivation, subsistence, drylands” which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land

South African National Land-Cover Change Map | Schoeman ...

African Journals Online (AJOL)

Globally, countries face a changing environment due to population growth, increase in agricultural production, increasing demand on natural resources, climate change and resultant degradation of the natural environment. One means of monitoring this changing scenario is through land-cover change mapping. Modern ...

Subpixel Snow Cover Mapping from MODIS Data by Nonparametric Regression Splines

Science.gov (United States)

Akyurek, Z.; Kuter, S.; Weber, G. W.

2016-12-01

Spatial extent of snow cover is often considered as one of the key parameters in climatological, hydrological and ecological modeling due to its energy storage, high reflectance in the visible and NIR regions of the electromagnetic spectrum, significant heat capacity and insulating properties. A significant challenge in snow mapping by remote sensing (RS) is the trade-off between the temporal and spatial resolution of satellite imageries. In order to tackle this issue, machine learning-based subpixel snow mapping methods, like Artificial Neural Networks (ANNs), from low or moderate resolution images have been proposed. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression tool that can build flexible models for high dimensional and complex nonlinear data. Although MARS is not often employed in RS, it has various successful implementations such as estimation of vertical total electron content in ionosphere, atmospheric correction and classification of satellite images. This study is the first attempt in RS to evaluate the applicability of MARS for subpixel snow cover mapping from MODIS data. Total 16 MODIS-Landsat ETM+ image pairs taken over European Alps between March 2000 and April 2003 were used in the study. MODIS top-of-atmospheric reflectance, NDSI, NDVI and land cover classes were used as predictor variables. Cloud-covered, cloud shadow, water and bad-quality pixels were excluded from further analysis by a spatial mask. MARS models were trained and validated by using reference fractional snow cover (FSC) maps generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also developed. The mutual comparison of obtained MARS and ANN models was accomplished on independent test areas. The MARS model performed better than the ANN model with an average RMSE of 0.1288 over the independent test areas; whereas the average RMSE of the ANN model

Meter-scale Urban Land Cover Mapping for EPA EnviroAtlas Using Machine Learning and OBIA Remote Sensing Techniques

Science.gov (United States)

Pilant, A. N.; Baynes, J.; Dannenberg, M.; Riegel, J.; Rudder, C.; Endres, K.

2013-12-01

US EPA EnviroAtlas is an online collection of tools and resources that provides geospatial data, maps, research, and analysis on the relationships between nature, people, health, and the economy (http://www.epa.gov/research/enviroatlas/index.htm). Using EnviroAtlas, you can see and explore information related to the benefits (e.g., ecosystem services) that humans receive from nature, including clean air, clean and plentiful water, natural hazard mitigation, biodiversity conservation, food, fuel, and materials, recreational opportunities, and cultural and aesthetic value. EPA developed several urban land cover maps at very high spatial resolution (one-meter pixel size) for a portion of EnviroAtlas devoted to urban studies. This urban mapping effort supported analysis of relations among land cover, human health and demographics at the US Census Block Group level. Supervised classification of 2010 USDA NAIP (National Agricultural Imagery Program) digital aerial photos produced eight-class land cover maps for several cities, including Durham, NC, Portland, ME, Tampa, FL, New Bedford, MA, Pittsburgh, PA, Portland, OR, and Milwaukee, WI. Semi-automated feature extraction methods were used to classify the NAIP imagery: genetic algorithms/machine learning, random forest, and object-based image analysis (OBIA). In this presentation we describe the image processing and fuzzy accuracy assessment methods used, and report on some sustainability and ecosystem service metrics computed using this land cover as input (e.g., carbon sequestration from USFS iTREE model; health and demographics in relation to road buffer forest width). We also discuss the land cover classification schema (a modified Anderson Level 1 after the National Land Cover Data (NLCD)), and offer some observations on lessons learned. Meter-scale urban land cover in Portland, OR overlaid on NAIP aerial photo. Streets, buildings and individual trees are identifiable.

Image Analysis for Facility Siting: a Comparison of Lowand High-altitude Image Interpretability for Land Use/land Cover Mapping

Science.gov (United States)

Borella, H. M.; Estes, J. E.; Ezra, C. E.; Scepan, J.; Tinney, L. R.

1982-01-01

For two test sites in Pennsylvania the interpretability of commercially acquired low-altitude and existing high-altitude aerial photography are documented in terms of time, costs, and accuracy for Anderson Level II land use/land cover mapping. Information extracted from the imagery is to be used in the evaluation process for siting energy facilities. Land use/land cover maps were drawn at 1:24,000 scale using commercially flown color infrared photography obtained from the United States Geological Surveys' EROS Data Center. Detailed accuracy assessment of the maps generated by manual image analysis was accomplished employing a stratified unaligned adequate class representation. Both 'area-weighted' and 'by-class' accuracies were documented and field-verified. A discrepancy map was also drawn to illustrate differences in classifications between the two map scales. Results show that the 1:24,000 scale map set was more accurate (99% to 94% area-weighted) than the 1:62,500 scale set, especially when sampled by class (96% to 66%). The 1:24,000 scale maps were also more time-consuming and costly to produce, due mainly to higher image acquisition costs.

Nielsen number of a covering map

Directory of Open Access Journals (Sweden)

Jerzy Jezierski

2006-02-01

Full Text Available We consider a finite regular covering pH:X˜H→X over a compact polyhedron and a map f:X→X admitting a lift f˜:X˜H→X˜H. We show some formulae expressing the Nielsen number N(f as a linear combination of the Nielsen numbers of its lifts.

Mapping Annual Forest Cover in Sub-Humid and Semi-Arid Regions through Analysis of Landsat and PALSAR Imagery

Directory of Open Access Journals (Sweden)

Yuanwei Qin

2016-11-01

Full Text Available Accurately mapping the spatial distribution of forests in sub-humid to semi-arid regions over time is important for forest management but a challenging task. Relatively large uncertainties still exist in the spatial distribution of forests and forest changes in the sub-humid and semi-arid regions. Numerous publications have used either optical or synthetic aperture radar (SAR remote sensing imagery, but the resultant forest cover maps often have large errors. In this study, we propose a pixel- and rule-based algorithm to identify and map annual forests from 2007 to 2010 in Oklahoma, USA, a transitional region with various climates and landscapes, using the integration of the L-band Advanced Land Observation Satellite (ALOS PALSAR Fine Beam Dual Polarization (FBD mosaic dataset and Landsat images. The overall accuracy and Kappa coefficient of the PALSAR/Landsat forest map were about 88.2% and 0.75 in 2010, with the user and producer accuracy about 93.4% and 75.7%, based on the 3270 random ground plots collected in 2012 and 2013. Compared with the forest products from Japan Aerospace Exploration Agency (JAXA, National Land Cover Database (NLCD, Oklahoma Ecological Systems Map (OKESM and Oklahoma Forest Resource Assessment (OKFRA, the PALSAR/Landsat forest map showed great improvement. The area of the PALSAR/Landsat forest was about 40,149 km2 in 2010, which was close to the area from OKFRA (40,468 km2, but much larger than those from JAXA (32,403 km2 and NLCD (37,628 km2. We analyzed annual forest cover dynamics, and the results show extensive forest cover loss (2761 km2, 6.9% of the total forest area in 2010 and gain (3630 km2, 9.0% in southeast and central Oklahoma, and the total area of forests increased by 684 km2 from 2007 to 2010. This study clearly demonstrates the potential of data fusion between PALSAR and Landsat images for mapping annual forest cover dynamics in sub-humid to semi-arid regions, and the resultant forest maps would be

Mapping wind erosion hazard in Australia using MODIS-derived ground cover, soil moisture and climate data

International Nuclear Information System (INIS)

Yang, X; Leys, J

2014-01-01

This paper describes spatial modeling methods to identify wind erosion hazard (WEH) areas across Australia using the recently available time-series products of satellite-derived ground cover, soil moisture and wind speed. We implemented the approach and data sets in a geographic information system to produce WEH maps for Australia at 500 m ground resolution on a monthly basis for the recent thirteen year period (2000–2012). These maps reveal the significant wind erosion hazard areas and their dynamic tendencies at paddock and regional scales. Dust measurements from the DustWatch network were used to validate the model and interpret the dust source areas. The modeled hazard areas and changes were compared with results from a rule-set approach and the Computational Environmental Management System (CEMSYS) model. The study demonstrates that the time series products of ground cover, soil moisture and wind speed can be jointly used to identify landscape erodibility and to map seasonal changes of wind erosion hazard across Australia. The time series wind erosion hazard maps provide detailed and useful information to assist in better targeting areas for investments and continuous monitoring, evaluation and reporting that will lead to reduced wind erosion and improved soil condition

Analysis of spatial distribution of land cover maps accuracy

Science.gov (United States)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain

Land Use and Land Cover - MO 2015 Silver Land Cover (GDB)

Data.gov (United States)

NSGIC State | GIS Inventory — MoRAP produced and integrated data to map land cover and wetlands for the Upper Silver Creek Watershed in Illinois. LiDAR elevation and vegetation height information...

Land Use and Land Cover - MO 2015 Meramec Land Cover (GDB)

Data.gov (United States)

NSGIC State | GIS Inventory — MoRAP produced and integrated data to map land cover and wetlands for the Meramec River bottomland in Missouri. LiDAR elevation and vegetation height information and...

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.

Image analysis for facility siting: a comparison of low- and high-altitude image interpretability for land use/land cover mapping

International Nuclear Information System (INIS)

Borella, H.M.; Estes, J.E.; Ezra, C.E.; Scepan, J.; Tinney, L.R.

1982-01-01

For two test sites in Pennsylvania the interpretability of commercially acquired low-altitude and existing high-altitude aerial photography are documented in terms of time, costs, and accuracy for Anderson Level II land use/land cover mapping. Information extracted from the imagery is to be used in the evaluation process for siting energy facilities. Land use/land cover maps were drawn at 1:24,000 scale using commercially flown color infrared photography obtained from the United States Geological Surveys' EROS Data Center. Detailed accuracy assessment of the maps generated by manual image analysis was accomplished employing a stratified unaligned adequate class representation. Both are-weighted and by-class accuracies were documented and field-verified. A discrepancy map was also drawn to illustrate differences in classifications between the two map scales. Results show that the 1:24,000 scale map set was accurate (99% to 94% area-weighted) than the 1:62,500 scale set, especially when sampled by class (96% to 66%). The 1:24,000 scale maps were also more time-consuming and costly to produce, due mainly to higher image acquisition costs

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.

Cover crops knowledge and implementation willingness by producers of several crops

Directory of Open Access Journals (Sweden)

Robin Gómez Gómez

2017-04-01

Full Text Available The objective of this study was to assess the knowledge on cover crops and native vegetation mulches and the willingness to implement them by papaya, oil palm, and banana producers in Costa Rica. An evaluation instrument with twenty eight questions to be answered as true or false was developed, and it was used to yield a knowledge indicator. Seven additional questions with responses on a scale from 0 to 5 were included to explore producers’ willingness to implement cover crops or native vegetation mulches on their farms. The evaluation was completed in 2014, and was filled out by 36 papaya producers, 30 oil palm producers, and 57 banana producers. Item analyses to determine reliability produced Cronbach’s alpha values above 90%. For this study a factors analysis was performed in order to determine the measurement of one single variable, knowledge on cover crops and native vegetation mulches. Global knowledge scores varied signi cantly between producer groups. Banana producers assessments yielded the highest mean with the lowest variability, whereas papaya producers had the lower mean and the highest variability. Likewise, answers to each of the questions differed importantly between producer groups. It was also determined that producers of these crops are willing to implement and get training on cover crops and native vegetation mulches.

A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses.

Science.gov (United States)

Estes, Lyndon; Chen, Peng; Debats, Stephanie; Evans, Tom; Ferreira, Stefanus; Kuemmerle, Tobias; Ragazzo, Gabrielle; Sheffield, Justin; Wolf, Adam; Wood, Eric; Caylor, Kelly

2018-01-01

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ∼45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higher-resolution imagery were most accurate, followed by multi-map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland-adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but ∼40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land

Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map

Directory of Open Access Journals (Sweden)

Myroslava Lesiv

2016-03-01

Full Text Available Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR, as well as classification and regression trees (CART. We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs.

Automatic crown cover mapping to improve forest inventory

Science.gov (United States)

Claude Vidal; Jean-Guy Boureau; Nicolas Robert; Nicolas Py; Josiane Zerubia; Xavier Descombes; Guillaume Perrin

2009-01-01

To automatically analyze near infrared aerial photographs, the French National Institute for Research in Computer Science and Control developed together with the French National Forest Inventory (NFI) a method for automatic crown cover mapping. This method uses a Reverse Jump Monte Carlo Markov Chain algorithm to locate the crowns and describe those using ellipses or...

Land Cover Mapping in Northern High Latitude Permafrost Regions with Satellite Data: Achievements and Remaining Challenges

Directory of Open Access Journals (Sweden)

Annett Bartsch

2016-11-01

Full Text Available Most applications of land cover maps that have been derived from satellite data over the Arctic require higher thematic detail than available in current global maps. A range of application studies has been reviewed, including up-scaling of carbon fluxes and pools, permafrost feature mapping and transition monitoring. Early land cover mapping studies were driven by the demand to characterize wildlife habitats. Later, in the 1990s, up-scaling of in situ measurements became central to the discipline of land cover mapping on local to regional scales at several sites across the Arctic. This includes the Kuparuk basin in Alaska, the Usa basin and the Lena Delta in Russia. All of these multi-purpose land cover maps have been derived from Landsat data. High resolution maps (from optical satellite data serve frequently as input for the characterization of periglacial features and also flux tower footprints in recent studies. The most used map to address circumpolar issues is the CAVM (Circum Arctic Vegetation Map based on AVHRR (1 km and has been manually derived. It provides the required thematic detail for many applications, but is confined to areas north of the treeline, and it is limited in spatial detail. A higher spatial resolution circumpolar land cover map with sufficient thematic content would be beneficial for a range of applications. Such a land cover classification should be compatible with existing global maps and applicable for multiple purposes. The thematic content of existing global maps has been assessed by comparison to the CAVM and regional maps. None of the maps provides the required thematic detail. Spatial resolution has been compared to used classes for local to regional applications. The required thematic detail increases with spatial resolution since coarser datasets are usually applied over larger areas covering more relevant landscape units. This is especially of concern when the entire Arctic is addressed. A spatial

Generation of Land Cover Maps Using High-Resolution Multispectral Aerial Cameras

DEFF Research Database (Denmark)

Höhle, Joachim

2013-01-01

. The classification had an overall accuracy of 79%. Suggestions for the improvements in the applied methodology are made. The potential of land cover maps lies in updating of topographic databases, quality control of maps, studies of town development, and other geo-spatial domain applications. The automatic...... for classification of land cover. A high degree of automation can be achieved. The obtained results of a practical example are checked with reference values derived from ortho-images in natural colour and from colour images using stereo-vision. An error matrix is applied in the evaluation of the results...

VT National Land Cover Dataset - 2001

Data.gov (United States)

Vermont Center for Geographic Information — (Link to Metadata) The NLCD2001 layer available from VCGI is a subset of the the National Land Cover Database 2001 land cover layer for mapping zone 65 was produced...

GAP Land Cover - Image

Data.gov (United States)

Minnesota Department of Natural Resources — This raster dataset is a simple image of the original detailed (1-acre minimum), hierarchically organized vegetation cover map produced by computer classification of...

GAP Land Cover - Vector

Data.gov (United States)

Minnesota Department of Natural Resources — This vector dataset is a detailed (1-acre minimum), hierarchically organized vegetation cover map produced by computer classification of combined two-season pairs of...

Calibration and Validation of Tundra Plant Functional Type Fractional Cover Mapping

Science.gov (United States)

Macander, M. J.; Nelson, P.; Frost, G. V., Jr.

2017-12-01

Fractional cover maps are being developed for selected tundra plant functional types (PFTs) across >500,000 sq. km of arctic Alaska and adjacent Canada at 30 m resolution. Training and validation data include a field-based training dataset based on point-intercept sampling method at hundreds of plots spanning bioclimatic and geomorphic gradients. We also compiled 50 blocks of 1-5 cm resolution RGB image mosaics in Alaska (White Mountains, North Slope, and Yukon-Kuskokwim Delta) and the Yukon Territory. The mosaics and associated surface and canopy height models were developed using a consumer drone and structure from motion processing. We summarized both the in situ measurements and drone imagery to determine cover of two PFTs: Low and Tall Deciduous Shrub, and Light Fruticose/Foliose Lichen. We applied these data to train 2 m (limited extent) and 30 m (wall to wall) maps of PFT fractional cover for shrubs and lichen. Predictors for 2 m models were commercial satellite imagery such as WorldView-2 and Worldview-3, analyzed on the ABoVE Science Cloud. Predictors for 30 m models were primarily reflectance composites and spectral metrics developed from Landsat imagery, using Google Earth Engine. We compared the performance of models developed from the in situ and drone-derived training data and identify best practices to improve the performance and efficiency of arctic PFT fractional cover mapping.

Land User and Land Cover Maps of Europe: a Webgis Platform

Science.gov (United States)

Brovelli, M. A.; Fahl, F. C.; Minghini, M.; Molinari, M. E.

2016-06-01

This paper presents the methods and implementation processes of a WebGIS platform designed to publish the available land use and land cover maps of Europe at continental scale. The system is built completely on open source infrastructure and open standards. The proposed architecture is based on a server-client model having GeoServer as the map server, Leaflet as the client-side mapping library and the Bootstrap framework at the core of the front-end user interface. The web user interface is designed to have typical features of a desktop GIS (e.g. activate/deactivate layers and order layers by drag and drop actions) and to show specific information on the activated layers (e.g. legend and simplified metadata). Users have the possibility to change the base map from a given list of map providers (e.g. OpenStreetMap and Microsoft Bing) and to control the opacity of each layer to facilitate the comparison with both other land cover layers and the underlying base map. In addition, users can add to the platform any custom layer available through a Web Map Service (WMS) and activate the visualization of photos from popular photo sharing services. This last functionality is provided in order to have a visual assessment of the available land coverages based on other user-generated contents available on the Internet. It is supposed to be a first step towards a calibration/validation service that will be made available in the future.

The Application of Remote Sensing Data to GIS Studies of Land Use, Land Cover, and Vegetation Mapping in the State of Hawaii

Science.gov (United States)

Hogan, Christine A.

1996-01-01

A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation

Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

Science.gov (United States)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are

A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment.

Directory of Open Access Journals (Sweden)

Jinwei Dong

Full Text Available Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6 km(2 (GlobCover to 2.69×10(6 km(2 (MCD12Q1 in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR onboard the Advanced Land Observing Satellite (ALOS became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%. The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+ implementation, and

High Resolution Urban Land Cover Mapping Using NAIP Aerial Photography and Image Processing for the USEPA National Atlas of Sustainability and Ecosystem Services

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Pilant, A. N.; Baynes, J.; Dannenberg, M.

2012-12-01

The US EPA National Atlas for Sustainability is a web-based, easy-to-use, mapping application that allows users to view and analyze multiple ecosystem services in a specific region. The Atlas provides users with a visual method for interpreting ecosystem services and understanding how they can be conserved and enhanced for a sustainable future. The Urban Atlas component of the National Atlas will provide fine-scale information linking human health and well-being to environmental conditions such as urban heat islands, near-road pollution, resource use, access to recreation, drinking water quality and other quality of life indicators. The National Land Cover Data (NLCD) derived from 30 m scale 2006 Landsat imagery provides the land cover base for the Atlas. However, urban features and phenomena occur at finer spatial scales, so higher spatial resolution and more current LC maps are required. We used 4 band USDA NAIP imagery (1 m pixel size) and various classification approaches to produce urban land cover maps with these classes: impervious surface, grass and herbaceous, trees and forest, soil and barren, and water. Here we present the remote sensing methods used and results from four pilot cities in this effort, highlighting the pros and cons of the approach, and the benefits to sustainability and ecosystem services analysis. Example of high resolution land cover map derived from USDA NAIP aerial photo. Compare 30 m and 1 m resolution land cover maps of downtown Durham, NC.

Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid SWAT Hydrologic Modeling

Science.gov (United States)

Spruce, J.; Bolten, J. D.; Srinivasan, R.

2017-12-01

This presentation discusses research to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB). Funded by a NASA ROSES Disasters grant, the main objective was to produce updated LULC maps to aid the Mekong River Commission's (MRC's) Soil and Water Assessment Tool (SWAT) hydrologic model. In producing needed LULC maps, temporally processed MODIS monthly NDVI data for 2010 were used as the primary data source for classifying regionally prominent forest and agricultural types. The MODIS NDVI data was derived from processing MOD09 and MYD09 8-day reflectance data with the Time Series Product Tool, a custom software package. Circa 2010 Landsat multispectral data from the dry season were processed into top of atmosphere reflectance mosaics and then classified to derive certain locally common LULC types, such as urban areas and industrial forest plantations. Unsupervised ISODATA clustering was used to derive most LULC classifications. GIS techniques were used to merge MODIS and Landsat classifications into final LULC maps for Sub-Basins (SBs) 1-8 of the LMB. The final LULC maps were produced at 250-meter resolution and delivered to the MRC for use in SWAT modeling for the LMB. A map accuracy assessment was performed for the SB 7 LULC map with 14 classes. This assessment was performed by comparing random locations for sampled LULC types to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data from Google Map/Earth, and other reference data from the MRC (e.g., crop calendars). LULC accuracy assessment results for SB 7 indicated an overall agreement to reference data of 81% at full scheme specificity. However, by grouping 3 deciduous forest classes into 1 class, the overall agreement improved to 87%. The project enabled updated LULC maps, plus more specific rice types were classified compared to the previous LULC maps. The LULC maps from this project should improve the use of SWAT for modeling

Application of Google Maps API service for creating web map of information retrieved from CORINE land cover databases

Directory of Open Access Journals (Sweden)

Kilibarda Milan

2010-01-01

Full Text Available Today, Google Maps API application based on Ajax technology as standard web service; facilitate users with publication interactive web maps, thus opening new possibilities in relation to the classical analogue maps. CORINE land cover databases are recognized as the fundamental reference data sets for numerious spatial analysis. The theoretical and applicable aspects of Google Maps API cartographic service are considered on the case of creating web map of change in urban areas in Belgrade and surround from 2000. to 2006. year, obtained from CORINE databases.

Design of a High Resolution Open Access Global Snow Cover Web Map Service Using Ground and Satellite Observations

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Kadlec, J.; Ames, D. P.

2014-12-01

The aim of the presented work is creating a freely accessible, dynamic and re-usable snow cover map of the world by combining snow extent and snow depth datasets from multiple sources. The examined data sources are: remote sensing datasets (MODIS, CryoLand), weather forecasting model outputs (OpenWeatherMap, forecast.io), ground observation networks (CUAHSI HIS, GSOD, GHCN, and selected national networks), and user-contributed snow reports on social networks (cross-country and backcountry skiing trip reports). For adding each type of dataset, an interface and an adapter is created. Each adapter supports queries by area, time range, or combination of area and time range. The combined dataset is published as an online snow cover mapping service. This web service lowers the learning curve that is required to view, access, and analyze snow depth maps and snow time-series. All data published by this service are licensed as open data; encouraging the re-use of the data in customized applications in climatology, hydrology, sports and other disciplines. The initial version of the interactive snow map is on the website snow.hydrodata.org. This website supports the view by time and view by site. In view by time, the spatial distribution of snow for a selected area and time period is shown. In view by site, the time-series charts of snow depth at a selected location is displayed. All snow extent and snow depth map layers and time series are accessible and discoverable through internationally approved protocols including WMS, WFS, WCS, WaterOneFlow and WaterML. Therefore they can also be easily added to GIS software or 3rd-party web map applications. The central hypothesis driving this research is that the integration of user contributed data and/or social-network derived snow data together with other open access data sources will result in more accurate and higher resolution - and hence more useful snow cover maps than satellite data or government agency produced data by

A Study on remote sensing method for drawing up and utilizing ecological and natural map - concentrated on drawing up of Land Cover Classification Map

Energy Technology Data Exchange (ETDEWEB)

Jun, Sung Woo; Chung, Sung Moon [Korea Environment Institute, Seoul (Korea)

1998-12-01

The drawing up of ecological and natural map, which is highly efficient using remote exploration method, was promoted in this study. As the first step of drawing up of ecological and natural map, this study is working on the drawing up of Land Cover using as a base map. Through the detailed and sufficient consideration on GAP analysis of USA, CORINE project of EU, and examples in Korea, it studied and proposed the Land Cover Classification system and method suitable for Korea. It will be helpful to draw up ecological and natural map by providing two strategies and principles for land cover classification. 26 refs., 33 figs., 9 tabs.

Towards automated statewide land cover mapping in Wisconsin using satellite remote sensing and GIS techniques

International Nuclear Information System (INIS)

Cosentino, B.L.; Lillesand, T.M.

1991-01-01

Attention is given to an initial research project being performed by the University of Wisconsin-Madison, Environmental Remote Sensing Center in conjunction with seven local, state, and federal agencies to implement automated statewide land cover mapping using satellite remote sensing and geographical information system (GIS) techniques. The basis, progress, and future research needs for this mapping program are presented. The research efforts are directed toward strategies that integrate satellite remote sensing and GIS techniques in the generation of land cover data for multiple users of land cover information. The project objectives are to investigate methodologies that integrate satellite data with other imagery and spatial data resident in emerging GISs in the state for particular program needs, and to develop techniques that can improve automated land cover mapping efficiency and accuracy. 10 refs

Land Cover Mapping Analysis and Urban Growth Modelling Using Remote Sensing Techniques in Greater Cairo Region—Egypt

Directory of Open Access Journals (Sweden)

Yasmine Megahed

2015-09-01

Full Text Available This study modeled the urban growth in the Greater Cairo Region (GCR, one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC maps (1984, 2003 and 2014 were produced from satellite images by using Support Vector Machines (SVM. Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modeled to predict the future scenarios for year 2025 using Land Change Modeler (LCM embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.

Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data

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Higginbottom, Thomas P.; Symeonakis, Elias; Meyer, Hanna; van der Linden, Sebastian

2018-05-01

Increasing attention is being directed at mapping the fractional woody cover of savannahs using Earth-observation data. In this study, we test the utility of Landsat TM/ ETM-based spectral-temporal variability metrics for mapping regional-scale woody cover in the Limpopo Province of South Africa, for 2010. We employ a machine learning framework to compare the accuracies of Random Forest models derived using metrics calculated from different seasons. We compare these results to those from fused Landsat-PALSAR data to establish if seasonal metrics can compensate for structural information from the PALSAR signal. Furthermore, we test the applicability of a statistical variable selection method, the recursive feature elimination (RFE), in the automation of the model building process in order to reduce model complexity and processing time. All of our tests were repeated at four scales (30, 60, 90, and 120 m-pixels) to investigate the role of spatial resolution on modelled accuracies. Our results show that multi-seasonal composites combining imagery from both the dry and wet seasons produced the highest accuracies (R2 = 0.77, RMSE = 9.4, at the 120 m scale). When using a single season of observations, dry season imagery performed best (R2 = 0.74, RMSE = 9.9, at the 120 m resolution). Combining Landsat and radar imagery was only marginally beneficial, offering a mean relative improvement of 1% in accuracy at the 120 m scale. However, this improvement was concentrated in areas with lower densities of woody coverage (continue to exploit the Landsat archive, but should aim to use multi-seasonal derived information. When the coarser 120 m pixel scale is adequate, integration of Landsat and SAR data should be considered, especially in areas with lower woody cover densities. The use of multiple seasonal compositing periods offers promise for large-area mapping of savannahs, even in regions with a limited historical Landsat coverage.

Land cover change mapping using MODIS time series to improve emissions inventories

Science.gov (United States)

López-Saldaña, Gerardo; Quaife, Tristan; Clifford, Debbie

2016-04-01

MELODIES is an FP7 funded project to develop innovative and sustainable services, based upon Open Data, for users in research, government, industry and the general public in a broad range of societal and environmental benefit areas. Understanding and quantifying land surface changes is necessary for estimating greenhouse gas and ammonia emissions, and for meeting air quality limits and targets. More sophisticated inventories methodologies for at least key emission source are needed due to policy-driven air quality directives. Quantifying land cover changes on an annual basis requires greater spatial and temporal disaggregation of input data. The main aim of this study is to develop a methodology for using Earth Observations (EO) to identify annual land surface changes that will improve emissions inventories from agriculture and land use/land use change and forestry (LULUCF) in the UK. First goal is to find the best sets of input features that describe accurately the surface dynamics. In order to identify annual and inter-annual land surface changes, a times series of surface reflectance was used to capture seasonal variability. Daily surface reflectance images from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution were used to invert a Bidirectional Reflectance Distribution Function (BRDF) model to create the seamless time series. Given the limited number of cloud-free observations, a BRDF climatology was used to constrain the model inversion and where no high-scientific quality observations were available at all, as a gap filler. The Land Cover Map 2007 (LC2007) produced by the Centre for Ecology & Hydrology (CEH) was used for training and testing purposes. A land cover product was created for 2003 to 2015 and a bayesian approach was created to identified land cover changes. We will present the results of the time series development and the first exercises when creating the land cover and land cover changes products.

Mekong Land Cover Dasboard: Regional Land Cover Mointoring Systems

Science.gov (United States)

Saah, D. S.; Towashiraporn, P.; Aekakkararungroj, A.; Phongsapan, K.; Triepke, J.; Maus, P.; Tenneson, K.; Cutter, P. G.; Ganz, D.; Anderson, E.

2016-12-01

SERVIR-Mekong, a USAID-NASA partnership, helps decision makers in the Lower Mekong Region utilize GIS and Remote Sensing information to inform climate related activities. In 2015, SERVIR-Mekong conducted a geospatial needs assessment for the Lower Mekong countries which included individual country consultations. The team found that many countries were dependent on land cover and land use maps for land resource planning, quantifying ecosystem services, including resilience to climate change, biodiversity conservation, and other critical social issues. Many of the Lower Mekong countries have developed national scale land cover maps derived in part from remote sensing products and geospatial technologies. However, updates are infrequent and classification systems do not always meet the needs of key user groups. In addition, data products stop at political boundaries and are often not accessible making the data unusable across country boundaries and with resource management partners. Many of these countries rely on global land cover products to fill the gaps of their national efforts, compromising consistency between data and policies. These gaps in national efforts can be filled by a flexible regional land cover monitoring system that is co-developed by regional partners with the specific intention of meeting national transboundary needs, for example including consistent forest definitions in transboundary watersheds. Based on these facts, key regional stakeholders identified a need for a land cover monitoring system that will produce frequent, high quality land cover maps using a consistent regional classification scheme that is compatible with national country needs. SERVIR-Mekong is currently developing a solution that leverages recent developments in remote sensing science and technology, such as Google Earth Engine (GEE), and working together with production partners to develop a system that will use a common set of input data sources to generate high

Moss and lichen cover mapping at local and regional scales in the boreal forest ecosystem of central Canada

Science.gov (United States)

Rapalee, G.; Steyaert, L.T.; Hall, F.G.

2001-01-01

Mosses and lichens are important components of boreal landscapes [Vitt et al., 1994; Bubier et al., 1997]. They affect plant productivity and belowground carbon sequestration and alter the surface runoff and energy balance. We report the use of multiresolution satellite data to map moss and lichens over the BOREAS region at a 10 m, 30 m, and 1 km scales. Our moss and lichen classification at the 10 m scale is based on ground observations of associations among soil drainage classes, overstory composition, and cover type among four broad classes of ground cover (feather, sphagnum, and brown mosses and lichens). For our 30 m map, we used field observations of ground cover-overstory associations to map mosses and lichens in the BOREAS southern study area (SSA). To scale up to a 1 km (AVHRR) moss map of the BOREAS region, we used the TM SSA mosaics plus regional field data to identify AVHRR overstory-ground cover associations. We found that: 1) ground cover, overstory composition and density are highly correlated, permitting inference of moss and lichen cover from satellite-based land cover classifications; 2) our 1 km moss map reveals that mosses dominate the boreal landscape of central Canada, thereby a significant factor for water, energy, and carbon modeling; 3) TM and AVHRR moss cover maps are comparable; 4) satellite data resolution is important; particularly in detecting the smaller wetland features, lakes, and upland jack pine sites; and 5) distinct regional patterns of moss and lichen cover correspond to latitudinal and elevational gradients. Copyright 2001 by the American Geophysical Union.

Forest Aboveground Biomass Mapping and Canopy Cover Estimation from Simulated ICESat-2 Data

Science.gov (United States)

Narine, L.; Popescu, S. C.; Neuenschwander, A. L.

2017-12-01

The assessment of forest aboveground biomass (AGB) can contribute to reducing uncertainties associated with the amount and distribution of terrestrial carbon. With a planned launch date of July 2018, the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) will provide data which will offer the possibility of mapping AGB at global scales. In this study, we develop approaches for utilizing vegetation data that will be delivered in ICESat-2's land-vegetation along track product (ATL08). The specific objectives are to: (1) simulate ICESat-2 photon-counting lidar (PCL) data using airborne lidar data, (2) utilize simulated PCL data to estimate forest canopy cover and AGB and, (3) upscale AGB predictions to create a wall-to-wall AGB map at 30-m spatial resolution. Using existing airborne lidar data for Sam Houston National Forest (SHNF) located in southeastern Texas and known ICESat-2 beam locations, PCL data are simulated from discrete return lidar points. We use multiple linear regression models to relate simulated PCL metrics for 100 m segments along the ICESat-2 ground tracks to AGB from a biomass map developed using airborne lidar data and canopy cover calculated from the same. Random Forest is then used to create an AGB map from predicted estimates and explanatory data consisting of spectral metrics derived from Landsat TM imagery and land cover data from the National Land Cover Database (NLCD). Findings from this study will demonstrate how data that will be acquired by ICESat-2 can be used to estimate forest structure and characterize the spatial distribution of AGB.

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

Land cover mapping of Greater Mesoamerica using MODIS data

Science.gov (United States)

Giri, Chandra; Jenkins, Clinton N.

2005-01-01

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: (i) qualitative assessment provided good insight in identifying and correcting gross errors; (ii) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest (r2 = 0.88), shrubland (r2 = 0.75), and cropland (r2 = 0.97) but weak positive association for grassland (r2 = 0.26); and (iii) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Mapping of Landscape Cover Using Remote Sensing and GIS in ...

African Journals Online (AJOL)

Humankind to fulfill its needs has put natural resources of the earth to a severe pressure. The rate of degradation and depletion of earth resources has accelerated tremendously in view of the overincreasing demographic pressure. Therefore, mapping of landscape cover types to evaluate it has been a great concern for ...

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

Directory of Open Access Journals (Sweden)

Ramesh K. Ningthoujam

2016-07-01

Full Text Available Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41 and 20 m (63% overall accuracy, κ = 0.27. The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its changes.

Snow Cover Maps from MODIS Images at 250 m Resolution, Part 2: Validation

Directory of Open Access Journals (Sweden)

Marc Zebisch

2013-03-01

Full Text Available The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS satellite images at 250 m resolution is validated using snow cover maps (SCA based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA MODIS snow products (MOD10 and MYD10. It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition.

Satellite Based Probabilistic Snow Cover Extent Mapping (SCE) at Hydro-Québec

Science.gov (United States)

Teasdale, Mylène; De Sève, Danielle; Angers, Jean-François; Perreault, Luc

2016-04-01

Over 40% of Canada's water resources are in Quebec and Hydro-Quebec has developed potential to become one of the largest producers of hydroelectricity in the world, with a total installed capacity of 36,643 MW. The Hydro-Québec fleet park includes 27 large reservoirs with a combined storage capacity of 176 TWh, and 668 dams and 98 controls. Thus, over 98% of all electricity used to supply the domestic market comes from water resources and the excess output is sold on the wholesale markets. In this perspective the efficient management of water resources is needed and it is based primarily on a good river flow estimation including appropriate hydrological data. Snow on ground is one of the significant variables representing 30% to 40% of its annual energy reserve. More specifically, information on snow cover extent (SCE) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in northern regions since the snowmelt provides the water that fills the reservoirs and is subsequently used for hydropower generation. For several years Hydro Quebec's research institute ( IREQ) developed several algorithms to map SCE and SWE. So far all the methods were deterministic. However, given the need to maximize the efficient use of all resources while ensuring reliability, the electrical systems must now be managed taking into account all risks. Since snow cover estimation is based on limited spatial information, it is important to quantify and handle its uncertainty in the hydrological forecasting system. This paper presents the first results of a probabilistic algorithm for mapping SCE by combining Bayesian mixture of probability distributions and multiple logistic regression models applied to passive microwave data. This approach allows assigning for each grid point, probabilities to the set of the mutually exclusive discrete outcomes: "snow" and "no snow". Its performance was evaluated using the Brier score since it is particularly appropriate to

Hybrid image classification technique for land-cover mapping in the Arctic tundra, North Slope, Alaska

Science.gov (United States)

Chaudhuri, Debasish

Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was

Using IKONOS and Aerial Videography to Validate Landsat Land Cover Maps of Central African Tropical Rain Forests

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Lin, T.; Laporte, N. T.

