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

NASA Technical Reports Server (NTRS)

Ransom, Kenneth J.; Montesano, Paul M.; Nelson, Ross F.

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 - 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 grouped pixels representing similar tree cover into polygonal features (objects) that form the map of the transition zone. Eachfeature represents an area much larger than the 500m MODIS pixel to characterize thepatterns of sparse forest patches on a regional scale. Comparisons of the adjusted average tree cover data were made with (1) two existing tree line definitions aggregated for each 1deg longitudinal interval in North America and Eurasia and (2) Landsat-derived Canadianproportion of forest cover for Canada. The adjusted TCC from MODIS VCF shows, on average, greater 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. Those polygons near the boreal/tundra interface with either (1) mean adjusted TCC values between 5-20% , or (2) mean adjusted TCC values <5% but with a standard deviation > 5% were used to identify the ecotone.

The 1980 land cover for the Puget Sound region

NASA Technical Reports Server (NTRS)

Shinn, R. D.; Westerlund, F. V.; Eby, J. R.

1982-01-01

Both LANDSAT imagery and the video information communications and retrieval software were used to develop a land cover classifiction of the Puget Sound of Washington. Planning agencies within the region were provided with a highly accurate land cover map registered to the 1980 census tracts which could subsequently be incorporated as one data layer in a multi-layer data base. Many historical activities related to previous land cover mapping studies conducted in the Puget Sound region are summarized. Valuable insight into conducting a project with a large community of users and in establishing user confidence in a multi-purpose land cover map derived from LANDSAT is provided.

Meteorological Effects of Land Cover Changes in Hungary during the 20th Century

NASA Astrophysics Data System (ADS)

Drüszler, Á.; Vig, P.; Csirmaz, K.

2012-04-01

Geological, paleontological and geomorphologic studies show that the Earth's climate has always been changing since it came into existence. The climate change itself is self-evident. Therefore the far more serious question is how much does mankind strengthen or weaken these changes beyond the natural fluctuation and changes of climate. The aim of the present study was to restore the historical land cover changes and to simulate the meteorological consequences of these changes. Two different land cover maps for Hungary were created in vector data format using GIS technology. The land cover map for 1900 was reconstructed based on statistical data and two different historical maps: the derived map of the 3rd Military Mapping Survey of the Austro-Hungarian Empire and the Synoptic Forestry Map of the Kingdom of Hungary. The land cover map for 2000 was derived from the CORINE land cover database. Significant land cover changes were found in Hungary during the 20th century according to the examinations of these maps and statistical databases. The MM5 non-hydrostatic dynamic model was used to further evaluate the meteorological effects of these changes. The lower boundary conditions for this mesoscale model were generated for two selected time periods (for 1900 and 2000) based on the reconstructed maps. The dynamic model has been run with the same detailed meteorological conditions of selected days from 2006 and 2007, but with modified lower boundary conditions. The set of the 26 selected initial conditions represents the whole set of the macrosynoptic situations for Hungary. In this way, 2×26 "forecasts" were made with 48 hours of integration. The effects of land cover changes under different weather situations were further weighted by the long-term (1961-1990) mean frequency of the corresponding macrosynoptic types, to assume the climatic effects from these stratified averages. The detailed evaluation of the model results were made for three different meteorological variables (temperature, dew point and precipitation).

A multitemporal (1979-2009) land-use/land-cover dataset of the binational Santa Cruz Watershed

USGS Publications Warehouse

2011-01-01

Trends derived from multitemporal land-cover data can be used to make informed land management decisions and to help managers model future change scenarios. We developed a multitemporal land-use/land-cover dataset for the binational Santa Cruz watershed of southern Arizona, United States, and northern Sonora, Mexico by creating a series of land-cover maps at decadal intervals (1979, 1989, 1999, and 2009) using Landsat Multispectral Scanner and Thematic Mapper data and a classification and regression tree classifier. The classification model exploited phenological changes of different land-cover spectral signatures through the use of biseasonal imagery collected during the (dry) early summer and (wet) late summer following rains from the North American monsoon. Landsat images were corrected to remove atmospheric influences, and the data were converted from raw digital numbers to surface reflectance values. The 14-class land-cover classification scheme is based on the 2001 National Land Cover Database with a focus on "Developed" land-use classes and riverine "Forest" and "Wetlands" cover classes required for specific watershed models. The classification procedure included the creation of several image-derived and topographic variables, including digital elevation model derivatives, image variance, and multitemporal Kauth-Thomas transformations. The accuracy of the land-cover maps was assessed using a random-stratified sampling design, reference aerial photography, and digital imagery. This showed high accuracy results, with kappa values (the statistical measure of agreement between map and reference data) ranging from 0.80 to 0.85.

Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

NASA Astrophysics Data System (ADS)

Höhle, J.

2014-09-01

A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.

DEVELOPMENT OF LAND COVER AND TERRAIN DATA BASES FOR THE INNOKO NATIONAL WILDLIFE REFUGE, ALASKA, USING LANDSAT AND DIGITAL TERRAIN DATA.

USGS Publications Warehouse

Markon, Carl J.; Talbot, Stephen

1986-01-01

Landsat-derived land cover maps and associated elevation, slope, and aspect class maps were produced for the Innoko National Wildlife Refuge (3,850,000 acres; 1,555,095 hectares) in northwestern Alaska. These maps and associated digital data products are being used by the U. S. Fish and Wildlife Service for wildlife management, research, and comprehensive conservation planning. Portions of two Landsat Multispectral Scanner (MSS) scenes and digital terrain data were used to produce 1:250,000 scale land cover and terrain maps. Prints of summer and winter Landsat MSS scenes were used to manually interpret broad physiographic strata. These strata were transferred to U. S. Geological Survey 1:250,000-scale topographic maps and digitized. Seven major land cover classes and 23 subclasses were identified. The major land cover classes include: forest, scrub, dwarf scrub and related types, herbaceous, scarcely vegetated areas, water, and shadow.

Agricultural land cover mapping in the context of a geographically referenced digital information system. [Carroll, Macon, and Gentry Counties, Missouri

NASA Technical Reports Server (NTRS)

Stoner, E. R.

1982-01-01

The introduction of soil map information to the land cover mapping process can improve discrimination of land cover types and reduce confusion among crop types that may be caused by soil-specific management practices and background reflectance characteristics. Multiple dates of LANDSAT MSS digital were analyzed for three study areas in northern Missouri to produce cover types for major agricultural land cover classes. Digital data bases were then developed by adding ancillary data such as digitized soil and transportation network information to the LANDSAT-derived cover type map. Procedures were developed to manipulate the data base parameters to extract information applicable to user requirements. An agricultural information system combining such data can be used to determine the productive capacity of land to grow crops, fertilizer needs, chemical weed control rates, irrigation suitability, and trafficability of soil for planting.

Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier

USGS Publications Warehouse

Friesz, Aaron M.; Wylie, Bruce K.; Howard, Daniel M.

2017-01-01

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.

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

NASA Astrophysics Data System (ADS)

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.

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

NASA Technical Reports Server (NTRS)

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%.

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

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

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

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

NASA Astrophysics Data System (ADS)

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.

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

USGS Publications Warehouse

Potapov, P.; Hansen, Matthew C.; Stehman, S.V.; Loveland, Thomas R.; Pittman, K.

2008-01-01

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.

Analysis of MODIS snow cover time series over the alpine regions as input for hydrological modeling

NASA Astrophysics Data System (ADS)

Notarnicola, Claudia; Rastner, Philipp; Irsara, Luca; Moelg, Nico; Bertoldi, Giacomo; Dalla Chiesa, Stefano; Endrizzi, Stefano; Zebisch, Marc

2010-05-01

Snow extent and relative physical properties are key parameters in hydrology, weather forecast and hazard warning as well as in climatological models. Satellite sensors offer a unique advantage in monitoring snow cover due to their temporal and spatial synoptic view. The Moderate Resolution Imaging Spectrometer (MODIS) from NASA is especially useful for this purpose due to its high frequency. However, in order to evaluate the role of snow on the water cycle of a catchment such as runoff generation due to snowmelt, remote sensing data need to be assimilated in hydrological models. This study presents a comparison on a multi-temporal basis between snow cover data derived from (1) MODIS images, (2) LANDSAT images, and (3) predictions by the hydrological model GEOtop [1,3]. The test area is located in the catchment of the Matscher Valley (South Tyrol, Northern Italy). The snow cover maps derived from MODIS-images are obtained using a newly developed algorithm taking into account the specific requirements of mountain regions with a focus on the Alps [2]. This algorithm requires the standard MODIS-products MOD09 and MOD02 as input data and generates snow cover maps at a spatial resolution of 250 m. The final output is a combination of MODIS AQUA and MODIS TERRA snow cover maps, thus reducing the presence of cloudy pixels and no-data-values due to topography. By using these maps, daily time series starting from the winter season (November - May) 2002 till 2008/2009 have been created. Along with snow maps from MODIS images, also some snow cover maps derived from LANDSAT images have been used. Due to their high resolution (< 30 m) they have been considered as an evaluation tool. The snow cover maps are then compared with the hydrological GEOtop model outputs. The main objectives of this work are: 1. Evaluation of the MODIS snow cover algorithm using LANDSAT data 2. Investigation of snow cover, and snow cover duration for the area of interest for South Tyrol 3. Derivation and interpretation of the snow line for the seven winter seasons 4. An evaluation of the model outputs in order to determine the situations in which the remotely sensed data can be used to improve the model prediction of snow coverage and related variables References [1] Rigon R., Bertoldi G. and Over T.M. 2006. GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets, Journal of Hydrometeorology, 7: 371-388. [2] Rastner P., Irsara L., Schellenberger T., Della Chiesa S., Bertoldi G., Endrizzi S., Notarnicola C., Steurer C., Zebisch M. 2009. Monitoraggio del manto nevoso in aree alpine con dati MODIS multi-temporali e modelli idrologici, 13th ASITA National Conference, 1-4.12.2009, Bari, Italy. [3] Zanotti F., Endrizzi S., Bertoldi G. and Rigon R. 2004. The GEOtop snow module. Hydrological Processes, 18: 3667-3679. DOI:10.1002/hyp.5794.

Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) Global Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; Scharfen, Greg R.

2000-01-01

Following the 1999 launch of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), the capability exists to produce global snow-cover maps on a daily basis at 500-m resolution. Eight-day composite snow-cover maps will also be available. MODIS snow-cover products are produced at Goddard Space Flight Center and archived and distributed by the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. The products are available in both orbital and gridded formats. An online search and order tool and user-services staff will be available at NSIDC to assist users with the snow products. The snow maps are available at a spatial resolution of 500 m, and 1/4 degree x 1/4 degree spatial resolution, and provide information on sub-pixel (fractional) snow cover. Pre-launch validation work has shown that the MODIS snow-mapping algorithms perform best under conditions of continuous snow cover in low vegetation areas, but can also map snow cover in dense forests. Post-launch validation activities will be performed using field and aircraft measurements from a February 2000 validation mission, as well as from existing satellite-derived snow-cover maps from NOAA and Landsat-7 Enhanced Thematic Mapper Plus (ETM+).

Development of a 2001 National Land Cover Database for the United States

USGS Publications Warehouse

Homer, Collin G.; Huang, Chengquan; Yang, Limin; Wylie, Bruce K.; Coan, Michael

2004-01-01

Multi-Resolution Land Characterization 2001 (MRLC 2001) is a second-generation Federal consortium designed to create an updated pool of nation-wide Landsat 5 and 7 imagery and derive a second-generation National Land Cover Database (NLCD 2001). The objectives of this multi-layer, multi-source database are two fold: first, to provide consistent land cover for all 50 States, and second, to provide a data framework which allows flexibility in developing and applying each independent data component to a wide variety of other applications. Components in the database include the following: (1) normalized imagery for three time periods per path/row, (2) ancillary data, including a 30 m Digital Elevation Model (DEM) derived into slope, aspect and slope position, (3) perpixel estimates of percent imperviousness and percent tree canopy, (4) 29 classes of land cover data derived from the imagery, ancillary data, and derivatives, (5) classification rules, confidence estimates, and metadata from the land cover classification. This database is now being developed using a Mapping Zone approach, with 66 Zones in the continental United States and 23 Zones in Alaska. Results from three initial mapping Zones show single-pixel land cover accuracies ranging from 73 to 77 percent, imperviousness accuracies ranging from 83 to 91 percent, tree canopy accuracies ranging from 78 to 93 percent, and an estimated 50 percent increase in mapping efficiency over previous methods. The database has now entered the production phase and is being created using extensive partnering in the Federal government with planned completion by 2006.

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

USGS Publications Warehouse

Hansen, M.C.; Reed, B.

2000-01-01

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

A long-term Northern Hemisphere snow cover extent product (JASMES) deriving from satellite-borne optical sensors using consistent objective criteria

NASA Astrophysics Data System (ADS)

Hori, M.; Sugiura, K.; Kobayashi, K.; Aoki, T.; Tanikawa, T.; Niwano, M.; Enomoto, H.

2017-12-01

A long-term Northern Hemisphere (NH) snow cover extent (SCE) product (JASMES SCE) was developed from the application of a consistent objective snow cover mapping algorithm to satellite-borne optical sensors (NOAA/AVHRR and NASA's optical sensor MODIS) from 1982 to the present. We estimated NH SCE from weekly composited snow cover maps and evaluated the accuracies of snow cover detection using in-situ snow data. As benchmark SCE product, we also evaluated the accuracy of SCE maps from the National Oceanic and Atmospheric Administration Climate Data Record (NOAA-CDR) product. The evaluation showed that JASMES SCE has more temporally stable accuracies. Seasonally averaged SCE derived from JASMES exhibited negative slopes in all seasons which is opposite to those of NOAA-CDR SCE in the fall and winter seasons. The spatial pattern of annual snow cover duration (SCD) trends exhibited noticeable asymmetric pattern between continents with the largest negative trends seen over western Eurasia. The NH SCE product will be connected to the data of the Japanese Earth Observing satellite named "Global Change Observation Mission for Climate (GCOM-C)" to be launched in late 2017.

Development and application of operational techniques for the inventory and monitoring of resources and uses for the Texas coastal zone. Volume 1: Text

NASA Technical Reports Server (NTRS)

Harwood, P. (Principal Investigator); Finley, R.; Mcculloch, S.; Malin, P. A.; Schell, J. A.

1977-01-01

The author has identified the following significant results. Image interpretation and computer-assisted techniques were developed to analyze LANDSAT scenes in support of resource inventory and monitoring requirements for the Texas coastal region. Land cover and land use maps, at a scale of 1:125,000 for the image interpretation product and 1:24,000 for the computer-assisted product, were generated covering four Texas coastal test sites. Classification schemes which parallel national systems were developed for each procedure, including 23 classes for image interpretation technique and 13 classes for the computer-assisted technique. Results indicate that LANDSAT-derived land cover and land use maps can be successfully applied to a variety of planning and management activities on the Texas coast. Computer-derived land/water maps can be used with tide gage data to assess shoreline boundaries for management purposes.