2003-12-01

Compared to the traditional validation methods, aerial videography is a relatively inexpensive and time-efficient approach to collect "field" data for validating satellite-derived land cover map over large areas. In particular, this approach is valuable in remote and inaccessible locations. In the Sangha Tri-National Park region of Central Africa, where road access is limited to industrial logging sites, we are using IKONOS imagery and aerial videography to assess the accuracy of Landsat-derived land cover maps. As part of a NASA Land Cover Land Use Change project (INFORMS) and in collaboration with the Wildlife Conservation Society in the Republic of Congo, over 1500km of aerial video transects were collected in the Spring of 2001. The use of MediaMapper software combined with a VMS 200 video mapping system enabled the collection of aerial transects to be registered with geographic locations from a Geographic Positioning System. Video frame were extracted, visually interpreted, and compared to land cover types mapped by Landsat. We addressed the limitations of accuracy assessment using aerial-base data and its potential for improving vegetation mapping in tropical rain forests. The results of the videography and IKONOS image analysis demonstrate the utility of very high resolution imagery for map validation and forest resource assessment.

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

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Adrian Fisher

2016-06-01

Full Text Available Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l’Observation de la Terre 5 (SPOT5 High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW, an area of over 800,000 km2. Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008–2011 for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC, a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (±0.51% standard error, while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r2 = 0.90 with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and

Stratifying FIA Ground Plots Using A 3-Year Old MRLC Forest Cover Map and Current TM Derived Variables Selected By "Decision Tree" Classification

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Michael Hoppus; Stan Arner; Andrew Lister

2001-01-01

A reduction in variance for estimates of forest area and volume in the state of Connecticut was accomplished by stratifying FIA ground plots using raw, transformed and classified Landsat Thematic Mapper (TM) imagery. A US Geological Survey (USGS) Multi-Resolution Landscape Characterization (MRLC) vegetation cover map for Connecticut was used to produce a forest/non-...

Urban Land Cover Mapping Accuracy Assessment - A Cost-benefit Analysis Approach

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Xiao, T.

2012-12-01

One of the most important components in urban land cover mapping is mapping accuracy assessment. Many statistical models have been developed to help design simple schemes based on both accuracy and confidence levels. It is intuitive that an increased number of samples increases the accuracy as well as the cost of an assessment. Understanding cost and sampling size is crucial in implementing efficient and effective of field data collection. Few studies have included a cost calculation component as part of the assessment. In this study, a cost-benefit sampling analysis model was created by combining sample size design and sampling cost calculation. The sampling cost included transportation cost, field data collection cost, and laboratory data analysis cost. Simple Random Sampling (SRS) and Modified Systematic Sampling (MSS) methods were used to design sample locations and to extract land cover data in ArcGIS. High resolution land cover data layers of Denver, CO and Sacramento, CA, street networks, and parcel GIS data layers were used in this study to test and verify the model. The relationship between the cost and accuracy was used to determine the effectiveness of each sample method. The results of this study can be applied to other environmental studies that require spatial sampling.

An operational methodology for riparian land cover fine scale regional mapping for the study of landscape influence on river ecological status

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Tormos, T.; Kosuth, P.; Souchon, Y.; Villeneuve, B.; Durrieu, S.; Chandesris, A.

2010-12-01

Preservation and restoration of river ecosystems require an improved understanding of the mechanisms through which they are influenced by landscape at multiple spatial scales and particularly at river corridor scale considering the role of riparian vegetation for regulating and protecting river ecological status and the relevance of this specific area for implementing efficient and realistic strategies. Assessing correctly this influence over large river networks involves accurate broad scale (i.e. at least regional) information on Land Cover within Riparian Areas (LCRA). As the structure of land cover along rivers is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose we developed a generic multi-scale Object Based Image Analysis (OBIA) scheme able to produce LCRA maps in different geographic context by exploiting information available from very high spatial resolution imagery (satellite or airborne) and/or metric to decametric spatial thematic data on a given study zone thanks to fuzzy expert knowledge classification rules. A first experimentation was carried out on the Herault river watershed (southern of France), a 2650 square kilometers basin that presents a contrasted landscape (different ecoregions) and a total stream length of 1150 Km, using high and very high multispectral remotely-sensed images (10m Spot5 multispectral images and 0.5m aerial photography) and existing spatial thematic data. Application of the OBIA scheme produced a detailed (22 classes) LCRA map with an overall accuracy of 89% and a Kappa index of 83% according to a land cover pressures typology (six categories). A second experimentation (using the same data sources) was carried out on a larger test zone, a part of the Normandy river network (25 000 square kilometers basin; 6000 km long river network; 155 ecological stations). This second work aimed at elaborating a robust statistical

Highlighting continued uncertainty in global land cover maps for the user community

International Nuclear Information System (INIS)

Fritz, Steffen; See, Linda; McCallum, Ian; Schill, Christian; Obersteiner, Michael; Van der Velde, Marijn; Boettcher, Hannes; Havlík, Petr; Achard, Frédéric

2011-01-01

In the last 10 years a number of new global datasets have been created and new, more sophisticated algorithms have been designed to classify land cover. GlobCover and MODIS v.5 are the most recent global land cover products available, where GlobCover (300 m) has the finest spatial resolution of other comparable products such as MODIS v.5 (500 m) and GLC-2000 (1 km). This letter shows that the thematic accuracy in the cropland domain has decreased when comparing these two latest products. This disagreement is also evident spatially when examining maps of cropland and forest disagreement between GLC-2000, MODIS and GlobCover. The analysis highlights the continued uncertainty surrounding these products, with a combined forest and cropland disagreement of 893 Mha (GlobCover versus MODIS v.5). This letter suggests that data sharing efforts and the provision of more in situ data for training, calibration and validation are very important conditions for improving future global land cover products.

A new strategy for snow-cover mapping using remote sensing data and ensemble based systems techniques

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Roberge, S.; Chokmani, K.; De Sève, D.

2012-04-01

The snow cover plays an important role in the hydrological cycle of Quebec (Eastern Canada). Consequently, evaluating its spatial extent interests the authorities responsible for the management of water resources, especially hydropower companies. The main objective of this study is the development of a snow-cover mapping strategy using remote sensing data and ensemble based systems techniques. Planned to be tested in a near real-time operational mode, this snow-cover mapping strategy has the advantage to provide the probability of a pixel to be snow covered and its uncertainty. Ensemble systems are made of two key components. First, a method is needed to build an ensemble of classifiers that is diverse as much as possible. Second, an approach is required to combine the outputs of individual classifiers that make up the ensemble in such a way that correct decisions are amplified, and incorrect ones are cancelled out. In this study, we demonstrate the potential of ensemble systems to snow-cover mapping using remote sensing data. The chosen classifier is a sequential thresholds algorithm using NOAA-AVHRR data adapted to conditions over Eastern Canada. Its special feature is the use of a combination of six sequential thresholds varying according to the day in the winter season. Two versions of the snow-cover mapping algorithm have been developed: one is specific for autumn (from October 1st to December 31st) and the other for spring (from March 16th to May 31st). In order to build the ensemble based system, different versions of the algorithm are created by varying randomly its parameters. One hundred of the versions are included in the ensemble. The probability of a pixel to be snow, no-snow or cloud covered corresponds to the amount of votes the pixel has been classified as such by all classifiers. The overall performance of ensemble based mapping is compared to the overall performance of the chosen classifier, and also with ground observations at meteorological

Object-Based Mapping of the Circumpolar Taiga-Tundra Ecotone with MODIS Tree Cover

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Ranson, K. J.; Montesano, P. M.; Nelson, R.

2011-01-01

The circumpolar taiga tundra ecotone was delineated using an image-segmentation-based mapping approach with multi-annual MODIS Vegetation Continuous Fields (VCF) tree cover data. Circumpolar tree canopy cover (TCC) throughout the ecotone was derived by averaging MODIS VCF data from 2000 to 2005 and adjusting the averaged values using linear equations relating MODIS TCC to Quickbird-derived tree cover estimates. The adjustment helped mitigate VCF's overestimation of tree cover in lightly forested regions. An image segmentation procedure was used to group pixels representing similar tree cover into polygonal features (segmentation objects) that form the map of the transition zone. Each polygon represents an area much larger than the 500 m MODIS pixel and characterizes the patterns of sparse forest patches on a regional scale. Those polygons near the boreal/tundra interface with either (1) mean adjusted TCC values from5 to 20%, or (2) mean adjusted TCC values greater than 5% but with a standard deviation less than 5% were used to identify the ecotone. Comparisons of the adjusted average tree cover data were made with (1) two existing tree line definitions aggregated for each 1 degree longitudinal interval in North America and Eurasia, (2) Landsat-derived Canadian proportion of forest cover for Canada, and (3) with canopy cover estimates extracted from airborne profiling lidar data that transected 1238 of the TCC polygons. The adjusted TCC from MODIS VCF shows, on average, less than 12% TCC for all but one regional zone at the intersection with independently delineated tree lines. Adjusted values track closely with Canadian proportion of forest cover data in areas of low tree cover. A comparison of the 1238 TCC polygons with profiling lidar measurements yielded an overall accuracy of 67.7%.

Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images

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Hassan Mohamed

2018-05-01

Full Text Available Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV ensembles for Support Vector Machines (SVM, K-Nearest Neighbor (K-NN, and Bagging (BAG classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy.

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

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Crawford, Christopher J.; Manson, Steven M.; Bauer, Marvin E.; Hall, Dorothy K.

2013-01-01

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent.

Multi-Sensor Approach to Mapping Snow Cover Using Data From NASA's EOS Aqua and Terra Spacecraft

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Armstrong, R. L.; Brodzik, M. J.

2003-12-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. For the period 1978 to 2002, we have shown earlier that both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are, depending on season, less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Snow mapping using optical data is based on the magnitude of the surface reflectance while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Our previous work has defined the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and it is clear that a blended product is optimal. We present a multi-sensor approach to snow mapping based both on historical data as well as data from current NASA EOS sensors. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets. An example of validation at the brightness temperature level is provided through the comparison of AMSR-E with data from the well-calibrated heritage SSM/I sensor over a large homogeneous snow-covered surface (Dome C, Antarctica). Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current blended product is being developed in the 25 km EASE-Grid while the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km

A multi-temporal analysis approach for land cover mapping in support of nuclear incident response

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Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.

2012-06-01

Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.

Land Cover Characterization and Mapping of South America for the Year 2010 Using Landsat 30 m Satellite Data

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Chandra Giri

2014-10-01

Full Text Available Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

An Automated Algorithm for Producing Land Cover Information from Landsat Surface Reflectance Data Acquired Between 1984 and Present

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Rover, J.; Goldhaber, M. B.; Holen, C.; Dittmeier, R.; Wika, S.; Steinwand, D.; Dahal, D.; Tolk, B.; Quenzer, R.; Nelson, K.; Wylie, B. K.; Coan, M.

2015-12-01

Multi-year land cover mapping from remotely sensed data poses challenges. Producing land cover products at spatial and temporal scales required for assessing longer-term trends in land cover change are typically a resource-limited process. A recently developed approach utilizes open source software libraries to automatically generate datasets, decision tree classifications, and data products while requiring minimal user interaction. Users are only required to supply coordinates for an area of interest, land cover from an existing source such as National Land Cover Database and percent slope from a digital terrain model for the same area of interest, two target acquisition year-day windows, and the years of interest between 1984 and present. The algorithm queries the Landsat archive for Landsat data intersecting the area and dates of interest. Cloud-free pixels meeting the user's criteria are mosaicked to create composite images for training the classifiers and applying the classifiers. Stratification of training data is determined by the user and redefined during an iterative process of reviewing classifiers and resulting predictions. The algorithm outputs include yearly land cover raster format data, graphics, and supporting databases for further analysis. Additional analytical tools are also incorporated into the automated land cover system and enable statistical analysis after data are generated. Applications tested include the impact of land cover change and water permanence. For example, land cover conversions in areas where shrubland and grassland were replaced by shale oil pads during hydrofracking of the Bakken Formation were quantified. Analytical analysis of spatial and temporal changes in surface water included identifying wetlands in the Prairie Pothole Region of North Dakota with potential connectivity to ground water, indicating subsurface permeability and geochemistry.

Improving Land Cover Mapping: a Mobile Application Based on ESA Sentinel 2 Imagery

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Melis, M. T.; Dessì, F.; Loddo, P.; La Mantia, C.; Da Pelo, S.; Deflorio, A. M.; Ghiglieri, G.; Hailu, B. T.; Kalegele, K.; Mwasi, B. N.

2018-04-01

The increasing availability of satellite data is a real value for the enhancement of environmental knowledge and land management. Possibilities to integrate different source of geo-data are growing and methodologies to create thematic database are becoming very sophisticated. Moreover, the access to internet services and, in particular, to web mapping services is well developed and spread either between expert users than the citizens. Web map services, like Google Maps or Open Street Maps, give the access to updated optical imagery or topographic maps but information on land cover/use - are not still provided. Therefore, there are many failings in the general utilization -non-specialized users- and access to those maps. This issue is particularly felt where the digital (web) maps could form the basis for land use management as they are more economic and accessible than the paper maps. These conditions are well known in many African countries where, while the internet access is becoming open to all, the local map agencies and their products are not widespread.

Validation of Satellite Snow Cover Maps in North America and Norway

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Hall, Dorothy K.; Solberg, Rune; Riggs, George A.

2002-01-01

Satellite-derived snow maps from NASA's Earth Observing System Moderate Resolution Imaging Spectroradiometer (MODIS) have been produced since February of 2000. The global maps are available daily at 500-m resolution, and at a climate-modeling grid (CMG) resolution of 1/20 deg (approximately 5.6 km). We compared the 8-day composite CMG MODIS-derived global maps from November 1,2001, through March 21,2002, and daily CMG maps from February 26 - March 5,2002, with National Oceanic and Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) 25-km resolution maps for North America. For the Norwegian study area, national snow maps, based on synoptic measurements as well as visual interpretation of AVHRR images, published by the Det Norske Meteorologiske Institutt (Norwegian Meteorological Institute) (MI) maps, as well as Landsat ETM+ images were compared with the MODIS maps. The MODIS-derived maps agreed over most areas with the IMS or MI maps, however, there are important areas of disagreement between the maps, especially when the 8-day composite maps were used. It is concluded that MODIS daily CMG maps should be studied for validation purposes rather than the 8-day composite maps, despite the limitations imposed by cloud obscuration when using the daily maps.

Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015)

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Li, Wei; MacBean, Natasha; Ciais, Philippe; Defourny, Pierre; Lamarche, Céline; Bontemps, Sophie; Houghton, Richard A.; Peng, Shushi

2018-01-01

Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km2 in the year 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013) or Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992-2015 are 1.5 and 0.9 million km2 respectively, resulting in a net forest loss of 0.6 million km2, mainly occurring in South and Central America. The magnitudes of gross changes in forest, cropland and grassland PFTs in the ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA and after 2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long-term time series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics

Advancing the quantification of humid tropical forest cover loss with multi-resolution optical remote sensing data: Sampling & wall-to-wall mapping

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Broich, Mark

Humid tropical forest cover loss is threatening the sustainability of ecosystem goods and services as vast forest areas are rapidly cleared for industrial scale agriculture and tree plantations. Despite the importance of humid tropical forest in the provision of ecosystem services and economic development opportunities, the spatial and temporal distribution of forest cover loss across large areas is not well quantified. Here I improve the quantification of humid tropical forest cover loss using two remote sensing-based methods: sampling and wall-to-wall mapping. In all of the presented studies, the integration of coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data enable advances in quantifying forest cover loss in the humid tropics. Imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used as the source of coarse spatial resolution, high temporal resolution data and imagery from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor are used as the source of moderate spatial, low temporal resolution data. In a first study, I compare the precision of different sampling designs for the Brazilian Amazon using the annual deforestation maps derived by the Brazilian Space Agency for reference. I show that sampling designs can provide reliable deforestation estimates; furthermore, sampling designs guided by MODIS data can provide more efficient estimates than the systematic design used for the United Nations Food and Agricultural Organization Forest Resource Assessment 2010. Sampling approaches, such as the one demonstrated, are viable in regions where data limitations, such as cloud contamination, limit exhaustive mapping methods. Cloud-contaminated regions experiencing high rates of change include Insular Southeast Asia, specifically Indonesia and Malaysia. Due to persistent cloud cover, forest cover loss in Indonesia has only been mapped at a 5-10 year interval using photo interpretation of single

Utilizing a Multi-Source Forest Inventory Technique, MODIS Data and Landsat TM Images in the Production of Forest Cover and Volume Maps for the Terai Physiographic Zone in Nepal

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Kalle Eerikäinen

2012-12-01

Full Text Available An approach based on the nearest neighbors techniques is presented for producing thematic maps of forest cover (forest/non-forest and total stand volume for the Terai region in southern Nepal. To create the forest cover map, we used a combination of Landsat TM satellite data and visual interpretation data, i.e., a sample grid of visual interpretation plots for which we obtained the land use classification according to the FAO standard. These visual interpretation plots together with the field plots for volume mapping originate from an operative forest inventory project, i.e., the Forest Resource Assessment of Nepal (FRA Nepal project. The field plots were also used in checking the classification accuracy. MODIS satellite data were used as a reference in a local correction approach conducted for the relative calibration of Landsat TM images. This study applied a non-parametric k-nearest neighbor technique (k-NN to the forest cover and volume mapping. A tree height prediction approach based on a nonlinear, mixed-effects (NLME modeling procedure is presented in the Appendix. The MODIS image data performed well as reference data for the calibration approach applied to make the Landsat image mosaic. The agreement between the forest cover map and the field observed values of forest cover was substantial in Western Terai (KHAT 0.745 and strong in Eastern Terai (KHAT 0.825. The forest cover and volume maps that were estimated using the k-NN method and the inventory data from the FRA Nepal project are already appropriate and valuable data for research purposes and for the planning of forthcoming forest inventories. Adaptation of the methods and techniques was carried out using Open Source software tools.

Annual land cover change mapping using MODIS time series to improve emissions inventories.

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López Saldaña, G.; Quaife, T. L.; Clifford, D.

2014-12-01

Understanding and quantifying land surface changes is necessary for estimating greenhouse gas and ammonia emissions, and for meeting air quality limits and targets. More sophisticated inventories methodologies for at least key emission source are needed due to policy-driven air quality directives. Quantifying land cover changes on an annual basis requires greater spatial and temporal disaggregation of input data. The main aim of this study is to develop a methodology for using Earth Observations (EO) to identify annual land surface changes that will improve emissions inventories from agriculture and land use/land use change and forestry (LULUCF) in the UK. First goal is to find the best sets of input features that describe accurately the surface dynamics. In order to identify annual and inter-annual land surface changes, a times series of surface reflectance was used to capture seasonal variability. Daily surface reflectance images from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution were used to invert a Bidirectional Reflectance Distribution Function (BRDF) model to create the seamless time series. Given the limited number of cloud-free observations, a BRDF climatology was used to constrain the model inversion and where no high-scientific quality observations were available at all, as a gap filler. The Land Cover Map 2007 (LC2007) produced by the Centre for Ecology & Hydrology (CEH) was used for training and testing purposes. A prototype land cover product was created for 2006 to 2008. Several machine learning classifiers were tested as well as different sets of input features going from the BRDF parameters to spectral Albedo. We will present the results of the time series development and the first exercises when creating the prototype land cover product.

An Operational Framework for Land Cover Classification in the Context of REDD+ Mechanisms. A Case Study from Costa Rica

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Alfredo Fernández-Landa

2016-07-01

Full Text Available REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.

the implications of land use/cover dynamics on resources

African Journals Online (AJOL)

2017-12-04

Dec 4, 2017 ... Land use maps were produced using the GIS software packages of ... Keywords: Land use/cover, Dynamics, Remote Sensing Techniques, Geographic Information System, .... sporadic floods and landslides in Bambui which.

MODIS snow cover mapping accuracy in a small mountain catchment – comparison between open and forest sites

Directory of Open Access Journals (Sweden)

G. Blöschl

2012-07-01

Full Text Available Numerous global and regional validation studies have examined MODIS snow mapping accuracy by using measurements at climate stations, which are mainly at open sites. MODIS accuracy in alpine and forested regions is, however, still not well understood. The main objective of this study is to evaluate MODIS (MOD10A1 and MYD10A1 snow cover products in a small experimental catchment by using extensive snow course measurements at open and forest sites. The MODIS accuracy is tested in the Jalovecky creek catchment (northern Slovakia in the period 2000–2011. The results show that the combined Terra and Aqua images enable snow mapping at an overall accuracy of 91.5%. The accuracies at forested, open and mixed land uses at the Červenec sites are 92.7%, 98.3% and 81.8%, respectively. The use of a 2-day temporal filter enables a significant reduction in the number of days with cloud coverage and an increase in overall snow mapping accuracy. In total, the 2-day temporal filter decreases the number of cloudy days from 61% to 26% and increases the snow mapping accuracy to 94%. The results indicate three possible factors leading to misclassification of snow as land: patchy snow cover, limited MODIS geolocation accuracy and mapping algorithm errors. Out of a total of 27 misclassification cases, patchy snow cover, geolocation issues and mapping errors occur in 12, 12 and 3 cases, respectively.

Analysis for Producing a Facsimile of the Cadastral Map of Varaždin

Directory of Open Access Journals (Sweden)

Mirko Husak

2012-12-01

Full Text Available This paper investigates and suggests methods for producing a facsimile of the 1 860 cadastral map of Varaždin. The methods used to produce the map, the coordinate systems, map contents, usage, maintenance and damage are described. Three samples from the map that display the elements of damage noted were researched, and the possibility of replacing damaged sections with undamaged sections using digital methods investigated. The sources available were the original cadastral colour map of Varaždin, along with the line art cadastral map and field cadastral sketches. The original colour and line art maps were scanned using the DeSkan Express scanning system for large formats. A flatbed UMAX Mirage II A3 scanner was used for scanning the field cadastral map. For the research image-processing, Adobe Photoshop CE 7.0 software was used, although it was primarily designed for processing photographs. The colour separation method was rejected from the start, since the map was made by hand. The paper discusses the possibility of copying and inserting missing parts from additional map sources, changing the colour of the paper to white or another colour, removing the map content added in red ink and lead pencil, and so on. The discussion is based on actual examples from the digital image. The conclusion suggests image-processing methods for achieving optimal results in producing a facsimile of the Varaždin cadastral map.Keywords: facsimile; cadastral map; map content; scanning; digital image processing

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

Science.gov (United States)

Ganguly, Sangram; Kalia, Subodh; Li, Shuang; Michaelis, Andrew; Nemani, Ramakrishna R.; Saatchi, Sassan A

2017-01-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

Science.gov (United States)

Ganguly, S.; Kalia, S.; Li, S.; Michaelis, A.; Nemani, R. R.; Saatchi, S.

2017-12-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above gound biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition/ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree/non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial/satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

Detailed forest formation mapping in the land cover map series for the Caribbean islands

Science.gov (United States)

Helmer, E. H.; Schill, S.; Pedreros, D. H.; Tieszen, L. L.; Kennaway, T.; Cushing, M.; Ruzycki, T.

2006-12-01

Forest formation and land cover maps for several Caribbean islands were developed from Landsat ETM+ imagery as part of a multi-organizational project. The spatially explicit data on forest formation types will permit more refined estimates of some forest attributes. The woody vegetation classification scheme relates closely to that of Areces-Malea et al. (1), who classify Caribbean vegetation according to standards of the US Federal Geographic Data Committee (FGDC, 1997), with modifications similar to those in Helmer et al. (2). For several of the islands, we developed image mosaics that filled cloudy parts of scenes with data from other scene dates after using regression tree normalization (3). The regression tree procedure permitted us to develop mosaics for wet and drought seasons for a few of the islands. The resulting multiseason imagery facilitated separation between classes such as seasonal evergreen forest, semi-deciduous forest (including semi-evergreen forest), and drought deciduous forest or woodland formations. We used decision tree classification methods to classify the Landsat image mosaics to detailed forest formations and land cover for Puerto Rico (4), St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines and Grenada. The decision trees classified a stack of raster layers for each mapping area that included the Landsat image bands and various ancillary raster data layers. For Puerto Rico, for example, the ancillary data included climate parameters (5). For some islands, the ancillary data included topographic derivatives such as aspect, slope and slope position, SRTM (6) or other topographic data. Mapping forest formations with decision tree classifiers, ancillary geospatial data, and cloud-free image mosaics, accurately distinguished spectrally similar forest formations, without the aid of ecological zone maps, on the islands where the approach was used. The approach resulted in maps of forest formations with comparable or better detail

[Application of biotope mapping model integrated with vegetation cover continuity attributes in urban biodiversity conservation].

Science.gov (United States)

Gao, Tian; Qiu, Ling; Chen, Cun-gen

2010-09-01

Based on the biotope classification system with vegetation structure as the framework, a modified biotope mapping model integrated with vegetation cover continuity attributes was developed, and applied to the study of the greenbelts in Helsingborg in southern Sweden. An evaluation of the vegetation cover continuity in the greenbelts was carried out by the comparisons of the vascular plant species richness in long- and short-continuity forests, based on the identification of woodland continuity by using ancient woodland indicator species (AWIS). In the test greenbelts, long-continuity woodlands had more AWIS. Among the forests where the dominant trees were more than 30-year-old, the long-continuity ones had a higher biodiversity of vascular plants, compared with the short-continuity ones with the similar vegetation structure. The modified biotope mapping model integrated with the continuity features of vegetation cover could be an important tool in investigating urban biodiversity, and provide corresponding strategies for future urban biodiversity conservation.

Dominant Benthic Structure and Biological Cover Habitat Maps for West Maui and West Hawaii

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Benthic habitat maps depict dominant substrate type and biological cover in depths between 0 and ~150 m for two priority sites in the Main Hawaiian Islands; the NOAA...

Gravity and isostatic anomaly maps of Greece produced

Science.gov (United States)

Lagios, E.; Chailas, S.; Hipkin, R. G.

A gravity anomaly map of Greece was first compiled in the early 1970s [Makris and Stavrou, 1984] from all available gravity data collected by different Hellenic institutions. However, to compose this map the data had to be smoothed to the point that many of the smaller-wavelength gravity anomalies were lost. New work begun in 1987 has resulted in the publication of an updated map [Lagios et al., 1994] and an isostatic anomaly map derived from it.The gravity data cover the area between east longitudes 19° and 27° and north latitudes 32° and 42°, organized in files of 100-km squares and grouped in 10-km squares using UTM zone 34 coordinates. Most of the data on land come from the gravity observations of Makris and Stavrou [1984] with additional data from the Institute of Geology and Mining Exploration, the Public Oil Corporation of Greece, and Athens University. These data were checked using techniques similar to those used in compiling the gravity anomaly map of the United States, but the horizontal gradient was used as a check rather than the gravity difference. Marine data were digitized from the maps of Morelli et al. [1975a, 1975b]. All gravity anomaly values are referred to the IGSN-71 system, reduced with the standard Bouger density of 2.67 Mg/m3. We estimate the errors of the anomalies in the continental part of Greece to be ±0.9 mGal; this is expected to be smaller over fairly flat regions. For stations whose height has been determined by leveling, the error is only ±0.3 mGal. For the marine areas, the errors are about ±5 mGal [Morelli, 1990].

Operational monitoring of land-cover change using multitemporal remote sensing data

Science.gov (United States)

Rogan, John

2005-11-01

Land-cover change, manifested as either land-cover modification and/or conversion, can occur at all spatial scales, and changes at local scales can have profound, cumulative impacts at broader scales. The implication of operational land-cover monitoring is that researchers have access to a continuous stream of remote sensing data, with the long term goal of providing for consistent and repetitive mapping. Effective large area monitoring of land-cover (i.e., >1000 km2) can only be accomplished by using remotely sensed images as an indirect data source in land-cover change mapping and as a source for land-cover change model projections. Large area monitoring programs face several challenges: (1) choice of appropriate classification scheme/map legend over large, topographically and phenologically diverse areas; (2) issues concerning data consistency and map accuracy (i.e., calibration and validation); (3) very large data volumes; (4) time consuming data processing and interpretation. Therefore, this dissertation research broadly addresses these challenges in the context of examining state-of-the-art image pre-processing, spectral enhancement, classification, and accuracy assessment techniques to assist the California Land-cover Mapping and Monitoring Program (LCMMP). The results of this dissertation revealed that spatially varying haze can be effectively corrected from Landsat data for the purposes of change detection. The Multitemporal Spectral Mixture Analysis (MSMA) spectral enhancement technique produced more accurate land-cover maps than those derived from the Multitemporal Kauth Thomas (MKT) transformation in northern and southern California study areas. A comparison of machine learning classifiers showed that Fuzzy ARTMAP outperformed two classification tree algorithms, based on map accuracy and algorithm robustness. Variation in spatial data error (positional and thematic) was explored in relation to environmental variables using geostatistical interpolation

High spatial resolution mapping of land cover types in a priority area for conservation in the Brazilian savanna

Science.gov (United States)

Ribeiro, F.; Roberts, D. A.; Hess, L. L.; Davis, F. W.; Caylor, K. K.; Nackoney, J.; Antunes Daldegan, G.

2017-12-01

Savannas are heterogeneous landscapes consisting of highly mixed land cover types that lack clear distinct boundaries. The Brazilian Cerrado is a Neotropical savanna considered a biodiversity hotspot for conservation due to its biodiversity richness and rapid transformation of its landscape by crop and pasture activities. The Cerrado is one of the most threatened Brazilian biomes and only 2.2% of its original extent is strictly protected. Accurate mapping and monitoring of its ecosystems and adjacent land use are important to select areas for conservation and to improve our understanding of the dynamics in this biome. Land cover mapping of savannas is difficult due to spectral similarity between land cover types resulting from similar vegetation structure, floristically similar components, generalization of land cover classes, and heterogeneity usually expressed as small patch sizes within the natural landscape. These factors are the major contributor to misclassification and low map accuracies among remote sensing studies in savannas. Specific challenges to map the Cerrado's land cover types are related to the spectral similarity between classes of land use and natural vegetation, such as natural grassland vs. cultivated pasture, and forest ecosystem vs. crops. This study seeks to classify and evaluate the land cover patterns across an area ranked as having extremely high priority for future conservation in the Cerrado. The main objective of this study is to identify the representativeness of each vegetation type across the landscape using high to moderate spatial resolution imagery using an automated scheme. A combination of pixel-based and object-based approaches were tested using RapidEye 3A imagery (5m spatial resolution) to classify the Cerrado's major land cover types. The random forest classifier was used to map the major ecosystems present across the area, and demonstrated to have an effective result with 68% of overall accuracy. Post

Sampling and Mapping Soil Erosion Cover Factor for Fort Richardson, Alaska. Integrating Stratification and an Up-Scaling Method

National Research Council Canada - National Science Library

Wang, Guangxing; Gertner, George; Anderson, Alan B; Howard, Heidi

2006-01-01

When a ground and vegetation cover factor related to soil erosion is mapped with the aid of remotely sensed data, a cost-efficient sample design to collect ground data and obtain an accurate map is required...

Land cover mapping with emphasis to burnt area delineation using co-orbital ALI and Landsat TM imagery

Science.gov (United States)

Petropoulos, George P.; Kontoes, Charalambos C.; Keramitsoglou, Iphigenia

2012-08-01

In this study, the potential of EO-1 Advanced Land Imager (ALI) radiometer for land cover and especially burnt area mapping from a single image analysis is investigated. Co-orbital imagery from the Landsat Thematic Mapper (TM) was also utilised for comparison purposes. Both images were acquired shortly after the suppression of a fire occurred during the summer of 2009 North-East of Athens, the capital of Greece. The Maximum Likelihood (ML), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) classifiers were parameterised and subsequently applied to the acquired satellite datasets. Evaluation of the land use/cover mapping accuracy was based on the error matrix statistics. Also, the McNemar test was used to evaluate the statistical significance of the differences between the approaches tested. Derived burnt area estimates were validated against the operationally deployed Services and Applications For Emergency Response (SAFER) Burnt Scar Mapping service. All classifiers applied to either ALI or TM imagery proved flexible enough to map land cover and also to extract the burnt area from other land surface types. The highest total classification accuracy and burnt area detection capability was returned from the application of SVMs to ALI data. This was due to the SVMs ability to identify an optimal separating hyperplane for best classes' separation that was able to better utilise ALI's advanced technological characteristics in comparison to those of TM sensor. This study is to our knowledge the first of its kind, effectively demonstrating the benefits of the combined application of SVMs to ALI data further implying that ALI technology may prove highly valuable in mapping burnt areas and land use/cover if it is incorporated into the development of Landsat 8 mission, planned to be launched in the coming years.

Snow Cover Mapping at the Continental to Global Scale Using Combined Visible and Passive Microwave Satellite Data

Science.gov (United States)

Armstrong, R. L.; Brodzik, M.; Savoie, M. H.

2007-12-01

Over the past several decades both visible and passive microwave satellite data have been utilized for snow mapping at the continental to global scale. Snow mapping using visible data has been based primarily on the magnitude of the surface reflectance, and in more recent cases on specific spectral signatures, while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. We describe the respective problems as well as the advantages and disadvantages of these two types of satellite data for snow cover mapping and demonstrate how a multi-sensor approach is optimal. For the period 1978 to present we combine data from the NOAA weekly snow charts with snow cover derived from the SMMR and SSM/I brightness temperature data. For the period since 2002 we blend NASA EOS MODIS and AMSR-E data sets. Our current product incorporates MODIS data from the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) with microwave-derived snow water equivalent (SWE) at 25 km, resulting in a blended product that includes percent snow cover in the larger grid cell whenever the microwave SWE signal is absent. Validation of AMSR-E at the brightness temperature level is provided through the comparison with data from the well-calibrated heritage SSM/I sensor over large homogeneous snow-covered surfaces (e.g. Dome C region, Antarctica). We also describe how the application of the higher frequency microwave channels (85 and 89 GHz)enhances accurate mapping of shallow and intermittent snow cover.

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

Effects of grain-producing cover crops on rice grain yield in Cabo Delgado, Mozambique

Directory of Open Access Journals (Sweden)

Adriano Stephan Nascente

Full Text Available ABSTRACT Besides providing benefits to the environment such as soil protection, release of nutrients, soil moisture maintenance, and weed control, cover crops can increase food production for grain production. The aim of this study was to evaluate the production of biomass and grain cover crops (and its respective effects on soil chemical and physical attributes, yield components, and grain yield of rice in Mozambique. The study was conducted in two sites located in the province of Cabo Delgado, in Mozambique. The experimental design was a randomized block in a 2 × 6 factorial, with four repetitions. Treatments were carried out in two locations (Cuaia and Nambaua with six cover crops: Millet (Pennisetum glaucum L.; namarra bean (Lablab purpureus (L. Sweet, velvet beans (Mucuna pruriens L., oloco beans (Vigna radiata (L. R. Wilczek, cowpea (Vigna unguiculata L., and fallow. Cover crops provided similar changes in chemical and physical properties of the soil. Lablab purpureus, Vigna unguiculata, and Mucuna pruriens produced the highest dry matter biomass. Vigna unguiculada produced the highest amount of grains. Rice grain yields were similar under all cover crops and higher in Cuaia than Nambaua.

Next Generation Snow Cover Mapping: Can Future Hyperspectral Satellite Spectrometer Systems Improve Subpixel Snow-covered Area and Grain Size in the Sierra Nevada?

Science.gov (United States)

Hill, R.; Calvin, W. M.; Harpold, A.

2017-12-01

Mountain snow storage is the dominant source of water for humans and ecosystems in western North America. Consequently, the spatial distribution of snow-covered area is fundamental to both hydrological, ecological, and climate models. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were collected along the entire Sierra Nevada mountain range extending from north of Lake Tahoe to south of Mt. Whitney during the 2015 and 2016 snow-covered season. The AVIRIS dataset used in this experiment consists of 224 contiguous spectral channels with wavelengths ranging 400-2500 nanometers at a 15-meter spatial pixel size. Data from the Sierras were acquired on four days: 2/24/15 during a very low snow year, 3/24/16 near maximum snow accumulation, and 5/12/16 and 5/18/16 during snow ablation and snow loss. Building on previous retrieval of subpixel snow-covered area algorithms that take into account varying grain size we present a model that analyzes multiple endmembers of varying snow grain size, vegetation, rock, and soil in segmented regions along the Sierra Nevada to determine snow-cover spatial extent, snow sub-pixel fraction, and approximate grain size. In addition, varying simulated models of the data will compare and contrast the retrieval of current snow products such as MODIS Snow-Covered Area and Grain Size (MODSCAG) and the Airborne Space Observatory (ASO). Specifically, does lower spatial resolution (MODIS), broader resolution bandwidth (MODIS), and limited spectral resolution (ASO) affect snow-cover area and grain size approximations? The implications of our findings will help refine snow mapping products for planned hyperspectral satellite spectrometer systems such as EnMAP (slated to launch in 2019), HISUI (planned for inclusion on the International Space Station in 2018), and HyspIRI (currently under consideration).