An integrated approach for automated cover-type mapping of large inaccessible areas in Alaska

USGS Publications Warehouse

Fleming, Michael D.

1988-01-01

The lack of any detailed cover type maps in the state necessitated that a rapid and accurate approach to be employed to develop maps for 329 million acres of Alaska within a seven-year period. This goal has been addressed by using an integrated approach to computer-aided analysis which combines efficient use of field data with the only consistent statewide spatial data sets available: Landsat multispectral scanner data, digital elevation data derived from 1:250 000-scale maps, and 1:60 000-scale color-infrared aerial photographs.

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

NASA Astrophysics Data System (ADS)

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 by late summer of 2017 and will be made available through the Commission for Environmental Cooperation (CEC) at www.cec.org

Assessment of 2001 NLCD percent tree and impervious cover estimates

Treesearch

Eric Greenfield; David J. Nowak; Jeffrey T. Walton

2009-01-01

The 2001 National Land Cover Database (NLCD) tree and impervious cover maps provide an opportunity to extract basic land-cover information helpful for natural resource assessments. To determine the potential utility and limitations of the 2001 NLCD data, this exploratory study compared 2001 NLCD-derived values of overall percent tree and impervious cover within...

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

PubMed

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 cover map users. © 2017 John Wiley & Sons Ltd.

Comparison of U.S. Forest Land AreaEstimates From Forest Inventory and Analysis, National Resources Inventory, and Four Satellite Image-Derived Land Cover Data Sets

Treesearch

Mark D. Nelson; Ronald E. McRoberts; Veronica C. Lessard

2005-01-01

Our objective was to test one application of remote sensing technology for complementing forest resource assessments by comparing a variety of existing satellite image-derived land cover maps with national inventory-derived estimates of United States forest land area. National Resources Inventory (NRI) 1997 estimates of non-Federal forest land area differed by 7.5...

Spectral signature selection for mapping unvegetated soils

NASA Technical Reports Server (NTRS)

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.

Mapping tree and impervious cover using Ikonos imagery: links with water quality and stream health

NASA Astrophysics Data System (ADS)

Wright, R.; Goetz, S. J.; Smith, A.; Zinecker, E.

2002-12-01

Precision georeferened Ikonos satellite imagery was used to map tree cover and impervious surface area in Montgomery county Maryland. The derived maps were used to assess riparian zone stream buffer tree cover and to predict, with multivariate logistic regression, stream health ratings across 246 small watersheds averaging 472 km2 in size. Stream health was assessed by state and county experts using a combination of physical measurements (e.g., dissolved oxygen) and biological indicators (e.g., benthic macroinvertebrates). We found it possible to create highly accurate (90+ per cent) maps of tree and impervious cover using decision tree classifiers, provided extensive field data were available for algorithm training. Impervious surface area was found to be the primary predictor of stream health, followed by tree cover in riparian buffers, and total tree cover within entire watersheds. A number of issues associated with mapping using Ikonos imagery were encountered, including differences in phenological and atmospheric conditions, shadowing within canopies and between scene elements, and limited spectral discrimination of cover types. We report on both the capabilities and limitations of Ikonos imagery for these applications, and considerations for extending these analyses to other areas.

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

NASA Technical Reports Server (NTRS)

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.

Development and characterization of a 3D high-resolution terrain database

NASA Astrophysics Data System (ADS)

Wilkosz, Aaron; Williams, Bryan L.; Motz, Steve

2000-07-01

A top-level description of methods used to generate elements of a high resolution 3D characterization database is presented. The database elements are defined as ground plane elevation map, vegetation height elevation map, material classification map, discrete man-made object map, and temperature radiance map. The paper will cover data collection by means of aerial photography, techniques of soft photogrammetry used to derive the elevation data, and the methodology followed to generate the material classification map. The discussion will feature the development of the database elements covering Fort Greely, Alaska. The developed databases are used by the US Army Aviation and Missile Command to evaluate the performance of various missile systems.

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

NASA Technical Reports Server (NTRS)

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

NASA Astrophysics Data System (ADS)

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.

AN EXPERIMENTAL ASSESSMENT OF MINIMUM MAPPING UNIT SIZE

EPA Science Inventory

Land-cover (LC) maps derived from remotely sensed data are often presented using a minimum mapping unit (MMU). The choice of a MMU that is appropriate for the projected use of a classification is important. The objective of this experiment was to determine the optimal MMU of a L...

Modeling Land Use/Cover Changes in an African Rural Landscape

NASA Astrophysics Data System (ADS)

Kamusoko, C.; Aniya, M.

2006-12-01

Land use/cover changes are analyzed in the Bindura district of Zimbabwe, Africa through the integration of data from a time series of Landsat imagery (1973, 1989 and 2000), a household survey and GIS coverages. We employed a hybrid supervised/unsupervised classification approach to generate land use/cover maps from which landscape metrics were calculated. Population and other household variables were derived from a sample of surveyed villages, while road accessibility and slope were obtained from topographic maps and digital elevation model, respectively. Markov-cellular automata modeling approach that incorporates Markov chain analysis, cellular automata and multi-criteria evaluation (MCE) / multi-objective allocation (MOLA) procedures was used to simulate land use/cover changes. A GIS-based MCE technique computed transition potential maps, whereas transition areas were derived from the 1973-2000 land use/cover maps using the Markov chain analysis. A 5 x 5 cellular automata filter was used to develop a spatially explicit contiguity- weighting factor to change the cells based on its previous state and those of its neighbors, while MOLA resolved land use/cover class allocation conflicts. The kappa index of agreement was used for model validation. Observed trends in land use/cover changes indicate that deforestation and the encroachment of cultivation in woodland areas is a continuous trend in the study area. This suggests that economic activities driven by agricultural expansion were the main causes of landscape fragmentation, leading to landscape degradation. Rigorous calibration of transition potential maps done by a MCE algorithm and Markovian transition probabilities produced accurate inputs for the simulation of land use/cover changes. Overall standard kappa index of agreement ranged from 0.73 to 0.83, which is sufficient for simulating land use/cover changes in the study area. Land use/cover simulations under the 1989 and 2000 scenario indicated further landscape degradation in the rural areas of the Bindura district. Keywords: Zimbabwe, land use/cover changes, landscape fragmentation, GIS, land use/cover change modeling, multi-criteria evaluation/multi-objective allocation procedures, Markov-cellular automata

Complexity in Climatic Controls on Plant Species Distribution: Satellite Data Reveal Unique Climate for Giant Sequoia in the California Sierra Nevada

NASA Astrophysics Data System (ADS)

Waller, Eric Kindseth

A better understanding of the environmental controls on current plant species distribution is essential if the impacts of such diverse challenges as invasive species, changing fire regimes, and global climate change are to be predicted and important diversity conserved. Climate, soil, hydrology, various biotic factors fire, history, and chance can all play a role, but disentangling these factors is a daunting task. Increasingly sophisticated statistical models relying on existing distributions and mapped climatic variables, among others, have been developed to try to answer these questions. Any failure to explain pattern with existing mapped climatic variables is often taken as a referendum on climate as a whole, rather than on the limitations of the particular maps or models. Every location has a unique and constantly changing climate so that any distribution could be explained by some aspect of climate. Chapter 1 of this dissertation reviews some of the major flaws in species distribution modeling and addresses concerns that climate may therefore not be predictive of, or even relevant to, species distributions. Despite problems with climate-based models, climate and climate-derived variables still have substantial merit for explaining species distribution patterns. Additional generation of relevant climate variables and improvements in other climate and climate-derived variables are still needed to demonstrate this more effectively. Satellite data have a long history of being used for vegetation mapping and even species distribution mapping. They have great potential for being used for additional climatic information, and for improved mapping of other climate and climate-derived variables. Improving the characterization of cloud cover frequency with satellite data is one way in which the mapping of important climate and climate-derived variables can be improved. An important input to water balance models, solar radiation maps could be vastly improved with a better mapping of spatial and temporal patterns in cloud cover. Chapter 2 of this dissertation describes the generation of custom daily cloud cover maps from Advanced Very High Resolution Radiometer (AVHRR) satellite data from 1981-1999 at ~5 km resolution and Moderate Resolution Imagine Spectroradiomter (MODIS) satellite reflectance data at ~500 meter resolution for much of the western U.S., from 2000 to 2012. Intensive comparisons of reflectance spectra from a variety of cloud and snow-covered scenes from the southwestern United States allowed the generation of new rules for the classification of clouds and snow in both the AVHRR and MODIS data. The resulting products avoid many of the problems that plague other cloud mapping efforts, such as the tendency for snow cover and bright desert soils to be mapped as cloud. This consistency in classification across cover types is critically important for any distribution modeling of a plant species that might be dependent on cloud cover. In Chapter 3, monthly cloud frequencies derived from the daily classifications were used directly in species distribution models for giant sequoia and were found to be the strongest predictors of giant sequoia distribution. A high frequency of cloud cover, especially in the spring, differentiated the climate of the west slope of the southern Sierra Nevada, where giant sequoia are prolific, from central and northern parts of the range, where the tree is rare and generally absent. Other mapped cloud products, contaminated by confusion with high elevation snow, would likely not have found this important result. The result illustrates the importance of accuracy in mapping as well as the importance of previously overlooked aspects of climate for species distribution modeling. But it also raises new questions about why the clouds form where they do and whether they might be associated with other aspects of climate important to giant sequoia distribution. What are the exact climatic mechanisms governing the distribution? Detailed aspects of the local climate warranted more investigation. Chapter 4 investigates the climate associated with the frequent cloud formation over the western slopes of the southern Sierra Nevada: the "sequoia belt". This region is climatically distinct in a number of ways, all of which could be factors in influencing the distribution of giant sequoia and other species. Satellite and micrometeorological flux tower data reveal characteristics of the sequoia belt that were not evident with surface climate measurements and maps derived from them. Results have implications for species distributions everywhere, but especially in rugged mountains, where climates are complex and poorly mapped. Chapter 5 summarizes some of the main conclusions from the work and suggests directions for related future research. (Abstract shortened by UMI.).

MODIS Snow and Ice Production

NASA Technical Reports Server (NTRS)

Hall, Dorthoy K.; Hoser, Paul (Technical Monitor)

2002-01-01

Daily, global snow cover maps, and sea ice cover and sea ice surface temperature (IST) maps are derived from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS), are available at no cost through the National Snow and Ice Data Center (NSIDC). Included on this CD-ROM are samples of the MODIS snow and ice products. In addition, an animation, done by the Scientific Visualization studio at Goddard Space Flight Center, is also included.

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

Treesearch

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-...

The natural resources inventory system ASVT project

NASA Technical Reports Server (NTRS)

Joyce, A. T.

1979-01-01

The hardware/software and the associated procedures for a natural resource inventory and information system based on the use of LANDSAT-acquired multispectral scanner digital data is described. The system is designed to derive land cover/vegetation information from LANDSAT data and geographically reference this information for the production of various types of maps and for the compilation of acreage by land cover/vegetation category. The system also provides for data base building so that the LANDSAT-derived information can be related to information digitized from other sources (e.g., soils maps) in a geographic context in order to address specific applications. These applications include agricultural crop production estimation, erosion hazard-reforestation need assessment, whitetail deer habitat assessment, and site selection. The system is tested in demonstration areas located in the state of Mississippi, and the results of these application demonstrations are presented. A cost-efficiency comparison of producing land cover/vegetation maps and statistics with this system versus the use of small-scale aerial photography is made.

Improving alpine-region spectral unmixing with optimal-fit snow endmembers

NASA Technical Reports Server (NTRS)

Painter, Thomas H.; Roberts, Dar A.; Green, Robert O.; Dozier, Jeff

1995-01-01

Surface albedo and snow-covered-area (SCA) are crucial inputs to the hydrologic and climatologic modeling of alpine and seasonally snow-covered areas. Because the spectral albedo and thermal regime of pure snow depend on grain size, areal distribution of snow grain size is required. Remote sensing has been shown to be an effective (and necessary) means of deriving maps of grain size distribution and snow-covered-area. Developed here is a technique whereby maps of grain size distribution improve estimates of SCA from spectral mixture analysis with AVIRIS data.

An Assessment of Differences in Tree Cover Measurements between Landsat and Lidar-derived Products

NASA Astrophysics Data System (ADS)

Tang, H.; Song, X. P.; Armston, J.; Hancock, S.; Duncanson, L.; Zhao, F. A.; Schaaf, C.; Strahler, A. H.; Huang, C.; Hansen, M.; Goetz, S. J.; Dubayah, R.

2016-12-01

Tree cover is one of the most important canopy structural variables describe interactions between atmosphere and biosphere, and is also linked to the function and quality of ecosystem services. Large-area tree cover measurements are traditionally based on multispectral satellite imagery, and there are several global products available at high to medium spatial resolution (30m-1km). Recent developments in lidar remote sensing, including the upcoming Global Ecosystem Dynamics Investigation (GEDI) lidar, offers an alternative means to map tree cover over broad geographical extents. However, differences in the definition of tree cover and the retrieval method can result in large discrepancies between products derived from multispectral imagery and lidar data, and can potentially impact their further use in ecosystem modelling and above-ground biomass mapping. To separate the effects of cover definition and retrieval method, we first conducted a meta-analysis of several tree cover data sets across different biogeographic regions using three publicly available Landsat-based tree cover products (GLCF, NLCD and GLAD), and two waveform and discrete return airborne lidar products. We found that, whereas Landsat products had low-moderate agreements (up to 40% mean difference) on tree cover estimates particularly at the high end (e.g. >80%), airborne lidar can provide more accurate and consistent measurements (mean difference < 5%) when compared with field data. The differences among Landsat products were mainly due to low measurement accuracy and those among lidar products were caused by different definitions of tree cover (e.g. crown cover vs. fractional cover). We further recommended the use of lidar data as a complement or alternative to ultra-fine resolution images in training/validating Landsat-class images for large-area tree cover mapping.

Thematic and positional accuracy assessment of digital remotely sensed data

Treesearch

Russell G. Congalton

2007-01-01

Accuracy assessment or validation has become a standard component of any land cover or vegetation map derived from remotely sensed data. Knowing the accuracy of the map is vital to any decisionmaking performed using that map. The process of assessing the map accuracy is time consuming and expensive. It is very important that the procedure be well thought out and...

Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: A case study of the Southwest Regional Gap Analysis Project (SWReGAP)

USGS Publications Warehouse

Lowry, J.; Ramsey, R.D.; Thomas, K.; Schrupp, D.; Sajwaj, T.; Kirby, J.; Waller, E.; Schrader, S.; Falzarano, S.; Langs, L.; Manis, G.; Wallace, C.; Schulz, K.; Comer, P.; Pohs, K.; Rieth, W.; Velasquez, C.; Wolk, B.; Kepner, W.; Boykin, K.; O'Brien, L.; Bradford, D.; Thompson, B.; Prior-Magee, J.