Planimetric Features Generalization for the Production of Small-Scale Map by Using Base Maps and the Existing Algorithms

Directory of Open Access Journals (Sweden)

M. Modiri

2014-10-01

Full Text Available Cartographic maps are representations of the Earth upon a flat surface in the smaller scale than it’s true. Large scale maps cover relatively small regions in great detail and small scale maps cover large regions such as nations, continents and the whole globe. Logical connection between the features and scale map must be maintained by changing the scale and it is important to recognize that even the most accurate maps sacrifice a certain amount of accuracy in scale to deliver a greater visual usefulness to its user. Cartographic generalization, or map generalization, is the method whereby information is selected and represented on a map in a way that adapts to the scale of the display medium of the map, not necessarily preserving all intricate geographical or other cartographic details. Due to the problems facing small-scale map production process and the need to spend time and money for surveying, today’s generalization is used as executive approach. The software is proposed in this paper that converted various data and information to certain Data Model. This software can produce generalization map according to base map using the existing algorithm. Planimetric generalization algorithms and roles are described in this article. Finally small-scale maps with 1:100,000, 1:250,000 and 1:500,000 scale are produced automatically and they are shown at the end.

Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data

Directory of Open Access Journals (Sweden)

Eufemia Tarantino

2012-06-01

Full Text Available Plastic covering is used worldwide to protect crops against damaging growing conditions. This agricultural practice raises some controversial issues. While it significantly impacts on local economic vitality, plasticulture also shows several environmental affects. In the Apulia Region (Italy the wide-spreading of artificial plastic coverings for vineyard protection has showed negative consequences on the hydrogeological balance of soils as well as on the visual quality of rural landscape. In order to monitor and manage this phenomenon, a detailed site mapping has become essential. In this study an efficient object-based classification procedure from Very High Spatial Resolution (VHSR true color aerial data was developed on eight test areas located in the Ionian area of the Apulia Region in order to support the updating of the existing land use database aimed at plastic covered vineyard monitoring.

Louisiana Land Cover Data Set, UTM Zone 15 NAD83, USGS [landcover_la_nlcd_usgs_2001.tif

Data.gov (United States)

Louisiana Geographic Information Center — The National Land Cover Database 2001 land cover layer for mapping zone 37A was produced through a cooperative project conducted by the Multi-Resolution Land...

Mapping of

Directory of Open Access Journals (Sweden)

Sayed M. Arafat

2014-06-01

Full Text Available Land cover map of North Sinai was produced based on the FAO-Land Cover Classification System (LCCS of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is covering a total area of 20,310.4 km2 (203,104 hectare. The landscape classification was based on SPOT4 data acquired in 2011 using combined multispectral bands of 20 m spatial resolution. Geographic Information System (GIS was used to manipulate the attributed layers of classification in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include bare rock, bare soils (stony, very stony and salt crusts, loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals and natural perennial water bodies as lakes (standing. The artificial surfaces include linear and non-linear features.

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

Science.gov (United States)

Hansen, M.C.; Egorov, Alexey; Potapov, P.V.; Stehman, S.V.; Tyukavina, A.; Turubanova, S.A.; Roy, David P.; Goetz, S.J.; Loveland, Thomas R.; Ju, J.; Kommareddy, A.; Kovalskyy, Valeriy; Forsyth, C.; Bents, T.

2014-01-01

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

Science.gov (United States)

Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

2014-06-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

International Nuclear Information System (INIS)

Deilmai, B Rokni; Ahmad, B Bin; Zabihi, H

2014-01-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification

QUALITY ASSESSMENT AND CONTROL OF OUTPUTS OF A NATIONWIDE AGRICULTURAL LAND COVER MAPPING PROGRAM USING LIDAR: PHIL-LIDAR 2 PARMAP EXPERIENCE

Directory of Open Access Journals (Sweden)

H. M. Pagkalinawan

2017-11-01

Full Text Available The Agricultural Resources Extraction from LiDAR Surveys (PARMAP project component of the Nationwide Detailed Resources Assessment using LiDAR (Phil-LiDAR 2 Program aims to produce detailed agricultural maps using LiDAR. Agricultural land cover at crop level was classified through object based image analysis using Support Vector Machine as classifier and LiDAR derivatives from point cloud (2 points per sq.m. and orthophoto (0.5-meter resolution as inputs. An accuracy of at least 90 %, assessed using validation points from the field and through image interpretation, was required before proceeding to post-processing and map lay-out. Knowledge sharing and capacity development facilitated by the University of the Philippines Diliman (UPD enabled partner universities across the Philippines to produce outputs for their assigned region. Considering output layers were generated by multiple teams working on different landscape complexities with some degree of data quality variability, quality checking is crucial to ensure accuracy standards were met. UPD PARMap devised a centralized and end-to-end scheme divided into four steps – land classification, GIS post-processing, schema application, and map lay-out. At each step, a block is reviewed and, subsequently, either approved or returned with documentation on required revisions. Turnaround time of review is at least one block (area ranging from 10 to 580 sq. km. per day. For coastal municipalities, an additional integration process to incorporate mapped coastal features was applied. Common problems observed during quality checking include misclassifications, gaps between features, incomplete attributes and missing map elements. Some issues are particular to specific blocks such as problematic LiDAR derivatives. UPD addressed these problems through discussion and mentoring visits to partner universities. As of March 2017, a total of 336 municipal agricultural maps have been turned-over to various

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

Science.gov (United States)

Hagensieker, Ron; Roscher, Ribana; Rosentreter, Johannes; Jakimow, Benjamin; Waske, Björn

2017-12-01

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.

Trajectory analysis of land use and land cover maps to improve spatial-temporal patterns, and impact assessment on groundwater recharge

Science.gov (United States)

Zomlot, Z.; Verbeiren, B.; Huysmans, M.; Batelaan, O.

2017-11-01

Land use/land cover (LULC) change is a consequence of human-induced global environmental change. It is also considered one of the major factors affecting groundwater recharge. Uncertainties and inconsistencies in LULC maps are one of the difficulties that LULC timeseries analysis face and which have a significant effect on hydrological impact analysis. Therefore, an accuracy assessment approach of LULC timeseries is needed for a more reliable hydrological analysis and prediction. The objective of this paper is to assess the impact of land use uncertainty and to improve the accuracy of a timeseries of CORINE (coordination of information on the environment) land cover maps by using a new approach of identifying spatial-temporal LULC change trajectories as a pre-processing tool. This ensures consistency of model input when dealing with land-use dynamics and as such improves the accuracy of land use maps and consequently groundwater recharge estimation. As a case study the impact of consistent land use changes from 1990 until 2013 on groundwater recharge for the Flanders-Brussels region is assessed. The change trajectory analysis successfully assigned a rational trajectory to 99% of all pixels. The methodology is shown to be powerful in correcting interpretation inconsistencies and overestimation errors in CORINE land cover maps. The overall kappa (cell-by-cell map comparison) improved from 0.6 to 0.8 and from 0.2 to 0.7 for forest and pasture land use classes respectively. The study shows that the inconsistencies in the land use maps introduce uncertainty in groundwater recharge estimation in a range of 10-30%. The analysis showed that during the period of 1990-2013 the LULC changes were mainly driven by urban expansion. The results show that the resolution at which the spatial analysis is performed is important; the recharge differences using original and corrected CORINE land cover maps increase considerably with increasing spatial resolution. This study indicates

Structural knowledge learning from maps for supervised land cover/use classification: Application to the monitoring of land cover/use maps in French Guiana

Science.gov (United States)

Bayoudh, Meriam; Roux, Emmanuel; Richard, Gilles; Nock, Richard

2015-03-01

The number of satellites and sensors devoted to Earth observation has become increasingly elevated, delivering extensive data, especially images. At the same time, the access to such data and the tools needed to process them has considerably improved. In the presence of such data flow, we need automatic image interpretation methods, especially when it comes to the monitoring and prediction of environmental and societal changes in highly dynamic socio-environmental contexts. This could be accomplished via artificial intelligence. The concept described here relies on the induction of classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system, combined with a multi-class classification procedure. This methodology was used to monitor changes in land cover/use of the French Guiana coastline. One hundred and fifty-eight classification rules were induced from 3 diachronic land cover/use maps including 38 classes. These rules were expressed in first order logic language, which makes them easily understandable by non-experts. A 10-fold cross-validation gave significant average values of 84.62%, 99.57% and 77.22% for classification accuracy, specificity and sensitivity, respectively. Our methodology could be beneficial to automatically classify new objects and to facilitate object-based classification procedures.

Global land cover mapping at 30 m resolution: A POK-based operational approach

Science.gov (United States)

Chen, Jun; Chen, Jin; Liao, Anping; Cao, Xin; Chen, Lijun; Chen, Xuehong; He, Chaoying; Han, Gang; Peng, Shu; Lu, Miao; Zhang, Weiwei; Tong, Xiaohua; Mills, Jon

2015-05-01

Global Land Cover (GLC) information is fundamental for environmental change studies, land resource management, sustainable development, and many other societal benefits. Although GLC data exists at spatial resolutions of 300 m and 1000 m, a 30 m resolution mapping approach is now a feasible option for the next generation of GLC products. Since most significant human impacts on the land system can be captured at this scale, a number of researchers are focusing on such products. This paper reports the operational approach used in such a project, which aims to deliver reliable data products. Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed. To handle the classification process of 10 land cover types, a split-and-merge strategy was employed, i.e. firstly each class identified in a prioritized sequence and then results are merged together. For the identification of each class, a robust integration of pixel-and object-based classification was developed. To improve the quality of the classification results, a knowledge-based interactive verification procedure was developed with the support of web service technology. The performance of the POK-based approach was tested using eight selected areas with differing landscapes from five different continents. An overall classification accuracy of over 80% was achieved. This indicates that the developed POK-based approach is effective and feasible

National Land Cover Database 2001 (NLCD01)

Science.gov (United States)

LaMotte, Andrew E.

2016-01-01

This 30-meter data set represents land use and land cover for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System (see http://water.usgs.gov/GIS/browse/nlcd01-partition.jpg). The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004), (see: http://www.mrlc.gov/mrlc2k.asp). The NLCD 2001 was created by partitioning the United States into mapping zones. A total of 68 mapping zones (see http://water.usgs.gov/GIS/browse/nlcd01-mappingzones.jpg), were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

Mapping lichen color-groups in western Arctic Alaska using seasonal Landsat composites

Science.gov (United States)

Nelson, P.; Macander, M. J.; Swingley, C. S.

2016-12-01

Mapping lichens at a landscape scale has received increased recent interest due to fears that terricolous lichen mats, primary winter caribou forage, may be decreasing across the arctic and boreal zones. However, previous efforts have produced taxonomically coarse, total lichen cover maps or have covered relatively small spatial extents. Here we attempt to map lichens of differing colors as species proxies across northwestern Alaska to produce the finest taxonomic and spatial- grained lichen maps covering the largest spatial extent to date. Lichen community sampling in five western Alaskan National Parks and Preserves from 2007-2012 generated 328 FIA-style 34.7 m radius plots on which species-level macrolichen community structure and abundance was estimated. Species were coded by color and plot lichen cover was aggregated by plot as the sum of the cover of each species in a color group. Ten different lichen color groupings were used for modeling to deduce which colors were most detectable. Reflectance signatures of each plot were extracted from a series of Landsat composites (circa 2000-2010) partitioned into two-week intervals from June 1 to Sept. 15. Median reflectance values for each band in each pixel were selected based on filtering criteria to reduce likelihood of snow cover. Lichen color group cover was regressed against plot reflectance plus additional abiotic predictors in two different data mining algorithms. Brown and grey lichens had the best models explaining approximately 40% of lichen cover in those color groups. Both data mining techniques produced similarly good fitting models. Spatial patterns of lichen color-group cover show distinctly different ecological patterns of these color-group species proxies.

Mapping Brazilian Cropland Expansion, 2000-2013

Science.gov (United States)

Zalles, V.; Hansen, M.; Potapov, P.

2016-12-01

Brazil is one of the world's leading producers and exporters of agricultural goods. Despite undergoing significant increases in its cropland area in the last decades, it remains one of the countries with the most potential for further agricultural expansion. Most notably, the expansion in production areas of commodity crops such as soybean, corn, and sugarcane has become the leading cause of land cover conversion in Brazil. Natural land covers, such as the Amazon and Cerrado forests, have been negatively affected by this agricultural expansion, causing carbon emissions, biodiversity loss, altered water cycles, and many other disturbances to ecosystem services. Monitoring of change in cropland area extent can provide relevant information to decision makers seeking to understand and manage land cover change drivers and their impacts. In this study, the freely-available Landsat archive was leveraged to produce a large-scale, methodologically consistent map of cropland cover at 30 m. resolution for the entire Brazilian territory in the year 2000. Additionally, we mapped cropland expansion from 2000 to 2013, and used statistical sampling techniques to accurately estimate cropland area per Brazilian state. Using the Global Forest Change product produced by Hansen et al. (2013), we can disaggregate forest cover loss due to cropland expansion by year, revealing spatiotemporal trends that could advance our understanding of the drivers of forest loss.

Evaluating rapid ground sampling and scaling estimated plant cover using UAV imagery up to Landsat for mapping arctic vegetation

Science.gov (United States)

Nelson, P.; Paradis, D. P.

2017-12-01

The small stature and spectral diversity of arctic plant taxa presents challenges in mapping arctic vegetation. Mapping vegetation at the appropriate scale is needed to visualize effects of disturbance, directional vegetation change or mapping of specific plant groups for other applications (eg. habitat mapping). Fine spatial grain of remotely sensed data (ca. 10 cm pixels) is often necessary to resolve patches of many arctic plant groups, such as bryophytes and lichens. These groups are also spectrally different from mineral, litter and vascular plants. We sought to explore method to generate high-resolution spatial and spectral data to explore better mapping methods for arctic vegetation. We sampled ground vegetation at seven sites north or west of tree-line in Alaska, four north of Fairbanks and three northwest of Bethel, respectively. At each site, we estimated cover of plant functional types in 1m2 quadrats spaced approximately every 10 m along a 100 m long transect. Each quadrat was also scanned using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm range) and photographed from multiple perspectives. We then flew our small UAV with a RGB camera over the transect and at least 50 m on either side collecting on imagery of the plot, which were used to generate a image mosaic and digital surface model of the plot. We compare plant functional group cover ocular estimated in situ to post-hoc estimation, either automated or using a human observer, using the quadrat photos. We also compare interpolated lichen cover from UAV scenes to estimated lichen cover using a statistical models using Landsat data, with focus on lichens. Light and yellow lichens are discernable in the UAV imagery but certain lichens, especially dark colored lichens or those with spectral signatures similar to graminoid litter, present challenges. Future efforts will focus on integrating UAV-upscaled ground cover estimates to hyperspectral sensors (eg. AVIRIS ng) for better combined

IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

Directory of Open Access Journals (Sweden)

Charles Gignac

2017-01-01

Full Text Available The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3–7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps.

Application of Modis Data to Assess the Latest Forest Cover Changes of Sri Lanka

Science.gov (United States)

Perera, K.; Herath, S.; Apan, A.; Tateishi, R.

2012-07-01

Assessing forest cover of Sri Lanka is becoming important to lower the pressure on forest lands as well as man-elephant conflicts. Furthermore, the land access to north-east Sri Lanka after the end of 30 years long civil war has increased the need of regularly updated land cover information for proper planning. This study produced an assessment of the forest cover of Sri Lanka using two satellite data based maps within 23 years of time span. For the old forest cover map, the study used one of the first island-wide digital land cover classification produced by the main author in 1988. The old land cover classification was produced at 80 m spatial resolution, using Landsat MSS data. A previously published another study by the author has investigated the application feasibility of MODIS and Landsat MSS imagery for a selected sub-section of Sri Lanka to identify the forest cover changes. Through the light of these two studies, the assessment was conducted to investigate the application possibility of MODIS 250 m over a small island like Sri Lanka. The relation between the definition of forest in the study and spatial resolution of the used satellite data sets were considered since the 2012 map was based on MODIS data. The forest cover map of 1988 was interpolated into 250 m spatial resolution to integrate with the GIS data base. The results demonstrated the advantages as well as disadvantages of MODIS data in a study at this scale. The successful monitoring of forest is largely depending on the possibility to update the field conditions at regular basis. Freely available MODIS data provides a very valuable set of information of relatively large green patches on the ground at relatively real-time basis. Based on the changes of forest cover from 1988 to 2012, the study recommends the use of MODIS data as a resalable method to forest assessment and to identify hotspots to be re-investigated. It's noteworthy to mention the possibility of uncounted small isolated pockets of

Land use/land cover mapping using multi-scale texture processing of high resolution data

Science.gov (United States)

Wong, S. N.; Sarker, M. L. R.

2014-02-01

Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road.

Land use/land cover mapping using multi-scale texture processing of high resolution data

International Nuclear Information System (INIS)

Wong, S N; Sarker, M L R

2014-01-01

Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road

Seafloor mapping of large areas using multibeam system - Indian experience

Digital Repository Service at National Institute of Oceanography (India)

Kodagali, V.N.; KameshRaju, K.A; Ramprasad, T.

averaged and merged to produce large area maps. Maps were generated in the scale of 1 mil. and 1.5 mil covering area of about 2 mil. sq.km in single map. Also, depth contour interval were generated. A computer program was developed to convert the depth data...

Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling

Directory of Open Access Journals (Sweden)

Brian A. Johnson

2018-01-01

Full Text Available The advent of very high resolution (VHR satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters. The scale parameter (SP is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water through meta-analysis and nonlinear regression tree (RT modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions, and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. Based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our

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

Manifestation of a neuro-fuzzy model to produce landslide susceptibility map using remote sensing data derived parameters

Science.gov (United States)

Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred

Landslides are the most common natural hazards in Malaysia. Preparation of landslide suscep-tibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this study, a new attempt is tried to produce landslide susceptibility map of a part of Cameron Valley of Malaysia. This paper develops an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment for landslide susceptibility mapping. To ob-tain the neuro-fuzzy relations for producing the landslide susceptibility map, landslide locations were identified from interpretation of aerial photographs and high resolution satellite images, field surveys and historical inventory reports. Landslide conditioning factors such as slope, plan curvature, distance to drainage lines, soil texture, lithology, and distance to lineament were extracted from topographic, soil, and lineament maps. Landslide susceptible areas were analyzed by the ANFIS model and mapped using the conditioning factors. Furthermore, we applied various membership functions (MFs) and fuzzy relations to produce landslide suscep-tibility maps. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. Results show that, triangular, trapezoidal, and polynomial MFs were the best individual MFs for modelling landslide susceptibility maps (86

Decision Tree and Texture Analysis for Mapping Debris-Covered Glaciers in the Kangchenjunga Area, Eastern Himalaya

Directory of Open Access Journals (Sweden)

Adina Racoviteanu

2012-10-01

Full Text Available In this study we use visible, short-wave infrared and thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER data validated with high-resolution Quickbird (QB and Worldview2 (WV2 for mapping debris cover in the eastern Himalaya using two independent approaches: (a a decision tree algorithm, and (b texture analysis. The decision tree algorithm was based on multi-spectral and topographic variables, such as band ratios, surface reflectance, kinetic temperature from ASTER bands 10 and 12, slope angle, and elevation. The decision tree algorithm resulted in 64 km2 classified as debris-covered ice, which represents 11% of the glacierized area. Overall, for ten glacier tongues in the Kangchenjunga area, there was an area difference of 16.2 km2 (25% between the ASTER and the QB areas, with mapping errors mainly due to clouds and shadows. Texture analysis techniques included co-occurrence measures, geostatistics and filtering in spatial/frequency domain. Debris cover had the highest variance of all terrain classes, highest entropy and lowest homogeneity compared to the other classes, for example a mean variance of 15.27 compared to 0 for clouds and 0.06 for clean ice. Results of the texture image for debris-covered areas were comparable with those from the decision tree algorithm, with 8% area difference between the two techniques.

An automated approach to mapping corn from Landsat imagery

Science.gov (United States)

Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.; Hoffer, R.M.

2004-01-01

Most land cover maps generated from Landsat imagery involve classification of a wide variety of land cover types, whereas some studies may only need spatial information on a single cover type. For example, we required a map of corn in order to estimate exposure to agricultural chemicals for an environmental epidemiology study. Traditional classification techniques, which require the collection and processing of costly ground reference data, were not feasible for our application because of the large number of images to be analyzed. We present a new method that has the potential to automate the classification of corn from Landsat satellite imagery, resulting in a more timely product for applications covering large geographical regions. Our approach uses readily available agricultural areal estimates to enable automation of the classification process resulting in a map identifying land cover as ‘highly likely corn,’ ‘likely corn’ or ‘unlikely corn.’ To demonstrate the feasibility of this approach, we produced a map consisting of the three corn likelihood classes using a Landsat image in south central Nebraska. Overall classification accuracy of the map was 92.2% when compared to ground reference data.

Increasing the availability of national mapping products.

Science.gov (United States)

Roney, J.I.; Ogilvie, B.C.

1981-01-01

A discussion of the means employed by the US Geological Survey to facilitate map usage, covering aspects of project Map Accessibility Program including special rolled and folded map packaging, new market testing, parks and campgrounds program, expanded map dealer program, new booklet-type State sales index and catalog and new USGS map reference code. The USGS is seen as the producer of a tremendous nation-wide inventory of topographic and related map products available in unprecedented types, formats and scales, and as endeavouring to increase access to its products. The new USGS map reference code is appended. -J.C.Stone

Utility of Mobile phones to support In-situ data collection for Land Cover Mapping

Science.gov (United States)

Oduor, P.; Omondi, S.; Wahome, A.; Mugo, R. M.; Flores, A.

2017-12-01

With the compelling need to create better monitoring tools for our landscapes to enhance better decision making processes, it becomes imperative to do so in much more sophisticated yet simple ways. Making it possible to leverage untapped potential of our "lay men" at the same time enabling us to respond to the complexity of the information we have to get out. SERVIR Eastern and Southern Africa has developed a mobile app that can be utilized with very little prior knowledge or no knowledge at all to collect spatial information on land cover. This set of in-situ data can be collected by masses because the tools is very simple to use, and have this information fed in classification algorithms than can then be used to map out our ever changing landscape. The LULC Mapper is a subset of JiMap system and is able to pull the google earth imagery and open street maps to enable user familiarize with their location. It uses phone GPS, phone network information to map location coordinates and at the same time gives the user sample picture of what to categorize their landscape. The system is able to work offline and when user gets access to internet they can push the information into an amazon database as bulk data. The location details including geotagged photos allows the data to be used in development of a lot of spatial information including land cover data. The app is currently available in Google Play Store and will soon be uploaded on Appstore for utilization by a wider community. We foresee a lot of potential in this tool in terms of making data collection cheaper and affordable. Taking advantage of the advances made in phone technology. We envisage to do a data collection campaign where we can have the tool used for crowdsourcing.

Assessment of dynamic probabilistic methods for mapping snow cover in Québec Canada

Science.gov (United States)

De Seve, D.; Perreault, L.; Vachon, F.; Guay, F.; choquette, Y.

2012-04-01

Hydro-Quebec is the leader in electricity production in North America and uses hydraulic resources to generate 97% of its overall production where snow represents 30% of its annual energy reserve. Information on snow cover extent (SC) and snow water equivalent (SWE) is crucial for hydrological forecasting, particularly in Nordic regions where a majority of total precipitations falls as snow. Accurate estimation of the spatial distribution of snow cover variables is required to measure the extent of this resource but snow surveys are expensive due to inaccessibility factors and to the large extent nature of the Quebec geography. Consequently, the follow-up of snowmelt is particularly challenging for operational forecasting resulting in the need to develop a new approach to assist forecasters. For improved understanding of the dynamics of snow melting over watersheds and to generate optimized power production, Hydro-Québec's Research Institute (IREQ) has developed expertise in in-situ, remote sensing monitoring and statistical treatment of such data. The main goal of this Hydro-Quebec project is to develop an automatic and dynamic snow mapping system providing a daily snow map by merging remote sensing (AVHRR and SSMI) and in situ data. This paper focuses on the work accomplished on passive microwave SSM/I data to follow up snow cover. In our problematic, it is highly useful to classify snow, more specifically during the snowmelt period. The challenge is to be able to discriminate ground from wet snow as it will react as a black body, therefore, adding noise to global brightness temperature. Two dynamic snow classifiers were developed and tested. For this purpose, channels at 19 and 37 GHz in vertical polarization have been used to feed each model. SWE values from gamma ray in situ stations (GMON) and data snow depth from ultrasonic sensor (SR50) were used to validate the output models. The first algorithm is based on a standard K-mean clustering approach, combined

Using the Landsat Archive to Estimate and Map Changes in Agriculture, Forests, and other Land Cover Types in East Africa

Science.gov (United States)

Healey, S. P.; Oduor, P.; Cohen, W. B.; Yang, Z.; Ouko, E.; Gorelick, N.; Wilson, S.

2017-12-01

Every country's land is distributed among different cover types, such as: agriculture; forests; rangeland; urban areas; and barren lands. Changes in the distribution of these classes can inform us about many things, including: population pressure; effectiveness of preservation efforts; desertification; and stability of the food supply. Good assessment of these changes can also support wise planning, use, and preservation of natural resources. We are using the Landsat archive in two ways to provide needed information about land cover change since the year 2000 in seven East African countries (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda, and Zambia). First, we are working with local experts to interpret historical land cover change from historical imagery at a probabilistic sample of 2000 locations in each country. This will provide a statistical estimate of land cover change since 2000. Second, we will use the same data to calibrate and validate annual land cover maps for each country. Because spatial context can be critical to development planning through the identification of hot spots, these maps will be a useful complement to the statistical, country-level estimates of change. The Landsat platform is an ideal tool for mapping land cover change because it combines a mix of appropriate spatial and spectral resolution with unparalleled length of service (Landsat 1 launched in 1972). Pilot tests have shown that time series analysis accessing the entire Landsat archive (i.e., many images per year) improves classification accuracy and stability. It is anticipated that this project will meet the civil needs of both governmental and non-governmental users across a range of disciplines.

Assessment of the thematic accuracy of land cover maps

DEFF Research Database (Denmark)

Høhle, Joachim

2015-01-01

were applied (‘Decision Tree’ and ‘Support Vector Machine’) using only two attributes (height above ground and normalized difference vegetation index) which both are derived from the images. The assessment of the thematic accuracy applied a stratified design and was based on accuracy measures...... methods perform equally for five classes. Trees are classified with a much better accuracy and a smaller confidence interval by means of the decision tree method. Buildings are classified by both methods with an accuracy of 99% (95% CI: 95%-100%) using independent 3D checkpoints. The average width......Several land cover maps are generated from aerial imagery and assessed by different approaches. The test site is an urban area in Europe for which six classes (‘building’, ‘hedge and bush’, ‘grass’, ‘road and parking lot’, ‘tree’, ‘wall and car port’) had to be derived. Two classification methods...

Land cover in Upper Egypt assessed using regional and global land cover products derived from MODIS imagery.

Science.gov (United States)

Fuller, Douglas O; Parenti, Michael S; Gad, Adel M; Beier, John C

2012-01-01

Irrigation along the Nile River has resulted in dramatic changes in the biophysical environment of Upper Egypt. In this study we used a combination of MODIS 250 m NDVI data and Landsat imagery to identify areas that changed from 2001-2008 as a result of irrigation and water-level fluctuations in the Nile River and nearby water bodies. We used two different methods of time series analysis -- principal components (PCA) and harmonic decomposition (HD), applied to the MODIS 250 m NDVI images to derive simple three-class land cover maps and then assessed their accuracy using a set of reference polygons derived from 30 m Landsat 5 and 7 imagery. We analyzed our MODIS 250 m maps against a new MODIS global land cover product (MOD12Q1 collection 5) to assess whether regionally specific mapping approaches are superior to a standard global product. Results showed that the accuracy of the PCA-based product was greater than the accuracy of either the HD or MOD12Q1 products for the years 2001, 2003, and 2008. However, the accuracy of the PCA product was only slightly better than the MOD12Q1 for 2001 and 2003. Overall, the results suggest that our PCA-based approach produces a high level of user and producer accuracies, although the MOD12Q1 product also showed consistently high accuracy. Overlay of 2001-2008 PCA-based maps showed a net increase of 12 129 ha of irrigated vegetation, with the largest increase found from 2006-2008 around the Districts of Edfu and Kom Ombo. This result was unexpected in light of ambitious government plans to develop 336 000 ha of irrigated agriculture around the Toshka Lakes.

Hydrograph sensitivity to estimates of map impervious cover: a WinHSPF BASINS case study

Science.gov (United States)

Endreny, Theodore A.; Somerlot, Christopher; Hassett, James M.

2003-04-01

The BASINS geographic information system hydrologic toolkit was designed to compute total maximum daily loads, which are often derived by combining water quantity estimates with pollutant concentration estimates. In this paper the BASINS toolkit PLOAD and WinHSPF sub-models are briefly described, and then a 0·45 km2 headwater watershed in the New York Croton River area is used for a case study illustrating a full WinHSPF implementation. The goal of the Croton study was to determine the sensitivity of WinHSPF hydrographs to changes in land cover map inputs. This scenario occurs when scaling the WinHSPF model from the smaller 0·45 km2 watershed to the larger 1000 km2 management basin of the entire Croton area. Methods used to test model sensitivity include first calibrating the WinHSPF hydrograph using research-monitored precipitation and discharge data together with high spatial resolution and accuracy land cover data of impervious and pervious areas, and then swapping three separate land cover files, known as GIRAS, MRLC, and DOQQ data, into the calibrated model. Research results indicated that the WinHSPF land cover swapping had peak flow sensitivity in December 2001 hydrographs between 35% underestimation and 20% overestimation, and that errors in land-cover-derived runoff ratios for storm totals and peak flows tracked with the land cover data estimates of impervious area.

Simultaneous comparison and assessment of eight remotely sensed maps of Philippine forests

Science.gov (United States)

Estoque, Ronald C.; Pontius, Robert G.; Murayama, Yuji; Hou, Hao; Thapa, Rajesh B.; Lasco, Rodel D.; Villar, Merlito A.

2018-05-01

This article compares and assesses eight remotely sensed maps of Philippine forest cover in the year 2010. We examined eight Forest versus Non-Forest maps reclassified from eight land cover products: the Philippine Land Cover, the Climate Change Initiative (CCI) Land Cover, the Landsat Vegetation Continuous Fields (VCF), the MODIS VCF, the MODIS Land Cover Type product (MCD12Q1), the Global Tree Canopy Cover, the ALOS-PALSAR Forest/Non-Forest Map, and the GlobeLand30. The reference data consisted of 9852 randomly distributed sample points interpreted from Google Earth. We created methods to assess the maps and their combinations. Results show that the percentage of the Philippines covered by forest ranges among the maps from a low of 23% for the Philippine Land Cover to a high of 67% for GlobeLand30. Landsat VCF estimates 36% forest cover, which is closest to the 37% estimate based on the reference data. The eight maps plus the reference data agree unanimously on 30% of the sample points, of which 11% are attributable to forest and 19% to non-forest. The overall disagreement between the reference data and Philippine Land Cover is 21%, which is the least among the eight Forest versus Non-Forest maps. About half of the 9852 points have a nested structure such that the forest in a given dataset is a subset of the forest in the datasets that have more forest than the given dataset. The variation among the maps regarding forest quantity and allocation relates to the combined effects of the various definitions of forest and classification errors. Scientists and policy makers must consider these insights when producing future forest cover maps and when establishing benchmarks for forest cover monitoring.

Evaluating The Land Use And Land Cover Dynamics In Borena ...

African Journals Online (AJOL)

The integration of satellite remote sensing and GIS was an effective approach for analyzing the direction, rate, and spatial pattern of land use change. Three land use and land cover maps were produced by analyzing remotely sensed images of Landsat satellite imageries at three time points (1972,1985,and 2003) .

Geotechnical Mapping of An-Najaf City, Iraq

Directory of Open Access Journals (Sweden)

Nadher Hassan Al-Baghdadi

2016-12-01

Full Text Available The present paper submits a set geotechnical maps for the area of An-Najaf city, by using contour lines to represent the different geotechnical properties of the soil. The present research work is very important step toward preparing a geotechnical database for this region, to complete the geotechnical database over all the country, (Iraq. Using such a database is very important in geotechincal investigation, reconnaissance phase, of construction projects. Within this phase of site investigation, numbers, depths and locations of the boreholes needed, will be determined. A well known commercial software (SURFER 11, was used to produce the all the contour maps of geotechnical properties presented herein. A forty nine (49 contour maps were produced to cover the variations, within the geotechnical properties of the soil, to produce realistic description to these soil properties. Both Google maps and Universal Transverse Mercator coordinate system (UTM have been used in the contour maps for easy use.

Accuracy Assessment of Satellite Derived Forest Cover Products in South and Southeast Asia

Science.gov (United States)

Gilani, H.; Xu, X.; Jain, A. K.

2017-12-01

South and Southeast Asia (SSEA) region occupies 16 % of worlds land area. It is home to over 50% of the world's population. The SSEA's countries are experiencing significant land-use and land-cover changes (LULCCs), primarily in agriculture, forest, and urban land. For this study, we compiled four existing global forest cover maps for year 2010 by Gong et al.(2015), Hansen et al. (2013), Sexton et al.(2013) and Shimada et al. (2014), which were all medium resolution (≤30 m) products based on Landsat and/or PALSAR satellite images. To evaluate the accuracy of these forest products, we used three types of information: (1) ground measurements, (2) high resolution satellite images and (3) forest cover maps produced at the national scale. The stratified random sampling technique was used to select a set of validation data points from the ground and high-resolution satellite images. Then the confusion matrix method was used to assess and rank the accuracy of the forest cover products for the entire SSEA region. We analyzed the spatial consistency of different forest cover maps, and further evaluated the consistency with terrain characteristics. Our study suggests that global forest cover mapping algorithms are trained and tested using limited ground measurement data. We found significant uncertainties in mountainous areas due to the topographical shadow effect and the dense tree canopies effects. The findings of this study will facilitate to improve our understanding of the forest cover dynamics and their impacts on the quantities and pathways of terrestrial carbon and nitrogen fluxes. Gong, P., et al. (2012). "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data." International Journal of Remote Sensing 34(7): 2607-2654. Hansen, M. C., et al. (2013). "High-Resolution Global Maps of 21st-Century Forest Cover Change." Science 342(6160): 850-853. Sexton, J. O., et al. (2013). "Global, 30-m resolution

The Effect Of Land Cover/Land Use On Groundwater Resources In Southern Egypt (Luxor Area): Remote Sensing And Field Studies

International Nuclear Information System (INIS)

Faid, A.M.; Hinz, E.A.; Montgomery, H.

2003-01-01

The impact of land cover/land use on groundwater can be critical. Land cover / land use maps give an early warning for planners and developers to protect groundwater resources from depletion and preserve its sustain ability. These land cover / land use maps can be used for the planning of groundwater development to prevent the deterioration of the aquifer. The Research Institute for Groundwater of Egypt (RIGW) has carried out hydrogeological studies in 1990 to evaluate the potentiality of groundwater in Luxor area in southern Egypt close to the Nile valley. The region is characterized by a rapid and continuous increase in land reclamation and development on the fringes which surround the already heavily cultivated land within the Nile valley. This presented a need for continuous monitoring and information updating over a vast region in a short time and at a reasonable cost. This study illustrates how remote sensing techniques can be effectively used for monitoring changes in land cover / land use in an effort to aid groundwater management. Landsat Thematic Mapper (TM) data collected in 1984 and 2000 were processed and analyzed over the study area to produce land cover/land use maps. The Normalized Difference Vegetation Index (NDVI) technique is used for Landsat TM images of to quantify areas which are covered by vegetation. Results indicated significant increase in cultivated areas. Remote sensing results are compared with iso-piezo metric maps and iso-salinity maps that were produced in 1984 and 2000. Comparison of these maps indicates groundwater depletion and salinity increase from 1984 to 2000. We relate this to the increase of the area being cultivated

Developing a New North American Land Cover Product at 30m Resolution: Methods, Results and Future Plans

Science.gov (United States)

Homer, C.; Colditz, R. R.; Latifovic, R.; Llamas, R. M.; Pouliot, D.; Danielson, P.; Meneses, C.; Victoria, A.; Ressl, R.; Richardson, K.; Vulpescu, M.

2017-12-01

Land cover and land cover change information at regional and continental scales has become fundamental for studying and understanding the terrestrial environment. With recent advances in computer science and freely available image archives, continental land cover mapping has been advancing to higher spatial resolution products. The North American Land Change Monitoring System (NALCMS) remains the principal provider of seamless land cover maps of North America. Founded in 2006, this collaboration among the governments of Canada, Mexico and the United States has released two previous products based on 250m MODIS images, including a 2005 land cover and a 2005-2010 land cover change product. NALCMS has recently completed the next generation North America land cover product, based upon 30m Landsat images. This product now provides the first ever 30m land cover produced for the North American continent, providing 19 classes of seamless land cover. This presentation provides an overview of country-specific image classification processes, describes the continental map production process, provides results for the North American continent and discusses future plans. NALCMS is coordinated by the Commission for Environmental Cooperation (CEC) and all products can be obtained at their website - www.cec.org.