2007-01-01

Land-cover mapping efforts within the USGS Gap Analysis Program have traditionally been state-centered; each state having the responsibility of implementing a project design for the geographic area within their state boundaries. The Southwest Regional Gap Analysis Project (SWReGAP) was the first formal GAP project designed at a regional, multi-state scale. The project area comprises the southwestern states of Arizona, Colorado, Nevada, New Mexico, and Utah. The land-cover map/dataset was generated using regionally consistent geospatial data (Landsat ETM+ imagery (1999-2001) and DEM derivatives), similar field data collection protocols, a standardized land-cover legend, and a common modeling approach (decision tree classifier). Partitioning of mapping responsibilities amongst the five collaborating states was organized around ecoregion-based "mapping zones". Over the course of 21/2 field seasons approximately 93,000 reference samples were collected directly, or obtained from other contemporary projects, for the land-cover modeling effort. The final map was made public in 2004 and contains 125 land-cover classes. An internal validation of 85 of the classes, representing 91% of the land area was performed. Agreement between withheld samples and the validated dataset was 61% (KHAT = .60, n = 17,030). This paper presents an overview of the methodologies used to create the regional land-cover dataset and highlights issues associated with large-area mapping within a coordinated, multi-institutional management framework. ?? 2006 Elsevier Inc. All rights reserved.

Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2017-04-01

The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

Quantifying scaling effects on satellite-derived forest area estimates for the conterminous USA

Treesearch

Daolan Zheng; L.S. Heath; M.J. Ducey; J.E. Smith

2009-01-01

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30m satellite-derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non-forest); and (2) coarser resolutions (1km and 10 km). Standard errors of the model estimates were 2.3%...

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

NASA Astrophysics Data System (ADS)

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 techniques. Finally, the land-cover modification maps generated for three time intervals (1985--1990--1996--2000), with nine change-classes revealed important variations in land-cover gain and loss between northern and southern California study areas.

Daily High-Resolution Flood Maps of Africa: 1992-present with Near Real Time Updates

NASA Astrophysics Data System (ADS)

Picton, J.; Galantowicz, J. F.; Root, B.

2016-12-01

The ability to characterize past and current flood extents frequently, accurately, and at high resolution is needed for many applications including risk assessment, wetlands monitoring, and emergency management. However, remote sensing methods have not been capable of meeting all of these requirements simultaneously. Cloud cover too often obscures the surface for visual and infrared sensors and observations from radar sensors are too infrequent to create consistent historical databases or monitor evolving events. Lower-resolution (10-50 km) passive microwave sensors, such as SSM/I, AMSR-E, and AMSR2, are sensitive to water cover, acquire useful data during clear and cloudy conditions, have revisit periods of up to twice daily, and provide a continuous record of data from 1992 to the present. What they lack most is the resolution needed to map flood extent. We will present results from a flood mapping system capable of producing high-resolution (90-m) flood extent depictions from lower resolution microwave data. The system uses the strong sensitivity of microwave data to surface water coverage combined with land surface and atmospheric data to derive daily flooded fraction estimates on a sensor-footprint basis. The system downscales flooded fraction to make high-resolution Boolean flood extent depictions that are spatially continuous and consistent with the lower resolution data. The downscaling step is based on a relative floodability (RF) index derived from higher-resolution topographic and hydrological data. We process RF to create a flooded fraction threshold map that relates each 90-m grid point to the surrounding terrain at the microwave scale. We have derived daily, 90-m resolution flood maps for Africa covering 1992-present using SSM/I, AMSR-E, and AMSR2 data and we are now producing new daily maps in near real time. The flood maps are being used by the African Risk Capacity (ARC) Agency to underpin an intergovernmental river flood insurance program in Africa. We will present results showing daily flood extents during major events and discuss: validation of the flood maps against MODIS-derived maps; analyses of minimum detectable flood size; aggregate analyses of flood extent over time; flood map use in ARC's insurance model; and results applying the system to the Americas.

Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

NASA Astrophysics Data System (ADS)

Mücher, C. A.; Roupioz, L.; Kramer, H.; Bogers, M. M. B.; Jongman, R. H. G.; Lucas, R. M.; Kosmidou, V. E.; Petrou, Z.; Manakos, I.; Padoa-Schioppa, E.; Adamo, M.; Blonda, P.

2015-05-01

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.

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

PubMed

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.

LARGE AREA LAND COVER MAPPING THROUGH SCENE-BASED CLASSIFICATION COMPOSITING

EPA Science Inventory

Over the past decade, a number of initiatives have been undertaken to create definitive national and global data sets consisting of precision corrected Landsat MSS and TM scenes. One important application of these data is the derivation of large area land cover products spanning ...

Classification of surface types using SIR-C/X-SAR, Mount Everest Area, Tibet

USGS Publications Warehouse

Albright, Thomas P.; Painter, Thomas H.; Roberts, Dar A.; Shi, Jiancheng; Dozier, Jeff; Fielding, Eric

1998-01-01

Imaging radar is a promising tool for mapping snow and ice cover in alpine regions. It combines a high-resolution, day or night, all-weather imaging capability with sensitivity to hydrologic and climatic snow and ice parameters. We use the spaceborne imaging radar-C/X-band synthetic aperture radar (SIR-C/X-SAR) to map snow and glacial ice on the rugged north slope of Mount Everest. From interferometrically derived digital elevation data, we compute the terrain calibration factor and cosine of the local illumination angle. We then process and terrain-correct radar data sets acquired on April 16, 1994. In addition to the spectral data, we include surface slope to improve discrimination among several surface types. These data sets are then used in a decision tree to generate an image classification. This method is successful in identifying and mapping scree/talus, dry snow, dry snow-covered glacier, wet snow-covered glacier, and rock-covered glacier, as corroborated by comparison with existing surface cover maps and other ancillary information. Application of the classification scheme to data acquired on October 7 of the same year yields accurate results for most surface types but underreports the extent of dry snow cover.

Improving snow fraction spatio-temporal continuity using a combination of MODIS and Fengyun-2 satellites over China

NASA Astrophysics Data System (ADS)

Jiang, L.; Wang, G.

2017-12-01

Snow cover is one of key elements in the investigations of weather, climatic change, water resource, and snow hazard. Satellites observations from on-board optical sensors provides the ability to snow cover mapping through the discrimination of snow from other surface features and cloud. MODIS provides maximum of snow cover data using 8-day composition data in order to reduce the cloud obscuration impacts. However, snow cover mapping is often required to obtain at the temporal scale of less than one day, especially in the case of disasters. Geostationary satellites provide much higher temporal resolution measurements (typically at 15 min or half or one hour), which has a great potential to reduce cloud cover problem and observe ground surface for identifying snow. The proposed method in this work is that how to take the advantages of polar-orbiting and geostationary optical sensors to accurately map snow cover without data gaps due to cloud. FY-2 geostationary satellites have high temporal resolution observations, however, they are lacking enough spectral bands essential for snow cover monitoring, such as the 1.6 μm band. Based on our recent work (Wang et al., 2017), we improved FY-2/VISSR fractional snow cover estimation with a linear spectral unmixing analysis method. The linear approach is applied then using the reflectance observed at the certain hourly image of FY-2 to calculate pixel-wise snow cover fraction. The composition of daily factional snow cover employs the sun zenith angle, where the snow fraction under lowest sun zenith angle is considered as the most confident result. FY-2/VISSR fractional snow cover map has less cloud due to the composition of multi-temporal snow maps in a single day. In order to get an accurate and cloud-reduced fractional snow cover map, both of MODIS and FY-2/VISSR daily snow fraction maps are blended together. With the combination of FY-2E/VISSR and MODIS, there are still some cloud existing in the daily snow fraction map. Then the combination snow fraction map is temporally reconstructed using MATLAB Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function to derive a completely daily cloud-free snow cover map under all the sky conditions.

National-scale cropland mapping based on spectral-temporal features and outdated land cover information.

PubMed

Waldner, François; Hansen, Matthew C; Potapov, Peter V; Löw, Fabian; Newby, Terence; Ferreira, Stefanus; Defourny, Pierre

2017-01-01

The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral-temporal features that target salient cropland characteristics. Second, we reduce the spatial variability of the class signatures by stratifying the country and by classifying each stratum independently. Finally, we remove speckle with a weighted majority filter accounting for per-pixel classification confidence. Capitalizing on a wall-to-wall validation data set, the method was tested in South Africa using a 16-year old land cover map and multi-sensor Landsat time series. The overall accuracy of the resulting cropland map reached 92%. A spatially explicit validation revealed large variations across the country and suggests that intensive grain-growing areas were better characterized than smallholder farming systems. Informative features in the classification process vary from one stratum to another but features targeting the minimum of vegetation as well as short-wave infrared features were consistently important throughout the country. Overall, the approach showed potential for routinely delivering consistent cropland maps over large areas as required for operational crop monitoring.

Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor

PubMed Central

Kobryn, Halina T.; Wouters, Kristin; Beckley, Lynnath E.; Heege, Thomas

2013-01-01

Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km2 of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km2). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas. PMID:23922921

National-scale cropland mapping based on spectral-temporal features and outdated land cover information

PubMed Central

Hansen, Matthew C.; Potapov, Peter V.; Löw, Fabian; Newby, Terence; Ferreira, Stefanus; Defourny, Pierre

2017-01-01

The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral-temporal features that target salient cropland characteristics. Second, we reduce the spatial variability of the class signatures by stratifying the country and by classifying each stratum independently. Finally, we remove speckle with a weighted majority filter accounting for per-pixel classification confidence. Capitalizing on a wall-to-wall validation data set, the method was tested in South Africa using a 16-year old land cover map and multi-sensor Landsat time series. The overall accuracy of the resulting cropland map reached 92%. A spatially explicit validation revealed large variations across the country and suggests that intensive grain-growing areas were better characterized than smallholder farming systems. Informative features in the classification process vary from one stratum to another but features targeting the minimum of vegetation as well as short-wave infrared features were consistently important throughout the country. Overall, the approach showed potential for routinely delivering consistent cropland maps over large areas as required for operational crop monitoring. PMID:28817618

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

NASA Astrophysics Data System (ADS)

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.

Mapping Land Cover Types in Amazon Basin Using 1km JERS-1 Mosaic

NASA Technical Reports Server (NTRS)

Saatchi, Sassan S.; Nelson, Bruce; Podest, Erika; Holt, John

2000-01-01

In this paper, the 100 meter JERS-1 Amazon mosaic image was used in a new classifier to generate a I km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages: 1) a supervised maximum a posteriori Bayesian approach to classify the mean backscatter image into 5 general land cover categories of forest, savannah, inundated, white sand, and anthropogenic vegetation classes, and 2) a texture measure decision rule approach to further discriminate subcategory classes based on taxonomic information and biomass levels. Fourteen classes were successfully separated at 1 km scale. The results were verified by examining the accuracy of the approach by comparison with the IBGE and the AVHRR 1 km resolution land cover maps.

Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment

NASA Astrophysics Data System (ADS)

Meusburger, K.; Konz, N.; Schaub, M.; Alewell, C.

2010-06-01

The focus of soil erosion research in the Alps has been in two categories: (i) on-site measurements, which are rather small scale point measurements on selected plots often constrained to irrigation experiments or (ii) off-site quantification of sediment delivery at the outlet of the catchment. Results of both categories pointed towards the importance of an intact vegetation cover to prevent soil loss. With the recent availability of high-resolution satellites such as IKONOS and QuickBird options for detecting and monitoring vegetation parameters in heterogeneous terrain have increased. The aim of this study is to evaluate the usefulness of QuickBird derived vegetation parameters in soil erosion models for alpine sites by comparison to Cesium-137 (Cs-137) derived soil erosion estimates. The study site (67 km 2) is located in the Central Swiss Alps (Urseren Valley) and is characterised by scarce forest cover and strong anthropogenic influences due to grassland farming for centuries. A fractional vegetation cover (FVC) map for grassland and detailed land-cover maps are available from linear spectral unmixing and supervised classification of QuickBird imagery. The maps were introduced to the Pan-European Soil Erosion Risk Assessment (PESERA) model as well as to the Universal Soil Loss Equation (USLE). Regarding the latter model, the FVC was indirectly incorporated by adapting the C factor. Both models show an increase in absolute soil erosion values when FVC is considered. In contrast to USLE and the Cs-137 soil erosion rates, PESERA estimates are low. For the USLE model also the spatial patterns improved and showed "hotspots" of high erosion of up to 16 t ha -1 a -1. In conclusion field measurements of Cs-137 confirmed the improvement of soil erosion estimates using the satellite-derived vegetation data.

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

NASA Astrophysics Data System (ADS)

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 (0.05 deg.) allowing the blended product to indicate percent snow cover over the larger grid cell. Relationships between the percent area covered by snow as indicated by the MODIS data and the threshold for the appearance of snow as indicated by the passive microwave data are presented. Both MODIS and AMSR-E data have enhanced spatial resolution compared to the earlier data sources and examples of how this increased spatial resolution results in more accurate snow cover maps are presented. A wide range of validation data sets are being employed in this study including the NASA Cold Lands Processes Field Experiment undertaken in Colorado during 2002 and 2003.

Global forest cover mapping for the United Nations Food and Agriculture Organization forest resources assessment 2000 program

USGS Publications Warehouse

Zhu, Z.; Waller, E.

2003-01-01

Many countries periodically produce national reports on the status and changes of forest resources, using statistical surveys and spatial mapping of remotely sensed data. At the global level, the Food and Agriculture Organization (FAO) of the United Nations has conducted a Forest Resources Assessment (FRA) program every 10 yr since 1980, producing statistics and analysis that give a global synopsis of forest resources in the world. For the year 2000 of the FRA program (FRA2000), a global forest cover map was produced to provide spatial context to the extensive survey. The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The first two forested classes at the global scale were delineated using combinations of temporal compositing, modified mixture analysis, geographic stratification, and other classification techniques. The remaining three FAO classes were derived primarily from the USGS global land cover characteristics database (Loveland et al. 1999). Validated on the basis of existing reference data sets, the map is estimated to be 77% accurate for the first four classes (no reference data were available for water), and 86% accurate for the forest and nonforest classification. The final map will be published as an insert to the FAO FRA2000 report.

Validation of Satellite Snow Cover Maps in North America and Norway

NASA Technical Reports Server (NTRS)

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.