Technique for large-scale structural mapping at uranium deposits i in non-metamorphosed sedimentary cover rocks

International Nuclear Information System (INIS)

Kochkin, B.T.

1985-01-01

The technique for large-scale construction (1:1000 - 1:10000), reflecting small amplitude fracture plicate structures, is given for uranium deposits in non-metamorphozed sedimentary cover rocks. Structure drill log sections, as well as a set of maps with the results of area analysis of hidden disturbances, structural analysis of iso-pachous lines and facies of platform mantle horizons serve as sour ce materials for structural mapplotting. The steps of structural map construction are considered: 1) structural carcass construction; 2) reconstruction of structure contour; 3) time determination of structure initiation; 4) plotting of an additional geologic load

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

Science.gov (United States)

Midekisa, Alemayehu; Holl, Felix; Savory, David J; Andrade-Pacheco, Ricardo; Gething, Peter W; Bennett, Adam; Sturrock, Hugh J W

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Scleractinian Coral Cover Maps Derived from Classified in situ Seafloor Imagery for Select U.S. Locations in the Pacific from 2001 to 2015

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Coral cover maps depict percentage of scleractinian (hard) coral cover along survey tracks, overlain on existing bathymetric grids and/or satellite images, for...

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

Science.gov (United States)

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O`Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting. PMID:26262681

Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

Science.gov (United States)

Chakraborty, S.; Banerjee, A.; Gupta, S. K. S.; Christensen, P. R.; Papandreou-Suppappola, A.

2017-12-01

Multitemporal observations acquired frequently by satellites with short revisit periods such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is an important source for modeling land cover. Due to the inherent seasonality of the land cover, harmonic modeling reveals hidden state parameters characteristic to it, which is used in classifying different land cover types and in detecting changes due to natural or anthropogenic factors. In this work, we use an eight day MODIS composite to create a Normalized Difference Vegetation Index (NDVI) time-series of ten years. Improved hidden parameter estimates of the nonlinear harmonic NDVI model are obtained using the Particle Filter (PF), a sequential Monte Carlo estimator. The nonlinear estimation based on PF is shown to improve parameter estimation for different land cover types compared to existing techniques that use the Extended Kalman Filter (EKF), due to linearization of the harmonic model. As these parameters are representative of a given land cover, its applicability in near real-time detection of land cover change is also studied by formulating a metric that captures parameter deviation due to change. The detection methodology is evaluated by considering change as a rare class problem. This approach is shown to detect change with minimum delay. Additionally, the degree of change within the change perimeter is non-uniform. By clustering the deviation in parameters due to change, this spatial variation in change severity is effectively mapped and validated with high spatial resolution change maps of the given regions.

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Directory of Open Access Journals (Sweden)

Gillian V. Lui

2015-03-01

Full Text Available Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite software package and the Global Forest Change dataset (GFCD being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001–2007 and 2007–2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite’s comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today’s continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application.

Evaluating maps produced by urban search and rescue robots: Lessons learned from RoboCup

NARCIS (Netherlands)

Balaguer, B.; Balakirsky, S.; Carpin, S.; Visser, A.

2009-01-01

This paper presents the map evaluation methodology developed for the Virtual Robots Rescue competition held as part of RoboCup. The procedure aims to evaluate the quality of maps produced by multi-robot systems with respect to a number of factors, including usability, exploration, annotation and

On retracting properties and covering homotopy theorem for S-maps into Sχ-cofibrations and Sχ-fibrations

Directory of Open Access Journals (Sweden)

Amin Saif

2016-10-01

Full Text Available In this paper we generalize the retracting property in homotopy theory for topological semigroups by introducing the notions of deformation S-retraction with its weaker forms and ES-homotopy extension property. Furthermore, the covering homotopy theorems for S-maps into Sχ-fibrations and Sχ-cofibrations are introduced and pullbacks for Sχ-fibrations behave properly.

An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data

Directory of Open Access Journals (Sweden)

Théo Masson

2018-04-01

Full Text Available The characterization of snow extent is critical for a wide range of applications. Since 1966, snow maps at different spatial resolutions have been produced using various satellite sensor images. Nowadays, the most widely used products are likely those derived from Moderate-Resolution Imaging Spectroradiometer (MODIS data, which cover the whole Earth at a near-daily frequency. There are a variety of snow mapping methods for MODIS data, based on different methodologies and applied at different spatial resolutions. Up to now, all these products have been tested and evaluated separately. This study aims to compare the methods currently available for retrieving snow from MODIS data. The focus is on fractional snow cover, which represents the snow cover area at the subpixel level. We examine the two main approaches available for generating such products from MODIS data; namely, linear regression of the Normalized Difference Snow Index (NDSI and spectral unmixing (SU. These two approaches have resulted in several methods, such as MOD10A1 (the NSIDC MODIS snow product for NDSI regression, and MODImLAB for SU. The assessment of these approaches was carried out using higher resolution binary snow maps (i.e., showing the presence or absence of snow at spatial resolutions of 10, 20, and 30 m, produced by SPOT 4, SPOT 5, and LANDSAT-8, respectively. Three areas were selected in order to provide landscape diversity: the French Alps (117 dates, the Pyrenees (30 dates, and the Moroccan Atlas (24 dates. This study investigates the impact of reference maps on accuracy assessments, and it is suggested that NDSI-based high spatial resolution reference maps advantage NDSI medium-resolution snow maps. For MODIS snow maps, the results show that applying an NDSI approach to accurate surface reflectance corrected for topographic and atmospheric effects generally outperforms other methods for the global retrieval of snow cover area. The improvements to the newer version

Meta-Analysis of Land Use / Land Cover Change Factors in the Conterminous US and Prediction of Potential Working Timberlands in the US South from FIA Inventory Plots and NLCD Cover Maps

Science.gov (United States)

Jeuck, James A.

-analysis provides insight into the general success of econometric independent variables for future forest use or cover change research. The second part of this dissertation developed a method for predicting area estimates and spatial distribution of PWT in the US South. This technique determined land use from USFS Forest Inventory and Analysis (FIA) and land cover from the National Land Cover Database (NLCD). Three dependent variable forms (DV Forms) were derived from the FIA data: DV Form 1, timberland, other; DV Form 2, short timberland, tall timberland, agriculture, other; and DV Form 3, short hardwood (HW) timberland, tall HW timberland, short softwood (SW) timberland, tall SW timberland, agriculture, other. The prediction accuracy of each DV Form was investigated using both random forest model and logistic regression model specifications and data optimization techniques. Model verification employing a "leave-group-out" Monte Carlo simulation determined the selection of a stratified version of the random forest model using one-year NLCD observations with an overall accuracy of 0.53-0.94. The lower accuracy side of the range was when predictions were made from an aggregated NLCD land cover class "grass_shrub". The selected model specification was run using 2011 NLCD and the other predictor variables to produce three levels of timberland prediction and probability maps for the US South. Spatial masks removed areas unlikely to be working forests (protected and urbanized lands) resulting in PWT maps. The area of the resulting maps compared well with USFS area estimates and masked PWT maps and had an 8-11% reduction of the USFS timberland estimate for the US South compared to the DV Form. Change analysis of the 2011 NLCD to PWT showed (1) the majority of the short timberland came from NLCD grass_shrub; (2) the majority of NLCD grass_shrub predicted into tall timberland, and (3) NLCD grass_shrub was more strongly associated with timberland in the Coastal Plain. Resulting map products

Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

Science.gov (United States)

Clark, M. L.; Kilham, N. E.

2015-12-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as

South African National Land-Cover Change Map

African Journals Online (AJOL)

Fritz Schoeman

monitoring land-cover change at a national scale over time using EO data. 2. .... assist with final results reporting and analysis on a sub-national level. ..... South African Land-Cover Characteristics Database: A synopsis of the landscape.

TREE CANOPY COVER MAPPING USING LiDAR IN URBAN BARANGAYS OF CEBU CITY, CENTRAL PHILIPPINES

Directory of Open Access Journals (Sweden)

J. A. Ejares

2016-06-01

Full Text Available This paper investigates tree canopy cover mapping of urban barangays (smallest administrative division in the Philippines in Cebu City using LiDAR (Light Detection and Ranging. Object-Based Image Analysis (OBIA was used to extract tree canopy cover. Multi-resolution segmentation and a series of assign-class algorithm in eCognition software was also performed to extract different land features. Contextual features of tree canopies such as height, area, roundness, slope, length-width and elliptic fit were also evaluated. The results showed that at the time the LiDAR data was collected (June 24, 2014, the tree cover was around 25.11 % (or 15,674,341.8 m2 of the city’s urban barangays (or 62,426,064.6 m2. Among all urban barangays in Cebu City, Barangay Busay had the highest cover (55.79 % while barangay Suba had the lowest (0.8 %. The 16 barangays with less than 10 % tree cover were generally located in the coastal area, presumably due to accelerated urbanization. Thirty-one barangays have tree cover ranging from 10.59–-27.3 %. Only 3 barangays (i.e., Lahug, Talamban, and Busay have tree cover greater than 30 %. The overall accuracy of the analysis was 96.6 % with the Kappa Index of Agreement or KIA of 0.9. From the study, a grouping can be made of the city’s urban barangays with regards to tree cover. The grouping will be useful to urban planners not only in allocating budget to the tree planting program of the city but also in planning and creation of urban parks and playgrounds.

Live Coral Cover Index Testing and Application with Hyperspectral Airborne Image Data

Directory of Open Access Journals (Sweden)

Karen E. Joyce

2013-11-01

Full Text Available Coral reefs are complex, heterogeneous environments where it is common for the features of interest to be smaller than the spatial dimensions of imaging sensors. While the coverage of live coral at any point in time is a critical environmental management issue, image pixels may represent mixed proportions of coverage. In order to address this, we describe the development, application, and testing of a spectral index for mapping live coral cover using CASI-2 airborne hyperspectral high spatial resolution imagery of Heron Reef, Australia. Field surveys were conducted in areas of varying depth to quantify live coral cover. Image statistics were extracted from co-registered imagery in the form of reflectance, derivatives, and band ratios. Each of the spectral transforms was assessed for their correlation with live coral cover, determining that the second derivative around 564 nm was the most sensitive to live coral cover variations(r2 = 0.63. Extensive field survey was used to transform relative to absolute coral cover, which was then applied to produce a live coral cover map of Heron Reef. We present the live coral cover index as a simple and viable means to estimate the amount of live coral over potentially thousands of km2 and in clear-water reefs.

Globalland30 Mapping Capacity of Land Surface Water in Thessaly, Greece

Directory of Open Access Journals (Sweden)

Ioannis Manakos

2014-12-01

Full Text Available The National Geomatics Center of China (NGCC produced Global Land Cover (GlobalLand30 maps with 30 m spatial resolution for the years 2000 and 2009–2010, responding to the need for harmonized, accurate, and high-resolution global land cover data. This study aims to assess the mapping accuracy of the land surface water layer of GlobalLand30 for 2009–2010. A representative Mediterranean region, situated in Greece, is considered as the case study area, with 2009 as the reference year. The assessment is realized through an object-based comparison of the GlobalLand30 water layer with the ground truth and visually interpreted data from the Hellenic Cadastre fine spatial resolution (0.5 m orthophoto map layer. GlobCover 2009, GlobCorine 2009, and GLCNMO 2008 corresponding thematic layers are utilized to show and quantify the progress brought along with the increment of the spatial resolution, from 500 m to 300 m and finally to 30 m with the newly produced GlobalLand30 maps. GlobalLand30 detected land surface water areas show a 91.9% overlap with the reference data, while the coarser resolution products are restricted to lower accuracies. Validation is extended to the drainage network elements, i.e., rivers and streams, where GlobalLand30 outperforms the other global map products, as well.

Mapping invasive alien Acacia dealbata Link using ASTER multispectral imagery: a case study in central-eastern of Portugal

Energy Technology Data Exchange (ETDEWEB)

Martins, F.; Alegria, C.; Artur, G.

2016-07-01

Aim of the study: Acacia dealbata is an alien invasive species that is widely spread in Portugal. The main goal of this study was to produce an accurate and detailed map for this invasive species using ASTER multispectral imagery. Area of study: The central-eastern zone of Portugal was used as study area. This whole area is represented in an ASTER scene covering about 321.1 x 103 ha. Material and methods: ASTER imagery of two dates (flowering season and dry season) were classified by applying three supervised classifiers (Maximum Likelihood, Support Vector Machine and Artificial Neural Networks) to five different land cover classifications (from most generic to most detailed land cover categories). The spectral separability of the land cover categories was analyzed and the accuracy of the 30 produced maps compared. Main results: The highest classification accuracy for acacia mapping was obtained using the flowering season imagery, the Maximum Likelihood classifier and the most detailed land cover classification (overall accuracy of 86%; Kappa statistics of 85%; acacia class Kappa statistics of 100%). As a result, the area occupied by acacia was estimated to be approximated 24,770 ha (i.e. 8% of the study area). Research highlights: The methodology explored proved to be a cost-effective solution for acacia mapping in central-eastern of Portugal. The obtained map enables a more accurate and detailed identification of this species’ invaded areas due to its spatial resolution (minimum mapping unit of 0.02 ha) providing a substantial improvement comparably to the existent national land cover maps to support monitoring and control activities. (Author)

Chemical composition of overland flow produced on soils covered with vegetative ash

Directory of Open Access Journals (Sweden)

M.B. Bodí

2013-05-01

Full Text Available The objective of this study was to ascertain the differences between the soluble elements of ash obtained under laboratory conditions and the dissolved in overland flow from soils covered with a layer of ash. The overland flow was obtained during series of rainfall simulations over soils covered with two different types of ash. This study indicates that the soluble elements released from ash can modify water quality increasing its pH, electrical conductivity and especially cation content. The nutrients solubilised are not necessarily the same as the elemental composition of ash itself. Runoff composition depends on the volume of water produced, on the solubility of the ash components and on the chemical interactions with water from rainfall and soil. After the first intense rain event, most of the elements are solubilised and lixiviated or washed out, however, some of them may increase in the runoff or soil water some weeks later due to chemical interactions with water from rainfall and soil nutrients.

Flood maps in Europe - methods, availability and use

Science.gov (United States)

de Moel, H.; van Alphen, J.; Aerts, J. C. J. H.

2009-03-01

To support the transition from traditional flood defence strategies to a flood risk management approach at the basin scale in Europe, the EU has adopted a new Directive (2007/60/EC) at the end of 2007. One of the major tasks which member states must carry out in order to comply with this Directive is to map flood hazards and risks in their territory, which will form the basis of future flood risk management plans. This paper gives an overview of existing flood mapping practices in 29 countries in Europe and shows what maps are already available and how such maps are used. Roughly half of the countries considered have maps covering as good as their entire territory, and another third have maps covering significant parts of their territory. Only five countries have very limited or no flood maps available yet. Of the different flood maps distinguished, it appears that flood extent maps are the most commonly produced floods maps (in 23 countries), but flood depth maps are also regularly created (in seven countries). Very few countries have developed flood risk maps that include information on the consequences of flooding. The available flood maps are mostly developed by governmental organizations and primarily used for emergency planning, spatial planning, and awareness raising. In spatial planning, flood zones delimited on flood maps mainly serve as guidelines and are not binding. Even in the few countries (e.g. France, Poland) where there is a legal basis to regulate floodplain developments using flood zones, practical problems are often faced which reduce the mitigating effect of such binding legislation. Flood maps, also mainly extent maps, are also created by the insurance industry in Europe and used to determine insurability, differentiate premiums, or to assess long-term financial solvency. Finally, flood maps are also produced by international river commissions. With respect to the EU Flood Directive, many countries already have a good starting point to map

Benthic Cover

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Benthic cover (habitat) maps are derived from aerial imagery, underwater photos, acoustic surveys, and data gathered from sediment samples. Shallow to moderate-depth...

Land Cover - Minnesota Land Cover Classification System

Data.gov (United States)

Minnesota Department of Natural Resources — Land cover data set based on the Minnesota Land Cover Classification System (MLCCS) coding scheme. This data was produced using a combination of aerial photograph...

Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps

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Peijun Sun

2018-03-01

Full Text Available Rescaled maps have been extensively utilized to provide data at the appropriate spatial resolution for use in various Earth science models. However, a simple and easy way to evaluate these rescaled maps has not been developed. We propose a similarity matrix approach using a contingency table to compute three measures: overall similarity (OS, omission error (OE, and commission error (CE to evaluate the rescaled maps. The Majority Rule Based aggregation (MRB method was employed to produce the upscaled maps to demonstrate this approach. In addition, previously created, coarser resolution land cover maps from other research projects were also available for comparison. The question of which is better, a map initially produced at coarse resolution or a fine resolution map rescaled to a coarse resolution, has not been quantitatively investigated. To address these issues, we selected study sites at three different extent levels. First, we selected twelve regions covering the continental USA, then we selected nine states (from the whole continental USA, and finally we selected nine Agriculture Statistical Districts (ASDs (from within the nine selected states as study sites. Crop/non-crop maps derived from the USDA Crop Data Layer (CDL at 30 m as base maps were used for the upscaling and existing maps at 250 m and 1 km were utilized for the comparison. The results showed that a similarity matrix can effectively provide the map user with the information needed to assess the rescaling. Additionally, the upscaled maps can provide higher accuracy and better represent landscape pattern compared to the existing coarser maps. Therefore, we strongly recommend that an evaluation of the upscaled map and the existing coarser resolution map using a similarity matrix should be conducted before deciding which dataset to use for the modelling. Overall, extending our understanding on how to perform an evaluation of the rescaled map and investigation of the applicability

A TEMPORAL MAP IN GEOSTATIONARY ORBIT: THE COVER ETCHING ON THE EchoStar XVI ARTIFACT

Energy Technology Data Exchange (ETDEWEB)

Weisberg, Joel M., E-mail: jweisber@carleton.edu [Department of Physics and Astronomy, Carleton College, Northfield, MN 55057 (United States); Paglen, Trevor, E-mail: trevor@paglen.com

2012-10-01

Geostationary satellites are unique among orbital spacecraft in that they experience no appreciable atmospheric drag. After concluding their respective missions, geostationary spacecraft remain in orbit virtually in perpetuity. As such, they represent some of human civilization's longest lasting artifacts. With this in mind, the EchoStar XVI satellite, to be launched in fall 2012, will play host to a time capsule intended as a message for the deep future. Inspired in part by the Pioneer Plaque and Voyager Golden Records, the EchoStar XVI Artifact is a pair of gold-plated aluminum jackets housing a small silicon disk containing 100 photographs. The Cover Etching, the subject of this paper, is etched onto one of the two jackets. It is a temporal map consisting of a star chart, pulsar timings, and other information describing the epoch from which EchoStar XVI came. The pulsar sample consists of 13 rapidly rotating objects, 5 of which are especially stable, having spin periods <10 ms and extremely small spin-down rates. In this paper, we discuss our approach to the time map etched onto the cover and the scientific data shown on it, and we speculate on the uses that future scientists may have for its data. The other portions of the EchoStar XVI Artifact will be discussed elsewhere.

A TEMPORAL MAP IN GEOSTATIONARY ORBIT: THE COVER ETCHING ON THE EchoStar XVI ARTIFACT

International Nuclear Information System (INIS)

Weisberg, Joel M.; Paglen, Trevor

2012-01-01

Geostationary satellites are unique among orbital spacecraft in that they experience no appreciable atmospheric drag. After concluding their respective missions, geostationary spacecraft remain in orbit virtually in perpetuity. As such, they represent some of human civilization's longest lasting artifacts. With this in mind, the EchoStar XVI satellite, to be launched in fall 2012, will play host to a time capsule intended as a message for the deep future. Inspired in part by the Pioneer Plaque and Voyager Golden Records, the EchoStar XVI Artifact is a pair of gold-plated aluminum jackets housing a small silicon disk containing 100 photographs. The Cover Etching, the subject of this paper, is etched onto one of the two jackets. It is a temporal map consisting of a star chart, pulsar timings, and other information describing the epoch from which EchoStar XVI came. The pulsar sample consists of 13 rapidly rotating objects, 5 of which are especially stable, having spin periods <10 ms and extremely small spin-down rates. In this paper, we discuss our approach to the time map etched onto the cover and the scientific data shown on it, and we speculate on the uses that future scientists may have for its data. The other portions of the EchoStar XVI Artifact will be discussed elsewhere.

Determinants of woody cover in African savannas

Science.gov (United States)

Sankaran, M.; Hanan, N.P.; Scholes, Robert J.; Ratnam, J.; Augustine, D.J.; Cade, B.S.; Gignoux, J.; Higgins, S.I.; Le, Roux X.; Ludwig, F.; Ardo, J.; Banyikwa, F.; Bronn, A.; Bucini, G.; Caylor, K.K.; Coughenour, M.B.; Diouf, A.; Ekaya, W.; Feral, C.J.; February, E.C.; Frost, P.G.H.; Hiernaux, P.; Hrabar, H.; Metzger, K.L.; Prins, H.H.T.; Ringrose, S.; Sea, W.; Tews, J.; Worden, J.; Zambatis, N.

2005-01-01

Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties 1-3. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover1,2,4,5, but perceptions differ on which of these are the primary drivers of savanna structure. Here we show, using data from 854 sites across Africa, that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than ???650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered 'stable' systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of ???650 mm, savannas are 'unstable' systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation 6 may considerably affect their distribution and dynamics. ?? 2005 Nature Publishing Group.

Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity

Science.gov (United States)

Paneque-Gálvez, Jaime; Mas, Jean-François; Moré, Gerard; Cristóbal, Jordi; Orta-Martínez, Martí; Luz, Ana Catarina; Guèze, Maximilien; Macía, Manuel J.; Reyes-García, Victoria

2013-08-01

Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines - SVM), and hybrid (unsupervised-supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different

Disruption of Transient Serotonin Accumulation by Non-Serotonin-Producing Neurons Impairs Cortical Map Development

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Xiaoning Chen

2015-01-01

Full Text Available Polymorphisms that alter serotonin transporter SERT expression and functionality increase the risks for autism and psychiatric traits. Here, we investigate how SERT controls serotonin signaling in developing CNS in mice. SERT is transiently expressed in specific sets of glutamatergic neurons and uptakes extrasynaptic serotonin during perinatal CNS development. We show that SERT expression in glutamatergic thalamocortical axons (TCAs dictates sensory map architecture. Knockout of SERT in TCAs causes lasting alterations in TCA patterning, spatial organizations of cortical neurons, and dendritic arborization in sensory cortex. Pharmacological reduction of serotonin synthesis during the first postnatal week rescues sensory maps in SERTGluΔ mice. Furthermore, knockdown of SERT expression in serotonin-producing neurons does not impair barrel maps. We propose that spatiotemporal SERT expression in non-serotonin-producing neurons represents a determinant in early life genetic programming of cortical circuits. Perturbing this SERT function could be involved in the origin of sensory and cognitive deficits associated with neurodevelopmental disorders.

EnviroAtlas - Percent Stream Buffer Zone As Natural Land Cover for the Conterminous United States

Science.gov (United States)

This EnviroAtlas dataset shows the percentage of land area within a 30 meter buffer zone along the National Hydrography Dataset (NHD) high resolution stream network, and along water bodies such as lakes and ponds that are connected via flow to the streams, that is classified as forest land cover, modified forest land cover, and natural land cover using the 2006 National Land Cover Dataset (NLCD) for each Watershed Boundary Dataset (WBD) 12-digit hydrological unit (HUC) in the conterminous United States. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

LACO-Wiki: A land cover validation tool and a new, innovative teaching resource for remote sensing and the geosciences

Science.gov (United States)

See, Linda; Perger, Christoph; Dresel, Christopher; Hofer, Martin; Weichselbaum, Juergen; Mondel, Thomas; Steffen, Fritz

2016-04-01

The validation of land cover products is an important step in the workflow of generating a land cover map from remotely-sensed imagery. Many students of remote sensing will be given exercises on classifying a land cover map followed by the validation process. Many algorithms exist for classification, embedded within proprietary image processing software or increasingly as open source tools. However, there is little standardization for land cover validation, nor a set of open tools available for implementing this process. The LACO-Wiki tool was developed as a way of filling this gap, bringing together standardized land cover validation methods and workflows into a single portal. This includes the storage and management of land cover maps and validation data; step-by-step instructions to guide users through the validation process; sound sampling designs; an easy-to-use environment for validation sample interpretation; and the generation of accuracy reports based on the validation process. The tool was developed for a range of users including producers of land cover maps, researchers, teachers and students. The use of such a tool could be embedded within the curriculum of remote sensing courses at a university level but is simple enough for use by students aged 13-18. A beta version of the tool is available for testing at: http://www.laco-wiki.net.

Characterizing Degradation Gradients through Land Cover Change Analysis in Rural Eastern Cape, South Africa

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Zahn Münch

2017-02-01

Full Text Available Land cover change analysis was performed for three catchments in the rural Eastern Cape, South Africa, for two time steps (2000 and 2014, to characterize landscape conversion trajectories for sustained landscape health. Land cover maps were derived: (1 from existing data (2000; and (2 through object-based image analysis (2014 of Landsat 8 imagery. Land cover change analysis was facilitated using land cover labels developed to identify landscape change trajectories. Land cover labels assigned to each intersection of the land cover maps at the two time steps provide a thematic representation of the spatial distribution of change. While land use patterns are characterized by high persistence (77%, the expansion of urban areas and agriculture has occurred predominantly at the expense of grassland. The persistence and intensification of natural or invaded wooded areas were identified as a degradation gradient within the landscape, which amounted to almost 10% of the study area. The challenge remains to determine significant signals in the landscape that are not artefacts of error in the underlying input data or scale of analysis. Systematic change analysis and accurate uncertainty reporting can potentially address these issues to produce authentic output for further modelling.

Rapid assessment and mapping of tree cover in southern African savanna woodlands using a new iPhone App and Landsat 8 imagery

Science.gov (United States)

Fuller, D. O.

2016-12-01

Tree cover is a key parameter in climate modeling. It strongly influences CO2 exchanges between the land surface and atmosphere and surface energy balance. We measured percent woody canopy cover (PWCC) in the savanna woodlands of eastern Zambia over a 10-day period in May 2016 using a new iPhone App (CanopyApp) and related these field measurements to Landsat 8 (L8) Band 4 (red) imagery acquired approximately the same time. We then used parameters from the band 4 digital numbers (DNs)-PWCC linear regression to derive a new map of PWCC for the entire L8 scene. Consistent with theory and previous empirical studies, we found that the relationship between L8 band 4 DNs- PWCC was negative and linear (r2 = 0.61, p reflectance was weaker (r2 = 0.46, p shadowing effects and other spatial inhomogeneities from variable soil and background reflectance. Our PWCC map agreed qualitatively with similar percent tree-cover maps based on Landsat level 1 products and past field studies in the area conducted using a hemispherical lens. Our results also compared favorably with other remote sensing studies that have used complex multivariate approaches to estimate tree cover, which suggests that use of a single L8 band 4 is sufficient to estimate PWCC when spectral contrast exists between the grass, soil and tree layers during the austral fall period in southern African savannas.

Carbon mapping of Argentine savannas: Using fractional tree cover to scale from field to region

Science.gov (United States)

González-Roglich, M.; Swenson, J. J.

2015-12-01

Programs which intend to maintain or enhance carbon (C) stocks in natural ecosystems are promising, but require detailed and spatially explicit C distribution models to monitor the effectiveness of management interventions. Savanna ecosystems are significant components of the global C cycle, covering about one fifth of the global land mass, but they have received less attention in C monitoring protocols. Our goal was to estimate C storage across a broad savanna ecosystem using field surveys and freely available satellite images. We first mapped tree canopies at 2.5 m resolution with a spatial subset of high resolution panchromatic images to then predict regional wall-to-wall tree percent cover using 30-m Landsat imagery and the Random Forests algorithms. We found that a model with summer and winter spectral indices from Landsat, climate and topography performed best. Using a linear relationship between C and % tree cover, we then predicted tree C stocks across the gradient of tree cover, explaining 87 % of the variability. The spatially explicit validation of the tree C model with field-measured C-stocks revealed an RMSE of 8.2 tC/ha which represented ~30% of the mean C stock for areas with tree cover, comparable to studies based on more advanced remote sensing methods, such as LiDAR and RADAR. Sample spatial distribution highly affected the performance of the RF models in predicting tree cover, raising concerns regarding the predictive capabilities of the model in areas for which training data is not present. The 50,000 km2 has ~41 Tg C, which could be released to the atmosphere if agricultural pressure intensifies in this semiarid savanna.

Open Land-Use Map: A Regional Land-Use Mapping Strategy for Incorporating OpenStreetMap with Earth Observations

Science.gov (United States)

Yang, D.; Fu, C. S.; Binford, M. W.

2017-12-01

The southeastern United States has high landscape heterogeneity, withheavily managed forestlands, highly developed agriculture lands, and multiple metropolitan areas. Human activities are transforming and altering land patterns and structures in both negative and positive manners. A land-use map for at the greater scale is a heavy computation task but is critical to most landowners, researchers, and decision makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating the classification maps at the regional scale: the necessity of large training point sets and the expensive computation cost-in terms of both money and time-in classifier modeling. Volunteered Geographic Information (VGI) opens a new era in mapping and visualizing our world, where the platform is open for collecting valuable georeferenced information by volunteer citizens, and the data is freely available to the public. As one of the most well-known VGI initiatives, OpenStreetMap (OSM) contributes not only road network distribution, but also the potential for using this data to justify land cover and land use classifications. Google Earth Engine (GEE) is a platform designed for cloud-based mapping with a robust and fast computing power. Most large scale and national mapping approaches confuse "land cover" and "land-use", or build up the land-use database based on modeled land cover datasets. Unlike most other large-scale approaches, we distinguish and differentiate land-use from land cover. By focusing our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach is developed by incorporating the OpenstreepMap dataset into Earth observation remote sensing imageries instead of the often-used land cover base maps.

Flood maps in Europe – methods, availability and use

Directory of Open Access Journals (Sweden)

J. C. J. H. Aerts

2009-03-01

Full Text Available To support the transition from traditional flood defence strategies to a flood risk management approach at the basin scale in Europe, the EU has adopted a new Directive (2007/60/EC at the end of 2007. One of the major tasks which member states must carry out in order to comply with this Directive is to map flood hazards and risks in their territory, which will form the basis of future flood risk management plans. This paper gives an overview of existing flood mapping practices in 29 countries in Europe and shows what maps are already available and how such maps are used. Roughly half of the countries considered have maps covering as good as their entire territory, and another third have maps covering significant parts of their territory. Only five countries have very limited or no flood maps available yet. Of the different flood maps distinguished, it appears that flood extent maps are the most commonly produced floods maps (in 23 countries, but flood depth maps are also regularly created (in seven countries. Very few countries have developed flood risk maps that include information on the consequences of flooding. The available flood maps are mostly developed by governmental organizations and primarily used for emergency planning, spatial planning, and awareness raising. In spatial planning, flood zones delimited on flood maps mainly serve as guidelines and are not binding. Even in the few countries (e.g. France, Poland where there is a legal basis to regulate floodplain developments using flood zones, practical problems are often faced which reduce the mitigating effect of such binding legislation. Flood maps, also mainly extent maps, are also created by the insurance industry in Europe and used to determine insurability, differentiate premiums, or to assess long-term financial solvency. Finally, flood maps are also produced by international river commissions. With respect to the EU Flood Directive, many countries already have a good starting

Implementation of forest cover and carbon mapping in the Greater Mekong subregion and Malaysia project - A case study of Thailand

Science.gov (United States)

Pungkul, S.; Suraswasdi, C.; Phonekeo, V.

2014-02-01

The Great Mekong Subregion (GMS) contains one of the world's largest tropical forests and plays a vital role in sustainable development and provides a range of economic, social and environmental benefits, including essential ecosystem services such as climate change mitigation and adaptation. However, the forest in this Subregion is experiencing deforestation rates at high level due to human activities. The reduction of the forest area has negative influence to the environmental and natural resources issues, particularly, more severe disasters have occurred due to global warming and the release of the greenhouse gases. Therefore, in order to conduct forest management in the Subregion efficiently, the Forest Cover and Carbon Mapping in Greater Mekong Subregion and Malaysia project was initialized by the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation (APFNet) with the collaboration of various research institutions including Institute of Forest Resource Information Technique (IFRIT), Chinese Academy of Forestry (CAF) and the countries in Sub region and Malaysia comprises of Cambodia, the People's Republic of China (Yunnan province and Guangxi province), Lao People's Democratic Republic, Malaysia, Myanmar, Thailand, and Viet Nam. The main target of the project is to apply the intensive use of recent satellite remote sensing technology, establishing regional forest cover maps, documenting forest change processes and estimating carbon storage in the GMS and Malaysia. In this paper, the authors present the implementation of the project in Thailand and demonstrate the result of forest cover mapping in the whole country in 2005 and 2010. The result of the project will contribute towards developing efficient tools to support decision makers to clearly understand the dynamic change of the forest cover which could benefit sustainable forest resource management in Thailand and the whole Subregion.

Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data

Science.gov (United States)

Steyaert, L.T.; Hall, F.G.; Loveland, Thomas R.

1997-01-01

A multitemporal 1 km advanced very high resolution radiometer (AVHRR) land cover analysis approach was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. The land cover classification was developed by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly normalized difference vegetation index (NDVI) image composites (April-September 1992). Quantitative areal proportions of the major boreal forest components were determined for a 821 km ?? 619 km region, ranging from the southern grasslands-boreal forest ecotone to the northern boreal transitional forest. The boreal wetlands (mostly lowland black spruce, tamarack, mosses, fens, and bogs) occupied approximately 33% of the region, while lakes accounted for another 13%. Upland mixed coniferous-deciduous forests represented 23% of the ecosystem. A SW-NE productivity gradient across the region is manifested by three levels of tree stand density for both the boreal wetland conifer and the mixed forest classes, which are generally aligned with isopleths of regional growing degree days. Approximately 30% of the region was directly affected by fire disturbance within the preceding 30-35 years, especially in the Canadian Shield Zone where large fire-regeneration patterns contribute to the heterogeneous boreal landscape. Intercomparisons with land cover classifications derived from 30-m Landsat Thematic Mapper (TM) data provided important insights into the relative accuracy of the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositing process, the 1 km AVHRR land cover classes have an effective spatial resolution in the 3-4 km range; therefore fens, bogs, small water bodies, and small patches of dry jack pine cannot be resolved within

Estimation of snow cover distribution in Beas basin, Indian Himalaya ...

Indian Academy of Sciences (India)

In the present paper, a methodology has been developed for the mapping of snow cover in Beas ... Different snow cover mapping methods using snow indices are compared to find the suitable ... cover are important factors for human activities,.

Mapping Urban Green Infrastructure: A Novel Landscape-Based Approach to Incorporating Land Use and Land Cover in the Mapping of Human-Dominated Systems

Directory of Open Access Journals (Sweden)

Matthew Dennis

2018-01-01

Full Text Available Common approaches to mapping green infrastructure in urbanised landscapes invariably focus on measures of land use or land cover and associated functional or physical traits. However, such one-dimensional perspectives do not accurately capture the character and complexity of the landscapes in which urban inhabitants live. The new approach presented in this paper demonstrates how open-source, high spatial and temporal resolution data with global coverage can be used to measure and represent the landscape qualities of urban environments. Through going beyond simple metrics of quantity, such as percentage green and blue cover, it is now possible to explore the extent to which landscape quality helps to unpick the mixed evidence presented in the literature on the benefits of urban nature to human well-being. Here we present a landscape approach, employing remote sensing, GIS and data reduction techniques to map urban green infrastructure elements in a large U.K. city region. Comparison with existing urban datasets demonstrates considerable improvement in terms of coverage and thematic detail. The characterisation of landscapes, using census tracts as spatial units, and subsequent exploration of associations with social–ecological attributes highlights the further detail that can be uncovered by the approach. For example, eight urban landscape types identified for the case study city exhibited associations with distinct socioeconomic conditions accountable not only to quantities but also qualities of green and blue space. The identification of individual landscape features through simultaneous measures of land use and land cover demonstrated unique and significant associations between the former and indicators of human health and ecological condition. The approach may therefore provide a promising basis for developing further insight into processes and characteristics that affect human health and well-being in urban areas, both in the United

National Land Cover Database (NLCD) Land Cover Collection

Data.gov (United States)

U.S. Geological Survey, Department of the Interior — The National Land Cover Database (NLCD) Land Cover Collection is produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC)...

A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi

Science.gov (United States)

Smith, Kathryn E.L.; Nayegandhi, Amar; Brock, John C.

2010-01-01

-based land-use classifications. Aerial photography is typically selected for smaller landscapes (watershed-basin scale), for greater definition of the land-use categories, and for increased spatial resolution. Disadvantages of using photography include time-consuming digitization, high costs for imagery collection, and lack of seasonal data. Recently, the availability of high-resolution satellite imagery has generated a new category of LULC data product. These new datasets have similar strengths to the aerial-photo-based LULC in that they possess the potential for refined definition of land-use categories and increased spatial resolution but also have the benefit of satellite-based classifications, such as repeatability for change analysis. LULC classification based on high-resolution satellite imagery is still in the early stages of development but merits greater attention because environmental-monitoring and landscape-modeling programs rely heavily on LULC data. This publication summarizes land-use and land-cover mapping activities for Alabama and Mississippi coastal areas within the U.S. Geological Survey (USGS) Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project boundaries. Existing LULC datasets will be described, as well as imagery data sources and ancillary data that may provide ground-truth or satellite training data for a forthcoming land-cover classification. Finally, potential areas for a high-resolution land-cover classification in the Alabama-Mississippi region will be identified.

Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections

Directory of Open Access Journals (Sweden)

Johannes H. Uhl

2018-04-01

Full Text Available Historical maps are unique sources of retrospective geographical information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographical information contained in such data archives makes it possible to extend geospatial analysis retrospectively beyond the era of digital cartography. However, given the large data volumes of such archives (e.g., more than 200,000 map sheets in the United States Geological Survey topographic map archive and the low graphical quality of older, manually-produced map sheets, the process to extract geographical information from these map archives needs to be automated to the highest degree possible. To understand the potential challenges (e.g., salient map characteristics and data quality variations in automating large-scale information extraction tasks for map archives, it is useful to efficiently assess spatio-temporal coverage, approximate map content, and spatial accuracy of georeferenced map sheets at different map scales. Such preliminary analytical steps are often neglected or ignored in the map processing literature but represent critical phases that lay the foundation for any subsequent computational processes including recognition. Exemplified for the United States Geological Survey topographic map and the Sanborn fire insurance map archives, we demonstrate how such preliminary analyses can be systematically conducted using traditional analytical and cartographic techniques, as well as visual-analytical data mining tools originating from machine learning and data science.

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

Directory of Open Access Journals (Sweden)

Arnaud Grüss

2018-01-01

Full Text Available To be able to simulate spatial patterns of predator-prey interactions, many spatially-explicit ecosystem modeling platforms, including Atlantis, need to be provided with distribution maps defining the annual or seasonal spatial distributions of functional groups and life stages. We developed a methodology combining extrapolation and interpolation of the predictions made by statistical habitat models to produce distribution maps for the fish and invertebrates represented in the Atlantis model of the Gulf of Mexico (GOM Large Marine Ecosystem (LME (“Atlantis-GOM”. This methodology consists of: (1 compiling a large monitoring database, gathering all the fisheries-independent and fisheries-dependent data collected in the northern (U.S. GOM since 2000; (2 compiling a large environmental database, storing all the environmental parameters known to influence the spatial distribution patterns of fish and invertebrates of the GOM; (3 fitting binomial generalized additive models (GAMs to the large monitoring and environmental databases, and geostatistical binomial generalized linear mixed models (GLMMs to the large monitoring database; and (4 employing GAM predictions to infer spatial distributions in the southern GOM, and GLMM predictions to infer spatial distributions in the U.S. GOM. Thus, our methodology allows for reasonable extrapolation in the southern GOM based on a large amount of monitoring and environmental data, and for interpolation in the U.S. GOM accurately reflecting the probability of encountering fish and invertebrates in that region. We used an iterative cross-validation procedure to validate GAMs. When a GAM did not pass the validation test, we employed a GAM for a related functional group/life stage to generate distribution maps for the southern GOM. In addition, no geostatistical GLMMs were fit for the functional groups and life stages whose depth, longitudinal and latitudinal ranges within the U.S. GOM are not entirely covered by

Sprite-producing Convective Storms within the Colorado Lightning Mapping Array

Science.gov (United States)

Lyons, W. A.; Cummer, S. A.; Rison, W.; Krehbiel, P. R.; Lang, T. J.; Rutledge, S. A.; Lu, G.; Stanley, M. A.; Ashcraft, T.; Nelson, T. E.

2012-12-01

The multi-year, multi-institution effort entitled Physical Origins of Coupling to the Upper Atmosphere from Lightning (PhOCAL), has among its goals to qualitatively understand the meteorology and lightning flash characteristics that produce the unusual and/or very energetic lightning responsible for phenomena such as sprites, halos, elves, blue jets and gigantic jets, collectively known as Transient Luminous Events (TLEs). A key task is to obtain simultaneous video, ideally with a high-speed imager (HSI), of both a TLE and its parent lightning discharge, within the domain of a 3-D Lightning Mapping Array (LMA). While conceptually simple, this task is logistically quite complicated. In 2012, a new 15-station Colorado LMA (COLMA) became operational, covering northeastern Colorado, with the Yucca Ridge Field Station (YRFS) near its western edge. The National Charge Moment Change Network (CMCN), which since 2007 has been documenting sprite-class +CGs (those with impulse change moment changes >100 C km), indicates that a strong gradient of energetic +CGs exists west-to-east through the COLMA, with the most likely region for sprite-producing storms being in the COLMA eastern fringes (western Kansas and Nebraska). Yet, on 8 and 25 June, 2012, intense convective systems formed in the COLMA along and just east of the Front Range, producing severe weather and intense lightning. On the 8th, four sprite parent +CGs were captured at 3000 fps from YRFS with the sprites confirmed by dual (conventional speed) cameras in New Mexico. In a second storm on the 25th, viewing conditions prevented +CG video acquisition, but sprites were logged over the COLMA and detailed reconstructions of the discharges are being made. The parent discharges often began as upward negative leaders propagating into a mid-level positive charge layer at 8-10 km. They often originated within or near the convective core before expanding outward into a stratiform region and involving several hundred square

Implementation of forest cover and carbon mapping in the Greater Mekong subregion and Malaysia project – A case study of Thailand

International Nuclear Information System (INIS)

Pungkul, S; Suraswasdi, C; Phonekeo, V

2014-01-01

The Great Mekong Subregion (GMS) contains one of the world's largest tropical forests and plays a vital role in sustainable development and provides a range of economic, social and environmental benefits, including essential ecosystem services such as climate change mitigation and adaptation. However, the forest in this Subregion is experiencing deforestation rates at high level due to human activities. The reduction of the forest area has negative influence to the environmental and natural resources issues, particularly, more severe disasters have occurred due to global warming and the release of the greenhouse gases. Therefore, in order to conduct forest management in the Subregion efficiently, the Forest Cover and Carbon Mapping in Greater Mekong Subregion and Malaysia project was initialized by the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation (APFNet) with the collaboration of various research institutions including Institute of Forest Resource Information Technique (IFRIT), Chinese Academy of Forestry (CAF) and the countries in Sub region and Malaysia comprises of Cambodia, the People's Republic of China (Yunnan province and Guangxi province), Lao People's Democratic Republic, Malaysia, Myanmar, Thailand, and Viet Nam. The main target of the project is to apply the intensive use of recent satellite remote sensing technology, establishing regional forest cover maps, documenting forest change processes and estimating carbon storage in the GMS and Malaysia. In this paper, the authors present the implementation of the project in Thailand and demonstrate the result of forest cover mapping in the whole country in 2005 and 2010. The result of the project will contribute towards developing efficient tools to support decision makers to clearly understand the dynamic change of the forest cover which could benefit sustainable forest resource management in Thailand and the whole Subregion

On the structure of finite-sheeted coverings of compact connected groups

OpenAIRE

Grigorian, S. A.; Gumerov, R. N.

2004-01-01

Finite-sheeted covering mappings onto compact connected groups are studied. It is shown that a finite-sheeted covering mapping from a connected Hausdorff topological space onto a compact connected abelian group G must be a homeomorphism provided that the character group of G admits division by the degree of given covering mapping. Using this result, we obtain criteria of triviality for finite coverings of G in terms of its character group and means on G. In order to establish these facts, for...

Analysis and Mapping of the Spectral Characteristics of Fractional Green Cover in Saline Wetlands (NE Spain Using Field and Remote Sensing Data

Directory of Open Access Journals (Sweden)

Manuela Domínguez-Beisiegel

2016-07-01

Full Text Available Inland saline wetlands are complex systems undergoing continuous changes in moisture and salinity and are especially vulnerable to human pressures. Remote sensing is helpful to identify vegetation change in semi-arid wetlands and to assess wetland degradation. Remote sensing-based monitoring requires identification of the spectral characteristics of soils and vegetation and their correspondence with the vegetation cover and soil conditions. We studied the spectral characteristics of soils and vegetation of saline wetlands in Monegros, NE Spain, through field and satellite images. Radiometric and complementary field measurements in two field surveys in 2007 and 2008 were collected in selected sites deemed as representative of different soil moisture, soil color, type of vegetation, and density. Despite the high local variability, we identified good relationships between field spectral data and Quickbird images. A methodology was established for mapping the fraction of vegetation cover in Monegros and other semi-arid areas. Estimating vegetation cover in arid wetlands is conditioned by the soil background and by the occurrence of dry and senescent vegetation accompanying the green component of perennial salt-tolerant plants. Normalized Difference Vegetation Index (NDVI was appropriate to map the distribution of the vegetation cover if the green and yellow-green parts of the plants are considered.

Global Land Survey Impervious Mapping Project Web Site

Science.gov (United States)

DeColstoun, Eric Brown; Phillips, Jacqueline

2014-01-01

The Global Land Survey Impervious Mapping Project (GLS-IMP) aims to produce the first global maps of impervious cover at the 30m spatial resolution of Landsat. The project uses Global Land Survey (GLS) Landsat data as its base but incorporates training data generated from very high resolution commercial satellite data and using a Hierarchical segmentation program called Hseg. The web site contains general project information, a high level description of the science, examples of input and output data, as well as links to other relevant projects.

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

High resolution population distribution maps for Southeast Asia in 2010 and 2015.

Science.gov (United States)

Gaughan, Andrea E; Stevens, Forrest R; Linard, Catherine; Jia, Peng; Tatem, Andrew J

2013-01-01

Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.

High resolution population distribution maps for Southeast Asia in 2010 and 2015.

Directory of Open Access Journals (Sweden)

Andrea E Gaughan

Full Text Available Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.

Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data

Science.gov (United States)

Beaumont, Benjamin; Grippa, Tais; Lennert, Moritz; Vanhuysse, Sabine; Stephenne, Nathalie; Wolff, Eléonore

2017-07-01

Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i.e., a rule- and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15 m2. Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed.

Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data

Science.gov (United States)

Giri, Chandra; Long, Jordan

2014-01-01

Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

Vegetation mapping with satellite data of the Forsmark and Tierp regions

Energy Technology Data Exchange (ETDEWEB)

Boresjoe-Bronge, Laine; Wester, Kjell [SwedPower, Stockholm (Sweden)

2002-04-01

SKB (Swedish Nuclear Fuel and Waste Management Co) performs a siting program for deep repository of spent nuclear fuel that includes survey of three potential sites. The SKB siting process has now reached the site investigation phase. There are several fields of investigations performed in this phase. One of them is description of the surface ecosystems. The surface ecosystems are mapped both on a regional (50-100 km{sup 2} ) and a local level (1 km{sup 2} ). Two inventory methods are used, remote sensing (satellite data/aerial photographs) for the regional level, and field inventory for the detailed level. As a part of the surface ecosystem characterisation on the regional level vegetation mapping using satellite data has been performed over the three potential deep depository sites, Forsmark, Tierp and Oskarshamn. The user requirements for the vegetation mapping of the potential sites are the following: Dominated species in the tree layer, shrub layer, field layer and ground layer shall be described both on regional and local level; Dominated species in all layers shall be quantified regarding share and percentage of ground cover, or absence of cover (vegetation free ground); The regional and the local inventory shall have identical or comparable classification systems; The classification system and the method used shall make it possible to scale the results from local to regional level and vice versa; The produced layers shall be presented in digital form and make it possible to model biomass and turnover of organic matter (carbon, nutrients, water); The produced information shall in a first phase be of use for planning and for making nature and environmental considerations. Data sources used in the study include geo-referenced SPOT4 XI data (20 m ground resolution), geo-referenced Landsat TM data (30 m ground resolution), soil type data, topographic map data and colour infrared aerial photographs. The production of vegetation layers has been carried out in two

Vegetation mapping with satellite data of the Forsmark and Tierp regions

International Nuclear Information System (INIS)

Boresjoe-Bronge, Laine; Wester, Kjell

2002-04-01

SKB (Swedish Nuclear Fuel and Waste Management Co) performs a siting program for deep repository of spent nuclear fuel that includes survey of three potential sites. The SKB siting process has now reached the site investigation phase. There are several fields of investigations performed in this phase. One of them is description of the surface ecosystems. The surface ecosystems are mapped both on a regional (50-100 km 2 ) and a local level (1 km 2 ). Two inventory methods are used, remote sensing (satellite data/aerial photographs) for the regional level, and field inventory for the detailed level. As a part of the surface ecosystem characterisation on the regional level vegetation mapping using satellite data has been performed over the three potential deep depository sites, Forsmark, Tierp and Oskarshamn. The user requirements for the vegetation mapping of the potential sites are the following: Dominated species in the tree layer, shrub layer, field layer and ground layer shall be described both on regional and local level; Dominated species in all layers shall be quantified regarding share and percentage of ground cover, or absence of cover (vegetation free ground); The regional and the local inventory shall have identical or comparable classification systems; The classification system and the method used shall make it possible to scale the results from local to regional level and vice versa; The produced layers shall be presented in digital form and make it possible to model biomass and turnover of organic matter (carbon, nutrients, water); The produced information shall in a first phase be of use for planning and for making nature and environmental considerations. Data sources used in the study include geo-referenced SPOT4 XI data (20 m ground resolution), geo-referenced Landsat TM data (30 m ground resolution), soil type data, topographic map data and colour infrared aerial photographs. The production of vegetation layers has been carried out in two steps. In

Development of a 30 m Spatial Resolution Land Cover of Canada: Contribution to the Harmonized North America Land Cover Dataset

Science.gov (United States)

Pouliot, D.; Latifovic, R.; Olthof, I.

2017-12-01

Land cover is needed for a large range of environmental applications regarding climate impacts and adaption, emergency response, wildlife habitat, air quality, water yield, etc. In Canada a 2008 user survey revealed that the most practical scale for provision of land cover data is 30 m, nationwide, with an update frequency of five years (Ball, 2008). In response to this need the Canada Centre for Remote Sensing has generated a 30 m land cover of Canada for the base year 2010 as part of a planned series of maps at the recommended five year update frequency. This land cover is the Canadian contribution to the North American Land Change Monitoring System initiative, which seeks to provide harmonized land cover across Canada, the United States, and Mexico. The methodology developed in this research utilized a combination of unsupervised and machine learning techniques to map land cover, blend results between mapping units, locally optimize results, and process some thematic attributes with specific features sets. Accuracy assessment with available field data shows it was on average 75% for the five study areas assessed. In this presentation an overview of the unique processing aspects, example results, and initial accuracy assessment will be discussed.

Estimating Accuracy of Land-Cover Composition From Two-Stage Clustering Sampling

Science.gov (United States)

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), ...

Expanding Thurston maps

CERN Document Server

Bonk, Mario

2017-01-01

This monograph is devoted to the study of the dynamics of expanding Thurston maps under iteration. A Thurston map is a branched covering map on a two-dimensional topological sphere such that each critical point of the map has a finite orbit under iteration. It is called expanding if, roughly speaking, preimages of a fine open cover of the underlying sphere under iterates of the map become finer and finer as the order of the iterate increases. Every expanding Thurston map gives rise to a fractal space, called its visual sphere. Many dynamical properties of the map are encoded in the geometry of this visual sphere. For example, an expanding Thurston map is topologically conjugate to a rational map if and only if its visual sphere is quasisymmetrically equivalent to the Riemann sphere. This relation between dynamics and fractal geometry is the main focus for the investigations in this work.

Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps

Directory of Open Access Journals (Sweden)

François Waldner

2015-06-01

Full Text Available Timely and accurate information on the global cropland extent is critical for applications in the fields of food security, agricultural monitoring, water management, land-use change modeling and Earth system modeling. On the one hand, it gives detailed location information on where to analyze satellite image time series to assess crop condition. On the other hand, it isolates the agriculture component to focus food security monitoring on agriculture and to assess the potential impacts of climate change on agricultural lands. The cropland class is often poorly captured in global land cover products due to its dynamic nature and the large variety of agro-systems. The overall objective was to evaluate the current availability of cropland datasets in order to propose a strategic planning and effort distribution for future cropland mapping activities and, therefore, to maximize their impact. Following a very comprehensive identification and collection of national to global land cover maps, a multi-criteria analysis was designed at the country level to identify the priority areas for cropland mapping. As a result, the analysis highlighted priority regions, such as Western Africa, Ethiopia, Madagascar and Southeast Asia, for the remote sensing community to focus its efforts. A Unified Cropland Layer at 250 m for the year 2014 was produced combining the fittest products. It was assessed using global validation datasets and yields an overall accuracy ranging from 82%–94%. Masking cropland areas with a global forest map reduced the commission errors from 46% down to 26%. Compared to the GLC-Share and the International Institute for Applied Systems Analysis-International Food Policy Research Institute (IIASA-IFPRI cropland maps, significant spatial disagreements were found, which might be attributed to discrepancies in the cropland definition. This advocates for a shared definition of cropland, as well as global validation datasets relevant for the

Mapping Hurricane Rita inland storm tide

Science.gov (United States)

Berenbrock, Charles; Mason, Jr., Robert R.; Blanchard, Stephen F.; Simonovic, Slobodan P.

2009-01-01

Flood-inundation data are most useful for decision makers when presented in the context of maps of effected communities and (or) areas. But because the data are scarce and rarely cover the full extent of the flooding, interpolation and extrapolation of the information are needed. Many geographic information systems (GIS) provide various interpolation tools, but these tools often ignore the effects of the topographic and hydraulic features that influence flooding. A barrier mapping method was developed to improve maps of storm tide produced by Hurricane Rita. Maps were developed for the maximum storm tide and at 3-hour intervals from midnight (0000 hour) through noon (1200 hour) on September 24, 2005. The improved maps depict storm-tide elevations and the extent of flooding. The extent of storm-tide inundation from the improved maximum storm-tide map was compared to the extent of flood-inundation from a map prepared by the Federal Emergency Management Agency (FEMA). The boundaries from these two maps generally compared quite well especially along the Calcasieu River. Also a cross-section profile that parallels the Louisiana coast was developed from the maximum storm-tide map and included FEMA high-water marks.

Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set

Directory of Open Access Journals (Sweden)

Jinshui Zhang

2017-04-01

Full Text Available This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD, to determine optimal parameters for support vector data description (SVDD model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C and kernel width (s, in mapping homogeneous specific land cover.

Generating a National Land Cover Dataset for Mexico at 30m Spatial Resolution in the Framework of the NALCMS Project.

Science.gov (United States)

Llamas, R. M.; Colditz, R. R.; Ressl, R.; Jurado Cruz, D. A.; Argumedo, J.; Victoria, A.; Meneses, C.

2017-12-01

The North American Land Change Monitoring System (NALCMS) is a tri-national initiative for mapping land cover across Mexico, United States and Canada, integrating efforts of institutions from the three countries. At the continental scale the group released land cover and change maps derived from MODIS image mosaics at 250m spatial resolution for 2005 and 2010. Current efforts are based on 30m Landsat images for 2010 ± 1 year. Each country uses its own mapping approach and sources for ancillary data, while ensuring that maps are produced in a coherent fashion across the continent. This paper presents the methodology and final land cover map of Mexico for the year 2010 that was later integrated into a continental map. The principal input for Mexico was the Monitoring Activity Data for Mexico (MAD-MEX) land cover map (version 4.3), derived from all available mostly cloud-free images for the year 2010. A total of 35 classes were regrouped to 15 classes of the NALCMS legend present in Mexico. Next, various issues of the automatically generated MAD-MEX land cover mosaic were corrected, such as: filling areas of no data due no cloud-free observation or gaps in Landsat 7 ETM+ images, filling inland water bodies which were left unclassified due to masking issues, relabeling isolated unclassified of falsely classified pixels, structural mislabeling due to data gaps, reclassifying areas of adjacent scenes with significant class disagreements and correcting obvious misclassifications, mostly of water and urban areas. In a second step minor missing areas and rare class snow and ice were digitized and a road network was added. A product such as NALCMS land cover map at 30m for North America is an unprecedented effort and will be without doubt an important source of information for many users around the world who need coherent land cover data over a continental domain as an input for a wide variety of environmental studies. The product release to the general public is expected

Cytogenetic characterization and AFLP-based genetic linkage mapping for the butterfly Bicyclus anynana, covering all 28 karyotyped chromosomes.

Directory of Open Access Journals (Sweden)

Arjen E Van't Hof

Full Text Available BACKGROUND: The chromosome characteristics of the butterfly Bicyclus anynana, have received little attention, despite the scientific importance of this species. This study presents the characterization of chromosomes in this species by means of cytogenetic analysis and linkage mapping. METHODOLOGY/PRINCIPAL FINDINGS: Physical genomic features in the butterfly B. anynana were examined by karyotype analysis and construction of a linkage map. Lepidoptera possess a female heterogametic W-Z sex chromosome system. The WZ-bivalent in pachytene oocytes of B. anynana consists of an abnormally small, heterochromatic W-chromosome with the Z-chromosome wrapped around it. Accordingly, the W-body in interphase nuclei is much smaller than usual in Lepidoptera. This suggests an intermediate stage in the process of secondary loss of the W-chromosome to a ZZ/Z sex determination system. Two nucleoli are present in the pachytene stage associated with an autosome and the WZ-bivalent respectively. Chromosome counts confirmed a haploid number of n = 28. Linkage mapping had to take account of absence of crossing-over in females, and of our use of a full-sib crossing design. We developed a new method to determine and exclude the non-recombinant uninformative female inherited component in offspring. The linkage map was constructed using a novel approach that uses exclusively JOINMAP-software for Lepidoptera linkage mapping. This approach simplifies the mapping procedure, avoids over-estimation of mapping distance and increases the reliability of relative marker positions. A total of 347 AFLP markers, 9 microsatellites and one single-copy nuclear gene covered all 28 chromosomes, with a mapping distance of 1354 cM. Conserved synteny of Tpi on the Z-chromosome in Lepidoptera was confirmed for B. anynana. The results are discussed in relation to other mapping studies in Lepidoptera. CONCLUSIONS/SIGNIFICANCE: This study adds to the knowledge of chromosome structure and

Discovering Land Cover Web Map Services from the Deep Web with JavaScript Invocation Rules

Directory of Open Access Journals (Sweden)

Dongyang Hou

2016-06-01

Full Text Available Automatic discovery of isolated land cover web map services (LCWMSs can potentially help in sharing land cover data. Currently, various search engine-based and crawler-based approaches have been developed for finding services dispersed throughout the surface web. In fact, with the prevalence of geospatial web applications, a considerable number of LCWMSs are hidden in JavaScript code, which belongs to the deep web. However, discovering LCWMSs from JavaScript code remains an open challenge. This paper aims to solve this challenge by proposing a focused deep web crawler for finding more LCWMSs from deep web JavaScript code and the surface web. First, the names of a group of JavaScript links are abstracted as initial judgements. Through name matching, these judgements are utilized to judge whether or not the fetched webpages contain predefined JavaScript links that may prompt JavaScript code to invoke WMSs. Secondly, some JavaScript invocation functions and URL formats for WMS are summarized as JavaScript invocation rules from prior knowledge of how WMSs are employed and coded in JavaScript. These invocation rules are used to identify the JavaScript code for extracting candidate WMSs through rule matching. The above two operations are incorporated into a traditional focused crawling strategy situated between the tasks of fetching webpages and parsing webpages. Thirdly, LCWMSs are selected by matching services with a set of land cover keywords. Moreover, a search engine for LCWMSs is implemented that uses the focused deep web crawler to retrieve and integrate the LCWMSs it discovers. In the first experiment, eight online geospatial web applications serve as seed URLs (Uniform Resource Locators and crawling scopes; the proposed crawler addresses only the JavaScript code in these eight applications. All 32 available WMSs hidden in JavaScript code were found using the proposed crawler, while not one WMS was discovered through the focused crawler

The National Map - Orthoimagery

Science.gov (United States)

Mauck, James; Brown, Kim; Carswell, William J.

2009-01-01

Orthorectified digital aerial photographs and satellite images of 1-meter (m) pixel resolution or finer make up the orthoimagery component of The National Map. The process of orthorectification removes feature displacements and scale variations caused by terrain relief and sensor geometry. The result is a combination of the image characteristics of an aerial photograph or satellite image and the geometric qualities of a map. These attributes allow users to: *Measure distance *Calculate areas *Determine shapes of features *Calculate directions *Determine accurate coordinates *Determine land cover and use *Perform change detection *Update maps The standard digital orthoimage is a 1-m or finer resolution, natural color or color infra-red product. Most are now produced as GeoTIFFs and accompanied by a Federal Geographic Data Committee (FGDC)-compliant metadata file. The primary source for 1-m data is the National Agriculture Imagery Program (NAIP) leaf-on imagery. The U.S. Geological Survey (USGS) utilizes NAIP imagery as the image layer on its 'Digital- Map' - a new generation of USGS topographic maps (http://nationalmap.gov/digital_map). However, many Federal, State, and local governments and organizations require finer resolutions to meet a myriad of needs. Most of these images are leaf-off, natural-color products at resolutions of 1-foot (ft) or finer.

Land Cover Mapping using GEOBIA to Estimate Loss of Salacca zalacca Trees in Landslide Area of Clapar, Madukara District of Banjarnegara

Science.gov (United States)

Permata, Anggi; Juniansah, Anwar; Nurcahyati, Eka; Dimas Afrizal, Mousafi; Adnan Shafry Untoro, Muhammad; Arifatha, Na'ima; Ramadhani Yudha Adiwijaya, Raden; Farda, Nur Mohammad

2016-11-01

Landslide is an unpredictable natural disaster which commonly happens in highslope area. Aerial photography in small format is one of acquisition method that can reach and obtain high resolution spatial data faster than other methods, and provide data such as orthomosaic and Digital Surface Model (DSM). The study area contained landslide area in Clapar, Madukara District of Banjarnegara. Aerial photographs of landslide area provided advantage in objects visibility. Object's characters such as shape, size, and texture were clearly seen, therefore GEOBIA (Geography Object Based Image Analysis) was compatible as method for classifying land cover in study area. Dissimilar with PPA (PerPixel Analyst) method that used spectral information as base object detection, GEOBIA could use spatial elements as classification basis to establish a land cover map with better accuracy. GEOBIA method used classification hierarchy to divide post disaster land cover into three main objects: vegetation, landslide/soil, and building. Those three were required to obtain more detailed information that can be used in estimating loss caused by landslide and establishing land cover map in landslide area. Estimating loss in landslide area related to damage in Salak (Salacca zalacca) plantations. This estimation towards quantity of Salak tree that were drifted away by landslide was calculated in assumption that every tree damaged by landslide had same age and production class with other tree that weren't damaged. Loss calculation was done by approximating quantity of damaged trees in landslide area with data of trees around area that were acquired from GEOBIA classification method.

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Science.gov (United States)

Hoffer, R. M.; Dean, M. E.; Knowlton, D. J.; Latty, R. S.

1982-01-01

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

Open land use map

OpenAIRE

Mildorf, T.; Charvát, K.; Jezek, J.; Templer, Simon; Malewski, Christian

2014-01-01

Open Land Use Map is an initiative that has been started by the Plan4business project and that will be extended as part of the SDI4Apps project in the future. This service aims to create an improved worldwide land use map. The initial map will be prepared using the CORINE Land Cover, Global Cover dataset and Open Street Map. Contributors, mainly volunteers, will able to change the geometry and assign up-to-date land use according to the HILUCS specification. For certain regions more detailed ...

Assessment of the mapping of fractional woody cover in southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images

CSIR Research Space (South Africa)

Urbazaev, M

2015-09-01

Full Text Available cover maps. The LiDAR survey was carried out in April 2008 with the Carnegie Airborne Observatory (CAO, http://cao.ciw.edu). The highest correlations to the reference data were obtained from SAR backscatters of the dry season, followed by the wet season...

Mapping tsunami impacts on land cover and related ecosystem service supply in Phang Nga, Thailand

Science.gov (United States)

Kaiser, G.; Burkhard, B.; Römer, H.; Sangkaew, S.; Graterol, R.; Haitook, T.; Sterr, H.; Sakuna-Schwartz, D.

2013-12-01

The 2004 Indian Ocean tsunami caused damages to coastal ecosystems and thus affected the livelihoods of the coastal communities who depend on services provided by these ecosystems. The paper presents a case study on evaluating and mapping the spatial and temporal impacts of the tsunami on land use and land cover (LULC) and related ecosystem service supply in the Phang Nga province, Thailand. The method includes local stakeholder interviews, field investigations, remote-sensing techniques, and GIS. Results provide an ecosystem services matrix with capacity scores for 18 LULC classes and 17 ecosystem functions and services as well as pre-/post-tsunami and recovery maps indicating changes in the ecosystem service supply capacities in the study area. Local stakeholder interviews revealed that mangroves, casuarina forest, mixed beach forest, coral reefs, tidal inlets, as well as wetlands (peat swamp forest) have the highest capacity to supply ecosystem services, while e.g. plantations have a lower capacity. The remote-sensing based damage and recovery analysis showed a loss of the ecosystem service supply capacities in almost all LULC classes for most of the services due to the tsunami. A fast recovery of LULC and related ecosystem service supply capacities within one year could be observed for e.g. beaches, while mangroves or casuarina forest needed several years to recover. Applying multi-temporal mapping the spatial variations of recovery could be visualised. While some patches of coastal forest were fully recovered after 3 yr, other patches were still affected and thus had a reduced capacity to supply ecosystem services. The ecosystem services maps can be used to quantify ecological values and their spatial distribution in the framework of a tsunami risk assessment. Beyond that they are considered to be a useful tool for spatial analysis in coastal risk management in Phang Nga.

A method for mapping corn using the US Geological Survey 1992 National Land Cover Dataset

Science.gov (United States)

Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.

2006-01-01

Long-term exposure to elevated nitrate levels in community drinking water supplies has been associated with an elevated risk of several cancers including non-Hodgkin's lymphoma, colon cancer, and bladder cancer. To estimate human exposure to nitrate, specific crop type information is needed as fertilizer application rates vary widely by crop type. Corn requires the highest application of nitrogen fertilizer of crops grown in the Midwest US. We developed a method to refine the US Geological Survey National Land Cover Dataset (NLCD) (including map and original Landsat images) to distinguish corn from other crops. Overall average agreement between the resulting corn and other row crops class and ground reference data was 0.79 kappa coefficient with individual Landsat images ranging from 0.46 to 0.93 kappa. The highest accuracies occurred in Regions where corn was the single dominant crop (greater than 80.0%) and the crop vegetation conditions at the time of image acquisition were optimum for separation of corn from all other crops. Factors that resulted in lower accuracies included the accuracy of the NLCD map, accuracy of corn areal estimates, crop mixture, crop condition at the time of Landsat overpass, and Landsat scene anomalies.

EXTRACTION OF BENTHIC COVER INFORMATION FROM VIDEO TOWS AND PHOTOGRAPHS USING OBJECT-BASED IMAGE ANALYSIS

Directory of Open Access Journals (Sweden)

M. T. L. Estomata

2012-07-01

Full Text Available Mapping benthic cover in deep waters comprises a very small proportion of studies in the field of research. Majority of benthic cover mapping makes use of satellite images and usually, classification is carried out only for shallow waters. To map the seafloor in optically deep waters, underwater videos and photos are needed. Some researchers have applied this method on underwater photos, but made use of different classification methods such as: Neural Networks, and rapid classification via down sampling. In this study, accurate bathymetric data obtained using a multi-beam echo sounder (MBES was attempted to be used as complementary data with the underwater photographs. Due to the absence of a motion reference unit (MRU, which applies correction to the data gathered by the MBES, accuracy of the said depth data was compromised. Nevertheless, even with the absence of accurate bathymetric data, object-based image analysis (OBIA, which used rule sets based on information such as shape, size, area, relative distance, and spectral information, was still applied. Compared to pixel-based classifications, OBIA was able to classify more specific benthic cover types other than coral and sand, such as rubble and fish. Through the use of rule sets on area, less than or equal to 700 pixels for fish and between 700 to 10,000 pixels for rubble, as well as standard deviation values to distinguish texture, fish and rubble were identified. OBIA produced benthic cover maps that had higher overall accuracy, 93.78±0.85%, as compared to pixel-based methods that had an average accuracy of only 87.30±6.11% (p-value = 0.0001, α = 0.05.

High-Precision Land-Cover-Land-Use GIS Mapping and Land Availability and Suitability Analysis for Grass Biomass Production in the Aroostook River Valley, Maine, USA

Directory of Open Access Journals (Sweden)

Chunzeng Wang

2015-03-01

Full Text Available High-precision land-cover-land-use GIS mapping was performed in four major townships in Maine’s Aroostook River Valley, using on-screen digitization and direct interpretation of very high spatial resolution satellite multispectral imagery (15–60 cm and high spatial resolution LiDAR data (2 m and the field mapping method. The project not only provides the first-ever high-precision land-use maps for northern Maine, but it also yields accurate hectarage estimates of different land-use types, in particular grassland, defined as fallow land, pasture, and hay field. This enables analysis of potential land availability and suitability for grass biomass production and other sustainable land uses. The results show that the total area of fallow land in the four towns is 7594 hectares, which accounts for 25% of total open land, and that fallow plots equal to or over four hectares in size total 4870, or 16% of open land. Union overlay analysis, using the Natural Resources Conservation Service (NRCS soil data, indicates that only a very small percentage of grassland (4.9% is on “poorly-drained” or “very-poorly-drained” soils, and that most grassland (85% falls into the “farmland of state importance” or “prime farmland” categories, as determined by NRCS. It is concluded that Maine’s Aroostook River Valley has an ample base of suitable, underutilized land for producing grass biomass.

A GIS Software Toolkit for Monitoring Areal Snow Cover and Producing Daily Hydrologic Forecasts using NASA Satellite Imagery, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Aniuk Consulting, LLC, proposes to create a GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts. This toolkit will be...

Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping

Science.gov (United States)

Tamiminia, H.; Homayouni, S.; Safari, A.

2015-12-01

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

Land cover change map comparisons using open source web mapping technologies

Science.gov (United States)

Erik Lindblom; Ian Housman; Tony Guay; Mark Finco; Kevin. Megown

2015-01-01

The USDA Forest Service is evaluating the status of current landscape change maps and assessing gaps in their information content. These activities have been occurring under the auspices of the Landscape Change Monitoring System (LCMS) project, which is a joint effort between USFS Research, USFS Remote Sensing Applications Center (RSAC), USGS Earth Resources...

Modeling percent tree canopy cover: a pilot study

Science.gov (United States)

John W. Coulston; Gretchen G. Moisen; Barry T. Wilson; Mark V. Finco; Warren B. Cohen; C. Kenneth Brewer

2012-01-01

Tree canopy cover is a fundamental component of the landscape, and the amount of cover influences fire behavior, air pollution mitigation, and carbon storage. As such, efforts to empirically model percent tree canopy cover across the United States are a critical area of research. The 2001 national-scale canopy cover modeling and mapping effort was completed in 2006,...

Disaggregating and mapping crop statistics using hypertemporal remote sensing

Science.gov (United States)

Khan, M. R.; de Bie, C. A. J. M.; van Keulen, H.; Smaling, E. M. A.; Real, R.

2010-02-01

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998-2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered "small scale maps". These maps can be used to monitor crop performance of

The Land Cover Dynamics and Conversion of Agricultural Land in Northwestern Bangladesh, 1973-2003.

Science.gov (United States)

Pervez, M.; Seelan, S. K.; Rundquist, B. C.

2006-05-01

The importance of land cover information describing the nature and extent of land resources and changes over time is increasing; this is especially true in Bangladesh, where land cover is changing rapidly. This paper presents research into the land cover dynamics of northwestern Bangladesh for the period 1973-2003 using Landsat satellite images in combination with field survey data collected in January and February 2005. Land cover maps were produced for eight different years during the study period with an average 73 percent overall classification accuracy. The classification results and post-classification change analysis showed that agriculture is the dominant land cover (occupying 74.5 percent of the study area) and is being reduced at a rate of about 3,000 ha per year. In addition, 6.7 percent of the agricultural land is vulnerable to temporary water logging annually. Despite this loss of agricultural land, irrigated agriculture increased substantially until 2000, but has since declined because of diminishing water availability and uncontrolled extraction of groundwater driven by population pressures and the extended need for food. A good agreement (r = 0.73) was found between increases in irrigated land and the depletion of the shallow groundwater table, a factor affecting widely practiced small-scale irrigation in northwestern Bangladesh. Results quantified the land cover change patterns and the stresses placed on natural resources; additionally, they demonstrated an accurate and economical means to map and analyze changes in land cover over time at a regional scale, which can assist decision makers in land and natural resources management decisions.

Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data

Science.gov (United States)

Sanford, Ward E.; Selnick, David L.

2013-01-01

Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water-balance method was combined with a climate and land-cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971-2000 across the U.S. to obtain long-term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land-cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land-cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land-cover data can also improve those predictions. Using the climate and land-cover data at an 800-m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land-cover data are plentiful.

Using the Remote Sensing and GIS Technology for Erosion Risk Mapping of Kartalkaya Dam Watershed in Kahramanmaras, Turkey

Directory of Open Access Journals (Sweden)

Abdullah E. Akay

2008-08-01

Full Text Available The soil erosion is the most serious environmental problem in watershed areas in Turkey. The main factors affecting the amount of soil erosion include vegetation cover, topography, soil, and climate. In order to describe the areas with high soil erosion risks and to develop adequate erosion prevention measures in the watersheds of dams, erosion risk maps should be generated considering these factors. Remote Sensing (RS and Geographic Information System (GIS technologies were used for erosion risk mapping in Kartalkaya Dam Watershed of Kahramanmaras, Turkey, based on the methodology implemented in COoRdination of INformation on the Environment (CORINE model. ASTER imagery was used to generate a land use/cover classification in ERDAS Imagine. The digital maps of the other factors (topography, soil types, and climate were generated in ArcGIS v9.2, and were then integrated as CORINE input files to produce erosion risk maps. The results indicate that 33.82%, 35.44%, and 30.74% of the study area were under low, moderate, and high actual erosion risks, respectively. The CORINE model integrated with RS and GIS technologies has great potential for producing accurate and inexpensive erosion risk maps in Turkey.

Inundation Mapping for Heterogeneous Land Covers with Synthetic Aperture Radar and Auxiliary Data

Science.gov (United States)

Aristizabal, F.; Judge, J.

2017-12-01

Synthetic Aperture Radar (SAR) has been widely used to detect surface water inundation and provides an advantage over multi-spectral instruments due to cloud penetration and higher spatial resolutions. However, detecting inundation for densely vegetated and urban areas with SAR remains a challenge due to corner reflection and diffuse scattering. Additionally, flat urban surfaces such as roads exhibit similar backscatter coefficients as urban surface water. Differences between inundated and non-inundated backscatter over vegetated land covers of static spatial domains have been demonstrated in previous studies. However, these backscatter differences are sensitive to changes in water depth, soil moisture, SAR sensor parameters, terrain, and vegetation properties. These factors tend to make accurate inundation mapping of heterogeneous regions across varying spatial and temporal extents difficult with exclusive use of SAR. This study investigates the utility of auxiliary data specifically high-resolution (10m) terrain information in conjunction with SAR (10m) for detecting inundated areas. Digital elevation models provide an absolute elevation which could enhance inundation mapping given a limited study extent with similar topography. To counter this limitation, a hydrologically relevant terrain index is proposed known as the Height Above Nearest Drainage (HAND) which normalizes topography to the local relative elevation of the nearest point along the relevant drainage line. HAND has been used for assisting remote sensing inundation mapping in the pre-processing stage as a terrain correction tool and as a post-processing mask that eliminates areas of low inundation risk. While the latter technique is useful for reduction of commission errors, it does not employ HAND for reducing omission errors that can occur from dense vegetation, spectral noise, and urban features. Sentinel-1 dual-pol SAR as well as auxiliary HAND will be used as predictors by various supervised and

Bamboo mapping of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery

Science.gov (United States)

Zhao, Yuanyuan; Feng, Duole; Jayaraman, Durai; Belay, Daniel; Sebrala, Heiru; Ngugi, John; Maina, Eunice; Akombo, Rose; Otuoma, John; Mutyaba, Joseph; Kissa, Sam; Qi, Shuhua; Assefa, Fiker; Oduor, Nellie Mugure; Ndawula, Andrew Kalema; Li, Yanxia; Gong, Peng

2018-04-01

Mapping the spatial distribution of bamboo in East Africa is necessary for biodiversity conservation, resource management and policy making for rural poverty reduction. In this study, we produced a contemporary bamboo cover map of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery series at 30 m spatial resolution. This is the first bamboo map generated using remotely sensed data for these three East African countries that possess most of the African bamboo resource. The producer's and user's accuracies of bamboos are 79.2% and 84.0%, respectively. The hotspots with large amounts of bamboo were identified and the area of bamboo coverage for each region was estimated according to the map. The seasonal growth status of two typical bamboo zones (one highland bamboo and one lowland bamboo) were analyzed and the multi-temporal imagery proved to be useful in differentiating bamboo from other vegetation classes. The images acquired in September to February are less contaminated by clouds and shadows, and the image series cover the dying back process of lowland bamboo, which were helpful for bamboo identification in East Africa.

Land cover mapping at Alkali Flat and Lake Lucero, White Sands, New Mexico, USA using multi-temporal and multi-spectral remote sensing data

Science.gov (United States)

Ghrefat, Habes A.; Goodell, Philip C.

2011-08-01

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR-SWIR (0.4-2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen

Removing non-urban roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011

Science.gov (United States)

Soulard, Christopher E.; Acevedo, William; Stehman, Stephen V.

2018-01-01

Quantifying change in urban land provides important information to create empirical models examining the effects of human land use. Maps of developed land from the National Land Cover Database (NLCD) of the conterminous United States include rural roads in the developed land class and therefore overestimate the amount of urban land. To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7 percent) than the NLCD maps (96.2 percent). More importantly, the urban maps resulted in lower commission error of the urban class (23 percent versus 57 percent for the NLCD in 2006) with the trade-off of slightly inflated omission error (20 percent for the urban map, 16 percent for NLCD in 2006). The removal of approximately 230,000 km2 of rural roads from the NLCD developed class resulted in maps that better characterize the urban footprint. These urban maps are more suited to modeling applications and policy decisions that rely on quantitative and spatially explicit information regarding urban lands.

Spectral signature selection for mapping unvegetated soils

Science.gov (United States)

May, G. A.; Petersen, G. W.

1975-01-01

Airborne multispectral scanner data covering the wavelength interval from 0.40-2.60 microns were collected at an altitude of 1000 m above the terrain in southeastern Pennsylvania. Uniform training areas were selected within three sites from this flightline. Soil samples were collected from each site and a procedure developed to allow assignment of scan line and element number from the multispectral scanner data to each sampling location. These soil samples were analyzed on a spectrophotometer and laboratory spectral signatures were derived. After correcting for solar radiation and atmospheric attenuation, the laboratory signatures were compared to the spectral signatures derived from these same soils using multispectral scanner data. Both signatures were used in supervised and unsupervised classification routines. Computer-generated maps using the laboratory and multispectral scanner derived signatures resulted in maps that were similar to maps resulting from field surveys. Approximately 90% agreement was obtained between classification maps produced using multispectral scanner derived signatures and laboratory derived signatures.

Measuring land-use and land-cover change using the U.S. department of agriculture's cropland data layer: Cautions and recommendations

Science.gov (United States)

Lark, Tyler J.; Mueller, Richard M.; Johnson, David M.; Gibbs, Holly K.

2017-10-01

Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps.

Spatial Simulation Modelling of Future Forest Cover Change Scenarios in Luangprabang Province, Lao PDR

Directory of Open Access Journals (Sweden)

Khamma Homsysavath

2011-08-01

Full Text Available Taking Luangprabang province in Lao Peoples’s Democratic Republic (PDR as an example, we simulated future forest cover changes under the business-as-usual (BAU, pessimistic and optimistic scenarios based on the Markov-cellular automata (MCA model. We computed transition probabilities from satellite-derived forest cover maps (1993 and 2000 using the Markov chains, while the “weights of evidence” technique was used to generate transition potential maps. The initial forest cover map (1993, the transition potential maps and the 1993–2000 transition probabilities were used to calibrate the model. Forest cover simulations were then performed from 1993 to 2007 at an annual time-step. The simulated forest cover map for 2007 was compared to the observed (actual forest cover map for 2007 in order to test the accuracy of the model. Following the successful calibration and validation, future forest cover changes were simulated up to 2014 under different scenarios. The MCA simulations under the BAU and pessimistic scenarios projected that current forest areas would decrease, whereas unstocked forest areas would increase in the future. Conversely, the optimistic scenario projected that current forest areas would increase in the future if strict forestry laws enforcing conservation in protected forest areas are implemented. The three simulation scenarios provide a very good case study for simulating future forest cover changes at the subnational level (Luangprabang province. Thus, the future simulated forest cover changes can possibly be used as a guideline to set reference scenarios as well as undertake REDD/REDD+ preparedness activities within the study area.

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

Science.gov (United States)

Ackers, Steven H.; Davis, Raymond J.; Olsen, K.; Dugger, Catherine

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map.Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors.We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the

The managed clearing: An overlooked land-cover type in urbanizing regions?

Science.gov (United States)

Madden, Marguerite; Gray, Josh; Meentemeyer, Ross K.

2018-01-01

Urban ecosystem assessments increasingly rely on widely available map products, such as the U.S. Geological Service (USGS) National Land Cover Database (NLCD), and datasets that use generic classification schemes to detect and model large-scale impacts of land-cover change. However, utilizing existing map products or schemes without identifying relevant urban class types such as semi-natural, yet managed land areas that account for differences in ecological functions due to their pervious surfaces may severely constrain assessments. To address this gap, we introduce the managed clearings land-cover type–semi-natural, vegetated land surfaces with varying degrees of management practices–for urbanizing landscapes. We explore the extent to which managed clearings are common and spatially distributed in three rapidly urbanizing areas of the Charlanta megaregion, USA. We visually interpreted and mapped fine-scale land cover with special attention to managed clearings using 2012 U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) images within 150 randomly selected 1-km2 blocks in the cities of Atlanta, Charlotte, and Raleigh, and compared our maps with National Land Cover Database (NLCD) data. We estimated the abundance of managed clearings relative to other land use and land cover types, and the proportion of land-cover types in the NLCD that are similar to managed clearings. Our study reveals that managed clearings are the most common land cover type in these cities, covering 28% of the total sampled land area– 6.2% higher than the total area of impervious surfaces. Managed clearings, when combined with forest cover, constitutes 69% of pervious surfaces in the sampled region. We observed variability in area estimates of managed clearings between the NAIP-derived and NLCD data. This suggests using high-resolution remote sensing imagery (e.g., NAIP) instead of modifying NLCD data for improved representation of spatial heterogeneity and

EnviroAtlas - Austin, TX - 51m Riparian Buffer Vegetated Cover

Science.gov (United States)

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. Vegetated cover is defined as Trees & Forest and Grass & Herbaceous. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Downscaling NASA Climatological Data to Produce Detailed Climate Zone Maps

Science.gov (United States)

Chandler, William S.; Hoell, James M.; Westberg, David J.; Whitlock, Charles H.; Zhang, Taiping; Stackhouse, P. W.

2011-01-01

The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of downscaling NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.

Computer-aided classification of forest cover types from small scale aerial photography

Science.gov (United States)

Bliss, John C.; Bonnicksen, Thomas M.; Mace, Thomas H.

1980-11-01

The US National Park Service must map forest cover types over extensive areas in order to fulfill its goal of maintaining or reconstructing presettlement vegetation within national parks and monuments. Furthermore, such cover type maps must be updated on a regular basis to document vegetation changes. Computer-aided classification of small scale aerial photography is a promising technique for generating forest cover type maps efficiently and inexpensively. In this study, seven cover types were classified with an overall accuracy of 62 percent from a reproduction of a 1∶120,000 color infrared transparency of a conifer-hardwood forest. The results were encouraging, given the degraded quality of the photograph and the fact that features were not centered, as well as the lack of information on lens vignetting characteristics to make corrections. Suggestions are made for resolving these problems in future research and applications. In addition, it is hypothesized that the overall accuracy is artificially low because the computer-aided classification more accurately portrayed the intermixing of cover types than the hand-drawn maps to which it was compared.

Land Use and Land Cover (LULC) Change Detection in Islamabad and its Comparison with Capital Development Authority (CDA) 2006 Master Plan

Science.gov (United States)

Hasaan, Zahra

2016-07-01

Remote sensing is very useful for the production of land use and land cover statistics which can be beneficial to determine the distribution of land uses. Using remote sensing techniques to develop land use classification mapping is a convenient and detailed way to improve the selection of areas designed to agricultural, urban and/or industrial areas of a region. In Islamabad city and surrounding the land use has been changing, every day new developments (urban, industrial, commercial and agricultural) are emerging leading to decrease in vegetation cover. The purpose of this work was to develop the land use of Islamabad and its surrounding area that is an important natural resource. For this work the eCognition Developer 64 computer software was used to develop a land use classification using SPOT 5 image of year 2012. For image processing object-based classification technique was used and important land use features i.e. Vegetation cover, barren land, impervious surface, built up area and water bodies were extracted on the basis of object variation and compared the results with the CDA Master Plan. The great increase was found in built-up area and impervious surface area. On the other hand vegetation cover and barren area followed a declining trend. Accuracy assessment of classification yielded 92% accuracies of the final land cover land use maps. In addition these improved land cover/land use maps which are produced by remote sensing technique of class definition, meet the growing need of legend standardization.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

Science.gov (United States)

Basu, Saikat; Ganguly, Sangram; Michaelis, Andrew; Votava, Petr; Roy, Anshuman; Mukhopadhyay, Supratik; Nemani, Ramakrishna

2015-01-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery using a Probabilistic Learning Framework

Science.gov (United States)

Basu, S.; Ganguly, S.; Michaelis, A.; Votava, P.; Roy, A.; Mukhopadhyay, S.; Nemani, R. R.

2015-12-01

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Science.gov (United States)

Riggs, George A.; Hall, Dorothy K.; Román, Miguel O.

2017-10-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Science.gov (United States)

Riggs, George A.; Hall, Dorothy K.; Roman, Miguel O.

2017-01-01

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

Science.gov (United States)

LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

2001-01-01

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

Using indigenous knowledge to link hyper-temporal land cover mapping with land use in the Venezuelan Amazon: "The Forest Pulse".

Science.gov (United States)

Olivero, Jesús; Ferri, Francisco; Acevedo, Pelayo; Lobo, Jorge M; Fa, John E; Farfán, Miguel Á; Romero, David; Real, Raimundo

2016-12-01

Remote sensing and traditional ecological knowledge (TEK) can be combined to advance conservation of remote tropical regions, e.g. Amazonia, where intensive in situ surveys are often not possible. Integrating TEK into monitoring and management of these areas allows for community participation, as well as for offering novel insights into sustainable resource use. In this study, we developed a 250 m resolution land-cover map of the Western Guyana Shield (Venezuela) based on remote sensing, and used TEK to validate its relevance for indigenous livelihoods and land uses. We first employed a hyper-temporal remotely sensed vegetation index to derive a land classification system. During a 1 300 km, eight day fluvial expedition in roadless areas in the Amazonas State (Venezuela), we visited six indigenous communities who provided geo-referenced data on hunting, fishing and farming activities. We overlaid these TEK data onto the land classification map, to link land classes with indigenous use. We characterized land classes using patterns of greenness temporal change and topo-hydrological information, and proposed 12 land-cover types, grouped into five main landscapes: 1) water bodies; 2) open lands/forest edges; 3) evergreen forests; 4) submontane semideciduous forests, and 5) cloud forests. Each land cover class was identified with a pulsating profile describing temporal changes in greenness, hence we labelled our map as "The Forest Pulse". These greenness profiles showed a slightly increasing trend, for the period 2000 to 2009, in the land classes representing grassland and scrubland, and a slightly decreasing trend in the classes representing forests. This finding is consistent with a gain in carbon in grassland as a consequence of climate warming, and also with some loss of vegetation in the forests. Thus, our classification shows potential to assess future effects of climate change on landscape. Several classes were significantly connected with agriculture, fishing

Towards Seamless Validation of Land Cover Data

Science.gov (United States)

Chuprikova, Ekaterina; Liebel, Lukas; Meng, Liqiu

2018-05-01

This article demonstrates the ability of the Bayesian Network analysis for the recognition of uncertainty patterns associated with the fusion of various land cover data sets including GlobeLand30, CORINE (CLC2006, Germany) and land cover data derived from Volunteered Geographic Information (VGI) such as Open Street Map (OSM). The results of recognition are expressed as probability and uncertainty maps which can be regarded as a by-product of the GlobeLand30 data. The uncertainty information may guide the quality improvement of GlobeLand30 by involving the ground truth data, information with superior quality, the know-how of experts and the crowd intelligence. Such an endeavor aims to pave a way towards a seamless validation of global land cover data on the one hand and a targeted knowledge discovery in areas with higher uncertainty values on the other hand.

Moving from proprietary to open-source solutions for academic research in remote sensing: Example with semi-automated land cover mapping

OpenAIRE

Grippa, Taïs

2017-01-01

GRASS GIS has recently experienced significant improvements for Object-Based Image Analysis. At ULB the choice was made to combine GRASS GIS and Python in a semi-automated processing chain for land-cover mapping. The later proved its ability of being quickly customized in order to match the requirements of different projects. In order to promote the OSGEO software, we decided to make it freely available, allowing anyone interested to review, reuse and/or enhance it for further studies.

USGS Land Cover (NLCD) Overlay Map Service from The National Map - National Geospatial Data Asset (NGDA) National Land Cover Database (NLCD)

Data.gov (United States)

U.S. Geological Survey, Department of the Interior — NLCD 1992, NLCD 2001, NLCD 2006, and NLCD 2011 are National Land Cover Database classification schemes based primarily on Landsat data along with ancillary data...

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

Directory of Open Access Journals (Sweden)

Akpona Okujeni

2014-07-01

Full Text Available Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR, kernel ridge regression (KRR, artificial neural networks (NN, random forest regression (RFR and partial least squares regression (PLSR. Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN or limited (RFR and PLSR performances when comprehensively mapping urban land cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.

Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources

Science.gov (United States)

Spruce, Joe; Berglund, Judith; Davis, Bruce

2006-01-01

This viewgraph presentation regards one element of a larger project on the integration of NASA science models and data into the Hazards U.S. Multi-Hazard (HAZUS-MH) Hurricane module for hurricane damage and loss risk assessment. HAZUS-MH is a decision support tool being developed by the National Institute of Building Sciences for the Federal Emergency Management Agency (FEMA). It includes the Hurricane Module, which employs surface roughness maps made from National Land Cover Data (NLCD) maps to estimate coastal hurricane wind damage and loss. NLCD maps are produced and distributed by the U.S. Geological Survey. This presentation discusses an effort to improve upon current HAZUS surface roughness maps by employing ASTER multispectral classifications with QuickBird "ground reference" imagery.

Construction of a reference genetic linkage map for carnation (Dianthus caryophyllus L.).

Science.gov (United States)

Yagi, Masafumi; Yamamoto, Toshiya; Isobe, Sachiko; Hirakawa, Hideki; Tabata, Satoshi; Tanase, Koji; Yamaguchi, Hiroyasu; Onozaki, Takashi

2013-10-26

Genetic linkage maps are important tools for many genetic applications including mapping of quantitative trait loci (QTLs), identifying DNA markers for fingerprinting, and map-based gene cloning. Carnation (Dianthus caryophyllus L.) is an important ornamental flower worldwide. We previously reported a random amplified polymorphic DNA (RAPD)-based genetic linkage map derived from Dianthus capitatus ssp. andrezejowskianus and a simple sequence repeat (SSR)-based genetic linkage map constructed using data from intraspecific F2 populations; however, the number of markers was insufficient, and so the number of linkage groups (LGs) did not coincide with the number of chromosomes (x = 15). Therefore, we aimed to produce a high-density genetic map to improve its usefulness for breeding purposes and genetic research. We improved the SSR-based genetic linkage map using SSR markers derived from a genomic library, expression sequence tags, and RNA-seq data. Linkage analysis revealed that 412 SSR loci (including 234 newly developed SSR loci) could be mapped to 17 linkage groups (LGs) covering 969.6 cM. Comparison of five minor LGs covering less than 50 cM with LGs in our previous RAPD-based genetic map suggested that four LGs could be integrated into two LGs by anchoring common SSR loci. Consequently, the number of LGs corresponded to the number of chromosomes (x = 15). We added 192 new SSRs, eight RAPD, and two sequence-tagged site loci to refine the RAPD-based genetic linkage map, which comprised 15 LGs consisting of 348 loci covering 978.3 cM. The two maps had 125 SSR loci in common, and most of the positions of markers were conserved between them. We identified 635 loci in carnation using the two linkage maps. We also mapped QTLs for two traits (bacterial wilt resistance and anthocyanin pigmentation in the flower) and a phenotypic locus for flower-type by analyzing previously reported genotype and phenotype data. The improved genetic linkage maps and SSR markers developed

EnviroAtlas - Des Moines, IA - 51m Riparian Buffer Vegetated Cover

Science.gov (United States)

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. Vegetated cover is defined as Trees & Forest and Grass & Herbaceous. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets)

Single-edition quadrangle maps

Science.gov (United States)

,

1998-01-01

In August 1993, the U.S. Geological Survey's (USGS) National Mapping Division and the U.S. Department of Agriculture's Forest Service signed an Interagency Agreement to begin a single-edition joint mapping program. This agreement established the coordination for producing and maintaining single-edition primary series topographic maps for quadrangles containing National Forest System lands. The joint mapping program saves money by eliminating duplication of effort by the agencies and results in a more frequent revision cycle for quadrangles containing national forests. Maps are revised on the basis of jointly developed standards and contain normal features mapped by the USGS, as well as additional features required for efficient management of National Forest System lands. Single-edition maps look slightly different but meet the content, accuracy, and quality criteria of other USGS products. The Forest Service is responsible for the land management of more than 191 million acres of land throughout the continental United States, Alaska, and Puerto Rico, including 155 national forests and 20 national grasslands. These areas make up the National Forest System lands and comprise more than 10,600 of the 56,000 primary series 7.5-minute quadrangle maps (15-minute in Alaska) covering the United States. The Forest Service has assumed responsibility for maintaining these maps, and the USGS remains responsible for printing and distributing them. Before the agreement, both agencies published similar maps of the same areas. The maps were used for different purposes, but had comparable types of features that were revised at different times. Now, the two products have been combined into one so that the revision cycle is stabilized and only one agency revises the maps, thus increasing the number of current maps available for National Forest System lands. This agreement has improved service to the public by requiring that the agencies share the same maps and that the maps meet a

Potential and limitations of webcam images for snow cover monitoring in the Swiss Alps

Science.gov (United States)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2017-04-01

In Switzerland, several thousands of outdoor webcams are currently connected to the Internet. They deliver freely available images that can be used to analyze snow cover variability on a high spatio-temporal resolution. To make use of this big data source, we have implemented a webcam-based snow cover mapping procedure, which allows to almost automatically derive snow cover maps from such webcam images. As there is mostly no information about the webcams and its parameters available, our registration approach automatically resolves these parameters (camera orientation, principal point, field of view) by using an estimate of the webcams position, the mountain silhouette, and a high-resolution digital elevation model (DEM). Combined with an automatic snow classification and an image alignment using SIFT features, our procedure can be applied to arbitrary images to generate snow cover maps with a minimum of effort. Resulting snow cover maps have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or hidden from webcams' positions. Up to now, we processed images of about 290 webcams from our archive, and evaluated images of 20 webcams using manually selected ground control points (GCPs) to evaluate the mapping accuracy of our procedure. We present methodological limitations and ongoing improvements, show some applications of our snow cover maps, and demonstrate that webcams not only offer a great opportunity to complement satellite-derived snow retrieval under cloudy conditions, but also serve as a reference for improved validation of satellite-based approaches.

Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

Directory of Open Access Journals (Sweden)

Sebastian Aleksandrowicz

2014-06-01

Full Text Available Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of data, a high level of automation is required. The semi-automatic change detection methodology described in this paper was developed to annually update land cover maps prepared in the context of the Geoland2. The proposed algorithm was tailored to work with different very high spatial resolution images acquired over different European landscapes. The methodology is a fusion of various change detection methods ranging from: (1 layer arithmetic; (2 vegetation indices (NDVI differentiating; (3 texture calculation; and methods based on (4 canonical correlation analysis (multivariate alteration detection (MAD. User intervention during the production of the change map is limited to the selection of the input data, the size of initial segments and the threshold for texture classification (optionally. To achieve a high level of automation, statistical thresholds were applied in most of the processing steps. Tests showed an overall change recognition accuracy of 89%, and the change type classification methodology can accurately classify transitions between classes.

GRANULOMETRIC MAPS FROM HIGH RESOLUTION SATELLITE IMAGES

Directory of Open Access Journals (Sweden)

Catherine Mering

2011-05-01

Full Text Available A new method of land cover mapping from satellite images using granulometric analysis is presented here. Discontinuous landscapes such as steppian bushes of semi arid regions and recently growing urban settlements are especially concerned by this study. Spatial organisations of the land cover are quantified by means of the size distribution analysis of the land cover units extracted from high resolution remotely sensed images. A granulometric map is built by automatic classification of every pixel of the image according to the granulometric density inside a sliding neighbourhood. Granulometric mapping brings some advantages over traditional thematic mapping by remote sensing by focusing on fine spatial events and small changes in one peculiar category of the landscape.

Maps and plans reliability in tourism activities

Directory of Open Access Journals (Sweden)

Олександр Донцов

2016-10-01

Full Text Available The paper is devoted to creation of an effective system of mapping at all levels of tourist-excursion functioning that will boost the promotion of tourist product in a domestic and foreign tourist market. The State Scientific - Production Enterprise «Kartographia» actively participates in cartographic tourism provision by producing travel pieces, survey, large-scale, route maps, atlases, travel guides, city plans. They produce maps covering different content of the territory of Ukraine, its individual regions, cities interested in tourist excursions. The list and scope of cartographic products has been prepared for publication and released for the last five years. The development of new types of tourism encourages publishers to create various cartographic products for the needs of tourists guaranteeing high accuracy, reliability of information, ease of use. A variety of scientific and practical problems in tourism and excursion activities that are solved using maps and plans makes it difficult to determine the criteria for assessing their reliability. The author proposes to introduce the concept of «relevance» - as maps suitability to solving specific problems. The basis of the peer review is suitability of maps for the objective results release criteria: appropriateness of the target maps tasks (area, theme, destination; accuracy of given parameters (projection, scale, height interval; year according to the shooting of location or mapping; selection methods, methods of results measurement processing algorithm; availability of assistive devices (instrumentation, computer technology, simulation devices. These criteria provide the reliability and accuracy of the result as acceptable to consumers as possible. The author proposes a set of measures aimed at improving the content, quality and reliability of cartographic production.

Land Cover Classification Using ALOS Imagery For Penang, Malaysia

International Nuclear Information System (INIS)

Sim, C K; Abdullah, K; MatJafri, M Z; Lim, H S

2014-01-01

This paper presents the potential of integrating optical and radar remote sensing data to improve automatic land cover mapping. The analysis involved standard image processing, and consists of spectral signature extraction and application of a statistical decision rule to identify land cover categories. A maximum likelihood classifier is utilized to determine different land cover categories. Ground reference data from sites throughout the study area are collected for training and validation. The land cover information was extracted from the digital data using PCI Geomatica 10.3.2 software package. The variations in classification accuracy due to a number of radar imaging processing techniques are studied. The relationship between the processing window and the land classification is also investigated. The classification accuracies from the optical and radar feature combinations are studied. Our research finds that fusion of radar and optical significantly improved classification accuracies. This study indicates that the land cover/use can be mapped accurately by using this approach

Landfill Top Covers

DEFF Research Database (Denmark)

Scheutz, Charlotte; Kjeldsen, Peter

2011-01-01

The purpose of the final cover of a landfill is to contain the waste and to provide for a physical separation between the waste and the environment for protection of public health. Most landfill covers are designed with the primary goal to reduce or prevent infiltration of precipitation...... into the landfill in order to minimize leachate generation. In addition the cover also has to control the release of gases produced in the landfill so the gas can be ventilated, collected and utilized, or oxidized in situ. The landfill cover should also minimize erosion and support vegetation. Finally the cover...... is landscaped in order to fit into the surrounding area/environment or meet specific plans for the final use of the landfill. To fulfill the above listed requirements landfill covers are often multicomponent systems which are placed directly on top of the waste. The top cover may be placed immediately after...

A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.

Directory of Open Access Journals (Sweden)

Dong Jiang

Full Text Available Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1 images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization with convenience.

Simulated impacts of land cover change on summer climate in the Tibetan Plateau

International Nuclear Information System (INIS)

Li Qian; Xue Yongkang

2010-01-01

The Tibetan Plateau (TP) is a key region of land-atmosphere interactions with severe eco-environment degradation. This study uses an atmospheric general circulation model, NCEP GCM/SSiB, to present the major TP summer climate features for six selected ENSO years and preliminarily assess the possible impact of land cover change on the summer circulation over the TP. Compared to Reanalysis II data, the GCM using satellite derived vegetation properties generally reproduces the main 6-year-mean TP summer circulation features despite some discrepancies in intensity and geographic locations of some climate features. Two existing vegetation maps with very different land cover conditions over the TP, one with bare ground and one with vegetation cover, derived from satellite derived data, are tested and produce clearer climate signals due to land cover change. It shows that land cover change from vegetated land to bare ground decreases the radiation absorbed by the surface and results in weaker surface thermal effects, which lead to lower atmospheric temperature, as well as weaker vertical ascending motion, low-layer cyclonic, upper level anticyclonic, and summer monsoon circulation. These changes in circulation cause a decrease in the precipitation in the southeastern TP.

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

Science.gov (United States)

Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

2018-05-01

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Quantitative assessment of errors in monitoring landcover changes by comparison of maps

Directory of Open Access Journals (Sweden)

Jean Francois Mas

2012-02-01

Full Text Available Many studies aimed at assessing land-cover changes are based upon the comparison of maps elaborated in different dates. This comparison allows the calculation of change rates as well as to generate more detailed data such as the transition matrix and the change map. In this study, we evaluated the errors incurred when comparing maps elaborated at different scales, obtained through independent digitalisation processes, elaborated using different classification schemes or when the maps were elaborated with inputs from different dates. Errors derived from the difference of scale or from the map-digitalisation processes led to false changes with a similar or greater scale to that of true changes. The comparison of maps based on different classification schemes invalidated the results of the comparison. By contrast, the different approaches used to tackle the issue of maps with multiple dates produced similar results. The paper discusses some methods aimed at reducing these problems and evaluating the reliability of multi-temporal databases.

EnviroAtlas - Cleveland, OH - 51m Riparian Buffer Vegetated Cover

Science.gov (United States)

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. In this community, vegetated cover is defined as Trees & Forest, Grass & Herbaceous, Woody Wetlands, and Emergent Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets)

EnviroAtlas - Memphis, TN - 51m Riparian Buffer Vegetated Cover

Science.gov (United States)

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. Vegetated cover is defined as Trees & Forest, Grass & Herbaceous, Woody Wetlands, and Emergent Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EVALUATION AND MAPPING OF RANGELANDS DEGRADATION USING REMOTELY SENSED DATA

Directory of Open Access Journals (Sweden)

Majid Ajorlo

2005-05-01

Full Text Available The empirical and scientifically documents prove that misuse of natural resource causes degradation in it. So natural resources conservation is important in approaching sustainable development aims. In current study, Landsat Thematic Mapper images and grazing gradient method have been used to map the extent and degree of rangeland degradation. In during ground-based data measuring, factors such as vegetation cover, litter, plant diversity, bare soil, and stone & gravels were estimated as biophysical indicators of degradation. The next stage, after geometric correction and doing some necessary pre-processing practices on the study area’s images; the best and suitable vegetation index has been selected to map rangeland degradation among the Normalized Difference Vegetation Index (NDVI, Soil Adjusted Vegetation Index (SAVI, and Perpendicular Vegetation Index (PVI. Then using suitable vegetation index and distance parameter was produced the rangelands degradation map. The results of ground-based data analysis reveal that there is a significant relation between increasing distance from critical points and plant diversity and also percentage of litter. Also there is significant relation between vegetation cover percent and distance from village, i.e. the vegetation cover percent increases by increasing distance from villages, while it wasn’t the same around the stock watering points. The result of analysis about bare soil and distance from critical point was the same to vegetation cover changes manner. Also there wasn’t significant relation between stones & gravels index and distance from critical points. The results of image processing show that, NDVI appears to be sensitive to vegetation changes along the grazing gradient and it can be suitable vegetation index to map rangeland degradation. The degradation map shows that there is high degradation around the critical points. These areas need urgent attention for soil conservation. Generally, it

Use of Land Use Land Cover Change Mapping Products in Aiding Coastal Habitat Conservation and Restoration Efforts of the Mobile Bay NEP

Science.gov (United States)

Spruce, Joseph P.; Swann, Roberta; Smooth, James

2010-01-01

The Mobile Bay region has undergone significant land use land cover change (LULC) over the last 35 years, much of which is associated with urbanization. These changes have impacted the region s water quality and wildlife habitat availability. In addition, much of the region is low-lying and close to the Gulf, which makes the region vulnerable to hurricanes, climate change (e.g., sea level rise), and sometimes man-made disasters such as the Deepwater Horizon (DWH) oil spill. Land use land cover change information is needed to help coastal zone managers and planners to understand and mitigate the impacts of environmental change on the region. This presentation discusses selective results of a current NASA-funded project in which Landsat data over a 34-year period (1974-2008) is used to produce, validate, refine, and apply land use land cover change products to aid coastal habitat conservation and restoration needs of the Mobile Bay National Estuary Program (MB NEP). The project employed a user defined classification scheme to compute LULC change mapping products for the entire region, which includes the majority of Mobile and Baldwin counties. Additional LULC change products have been computed for select coastal HUC-12 sub-watersheds adjacent to either Mobile Bay or the Gulf of Mexico, as part of the MB NEP watershed profile assessments. This presentation will include results of additional analyses of LULC change for sub-watersheds that are currently high priority areas, as defined by MB NEP. Such priority sub-watersheds include those that are vulnerable to impacts from the DWH oil spill, as well as sub-watersheds undergoing urbanization. Results demonstrating the nature and permanence of LULC change trends for these higher priority sub-watersheds and results characterizing change for the entire 34-year period and at approximate 10-year intervals across this period will also be presented. Future work will include development of value-added coastal habitat quality

A Semi-Automated Machine Learning Algorithm for Tree Cover Delineation from 1-m Naip Imagery Using a High Performance Computing Architecture

Science.gov (United States)

Basu, S.; Ganguly, S.; Nemani, R. R.; Mukhopadhyay, S.; Milesi, C.; Votava, P.; Michaelis, A.; Zhang, G.; Cook, B. D.; Saatchi, S. S.; Boyda, E.

2014-12-01

Accurate tree cover delineation is a useful instrument in the derivation of Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree cover delineation in high to coarse resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR datasets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree cover estimates for the whole of Continental United States, using a High Performance Computing Architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field (CRF), which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the state of California, which covers a total of 11,095 NAIP tiles and spans a total geographical area of 163,696 sq. miles. Our framework produced correct detection rates of around 85% for fragmented forests and 70% for urban tree cover areas, with false positive rates lower than 3% for both regions. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR high-resolution canopy height model shows the effectiveness of our algorithm in generating accurate high-resolution tree cover maps.

2005 Kansas Land Cover Patterns, Level IV, Kansas River Watershed

Data.gov (United States)

Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns (KLCP) Mapping Initiative was a two-phase mapping endeavor that occurred over a three-year period (2007-2009). Note that while...

National Land Cover Database 2001 (NLCD01) Imperviousness Layer Tile 3, Southwest United States: IMPV01_3

Science.gov (United States)

LaMotte, Andrew E.; Wieczorek, Michael

2010-01-01

This 30-meter resolution data set represents the imperviousness layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp.. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

National Land Cover Database 2001 (NLCD01) Imperviousness Layer Tile 4, Southeast United States: IMPV01_4

Science.gov (United States)

Wieczorek, Michael; LaMotte, Andrew E.

2010-01-01

This 30-meter resolution data set represents the imperviousness layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp.. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

National Land Cover Database 2001 (NLCD01) Imperviousness Layer Tile 2, Northeast United States: IMPV01_2

Science.gov (United States)

LaMotte, Andrew E.; Wieczorek, Michael

2010-01-01

This 30-meter resolution data set represents the imperviousness layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp.. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

National Land Cover Database 2001 (NLCD01) Imperviousness Layer Tile 1, Northwest United States: IMPV01_1

Science.gov (United States)

LaMotte, Andrew E.; Wieczorek, Michael

2010-01-01

This 30-meter resolution data set represents the imperviousness layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp.. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

2005 Kansas Land Cover Patterns, Level I, Kansas River Watershed

Data.gov (United States)

Kansas Data Access and Support Center — The Upper Kansas River Watershed Land Cover Patterns map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period as part of a...