Evaluating MODIS snow products for modelling snowmelt runoff: Case study of the Rio Grande headwaters

NASA Astrophysics Data System (ADS)

Steele, Caitriana; Dialesandro, John; James, Darren; Elias, Emile; Rango, Albert; Bleiweiss, Max

2017-12-01

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM) and in other models for simulating discharge from snowmelt. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM +) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and may not always provide sufficient cloud-free dates. The coarser spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) offers better temporal resolution and in cloudy years, MODIS data offer the best alternative for mapping snow cover when finer spatial resolution data are unavailable. However, MODIS' coarse spatial resolution (500 m) can obscure fine spatial patterning in snow cover and some MODIS products are not sensitive to end-of-season snow cover. In this study, we aimed to test MODIS snow products for use in simulating snowmelt runoff from smaller headwater basins by a) comparing maps of TM and MODIS-based SCA and b) determining how SRM streamflow simulations are changed by the different estimates of seasonal snow depletion. We compared gridded MODIS snow products (Collection 5 MOD10A1 fractional and binary SCA; SCA derived from Collection 6 MOD10A1 Normalised Difference Snow Index (NDSI) Snow Cover), and the MODIS Snow Covered-Area and Grain size retrieval (MODSCAG) canopy-corrected fractional SCA (SCAMG), with reference SCA maps (SCAREF) generated from binary classification of TM imagery. SCAMG showed strong agreement with SCAREF; excluding true negatives (where both methods agreed no snow was present) the median percent difference between SCAREF and SCAMG ranged between -2.4% and 4.7%. We simulated runoff for each of the four study years using SRM populated with and calibrated for snow depletion curves derived from SCAREF. We then substituted in each of the MODIS-derived depletion curves. With efficiency coefficients ranging between 0.73 and 0.93, SRM simulation results from the SCAMG runs yielded the best results of all the MODIS products and only slightly underestimated discharge volume (between 7 and 11% of measured annual discharge). SRM simulations that used SCA derived from Collection 6 NDSI Snow Cover also yielded promising results, with efficiency coefficients ranging between 0.73 and 0.91. In conclusion, we recommend that when simulating snowmelt runoff from small basins (<4000 km2) with SRM, we recommend that users select either canopy-corrected MODSCAG or create their own site-specific products from the Collection 6 MOD10A1 NDSI.

Landsat and water pollution

NASA Technical Reports Server (NTRS)

Castruccio, P.; Fowler, T.; Loats, H., Jr.

1979-01-01

Report presents data derived from satellite images predicting pollution loads after rainfall. It explains method for converting Landsat images of Eastern United States into cover maps for Baltimore/five county region.

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

PubMed Central

2013-01-01

Background 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. Results 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. Conclusions The improved genetic linkage maps and SSR markers developed in this study will serve as reference genetic linkage maps for members of the genus Dianthus, including carnation, and will be useful for mapping QTLs associated with various traits, and for improving carnation breeding programs. PMID:24160306

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

PubMed

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 in this study will serve as reference genetic linkage maps for members of the genus Dianthus, including carnation, and will be useful for mapping QTLs associated with various traits, and for improving carnation breeding programs.

Merging the MODIS and NESDIS Monthly Snow-Cover Records to Study Decade-Scale Changes in Northern Hemisphere Snow Cover

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Robinson, David A.; Riggs, George A.

2004-01-01

A decade-scale record of Northern Hemisphere snow cover has been available from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite Data and Information Service (NESDIS) and has been reconstructed and validated by Rutgers University following adjustments for inconsistencies that were discovered in the early years of the data set. This record provides weekly, monthly (and, in recent years, daily) snow cover from 1966 to the present for the Northern Hemisphere. With the December 1999 launch of NASA's Earth observing System (EOS) Terra satellite, snow maps are being produced globally, using automated algorithms, on a daily, weekly and monthly basis from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument. The resolution of the MODIS monthly snow maps (0.05deg or about 5 km) is an improvement over that of the NESDIS-derived monthly snow maps (>approx.10 km) the maps, it is necessary to study the datasets carefully to determine if it is possible to merge the datasets into a continuous record. The months in which data are available for both the NESDIS and MODIS maps (March 2000 to the present) will be compared quantitatively to analyze differences in North American and Eurasian snow cover. Results from the NESDIS monthly maps show that for North America (including all 12 months), there is a trend toward slightly less snow cover in each succeeding decade. Interannual snow-cover extent has varied significantly since 2000 as seen in both the NESDIS and MODIS maps. As the length of the satellite record increases through the MODIS era, and into the National Polar-orbiting Environmental Satellite System (NPOESS) era, it should become easier to identify trends in areal extent of snow cover, if present, that may have climatic significance. Thus it is necessary to analyze the validity of merging the NESDIS and MODIS, and, in the future, the NPOESS datasets for determination of long-term continuity in measurement of Northern Hemisphere snow cover.

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Technical Reports Server (NTRS)

Kumar, Uttam; Nemani, Ramakrishna R.; Ganguly, Sangram; Kalia, Subodh; Michaelis, Andrew

2017-01-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS-national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91 percent was achieved, which is a 6 percent improvement in unmixing based classification relative to per-pixel-based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2017-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Urban Land Cover/use Change Detection Using High Resolution SPOT 5 and SPOT 6 Images and Urban Atlas Nomenclature

NASA Astrophysics Data System (ADS)

Akay, S. S.; Sertel, E.

2016-06-01

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.

THERMAL-INERTIA MAPPING IN VEGETATED TERRAIN FROM HEAT CAPACITY MAPPING MISSION SATELLITE DATA.

USGS Publications Warehouse

Watson, Ken; Hummer-Miller, Susanne

1984-01-01

Thermal-inertia data, derived from the Heat Capacity Mapping Mission (HCMM) satellite, were analyzed in areas of varying amounts of vegetation cover. Thermal differences which appear to correlate with lithologic differences have been observed previously in areas of substantial vegetation cover. However, the energy exchange occurring within the canopy is much more complex than that used to develop the methods employed to produce thermal-inertia images. Because adequate models are lacking at present, the interpretation is largely dependent on comparison, correlation, and inference. Two study areas were selected in the western United States: the Richfield, Utah and the Silver City, Arizona-New Mexico, 1 degree multiplied by 2 degree quadrangles. Many thermal-inertia highs were found to be associated with geologic-unit boundaries, faults, and ridges. Lows occur in valleys with residual soil cover.

Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region

PubMed Central

Penížek, Vít; Zádorová, Tereza; Kodešová, Radka; Vaněk, Aleš

2016-01-01

The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters) influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index) were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree) to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area. PMID:27846230

Estimating accuracy of land-cover composition from two-stage cluster sampling

USGS Publications Warehouse

Stehman, S.V.; Wickham, J.D.; Fattorini, L.; Wade, T.D.; Baffetta, F.; Smith, J.H.

2009-01-01

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), root mean square error (RMSE), and correlation (CORR) to quantify accuracy of land-cover composition for a general two-stage cluster sampling design, and for the special case of simple random sampling without replacement (SRSWOR) at each stage. The bias of the estimators for the two-stage SRSWOR design is evaluated via a simulation study. The estimators of RMSE and CORR have small bias except when sample size is small and the land-cover class is rare. The estimator of MAD is biased for both rare and common land-cover classes except when sample size is large. A general recommendation is that rare land-cover classes require large sample sizes to ensure that the accuracy estimators have small bias. ?? 2009 Elsevier Inc.