SAR China Land Mapping Project: Development, Production and Potential Applications

International Nuclear Information System (INIS)

Zhang, Lu; Guo, Huadong; Liu, Guang; Fu, Wenxue; Yan, Shiyong; Song, Rui; Ji, Peng; Wang, Xinyuan

2014-01-01

Large-area, seamless synthetic aperture radar (SAR) mosaics can reflect overall environmental conditions and highlight general trends in observed areas from a macroscopic standpoint, and effectively support research at the global scale, which is in high demand now across scientific fields. The SAR China Land Mapping Project (SCLM), supported by the Digital Earth Science Platform Project initiated and managed by the Center for Earth Observation and Digital Earth, Chinese Academy of Sciences (CEODE), is introduced in this paper. This project produced a large-area SAR mosaic dataset and generated the first complete seamless SAR map covering the entire land area of China using EnviSat-ASAR images. The value of the mosaic map is demonstrated by some potential applications in studies of urban distribution, rivers and lakes, geologic structures, geomorphology and paleoenvironmental change

The National Map - Missouri Pilot Project

Science.gov (United States)

,

2001-01-01

Governments depend on a common set of geographic base information as a tool for economic and community development, land and natural resource management, and health and safety services. Emergency management and defense operations rely on this information. Private industry, nongovernmental organizations, and individual citizens use the same geographic data. Geographic information underpins an increasingly large part of the Nation's economy. Available geographic data often have the following problems: * They do not align with each other because layers are frequently created or revised separately, * They do not match across administrative boundaries because each producing organization uses different methods and standards, and * They are not up to date because of the complexity and cost of revision. The U.S. Geological Survey (USGS) is developing The National Map to be a seamless, continuously maintained, and nationally consistent set of online, public domain, geographic base information to address these issues. The National Map will serve as a foundation for integrating, sharing, and using other data easily and consistently. In collaboration with other government agencies, the private sector, academia, and volunteer groups, the USGS will coordinate, integrate, and, where needed, produce and maintain base geographic data. The National Map will include digital orthorectified imagery; elevation data; vector data for hydrography, transportation, boundary, and structure features; geographic names; and land cover information. The data will be the source of revised paper topographic maps. Many technical and institutional issues must be resolved as The National Map is implemented. To begin the refinement of this new paradigm, pilot projects are being designed to identify and investigate these issues. The pilots are the foundation upon which future partnerships for data sharing and maintenance will be built.

The National Map - Delaware Pilot Project

Science.gov (United States)

,

2001-01-01

Governments depend on a common set of geographic base information as a tool for economic and community development, land and natural resource management, and health and safety services. Emergency management and defense operations rely on this information. Private industry, nongovernmental organizations, and individual citizens use the same geographic data. Geographic information underpins an increasingly large part of the Nation's economy. Available geographic data often have the following problems: * They do not align with each other because layers are frequently created or revised separately, * They do not match across administrative boundaries because each producing organization uses different methods and standards, and * They are not up to date because of the complexity and cost of revision. The U.S. Geological Survey (USGS) is developing The National Map to be a seamless, continuously maintained, and nationally consistent set of online, public domain, geographic base information to address these issues. The National Map will serve as a foundation for integrating, sharing, and using other data easily and consistently. In collaboration with other government agencies, the private sector, academia, and volunteer groups, the USGS will coordinate, integrate, and, where needed, produce and maintain base geographic data. The National Map will include digital orthorectified imagery; elevation data; vector data for hydrography, transportation, boundary, and structure features; geographic names; and land cover information. The data will be the source of revised paper topographic maps. Many technical and institutional issues must be resolved as The National Map is implemented. To begin the refinement of this new paradigm, pilot projects are being designed to identify and investigate these issues. The pilots are the foundation upon which future partnerships for data sharing and maintenance will be built.

The National Map - Pennsylvania Pilot Project

Science.gov (United States)

,

2001-01-01

Governments depend on a common set of geographic base information as a tool for economic and community development, land and natural resource management, and health and safety services. Emergency management and defense operations rely on this information. Private industry, nongovernmental organizations, and individual citizens use the same geographic data. Geographic information underpins an increasingly large part of the Nation's economy. Available geographic data often have the following problems: * They do not align with each other because layers are frequently created or revised separately, * They do not match across administrative boundaries because each producing organization uses different methods and standards, and * They are not up to date because of the complexity and cost of revision. The U.S. Geological Survey (USGS) is developing The National Map to be a seamless, continuously maintained, and nationally consistent set of online, public domain, geographic base information to address these issues. The National Map will serve as a foundation for integrating, sharing, and using other data easily and consistently. In collaboration with other government agencies, the private sector, academia, and volunteer groups, the USGS will coordinate, integrate, and, where needed, produce and maintain base geographic data. The National Map will include digital orthorectified imagery; elevation data; vector data for hydrography, transportation, boundary, and structure features; geographic names; and land cover information. The data will be the source of revised paper topographic maps. Many technical and institutional issues must be resolved as The National Map is implemented. To begin the refinement of this new paradigm, pilot projects are being designed to identify and investigate these issues. The pilots are the foundation upon which future partnerships for data sharing and maintenance will be built.

The National Map - Texas Pilot Project

Science.gov (United States)

,

2001-01-01

Governments depend on a common set of geographic base information as a tool for economic and community development, land and natural resource management, and health and safety services. Emergency management and defense operations rely on this information. Private industry, nongovernmental organizations, and individual citizens use the same geographic data. Geographic information underpins an increasingly large part of the Nation's economy. Available geographic data often have the following problems: * They do not align with each other because layers are frequently created or revised separately, * They do not match across administrative boundaries because each producing organization uses different methods and standards, and * They are not up to date because of the complexity and cost of revision. The U.S. Geological Survey (USGS) is developing The National Map to be a seamless, continuously maintained, and nationally consistent set of online, public domain, geographic base information to address these issues. The National Map will serve as a foundation for integrating, sharing, and using other data easily and consistently. In collaboration with other government agencies, the private sector, academia, and volunteer groups, the USGS will coordinate, integrate, and, where needed, produce and maintain base geographic data. The National Map will include digital orthorectified imagery; elevation data; vector data for hydrography, transportation, boundary, and structure features; geographic names; and land cover information. The data will be the source of revised paper topographic maps. Many technical and institutional issues must be resolved as The National Map is implemented. To begin the refinement of this new paradigm, pilot projects are being designed to identify and investigate these issues. The pilots are the foundation upon which future partnerships for data sharing and maintenance will be built.

The National Map - Florida Pilot Project

Science.gov (United States)

,

2001-01-01

Governments depend on a common set of geographic base information as a tool for economic and community development, land and natural resource management, and health and safety services. Emergency management and defense operations rely on this information. Private industry, nongovernmental organizations, and individual citizens use the same geographic data. Geographic information underpins an increasingly large part of the Nation's economy. Available geographic data often have the following problems: * They do not align with each other because layers are frequently created or revised separately, * They do not match across administrative boundaries because each producing organization uses different methods and standards, and * They are not up to date because of the complexity and cost of revision. The U.S. Geological Survey (USGS) is developing The National Map to be a seamless, continuously maintained, and nationally consistent set of online, public domain, geographic base information to address these issues. The National Map will serve as a foundation for integrating, sharing, and using other data easily and consistently. In collaboration with other government agencies, the private sector, academia, and volunteer groups, the USGS will coordinate, integrate, and, where needed, produce and maintain base geographic data. The National Map will include digital orthorectified imagery; elevation data; vector data for hydrography, transportation, boundary, and structure features; geographic names; and land cover information. The data will be the source of revised paper topographic maps. Many technical and institutional issues must be resolved as The National Map is implemented. To begin the refinement of this new paradigm, pilot projects are being designed to identify and investigate these issues. The pilots are the foundation upon which future partnerships for data sharing and maintenance will be built.

On parabolic external maps

DEFF Research Database (Denmark)

Lomonaco, Luna; Petersen, Carsten Lunde; Shen, Weixiao

2017-01-01

We prove that any C1+BV degree d ≥ 2 circle covering h having all periodic orbits weakly expanding, is conjugate by a C1+BV diffeomorphism to a metrically expanding map. We use this to connect the space of parabolic external maps (coming from the theory of parabolic-like maps) to metrically expan...

Assessment of land cover changes in Lampedusa Island (Italy) using Landsat TM and OLI data

Science.gov (United States)

Mei, Alessandro; Manzo, Ciro; Fontinovo, Giuliano; Bassani, Cristiana; Allegrini, Alessia; Petracchini, Francesco

2016-10-01

The Lampedusa Island displays important socio-economic criticalities related to an intensive touristic activity, which implies an increase in electricity consumption and waste production. An adequate island conversion to a more environmental, sustainable community needs to be faced by the local Management Plans establishment. For this purpose, several thematic datasets have to be produced and evaluated. Socio-economic and bio-ecological components as well as land cover/use assessment are some of the main topics to be managed within the Decision Support Systems. Considering the lack of Land Cover (LC) and vegetation change detection maps in Lampedusa Island (Italy), this paper focuses on the retrieval of these topics by remote sensing techniques. The analysis was carried out by Landsat 5 TM and Landsat 8 OLI multispectral images from 1984 to 2014 in order to obtain spatial and temporal information of changes occurred in the island. Firstly, imagery was co-registered and atmospherically corrected; secondly, it was then classified for land cover and vegetation distribution analysis with the use of QGIS and Saga GIS open source softwares. The Maximum Likelihood Classifier (MLC) was used for LC maps production, while the Normalized Difference Vegetation Index (NDVI) was used for vegetation examination and distribution. Topographic maps, historical aerial photos, ortophotos and field data are merged in the GIS for accuracy assessment. Finally, change detection of MLC and NDVI are provided respectively by Post-Classification Comparison (PCC) and Image Differencing (ID). The provided information, combined with local socio-economic parameters, is essential for the improvement of environmental sustainability of anthropogenic activities in Lampedusa.

Application of a niche-based model for forest cover classification

Directory of Open Access Journals (Sweden)

Amici V

2012-05-01

Full Text Available In recent years, a surge of interest in biodiversity conservation have led to the development of new approaches to facilitate ecologically-based conservation policies and management plans. In particular, image classification and predictive distribution modeling applied to forest habitats, constitute a crucial issue as forests constitute the most widespread vegetation type and play a key role for ecosystem functioning. Then, the general purpose of this study is to develop a framework that in the absence of large amounts of field data for large areas may allow to select the most appropriate classification. In some cases, a hard division of classes is required, especially as support to environmental policies; despite this it is necessary to take into account problems which derive from a crisp view of ecological entities being mapped, since habitats are expected to be structurally complex and continuously vary within a landscape. In this paper, a niche model (MaxEnt, generally used to estimate species/habitat distribution, has been applied to classify forest cover in a complex Mediterranean area and to estimate the probability distribution of four forest types, producing continuous maps of forest cover. The use of the obtained models as validation of model for crisp classifications, highlighted that crisp classification, which is being continuously used in landscape research and planning, is not free from drawbacks as it is showing a high degree of inner variability. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classification, may represent an useful approach to making more efficient and effective field inventories and to developing effective forest conservation policies.

Texture analysis for mapping Tamarix parviflora using aerial photographs along the Cache Creek, California.

Science.gov (United States)

Ge, Shaokui; Carruthers, Raymond; Gong, Peng; Herrera, Angelica

2006-03-01

Natural color photographs were used to detect the coverage of saltcedar, Tamarix parviflora, along a 40 km portion of Cache Creek near Woodland, California. Historical aerial photographs from 2001 were retrospectively evaluated and compared with actual ground-based information to assess accuracy of the assessment process. The color aerial photos were sequentially digitized, georeferenced, classified using color and texture methods, and mosaiced into maps for field use. Eight types of ground cover (Tamarix, agricultural crops, roads, rocks, water bodies, evergreen trees, non-evergreen trees and shrubs (excluding Tamarix)) were selected from the digitized photos for separability analysis and supervised classification. Due to color similarities among the eight cover types, the average separability, based originally only on color, was very low. The separability was improved significantly through the inclusion of texture analysis. Six types of texture measures with various window sizes were evaluated. The best texture was used as an additional feature along with the color, for identifying Tamarix. A total of 29 color photographs were processed to detect Tamarix infestations using a combination of the original digital images and optimal texture features. It was found that the saltcedar covered a total of 3.96 km(2) (396 hectares) within the study area. For the accuracy assessment, 95 classified samples from the resulting map were checked in the field with a global position system (GPS) unit to verify Tamarix presence. The producer's accuracy was 77.89%. In addition, 157 independently located ground sites containing saltcedar were compared with the classified maps, producing a user's accuracy of 71.33%.

Design and analysis for thematic map accuracy assessment: Fundamental principles

Science.gov (United States)

Stephen V. Stehman; Raymond L. Czaplewski

1998-01-01

Land-cover maps are used in numerous natural resource applications to describe the spatial distribution and pattern of land-cover, to estimate areal extent of various cover classes, or as input into habitat suitability models, land-cover change analyses, hydrological models, and risk analyses. Accuracy assessment quantifies data quality so that map users may evaluate...

Quantifying Savanna Woody Cover in the Field and on Historical ...

African Journals Online (AJOL)

jed1z

... mapping woody cover on such imagery in bush encroachment studies are the use of traditional pixel-based ... cover by testing it against detailed field validation data. We then assess ..... Mexico', Remote Sensing of Environment, vol. 93, pp.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

Science.gov (United States)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2015-03-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Text 2 Mind Map

OpenAIRE

Iona, John

2017-01-01

This is a review of the web resource 'Text 2 Mind Map' www.Text2MindMap.com. It covers what the resource is, and how it might be used in Library and education context, in particular for School Librarians.

Mapping in the cloud

CERN Document Server

Peterson, Michael P

2014-01-01

This engaging text provides a solid introduction to mapmaking in the era of cloud computing. It takes students through both the concepts and technology of modern cartography, geographic information systems (GIS), and Web-based mapping. Conceptual chapters delve into the meaning of maps and how they are developed, covering such topics as map layers, GIS tools, mobile mapping, and map animation. Methods chapters take a learn-by-doing approach to help students master application programming interfaces and build other technical skills for creating maps and making them available on the Internet. Th

SYNERGY OF OPTICAL AND SAR DATA FOR MAPPING AND MONITORING MANGROVES

Directory of Open Access Journals (Sweden)

A. K. Monzon

2016-06-01

Full Text Available Quantitative information on mangrove cover extents is essential in producing relevant resource management plans and conservation strategies. In the Philippines, mangrove rehabilitation was made a priority in relation to disaster risk response and mitigation following the calamities in the coastal communities during typhoon Haiyan/Yolanda; hence, baseline information on the extent of remaining mangrove cover was essential for effective site interventions. Although mangrove cover maps for the country already exists, analysis of mangrove cover changes were limited to the application of fixed annual deforestation rates due to the challenge of acquiring consistent temporal cloud-free optical satellite data over large landscapes. This study presents an initial analysis of SAR and optical imagery combined with field-based observations for detecting mangrove cover extent and changes through a straightforward graphical approach. The analysis is part of a larger study evaluating the synergistic use of time-series L-band SAR and optical data for mapping and monitoring of mangroves. Image segmentation was implemented on the 25-meter ALOS/PALSAR image mosaics, in which the generated objects were subjected to statistical analysis using the software R. In combination with selected Landsat bands, the class statistics from the image bands were used to generate decision trees and thresholds for the hierarchical image classification. The results were compared with global mangrove cover dataset and validated using collected ground truth data. This study developed an integrated replicable approach for analyzing future radar and optical datasets, essential in national level mangrove cover change monitoring and assessment for long-term conservation targets and strategies.

Monitoring land Cover Changes and Fragmentation dynamics in the ...

African Journals Online (AJOL)

Monitoring land Cover Changes and Fragmentation dynamics in the subtropical thicket of the Eastern Cape Province, South Africa. ... Baseline land use/cover maps and fragmentation analyses in a temporal framework are valuable for gaining insights into, among other things, carbon stock change trends. Keywords: Land ...

Standard practice for detection sensitivity mapping of In-Plant Walk-through metal detectors

CERN Document Server

American Society for Testing and Materials. Philadelphia

1997-01-01

1.1 This standard practice covers a procedure for determining the weakest detection path through the portal aperture and the worst-case orthogonal orientation of metallic test objects. It results in detection sensitivity maps, which model the detection zone in terms related to detection sensitivity and identify the weakest detection paths. Detection sensitivity maps support sensitivity adjustment and performance evaluation procedures (see Practices C1269 and C1309). Note 1—Unsymmetrical metal objects possessing a primary longitudinal component, such as handguns and knives, usually have one particular orientation that produces the weakest detection signal. The orientation and the path through the detector aperture where the weakest response is produced may not be the same for all test objects, even those with very similar appearance. Note 2—In the case of multiple specified test objects or for test objects that are orientation sensitive, it may be necessary to map each object several times to determine ...

Reconnaissance geologic mapping of a portion of the rain‐forest‐covered Guiana Shield, Northwestern Brazil, using SIR-B and digital aeromagnetic data

Science.gov (United States)

Pellon de Miranda, Fernando; McCafferty, Anne E.; Taranik, James V.

1994-01-01

This paper documents the result of an integrated analysis of spaceborne radar (SIR-B) and digital aeromagnetic data carried out in the heavily forested Guiana Shield. The objective of the research is to interpret the geophysical data base to its limit to produce a reconnaissance geologic map as an aid to ground work planning in a worst‐case setting. Linear geomorphic features were identified based on the interpretation of the SIR-B image. Digital manipulation of aeromagnetic data allowed the development of a color‐shaded relief map of reduced‐to‐pole magnetic anomalies, a terrace‐magnetization map, and a map showing the location of maximum values of the horizontal component of the pseudogravity gradient (magnetization boundary lines). The resultant end product was a reconnaissance geologic map where broad terrane categories were delineated and geologic faults with both topographic and magnetic expression were defined. The availability of global spaceborne radar coverage in the 1990s and the large number of existing digital aeromagnetic surveys in northwestern Brazil indicate that this approach can be potentially useful for reconnaissance geologic mapping elsewhere in the Guiana Shield.

St. Louis Area Earthquake Hazards Mapping Project - A Progress Report-November 2008

Science.gov (United States)

Karadeniz, D.; Rogers, J.D.; Williams, R.A.; Cramer, C.H.; Bauer, R.A.; Hoffman, D.; Chung, J.; Hempen, G.L.; Steckel, P.H.; Boyd, O.L.; Watkins, C.M.; McCallister, N.S.; Schweig, E.

2009-01-01

St. Louis has experienced minor earthquake damage at least 12 times in the past 200 years. Because of this history and its proximity to known active earthquake zones, the St. Louis Area Earthquake Hazards Mapping Project (SLAEHMP) is producing digital maps that show variability of earthquake hazards, including liquefaction and ground shaking, in the St. Louis area. The maps will be available free via the internet. Although not site specific enough to indicate the hazard at a house-by-house resolution, they can be customized by the user to show specific areas of interest, such as neighborhoods or transportation routes. Earthquakes currently cannot be predicted, but scientists can estimate how strongly the ground is likely to shake as the result of an earthquake. Earthquake hazard maps provide one way of conveying such estimates. The U.S. Geological Survey (USGS), which produces earthquake hazard maps for the Nation, is working with local partners to develop detailed maps for urban areas vulnerable to strong ground shaking. These partners, which along with the USGS comprise the SLAEHMP, include the Missouri University of Science and Technology-Rolla (Missouri S&T), Missouri Department of Natural Resources (MDNR), Illinois State Geological Survey (ISGS), Saint Louis University, Missouri State Emergency Management Agency, and URS Corporation. Preliminary hazard maps covering a test portion of the 29-quadrangle St. Louis study area have been produced and are currently being evaluated by the SLAEHMP. A USGS Fact Sheet summarizing this project was produced and almost 1000 copies have been distributed at several public outreach meetings and field trips that have featured the SLAEHMP (Williams and others, 2007). In addition, a USGS website focusing on the SLAEHMP, which provides links to project results and relevant earthquake hazard information, can be found at: http://earthquake.usgs.gov/regional/ceus/urban_map/st_louis/index.php. This progress report summarizes the

Mapping Decadal Land Cover Changes in the Woodlands of North Eastern Namibia from 1975 to 2014 Using the Landsat Satellite Archived Data

Directory of Open Access Journals (Sweden)

Vladimir R. Wingate

2016-08-01

Full Text Available Woodlands and savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to subsistence and intensive agriculture or urbanized. This study investigates changes in land cover over four administrative regions of North Eastern Namibia within the Kalahari woodland savannah biome, covering a total of 107,994 km2. Land cover is mapped using multi-sensor Landsat imagery at decadal intervals from 1975 to 2014, with a post-classification change detection method. The dominant change observed was a reduction in the area of woodland savannah due to the expansion of agriculture, primarily in the form of small-scale cereal and pastoral production. More specifically, woodland savannah area decreased from 90% of the study area in 1975 to 83% in 2004, and then increased to 86% in 2014, while agricultural land increased from 6% to 12% between 1975 and 2014. We assess land cover changes in relation to towns, villages, rivers and roads and find most changes occurred in proximity to these. In addition, we find that most land cover changes occur within land designated as communally held, followed by state protected land. With widespread changes occurring across the African continent, this study provides important data for understanding drivers of change in the region and their impacts on the distribution of woodland savannahs.

National Land Cover Database 2001 (NLCD01) Tree Canopy Layer Tile 4, Southeast United States: CNPY01_4

Science.gov (United States)

LaMotte, Andrew E.; Wieczorek, Michael

2010-01-01

This 30-meter resolution data set represents the tree canopy layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition.jpg The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

National Land Cover Database 2001 (NLCD01) Tree Canopy Layer Tile 2, Northeast United States: CNPY01_2

Science.gov (United States)

LaMotte, Andrew E.; Wieczorek, Michael

2010-01-01

This 30-meter resolution data set represents the tree canopy layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition.jpg The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

National Land Cover Database 2001 (NLCD01) Tree Canopy Layer Tile 1, Northwest United States: CNPY01_1

Science.gov (United States)

LaMotte, Andrew E.; Wieczorek, Michael

2010-01-01

This 30-meter resolution data set represents the tree canopy layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition.jpg. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov

National Land Cover Database 2001 (NLCD01) Tree Canopy Layer Tile 3, Southwest United States: CNPY01_3

Science.gov (United States)

LaMotte, Andrew E.; Wieczorek, Michael

2010-01-01

This 30-meter resolution data set represents the tree canopy layer for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System, browse graphic: nlcd01-partition.jpg The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004) and http://www.mrlc.gov/mrlc2k.asp. The NLCD 2001 was created by partitioning the United States into mapping-zones. A total of 68 mapping-zones browse graphic: nlcd01-mappingzones.jpg were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping-zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

EnviroAtlas - New York, NY - 51m Riparian Buffer Vegetated Cover

Science.gov (United States)

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. In this community, vegetated cover is defined as Trees & Forest and Grass & Herbaceous. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets)

Land-Cover Change Analysis and Simulation in Conakry (Guinea, Using Hybrid Cellular-Automata and Markov Model

Directory of Open Access Journals (Sweden)

Arafan Traore

2018-04-01

Full Text Available In this study, land-cover change in the capital Conakry of Guinea was simulated using the integrated Cellular Automata and Markov model (CA-Markov in the Geographic Information System (GIS and Remote Sensing (RS. Historical land-cover change information was derived from 1986, 2000 and 2016 Landsat data. Using the land-cover change maps of 1986 and 2000, the land-cover change map for 2016 was simulated based on the Markov model in IDRISSI software (Clark University, Worcester, MA, USA. The simulated result was compared with the 2016 land-cover map for validation using the Relative Operating Characteristic (ROC. The ROC result showed a very strong agreement between the two maps. From this result, the land-cover change map for 2025 was simulated using CA-Markov model. The result has indicated that the proportion of the urban area was 49% in 2016, and it is expected to increase to 52% by 2025, while vegetation will decrease from 35% in 2016 to 32% in 2025. This study suggests that the rapid land-cover change has been led by both rapid population growth and extreme poverty in rural areas, which will result in migration into Conakry. The results of this study will provide bases for assessing the sustainability and the management of the urban area and for taking actions to mitigate the degradation of the urban environment.

Mapping Vesta Mid-Latitude Quadrangle V-12EW: Mapping the Edge of the South Polar Structure

Science.gov (United States)

Hoogenboom, T.; Schenk, P.; Williams, D. A.; Hiesinger, H.; Garry, W. B.; Yingst, R.; Buczkowski, D.; McCord, T. B.; Jaumann, R.; Pieters, C. M.; Gaskell, R. W.; Neukum, G.; Schmedemann, N.; Marchi, S.; Nathues, A.; Le Corre, L.; Roatsch, T.; Preusker, F.; White, O. L.; DeSanctis, C.; Filacchione, G.; Raymond, C. A.; Russell, C. T.

2011-12-01

NASA's Dawn spacecraft arrived at the asteroid 4Vesta on July 15, 2011, and is now collecting imaging, spectroscopic, and elemental abundance data during its one-year orbital mission. As part of the geological analysis of the surface, a series of 15 quadrangle maps are being produced based on Framing Camera images (FC: spatial resolution: ~65 m/pixel) along with Visible & Infrared Spectrometer data (VIR: spatial resolution: ~180 m/pixel) obtained during the High-Altitude Mapping Orbit (HAMO). This poster presentation concentrates on our geologic analysis and mapping of quadrangle V-12EW. This quadrangle is dominated by the arcuate edge of the large 460+ km diameter south polar topographic feature first observed by HST (Thomas et al., 1997). Sparsely cratered, the portion of this feature covered in V-12EW is characterized by arcuate ridges and troughs forming a generalized arcuate pattern. Mapping of this terrain and the transition to areas to the north will be used to test whether this feature has an impact or other (e.g., internal) origin. We are also using FC stereo and VIR images to assess whether their are any compositional differences between this terrain and areas further to the north, and image data to evaluate the distribution and age of young impact craters within the map area. The authors acknowledge the support of the Dawn Science, Instrument and Operations Teams.

Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels

Directory of Open Access Journals (Sweden)

Frank Canters

2008-06-01

Full Text Available Urban growth and its related environmental problems call for sustainable urban management policies to safeguard the quality of urban environments. Vegetation plays an important part in this as it provides ecological, social, health and economic benefits to a city’s inhabitants. Remotely sensed data are of great value to monitor urban green and despite the clear advantages of contemporary high resolution images, the benefits of medium resolution data should not be discarded. The objective of this research was to estimate fractional vegetation cover from a Landsat ETM+ image with sub-pixel classification, and to compare accuracies obtained with multiple stepwise regression analysis, linear spectral unmixing and multi-layer perceptrons (MLP at the level of meaningful urban spatial entities. Despite the small, but nevertheless statistically significant differences at pixel level between the alternative approaches, the spatial pattern of vegetation cover and estimation errors is clearly distinctive at neighbourhood level. At this spatially aggregated level, a simple regression model appears to attain sufficient accuracy. For mapping at a spatially more detailed level, the MLP seems to be the most appropriate choice. Brightness normalisation only appeared to affect the linear models, especially the linear spectral unmixing.

Flood mapping from Sentinel-1 and Landsat-8 data: a case study from river Evros, Greece

Science.gov (United States)

Kyriou, Aggeliki; Nikolakopoulos, Konstantinos

2015-10-01

Floods are suddenly and temporary natural events, affecting areas which are not normally covered by water. The influence of floods plays a significant role both in society and the natural environment, therefore flood mapping is crucial. Remote sensing data can be used to develop flood map in an efficient and effective way. This work is focused on expansion of water bodies overtopping natural levees of the river Evros, invading the surroundings areas and converting them in flooded. Different techniques of flood mapping were used using data from active and passive remote sensing sensors like Sentinlel-1 and Landsat-8 respectively. Space borne pairs obtained from Sentinel-1 were processed in this study. Each pair included an image during the flood, which is called "crisis image" and another one before the event, which is called "archived image". Both images covering the same area were processed producing a map, which shows the spread of the flood. Multispectral data From Landsat-8 were also processed in order to detect and map the flooded areas. Different image processing techniques were applied and the results were compared to the respective results of the radar data processing.

A procedure for merging land cover/use data from LANDSAT, aerial photography, and map sources: Compatibility, accuracy, and cost. Remote Sensing Project

Science.gov (United States)

Enslin, W. R.; Tilmann, S. E.; Hill-Rowley, R.; Rogers, R. H.

1977-01-01

Regional planning agencies are currently expressing a need for detailed land cover/use information to effectively meet the requirements of various federal programs. Individual data sources have advantages and limitations in fulfilling this need, both in terms of time/cost and technological capability. A methodology has been developed to merge land cover/use data from LANDSAT, aerial photography and map sources to maximize the effective use of a variety of data sources in the provision of an integrated information system for regional analysis. A test of the proposed inventory method is currently under way in four central Michigan townships. This test will evaluate the compatibility, accuracy and cost of the integrated method with reference to inventories developed from a single data source, and determine both the technological feasibility and analytical potential of such a system.

Impact of Land Use Land Cover Change on East Asian monsoon

Science.gov (United States)

Chilukoti, N.; Xue, Y.; Liu, Y.; Lee, J.

2017-12-01

Humans modify the Earth's terrestrial surface on a continental scale by removing natural vegetation for crops/grazing. The current rates, extents and intensities of Land Use and Land Cover Change (LULCC) are greater than ever in history. The earlier studies of Land-atmosphere interactions used specified land surface conditions without interannual variations. In this study using NCEP CFSv2 coupled with Simplified Simple Biosphere (SSiB) model, biogeophysical impacts of LULCC on climate variability, anomaly, and changes are investigated by using the LULCC map from the Hurtt et al. (2006, 2011), which covered 66 years from 1950-2015 with annual variability. We combined the changes in crop and pasture fractions and consider as LULCC. A methodology had been developed to convert the Hurtt LULCC change map with 1° resolution to the GCM grid points. Since the GCM has only one dominant type, when the crop and pasture frction value at one point was larger than the critical value, that grid was assigned as degraded. Comprehensive evaluation was conducted to ensure the consistence of the trend of land degradation in the Hurtt's map and in the GCM LULCC map. In the degraded point, trees were changed to low vegetation or grasses, and low vegetation to bare soil. A set of surface parameters such as leaf area index, vegetation height, roughness length, and soil parameters, associated with vegetation are changed to show the degradation effects. We integrated the model with the potential vegetation map and the map with LULCC from 1950 to 2015, and the results indicate the LULCC causes precipitation reduction globally, with the strongest signals over monsoon regions. For instance, the degradation in Mexico, West Africa, south and East Asia and South America produced significant precipitation anomalies, some of which are consistent with observed regional precipitation anomalies. Meanwhile, it has also found that the LULCC enhances the surface warming during the summer in monsoon

A methodology for producing small scale rural land use maps in semi-arid developing countries using orbital imagery

Science.gov (United States)

Vangenderen, J. L. (Principal Investigator); Lock, B. F.

1976-01-01

The author has identified the following significant results. Results have shown that it is feasible to design a methodology that can provide suitable guidelines for operational production of small scale rural land use maps of semiarid developing regions from LANDSAT MSS imagery, using inexpensive and unsophisticated visual techniques. The suggested methodology provides immediate practical benefits to map makers attempting to produce land use maps in countries with limited budgets and equipment. Many preprocessing and interpretation techniques were considered, but rejected on the grounds that they were inappropriate mainly due to the high cost of imagery and/or equipment, or due to their inadequacy for use in operational projects in the developing countries. Suggested imagery and interpretation techniques, consisting of color composites and monocular magnification proved to be the simplest, fastest, and most versatile methods.

Map showing principal drainage basins, principal runoff-producing areas, and selected stream flow data in the Kaiparowits coal-basin area, Utah

Science.gov (United States)

Price, Don

1978-01-01

This is one of a series of maps that describe the geology and related natural resources in the Kaiparowits coal-basin area. Streamflow records used to compile this map and the accompanying table were collected by the U.S. Geological Survey in cooperation with the Utah State Engineer and the Utah Department of Transportation. The principal runoff-producing areas were delineated from a work map (scale 1:250,000) compiled to estimate water yields in Utah (Bagley and others, 1964). Information about Lake Powell was furnished by the U.S. Bureau of Reclamation.

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

Directory of Open Access Journals (Sweden)

Yun Du

2016-04-01

Full Text Available Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI calculated from the green and Shortwave-Infrared (SWIR bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies’ mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA, Intensity Hue Saturation (IHS, High Pass Filter (HPF and À Trous Wavelet Transform (ATWT, were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt

2005 Kansas Land Cover Patterns, Level I, State of Kansas (300m buffer)

Data.gov (United States)

Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly...

Mapping Congo Basin vegetation types from 300 m and 1 km multi-sensor time series for carbon stocks and forest areas estimation

Directory of Open Access Journals (Sweden)

A. Verhegghen

2012-12-01

Full Text Available This study aims to contribute to the understanding of the Congo Basin forests by delivering a detailed map of vegetation types with an improved spatial discrimination and coherence for the whole Congo Basin region. A total of 20 land cover classes were described with the standardized Land Cover Classification System (LCCS developed by the FAO. Based on a semi-automatic processing chain, the Congo Basin vegetation types map was produced by combining 19 months of observations from the Envisat MERIS full resolution products (300 m and 8 yr of daily SPOT VEGETATION (VGT reflectances (1 km. Four zones (north, south and two central were delineated and processed separately according to their seasonal and cloud cover specificities. The discrimination between different vegetation types (e.g. forest and savannas was significantly improved thanks to the MERIS sharp spatial resolution. A better discrimination was achieved in cloudy areas by taking advantage of the temporal consistency of the SPOT VGT observations. This resulted in a precise delineation of the spatial extent of the rural complex in the countries situated along the Atlantic coast. Based on this new map, more accurate estimates of the surface areas of forest types were produced for each country of the Congo Basin. Carbon stocks of the Basin were evaluated to a total of 49 360 million metric tons. The regional scale of the map was an opportunity to investigate what could be an appropriate tree cover threshold for a forest class definition in the Congo Basin countries. A 30% tree cover threshold was suggested. Furthermore, the phenology of the different vegetation types was illustrated systematically with EVI temporal profiles. This Congo Basin forest types map reached a satisfactory overall accuracy of 71.5% and even 78.9% when some classes are aggregated. The values of the Cohen's kappa coefficient, respectively 0.64 and 0.76 indicates a result significantly better than random.

It's time for a crisper image of the Face of the Earth: Landsat and climate time series for massive land cover & climate change mapping at detailed resolution.

Science.gov (United States)

Pons, Xavier; Miquel, Ninyerola; Oscar, González-Guerrero; Cristina, Cea; Pere, Serra; Alaitz, Zabala; Lluís, Pesquer; Ivette, Serral; Joan, Masó; Cristina, Domingo; Maria, Serra Josep; Jordi, Cristóbal; Chris, Hain; Martha, Anderson; Juanjo, Vidal

2014-05-01

Combining climate dynamics and land cover at a relative coarse resolution allows a very interesting approach to global studies, because in many cases these studies are based on a quite high temporal resolution, but they may be limited in large areas like the Mediterranean. However, the current availability of long time series of Landsat imagery and spatially detailed surface climate models allow thinking on global databases improving the results of mapping in areas with a complex history of landscape dynamics, characterized by fragmentation, or areas where relief creates intricate climate patterns that can be hardly monitored or modeled at coarse spatial resolutions. DinaCliVe (supported by the Spanish Government and ERDF, and by the Catalan Government, under grants CGL2012-33927 and SGR2009-1511) is the name of the project that aims analyzing land cover and land use dynamics as well as vegetation stress, with a particular emphasis on droughts, and the role that climate variation may have had in such phenomena. To meet this objective is proposed to design a massive database from long time series of Landsat land cover products (grouped in quinquennia) and monthly climate records (in situ climate data) for the Iberian Peninsula (582,000 km2). The whole area encompasses 47 Landsat WRS2 scenes (Landsat 4 to 8 missions, from path 197 to 202 and from rows 30 to 34), and 52 Landsat WRS1 scenes (for the previous Landsat missions, 212 to 221 and 30 to 34). Therefore, a mean of 49.5 Landsat scenes, 8 quinquennia per scene and a about 6 dates per quinquennium , from 1975 to present, produces around 2376 sets resulting in 30 m x 30 m spatial resolution maps. Each set is composed by highly coherent geometric and radiometric multispectral and multitemporal (to account for phenology) imagery as well as vegetation and wetness indexes, and several derived topographic information (about 10 Tbyte of data). Furthermore, on the basis on a previous work: the Digital Climatic Atlas of

A web-based system for supporting global land cover data production

Science.gov (United States)

Han, Gang; Chen, Jun; He, Chaoying; Li, Songnian; Wu, Hao; Liao, Anping; Peng, Shu

2015-05-01

Global land cover (GLC) data production and verification process is very complicated, time consuming and labor intensive, requiring huge amount of imagery data and ancillary data and involving many people, often from different geographic locations. The efficient integration of various kinds of ancillary data and effective collaborative classification in large area land cover mapping requires advanced supporting tools. This paper presents the design and development of a web-based system for supporting 30-m resolution GLC data production by combining geo-spatial web-service and Computer Support Collaborative Work (CSCW) technology. Based on the analysis of the functional and non-functional requirements from GLC mapping, a three tiers system model is proposed with four major parts, i.e., multisource data resources, data and function services, interactive mapping and production management. The prototyping and implementation of the system have been realised by a combination of Open Source Software (OSS) and commercially available off-the-shelf system. This web-based system not only facilitates the integration of heterogeneous data and services required by GLC data production, but also provides online access, visualization and analysis of the images, ancillary data and interim 30 m global land-cover maps. The system further supports online collaborative quality check and verification workflows. It has been successfully applied to China's 30-m resolution GLC mapping project, and has improved significantly the efficiency of GLC data production and verification. The concepts developed through this study should also benefit other GLC or regional land-cover data production efforts.

Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles

Science.gov (United States)

Niedzielski, Tomasz; Spallek, Waldemar; Witek-Kasprzak, Matylda

2018-04-01

The paper presents the application of the k-means clustering in the process of automated snow extent mapping using orthophoto images generated using the Structure-from-Motion (SfM) algorithm from oblique aerial photographs taken by unmanned aerial vehicle (UAV). A simple classification approach has been implemented to discriminate between snow-free and snow-covered terrain. The procedure uses the k-means clustering and classifies orthophoto images based on the three-dimensional space of red-green-blue (RGB) or near-infrared-red-green (NIRRG) or near-infrared-green-blue (NIRGB) bands. To test the method, several field experiments have been carried out, both in situations when snow cover was continuous and when it was patchy. The experiments have been conducted using three fixed-wing UAVs (swinglet CAM by senseFly, eBee by senseFly, and Birdie by FlyTech UAV) on 10/04/2015, 23/03/2016, and 16/03/2017 within three test sites in the Izerskie Mountains in southwestern Poland. The resulting snow extent maps, produced automatically using the classification method, have been validated against real snow extents delineated through a visual analysis and interpretation offered by human analysts. For the simplest classification setup, which assumes two classes in the k-means clustering, the extent of snow patches was estimated accurately, with areal underestimation of 4.6% (RGB) and overestimation of 5.5% (NIRGB). For continuous snow cover with sparse discontinuities at places where trees or bushes protruded from snow, the agreement between automatically produced snow extent maps and observations was better, i.e. 1.5% (underestimation with RGB) and 0.7-0.9% (overestimation, either with RGB or with NIRRG). Shadows on snow were found to be mainly responsible for the misclassification.

On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales

Science.gov (United States)

Härer, Stefan; Bernhardt, Matthias; Siebers, Matthias; Schulz, Karsten

2018-05-01

Knowledge of current snow cover extent is essential for characterizing energy and moisture fluxes at the Earth's surface. The snow-covered area (SCA) is often estimated by using optical satellite information in combination with the normalized-difference snow index (NDSI). The NDSI thereby uses a threshold for the definition if a satellite pixel is assumed to be snow covered or snow free. The spatiotemporal representativeness of the standard threshold of 0.4 is however questionable at the local scale. Here, we use local snow cover maps derived from ground-based photography to continuously calibrate the NDSI threshold values (NDSIthr) of Landsat satellite images at two European mountain sites of the period from 2010 to 2015. The Research Catchment Zugspitzplatt (RCZ, Germany) and Vernagtferner area (VF, Austria) are both located within a single Landsat scene. Nevertheless, the long-term analysis of the NDSIthr demonstrated that the NDSIthr at these sites are not correlated (r = 0.17) and different than the standard threshold of 0.4. For further comparison, a dynamic and locally optimized NDSI threshold was used as well as another locally optimized literature threshold value (0.7). It was shown that large uncertainties in the prediction of the SCA of up to 24.1 % exist in satellite snow cover maps in cases where the standard threshold of 0.4 is used, but a newly developed calibrated quadratic polynomial model which accounts for seasonal threshold dynamics can reduce this error. The model minimizes the SCA uncertainties at the calibration site VF by 50 % in the evaluation period and was also able to improve the results at RCZ in a significant way. Additionally, a scaling experiment shows that the positive effect of a locally adapted threshold diminishes using a pixel size of 500 m or larger, underlining the general applicability of the standard threshold at larger scales.

Ionospheric TEC Weather Map Over South America

Science.gov (United States)

Takahashi, H.; Wrasse, C. M.; Denardini, C. M.; Pádua, M. B.; de Paula, E. R.; Costa, S. M. A.; Otsuka, Y.; Shiokawa, K.; Monico, J. F. Galera; Ivo, A.; Sant'Anna, N.

2016-11-01

Ionospheric weather maps using the total electron content (TEC) monitored by ground-based Global Navigation Satellite Systems (GNSS) receivers over South American continent, TECMAP, have been operationally produced by Instituto Nacional de Pesquisas Espaciais's Space Weather Study and Monitoring Program (Estudo e Monitoramento Brasileiro de Clima Especial) since 2013. In order to cover the whole continent, four GNSS receiver networks, (Rede Brasileiro de Monitoramento Contínuo) RBMC/Brazilian Institute for Geography and Statistics, Low-latitude Ionospheric Sensor Network, International GNSS Service, and Red Argentina de Monitoreo Satelital Continuo, in total 140 sites, have been used. TECMAPs with a time resolution of 10 min are produced in 12 h time delay. Spatial resolution of the map is rather low, varying between 50 and 500 km depending on the density of the observation points. Large day-to-day variabilities of the equatorial ionization anomaly have been observed. Spatial gradient of TEC from the anomaly trough (total electron content unit, 1 TECU = 1016 el m-2 (TECU) 80) causes a large ionospheric range delay in the GNSS positioning system. Ionospheric plasma bubbles, their seeding and development, could be monitored. This plasma density (spatial and temporal) variability causes not only the GNSS-based positioning error but also radio wave scintillations. Monitoring of these phenomena by TEC mapping becomes an important issue for space weather concern for high-technology positioning system and telecommunication.

National Level Assessment of Mangrove Forest Cover in Pakistan

Science.gov (United States)

Abbas, S.; Qamer, F. M.; Hussain, N.; Saleem, R.; Nitin, K. T.

2011-09-01

. GIS and Remote Sensing based technologies and methods are in use to map forest cover since the last two decades in Pakistan. The national level forest cover studies based upon satellite images include, Forestry Sector Master Plan (FSMP) and National Forest & Range Resources Assessment Study (NFRRAS). In FSMP, the mangrove forest extent was visually determined from Landsat images of 1988 - 1991, and was estimated to be 155,369 ha; whereas, in NFRRAS, Landsat images of 1997 - 2001 were automated processed and the mangroves areas was estimated to be 158,000 ha. To our knowledge, a comprehensive assessment of current mangroves cover of Pakistan has not been made over the last decade, although the mangroves ecosystems have become the focus of intention in context of recent climate change scenarios. This study was conducted to support the informed decision making for sustainable development in coastal areas of Pakistan by providing up-todate mangroves forest cover assessment of Pakistan. Various types of Earth Observation satellite images and processing methods have been tested in relation to mangroves mapping. Most of the studies have applied classical pixel - based approached, there are a few studies which used object - based methods of image analysis to map the mangroves ecosystems. Object - based methods have the advantage of incorporating spatial neighbourhood properties and hierarchical structures into the classification process to produce more accurate surface patterns recognition compared with classical pixel - based approaches. In this research, we applied multi-scale hierarchical approach of object-based methods of image analysis to ALOS - AVNIR-2 images of the year 2008-09 to map the land cover in the mangroves ecosystems of Pakistan. Considering the tide height and phonological effects of vegetation, particularly the algal mats, these data sets were meticulously chosen. Incorporation of multi-scale hierarchical structures made it easy to effectively discriminate

Accuracy assessment of seven global land cover datasets over China

Science.gov (United States)

Yang, Yongke; Xiao, Pengfeng; Feng, Xuezhi; Li, Haixing

2017-03-01

Land cover (LC) is the vital foundation to Earth science. Up to now, several global LC datasets have arisen with efforts of many scientific communities. To provide guidelines for data usage over China, nine LC maps from seven global LC datasets (IGBP DISCover, UMD, GLC, MCD12Q1, GLCNMO, CCI-LC, and GlobeLand30) were evaluated in this study. First, we compared their similarities and discrepancies in both area and spatial patterns, and analysed their inherent relations to data sources and classification schemes and methods. Next, five sets of validation sample units (VSUs) were collected to calculate their accuracy quantitatively. Further, we built a spatial analysis model and depicted their spatial variation in accuracy based on the five sets of VSUs. The results show that, there are evident discrepancies among these LC maps in both area and spatial patterns. For LC maps produced by different institutes, GLC 2000 and CCI-LC 2000 have the highest overall spatial agreement (53.8%). For LC maps produced by same institutes, overall spatial agreement of CCI-LC 2000 and 2010, and MCD12Q1 2001 and 2010 reach up to 99.8% and 73.2%, respectively; while more efforts are still needed if we hope to use these LC maps as time series data for model inputting, since both CCI-LC and MCD12Q1 fail to represent the rapid changing trend of several key LC classes in the early 21st century, in particular urban and built-up, snow and ice, water bodies, and permanent wetlands. With the highest spatial resolution, the overall accuracy of GlobeLand30 2010 is 82.39%. For the other six LC datasets with coarse resolution, CCI-LC 2010/2000 has the highest overall accuracy, and following are MCD12Q1 2010/2001, GLC 2000, GLCNMO 2008, IGBP DISCover, and UMD in turn. Beside that all maps exhibit high accuracy in homogeneous regions; local accuracies in other regions are quite different, particularly in Farming-Pastoral Zone of North China, mountains in Northeast China, and Southeast Hills. Special

CRED Cumulative Map of Percent Scleractinian Coral Cover at Sarigan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

CRED Cumulative Map of Percent Scleractinian Coral Cover at Saipan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

CRED Cumulative Map of Percent Scleractinian Coral Cover at Tutuila

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

CRED Cumulative Map of Percent Scleractinian Coral Cover at Anatahan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

CRED Cumulative Map of Percent Scleractinian Coral Cover at Alamagan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

CRED Cumulative Map of Percent Scleractinian Coral Cover at Agrihan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

CRED Cumulative Map of Percent Scleractinian Coral Cover at Pagan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

CRED Cumulative Map of Percent Scleractinian Coral Cover at Asuncion

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

CRED Cumulative Map of Percent Scleractinian Coral Cover at Aguijan

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This map displays optical validation observation locations and percent coverage of scleractinian coral overlaid on bathymetry.

USGS Topo Base Map Service from The National Map

Data.gov (United States)

U.S. Geological Survey, Department of the Interior — USGS Topo is a topographic tile cache base map that combines the most current data (Boundaries, Names, Transportation, Elevation, Hydrography, Land Cover, and other...

Mapping Forest Biomass Using Remote Sensing and National Forest Inventory in China

Directory of Open Access Journals (Sweden)

Ling Du

2014-06-01

Full Text Available Quantifying the spatial pattern of large-scale forest biomass can provide a general picture of the carbon stocks within a region and is of great scientific and political importance. The combination of the advantages of remote sensing data and field survey data can reduce uncertainty as well as demonstrate the spatial distribution of forest biomass. In this study, the seventh national forest inventory statistics (for the period 2004–2008 and the spatially explicit MODIS Land Cover Type product (MCD12C1 were used together to quantitatively estimate the spatially-explicit distribution of forest biomass in China (with a resolution of 0.05°, ~5600 m. Our study demonstrated that the calibrated forest cover proportion maps allow proportionate downscaling of regional forest biomass statistics to forest cover pixels to produce a relatively fine-resolution biomass map. The total stock of forest biomass in China was 11.9 Pg with an average of 76.3 Mg ha−1 during the study period; the high values were located in mountain ranges in northeast, southwest and southeast China and were strongly correlated with forest age and forest density.

Prevalence of Pure Versus Mixed Snow Cover Pixels across Spatial Resolutions in Alpine Environments

Directory of Open Access Journals (Sweden)

David J. Selkowitz

2014-12-01

Full Text Available Remote sensing of snow-covered area (SCA can be binary (indicating the presence/absence of snow cover at each pixel or fractional (indicating the fraction of each pixel covered by snow. Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days

Mapping seabird sensitivity to offshore wind farms.

Science.gov (United States)

Bradbury, Gareth; Trinder, Mark; Furness, Bob; Banks, Alex N; Caldow, Richard W G; Hume, Duncan

2014-01-01

We present a Geographic Information System (GIS) tool, SeaMaST (Seabird Mapping and Sensitivity Tool), to provide evidence on the use of sea areas by seabirds and inshore waterbirds in English territorial waters, mapping their relative sensitivity to offshore wind farms. SeaMaST is a freely available evidence source for use by all connected to the offshore wind industry and will assist statutory agencies in assessing potential risks to seabird populations from planned developments. Data were compiled from offshore boat and aerial observer surveys spanning the period 1979-2012. The data were analysed using distance analysis and Density Surface Modelling to produce predicted bird densities across a grid covering English territorial waters at a resolution of 3 km×3 km. Coefficients of Variation were estimated for each grid cell density, as an indication of confidence in predictions. Offshore wind farm sensitivity scores were compiled for seabird species using English territorial waters. The comparative risks to each species of collision with turbines and displacement from operational turbines were reviewed and scored separately, and the scores were multiplied by the bird density estimates to produce relative sensitivity maps. The sensitivity maps reflected well the amassed distributions of the most sensitive species. SeaMaST is an important new tool for assessing potential impacts on seabird populations from offshore development at a time when multiple large areas of development are proposed which overlap with many seabird species' ranges. It will inform marine spatial planning as well as identifying priority areas of sea usage by marine birds. Example SeaMaST outputs are presented.

Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project

Directory of Open Access Journals (Sweden)

Recep Gundogan

2008-02-01

Full Text Available The satellite imagery has been effectively utilized for classifying land covertypes and detecting land cover conditions. The Advanced Spaceborne Thermal Emissionand Reflection Radiometer (ASTER sensor imagery has been widely used in classificationprocess of land cover. However, atmospheric corrections have to be made by preprocessingsatellite sensor imagery since the electromagnetic radiation signals received by the satellitesensors can be scattered and absorbed by the atmospheric gases and aerosols. In this study,an ASTER sensor imagery, which was converted into top-of-atmosphere reflectance(TOA, was used to classify the land use/cover types, according to COoRdination ofINformation on the Environment (CORINE land cover nomenclature, for an arearepresenting the heterogonous characteristics of eastern Mediterranean regions inKahramanmaras, Turkey. The results indicated that using the surface reflectance data ofASTER sensor imagery can provide accurate (i.e. overall accuracy and kappa values of83.2% and 0.79, respectively and low-cost cover mapping as a part of inventory forCORINE Land Cover Project.

ASSESSING LAND COVER CHANGES CAUSED BY GRANITE QUARRYING USING REMOTE SENSING

Directory of Open Access Journals (Sweden)

R. S. Moeletsi

2017-11-01

Full Text Available Dimension stone quarrying in the area between Rustenburg and Brits in the North West Province of South Africa has been in existence for over 70 decades. The unique characteristics of the granite deposits in South Africa resulted in making the country a global producer of the granite rocks. This led to intensified quarrying activities between Rustenburg and Brits town. However, this surface mining method, has a potential to impact the environment in a negative way causing loss in vegetation, depletion of natural resources, loss of scenic beauty and contamination of surface water resources. To assess the land cover changes caused by granite quarrying activities, remotely sensed data in the form of Landsat images between 1998 and 2015 were used. Supervised classification was used to create maps. Accuracy assessment using Google EarthTM as a reference data yielded an overall accuracy of 78 %. The post classification change detection method was used to assess land cover changes within the granite quarries. Granite quarries increased by 1174.86 ha while formation of quarry lakes increased to 5.3 ha over the 17-year period. Vegetation cover decreased by 1308 ha in area while 18.3 ha bare land was lost during the same period. This study demonstrated the utility of remote sensing to detect changes in land cover within granite quarries.

EnviroAtlas - Minneapolis/St. Paul, MN - 51m Riparian Buffer Vegetated Cover

Science.gov (United States)

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. In this community, vegetated cover is defined as Trees and Forest, Grass and Herbaceous, Woody Wetlands, and Emergent Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Making maps a visual guide to map design for GIS

CERN Document Server

Krygier, John

2016-01-01

Lauded for its accessibility and beautiful design, this text has given thousands of students and professionals the tools to create effective, compelling maps. Using a wealth of illustrations--with 74 in full color--to elucidate each concisely presented point, the revised and updated third edition continues to emphasize how design choices relate to the reasons for making a map and its intended purpose. All components of map making are covered: titles, labels, legends, visual hierarchy, font selection, how to turn phenomena into visual data, data organization, symbolization, and more. Innovative

CORINE land cover and floristic variation in a Mediterranean wetland.

Science.gov (United States)

Giallonardo, Tommaso; Landi, Marco; Frignani, Flavio; Geri, Francesco; Lastrucci, Lorenzo; Angiolini, Claudia

2011-11-01

The aims of the present study were to: (1) investigate whether CORINE land cover classes reflect significant differences in floristic composition, using a very detailed CORINE land cover map (scale 1:5000); (2) decompose the relationships between floristic assemblages and three groups of explanatory variables (CORINE land cover classes, environmental characteristics and spatial structure) into unique and interactive components. Stratified sampling was used to select a set of 100-m(2) plots in each land cover class identified in the semi-natural wetland surrounding a lake in central Italy. The following six classes were considered: stable meadows, deciduous oak dominated woods, hygrophilous broadleaf dominated woods, heaths and shrublands, inland swamps, canals or watercourses. The relationship between land cover classes and floristic composition was tested using several statistical techniques in order to determine whether the results remained consistent with different procedures. The variation partitioning approach was applied to identify the relative importance of three groups of explanatory variables in relation to floristic variation. The most important predictor was land cover, which explained 20.7% of the variation in plant distribution, although the hypothesis that each land cover class could be associated with a particular floristic pattern was not verified. Multi Response Permutation Analysis did not indicate a strong floristic separability between land cover classes and only 9.5% of species showed a significant indicator value for a specific land cover class. We suggest that land cover classes linked with hygrophilous and herbaceous communities in a wetland may have floristic patterns that vary with fine scale and are not compatible with a land cover map.

Topographic Map of Quadrangle 3464, Shahrak (411) and Kasi (412) Quadrangles, Afghanistan

Science.gov (United States)

Bohannon, Robert G.

2006-01-01

This map was produced from several larger digital datasets. Topography was derived from Shuttle Radar Topography Mission (SRTM) 85-meter digital data. Gaps in the original dataset were filled with data digitized from contours on 1:200,000-scale Soviet General Staff Sheets (1978-1997). Contours were generated by cubic convolution averaged over four pixels using TNTmips surface-modeling capabilities. Minor artifacts resulting from the auto-contouring technique are present. Streams were auto-generated from the SRTM data in TNTmips as flow paths. Flow paths were limited in number by their Horton value on a quadrangle-by-quadrangle basis. Peak elevations were averaged over an area measuring 85 m by 85 m (represented by one pixel), and they are slightly lower than the highest corresponding point on the ground. Cultural data were extracted from files downloaded from the Afghanistan Information Management Service (AIMS) Web site (http://www.aims.org.af). The AIMS files were originally derived from maps produced by the Afghanistan Geodesy and Cartography Head Office (AGCHO). Because cultural features were not derived from the SRTM base, they do not match it precisely. Province boundaries are not exactly located. This map is part of a series that includes a geologic map, a topographic map, a Landsat natural-color-image map, and a Landsat false-color-image map for the USGS/AGS (Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file number but a different letter suffix, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively. The open-file report (OFR) numbers for each quadrangle range in sequence from 1092 - 1123. The present map series is to be followed by a second series, in which the geology is reinterpreted on the basis of analysis of remote-sensing data, limited fieldwork, and library research. The second series is to be produced by the USGS

Topographic Map of Quadrangle 3266, Ourzgan (519) and Moqur (520) Quadrangles, Afghanistan

Science.gov (United States)

Bohannon, Robert G.

2006-01-01

This map was produced from several larger digital datasets. Topography was derived from Shuttle Radar Topography Mission (SRTM) 85-meter digital data. Gaps in the original dataset were filled with data digitized from contours on 1:200,000-scale Soviet General Staff Sheets (1978-1997). Contours were generated by cubic convolution averaged over four pixels using TNTmips surface-modeling capabilities. Minor artifacts resulting from the auto-contouring technique are present. Streams were auto-generated from the SRTM data in TNTmips as flow paths. Flow paths were limited in number by their Horton value on a quadrangle-by-quadrangle basis. Peak elevations were averaged over an area measuring 85 m by 85 m (represented by one pixel), and they are slightly lower than the highest corresponding point on the ground. Cultural data were extracted from files downloaded from the Afghanistan Information Management Service (AIMS) Web site (http://www.aims.org.af). The AIMS files were originally derived from maps produced by the Afghanistan Geodesy and Cartography Head Office (AGCHO). Because cultural features were not derived from the SRTM base, they do not match it precisely. Province boundaries are not exactly located. This map is part of a series that includes a geologic map, a topographic map, a Landsat natural-color-image map, and a Landsat false-color-image map for the USGS/AGS (Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file number but a different letter suffix, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively. The open-file report (OFR) numbers for each quadrangle range in sequence from 1092 - 1123. The present map series is to be followed by a second series, in which the geology is reinterpreted on the basis of analysis of remote-sensing data, limited fieldwork, and library research. The second series is to be produced by the USGS

Topographic Map of Quadrangle 3568, Polekhomri (503) and Charikar (504) Quadrangles, Afghanistan

Science.gov (United States)

Bohannon, Robert G.

2006-01-01

This map was produced from several larger digital datasets. Topography was derived from Shuttle Radar Topography Mission (SRTM) 85-meter digital data. Gaps in the original dataset were filled with data digitized from contours on 1:200,000-scale Soviet General Staff Sheets (1978-1997). Contours were generated by cubic convolution averaged over four pixels using TNTmips surface-modeling capabilities. Minor artifacts resulting from the auto-contouring technique are present. Streams were auto-generated from the SRTM data in TNTmips as flow paths. Flow paths were limited in number by their Horton value on a quadrangle-by-quadrangle basis. Peak elevations were averaged over an area measuring 85 m by 85 m (represented by one pixel), and they are slightly lower than the highest corresponding point on the ground. Cultural data were extracted from files downloaded from the Afghanistan Information Management Service (AIMS) Web site (http://www.aims.org.af). The AIMS files were originally derived from maps produced by the Afghanistan Geodesy and Cartography Head Office (AGCHO). Because cultural features were not derived from the SRTM base, they do not match it precisely. Province boundaries are not exactly located. This map is part of a series that includes a geologic map, a topographic map, a Landsat natural-color-image map, and a Landsat false-color-image map for the USGS/AGS (Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file number but a different letter suffix, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively. The open-file report (OFR) numbers for each quadrangle range in sequence from 1092 - 1123. The present map series is to be followed by a second series, in which the geology is reinterpreted on the basis of analysis of remote-sensing data, limited fieldwork, and library research. The second series is to be produced by the USGS

Topographic Map of Quadrangle 3366, Gizab (513) and Nawer (514) Quadrangles, Afghanistan

Science.gov (United States)

Bohannon, Robert G.

2006-01-01

This map was produced from several larger digital datasets. Topography was derived from Shuttle Radar Topography Mission (SRTM) 85-meter digital data. Gaps in the original dataset were filled with data digitized from contours on 1:200,000-scale Soviet General Staff Sheets (1978-1997). Contours were generated by cubic convolution averaged over four pixels using TNTmips surface-modeling capabilities. Minor artifacts resulting from the auto-contouring technique are present. Streams were auto-generated from the SRTM data in TNTmips as flow paths. Flow paths were limited in number by their Horton value on a quadrangle-by-quadrangle basis. Peak elevations were averaged over an area measuring 85 m by 85 m (represented by one pixel), and they are slightly lower than the highest corresponding point on the ground. Cultural data were extracted from files downloaded from the Afghanistan Information Management Service (AIMS) Web site (http://www.aims.org.af). The AIMS files were originally derived from maps produced by the Afghanistan Geodesy and Cartography Head Office (AGCHO). Because cultural features were not derived from the SRTM base, they do not match it precisely. Province boundaries are not exactly located. This map is part of a series that includes a geologic map, a topographic map, a Landsat natural-color-image map, and a Landsat false-color-image map for the USGS/AGS (Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file number but a different letter suffix, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively. The open-file report (OFR) numbers for each quadrangle range in sequence from 1092 - 1123. The present map series is to be followed by a second series, in which the geology is reinterpreted on the basis of analysis of remote-sensing data, limited fieldwork, and library research. The second series is to be produced by the USGS

Topographic Map of Quadrangle 3162, Chakhansur (603) and Kotalak (604) Quadrangles, Afghanistan

Science.gov (United States)

Bohannon, Robert G.

2006-01-01

This map was produced from several larger digital datasets. Topography was derived from Shuttle Radar Topography Mission (SRTM) 85-meter digital data. Gaps in the original dataset were filled with data digitized from contours on 1:200,000-scale Soviet General Staff Sheets (1978-1997). Contours were generated by cubic convolution averaged over four pixels using TNTmips surface-modeling capabilities. Minor artifacts resulting from the auto-contouring technique are present. Streams were auto-generated from the SRTM data in TNTmips as flow paths. Flow paths were limited in number by their Horton value on a quadrangle-by-quadrangle basis. Peak elevations were averaged over an area measuring 85 m by 85 m (represented by one pixel), and they are slightly lower than the highest corresponding point on the ground. Cultural data were extracted from files downloaded from the Afghanistan Information Management Service (AIMS) Web site (http://www.aims.org.af). The AIMS files were originally derived from maps produced by the Afghanistan Geodesy and Cartography Head Office (AGCHO). Because cultural features were not derived from the SRTM base, they do not match it precisely. Province boundaries are not exactly located. This map is part of a series that includes a geologic map, a topographic map, a Landsat natural-color-image map, and a Landsat false-color-image map for the USGS/AGS (Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file number but a different letter suffix, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively. The open-file report (OFR) numbers for each quadrangle range in sequence from 1092 - 1123. The present map series is to be followed by a second series, in which the geology is reinterpreted on the basis of analysis of remote-sensing data, limited fieldwork, and library research. The second series is to be produced by the USGS

Topographic Map of Quadrangle 3164, Lashkargah (605) and Kandahar (606) Quadrangles, Afghanistan

Science.gov (United States)

Bohannon, Robert G.

2006-01-01

This map was produced from several larger digital datasets. Topography was derived from Shuttle Radar Topography Mission (SRTM) 85-meter digital data. Gaps in the original dataset were filled with data digitized from contours on 1:200,000-scale Soviet General Staff Sheets (1978-1997). Contours were generated by cubic convolution averaged over four pixels using TNTmips surface-modeling capabilities. Minor artifacts resulting from the auto-contouring technique are present. Streams were auto-generated from the SRTM data in TNTmips as flow paths. Flow paths were limited in number by their Horton value on a quadrangle-by-quadrangle basis. Peak elevations were averaged over an area measuring 85 m by 85 m (represented by one pixel), and they are slightly lower than the highest corresponding point on the ground. Cultural data were extracted from files downloaded from the Afghanistan Information Management Service (AIMS) Web site (http://www.aims.org.af). The AIMS files were originally derived from maps produced by the Afghanistan Geodesy and Cartography Head Office (AGCHO). Because cultural features were not derived from the SRTM base, they do not match it precisely. Province boundaries are not exactly located. This map is part of a series that includes a geologic map, a topographic map, a Landsat natural-color-image map, and a Landsat false-color-image map for the USGS/AGS (Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file number but a different letter suffix, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively. The open-file report (OFR) numbers for each quadrangle range in sequence from 1092 - 1123. The present map series is to be followed by a second series, in which the geology is reinterpreted on the basis of analysis of remote-sensing data, limited fieldwork, and library research. The second series is to be produced by the USGS

Vegetation mapping of the Mond Protected Area of Bushehr Province (south-west Iran).

Science.gov (United States)

Mehrabian, Ahmadreza; Naqinezhad, Alireza; Mahiny, Abdolrassoul Salman; Mostafavi, Hossein; Liaghati, Homan; Kouchekzadeh, Mohsen

2009-03-01

Arid regions of the world occupy up to 35% of the earth's surface, the basis of various definitions of climatic conditions, vegetation types or potential for food production. Due to their high ecological value, monitoring of arid regions is necessary and modern vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps. Sixteen map units covering 12 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata, Salicornia europaea-Suaeda heterophylla vegetation types were the smallest. Vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.

2005 Kansas Land Cover Patterns, Level I, Kansas River Watershed (1,000m buffer)

Data.gov (United States)

Kansas Data Access and Support Center — The 2005 Kansas Land Cover Patterns map represents Phase 1 of a two-phase mapping initiative occurring over a three-year period. The map is designed to be explicitly...

Generating Topographic Map Data from Classification Results

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Joachim Höhle

2017-03-01

Full Text Available The use of classification results as topographic map data requires cartographic enhancement and checking of the geometric accuracy. Urban areas are of special interest. The conversion of the classification result into topographic map data of high thematic and geometric quality is subject of this contribution. After reviewing the existing literature on this topic, a methodology is presented. The extraction of point clouds belonging to line segments is solved by the Hough transform. The mathematics for deriving polygons of orthogonal, parallel and general line segments by least squares adjustment is presented. A unique solution for polylines, where the Hough parameters are optimized, is also given. By means of two data sets land cover maps of six classes were produced and then enhanced by the proposed method. The classification used the decision tree method applying a variety of attributes including object heights derived from imagery. The cartographic enhancement is carried out with two different levels of quality. The user’s accuracies for the classes “impervious surface” and “building” were above 85% in the “Level 1” map of Example 1. The geometric accuracy of building corners at the “Level 2” maps is assessed by means of reference data derived from ortho-images. The obtained root mean square errors (RMSE of the generated coordinates (x, y were RMSEx = 1.2 m and RMSEy = 0.7 m (Example 1 and RMSEx = 0.8 m and RMSEy = 1.0 m (Example 2 using 31 and 62 check points, respectively. All processing for Level 1 (raster data could be carried out with a high degree of automation. Level 2 maps (vector data were compiled for the classes “building” and “road and parking lot”. For urban areas with numerous classes and of large size, universal algorithms are necessary to produce vector data fully automatically. The recent progress in sensors and machine learning methods will support the generation of topographic map data of high

National forest cover monitoring in mainland South and Southeast Asia: method development and capacity building

Science.gov (United States)

Tyukavina, A.; Potapov, P.; Hansen, M.; Talero, Y.; Turubanova, S.; Pickering, J.; Pickens, A. H.; Quyen, N. H.; Spirovska Kono, M.

2017-12-01

Timely forest monitoring data produced following good practice guidance are required for national reporting on greenhouse gas emissions, national forest resource assessments, and monitoring for REDD+ projects. Remote sensing provides a cost-efficient supplement to national forest inventories, and is often the single viable source of data on forest extent for countries still in the process of establishing field-based inventories. Operational forest monitoring using remotely sensed data requires technical capacity to store, process, and analyze high volumes of satellite imagery. The University of Maryland Global Land Analysis and Discovery (UMD GLAD) lab possesses such technical capacity and is seeking to transfer it to national agencies responsible for forest reporting, national academic institutions, and NGOs. Our projects in South and Southeast Asia include regional forest monitoring in the lower Mekong region in support of the Regional Land Cover Monitoring System (funded by the NASA SERVIR program) and building capacity for forest monitoring in Nepal, Bangladesh, Vietnam, Cambodia, Laos, and Thailand (funded by the SilvaCarbon program). Our forest monitoring approach is a regional scale adaptation of methods developed for the global analysis (Hansen et al. 2013). The methodology to track large-scale clearing of natural forests (e.g. in Brazil and Indonesia) is well established; however, the methods for small-scale disturbance mapping and tree cover rotation assessment are still in development. In Bangladesh our mapping of tree cover change between 2000-2014 revealed that 54% of the tree canopy cover was outside forests, and the majority of canopy changes were smaller than 0.1 ha. Landsat's 30-m resolution was therefore insufficient to monitor changes in tree cover. By using a probability sample of high resolution (circa 1 m) imagery we were able to quantify change in tree canopy cover outside forests (including village woodlots, tree plantations and agroforestry

Calibration and Validation of Landsat Tree Cover in the Taiga−Tundra Ecotone

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Paul Mannix Montesano

2016-06-01

Full Text Available Monitoring current forest characteristics in the taiga−tundra ecotone (TTE at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover >80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010 by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

European Forest Cover During the Past 12,000 Years: A Palynological Reconstruction Based on Modern Analogs and Remote Sensing.

Science.gov (United States)

Zanon, Marco; Davis, Basil A S; Marquer, Laurent; Brewer, Simon; Kaplan, Jed O

2018-01-01

Characterization of land cover change in the past is fundamental to understand the evolution and present state of the Earth system, the amount of carbon and nutrient stocks in terrestrial ecosystems, and the role played by land-atmosphere interactions in influencing climate. The estimation of land cover changes using palynology is a mature field, as thousands of sites in Europe have been investigated over the last century. Nonetheless, a quantitative land cover reconstruction at a continental scale has been largely missing. Here, we present a series of maps detailing the evolution of European forest cover during last 12,000 years. Our reconstructions are based on the Modern Analog Technique (MAT): a calibration dataset is built by coupling modern pollen samples with the corresponding satellite-based forest-cover data. Fossil reconstructions are then performed by assigning to every fossil sample the average forest cover of its closest modern analogs. The occurrence of fossil pollen assemblages with no counterparts in modern vegetation represents a known limit of analog-based methods. To lessen the influence of no-analog situations, pollen taxa were converted into plant functional types prior to running the MAT algorithm. We then interpolate site-specific reconstructions for each timeslice using a four-dimensional gridding procedure to create continuous gridded maps at a continental scale. The performance of the MAT is compared against methodologically independent forest-cover reconstructions produced using the REVEALS method. MAT and REVEALS estimates are most of the time in good agreement at a trend level, yet MAT regularly underestimates the occurrence of densely forested situations, requiring the application of a bias correction procedure. The calibrated MAT-based maps draw a coherent picture of the establishment of forests in Europe in the Early Holocene with the greatest forest-cover fractions reconstructed between ∼8,500 and 6,000 calibrated years BP. This

Forest cover of Champaign County, Illinois in 1993

Science.gov (United States)

Jesus Danilo Chinea; Louis R. Iverson

1997-01-01

The forest cover of Champaign County, in east-central Illinois, was mapped from 1993 aerial photography and entered in a geographical information system database. One hundred and six forest patches cover 3,380 ha. These patches have a mean area of 32 ha, a mean perimeter of 4,851 m, a mean perimeter to area ratio of 237, a fractal dimension of 1.59, and a mean nearest...

The Entropy of Co-Compact Open Covers

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Steven Bourquin

2013-06-01

Full Text Available Co-compact entropy is introduced as an invariant of topological conjugation for perfect mappings defined on any Hausdorff space (compactness and metrizability are not necessarily required. This is achieved through the consideration of co-compact covers of the space. The advantages of co-compact entropy include: (1 it does not require the space to be compact and, thus, generalizes Adler, Konheim and McAndrew’s topological entropy of continuous mappings on compact dynamical systems; and (2 it is an invariant of topological conjugation, compared to Bowen’s entropy, which is metric-dependent. Other properties of co-compact entropy are investigated, e.g., the co-compact entropy of a subsystem does not exceed that of the whole system. For the linear system, (R; f, defined by f(x = 2x, the co-compact entropy is zero, while Bowen’s entropy for this system is at least log 2. More generally, it is found that co-compact entropy is a lower bound of Bowen’s entropies, and the proof of this result also generates the Lebesgue Covering Theorem to co-compact open covers of non-compact metric spaces.

Spatial resolution influence on the identification of land cover classes in the Amazon environment

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PONZONI FLÁVIO J.

2002-01-01

Full Text Available To evaluate the role played by the spatial resolution in distinguishing land cover classes in the Amazon region, different levels of spatial resolution (60, 100, 120, 200 and 250 meters were simulated from a Landsat_5 Thematic Mapper (TM image. Thematic maps were produced by visual interpretation from the original (30 x 30 meters and simulated set of images. The map legend included primary forest, old and young woody secondary succession, and non-forest. The results indicated that for the discrimination between primary forest and non-forest, spatial resolution did not have great influence for pixel size equal or lower than 200 meters. The contrary was verified for the identification of old and young woody secondary vegetation due to their occurrence in small polygons. To avoid significant changes in the calculated area of these land cover types, a spatial resolution better than 100 meters is required. This result is an indication that the use of the future Brazilian remote sensing satellite (SSR-1 for secondary succession identification may be unreliable, especially for latitudes between S10degrees and S15degrees where critical areas of deforestation are located and pixel size is expected to vary within the same scene from 100 meters (S10degrees to 200 meters (S15degrees.

Results of radiometric and geochemical measurement for the natural radioactivity map of Slovenia

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Mišo Andjelov

1994-12-01

Full Text Available In 1990, a program was initiated to cover Slovenia with portable gamma-ray spectrometer measurements on a 5 x 5 km grid. The measurements were performed with a four channel Scintrex GAD-6 spectrometer. Five gamma-ray measurements were taken at each of 816 locations. Samples of the upper 10 cm of soil profile were collected for laboratory analysis. Uranium in samples was determinedby delayed neutron method (DNC. Other 35 elements: Ag, Al, As, Au, Ba, Be,Bi, Ca, Co, Cr, Cu, Fe, K, La, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Sb, Se, Sn, Sr, Th, Ti,U, V, W, Y, Zn and Zr were analyzed by plasma-coupled emission spectrometry (ICP. The field gamma-ray measurements were converted to ground concentrationsof potassium, uranium and thorium. These show good correlation with the laboratory analyses of soil samples. Regardless of the wide spaced sampling, the produced maps show relatively good correlation with main geological units. They demonstrated that the methodology can be successfully implemented for environmental monitoring, geological mapping and mineral exploration. The product ofthis project is the frist natural background radioactivity map of Slovenia covering the entire country.