Development of Ground Reference GIS for Assessing Land Cover Maps of Northeast Yellowstone National Park

NASA Technical Reports Server (NTRS)

Spruce, Joe; Warner, Amanda; Terrie, Greg; Davis, Bruce

2001-01-01

GIS technology and ground reference data often play vital roles in assessing land cover maps derived from remotely sensed data. This poster illustrates these roles, using results from a study done in Northeast Yellowstone National Park. This area holds many forest, range, and wetland cover types of interest to park managers. Several recent studies have focused on this locale, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project performed by Yellowstone Ecosystems Studies (YES) on riparian and in-stream habitat mapping. This poster regards a spin-off to the EOCAP project in which YES and NASA's Earth Science Applications Directorate explored the potential for synergistic use of hyperspecral, synthetic aperture radar, and multiband thermal imagery in mapping land cover types. The project included development of a ground reference GIS for site-specific data needed to evaluate maps from remotely sensed imagery. Field survey data included reflectance of plant communities, native and exotic plant species, and forest health conditions. Researchers also collected GPS points, annotated aerial photographs, and took hand held photographs of reference sites. The use of ESRI, ERDAS, and ENVI software enabled reference data entry into a GIS for comparision to georeferenced imagery and thematic maps. The GIS-based ground reference data layers supported development and assessment of multiple maps from remotely sensed data sets acquired over the study area.

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

NASA Astrophysics Data System (ADS)

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

1997-12-01

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 the wet conifer mosaic. Major differences in the 1-km AVHRR and 30-m Landsat TM-derived land cover classes are most likely due to differences in the spatial resolution of the data sets. In general, the 1 km AVHRR land cover classes are vegetation mosaics consisting of mixed combinations of the Landsat classes. Detailed mapping of the global boreal forest with this approach will benefit from algorithms for cloud screening and to atmospherically correct reflectance data for both aerosol and water vapor effects. We believe that this 1 km AVHRR land cover analysis provides new and useful information for regional water, energy, carbon, and trace gases studies in BOREAS, especially given the significant spatial variability in land cover type and associated biophysical land cover parameters (e.g., albedo, leaf area index, FPAR, and surface roughness). Multiresolution land cover comparisons (30 m, l km, and 100 km grid cells) also illustrated how heterogeneous landscape patterns are represented in land cover maps with differing spatial scales and provided insights on the requirements and challenges for parameterizing landscape heterogeneity as part of land surface process research.

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

USGS Publications Warehouse

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 the wet conifer mosaic. Major differences in the 1-km AVHRR and 30-m Landsat TM-derived land cover classes are most likely due to differences in the spatial resolution of the data sets. In general, the 1 km AVHRR land cover classes are vegetation mosaics consisting of mixed combinations of the Landsat classes. Detailed mapping of the global boreal forest with this approach will benefit from algorithms for cloud screening and to atmospherically correct reflectance data for both aerosol and water vapor effects. We believe that this 1 km AVHRR land cover analysis provides new and useful information for regional water, energy, carbon, and trace gases studies in BOREAS, especially given the significant spatial variability in land cover type and associated biophysical land cover parameters (e.g., albedo, leaf area index, FPAR, and surface roughness). Multiresolution land cover comparisons (30 m, 1 km, and 100 km grid cells) also illustrated how heterogeneous landscape patterns are represented in land cover maps with differing spatial scales and provided insights on the requirements and challenges for parameterizing landscape heterogeneity as part of land surface process research.

Assessing Habitat Suitability at Multiple Scales: A Landscape-Level Approach

Treesearch

Kurt H. Riitters; R.V. O' Neill; K.B. Jones

1997-01-01

The distribution and abundance of many plants and animals are influenced by the spatial arrangement of suitable habitats across landscapes. We derived habitat maps from a digital land cover map of the ~178,000 km2 Chesapeake Bay Watershed by using a spatial filtering algorithm. The regional amounts and patterns of habitats were different for...

 A global evaluation of forest interior area dynamics using tree cover data from 2000 to 2012

Treesearch

Kurt Riitters; James Wickham; Jennifer K. Costanza; Peter Vogt

2016-01-01

Context Published maps of global tree cover derived from Landsat data have indicated substantial changes in forest area from 2000 to 2012. The changes can be arranged in different patterns, with different consequences for forest fragmentation. Thus, the changes in forest area do not necessarily equate to changes in...

Development and applications of the LANDFIRE forest structure layers

Treesearch

Chris Toney; Birgit Peterson; Don Long; Russ Parsons; Greg Cohn

2012-01-01

The LANDFIRE program is developing 2010 maps of vegetation and wildland fuel attributes for the United States at 30-meter resolution. Currently available vegetation layers include ca. 2001 and 2008 forest canopy cover and canopy height derived from Landsat and Forest Inventory and Analysis (FIA) plot measurements. The LANDFIRE canopy cover layer for the conterminous...

Construction of an ultra-high density consensus genetic map, and enhancement of the physical map from genome sequencing in Lupinus angustifolius.

PubMed

Zhou, Gaofeng; Jian, Jianbo; Wang, Penghao; Li, Chengdao; Tao, Ye; Li, Xuan; Renshaw, Daniel; Clements, Jonathan; Sweetingham, Mark; Yang, Huaan

2018-01-01

An ultra-high density genetic map containing 34,574 sequence-defined markers was developed in Lupinus angustifolius. Markers closely linked to nine genes of agronomic traits were identified. A physical map was improved to cover 560.5 Mb genome sequence. Lupin (Lupinus angustifolius L.) is a recently domesticated legume grain crop. In this study, we applied the restriction-site associated DNA sequencing (RADseq) method to genotype an F 9 recombinant inbred line population derived from a wild type × domesticated cultivar (W × D) cross. A high density linkage map was developed based on the W × D population. By integrating sequence-defined DNA markers reported in previous mapping studies, we established an ultra-high density consensus genetic map, which contains 34,574 markers consisting of 3508 loci covering 2399 cM on 20 linkage groups. The largest gap in the entire consensus map was 4.73 cM. The high density W × D map and the consensus map were used to develop an improved physical map, which covered 560.5 Mb of genome sequence data. The ultra-high density consensus linkage map, the improved physical map and the markers linked to genes of breeding interest reported in this study provide a common tool for genome sequence assembly, structural genomics, comparative genomics, functional genomics, QTL mapping, and molecular plant breeding in lupin.

Incorporating Sentinel-2-like remote sensing products in the hydrometeorological modelling over an agricultural area in south west France

NASA Astrophysics Data System (ADS)

Rivalland, Vincent; Gascoin, Simon; Etchanchu, Jordi; Coustau, Mathieu; Cros, Jérôme; Tallec, Tiphaine

2016-04-01

The Sentinel-2 mission will enable to monitor the land cover and the vegetation phenology at high-resolution (HR) every 5 days. However, current Land Surface Models (LSM) typically use land cover and vegetation parameters derived from previous low to mid resolution satellite missions. Here we studied the effect of introducing Sentinel-2-like data in the simulation of the land surface energy and water fluxes in a region dominated by cropland. Simulations were performed with the ISBA-SURFEX LSM, which is used in the operational hydrometeorological chain of Meteo-France for hydrological forecasts and drought monitoring. By default, SURFEX vegetation land surface parameters and temporal evolution are from the ECOCLIMAP II European database mostly derived from MODIS products at 1 km resolution. The model was applied to an experimental area of 30 km by 30 km in south west France. In this area the resolution of ECOCLIMAP is coarser than the typical size of a crop field. This means that several crop types can be mixed in a pixel. In addition ECOCLIMAP provides a climatology of the vegetation phenology and thus does not account for the interannual effects of the climate and land management on the crop growth. In this work, we used a series of 26 Formosat-2 images at 8-m resolution acquired in 2006. From this dataset, we derived a land cover map and a leaf area index map (LAI) at each date, which were substituted to the ECOCLIMAP land cover map and the LAI maps. The model output water and energy fluxes were compared to a standard simulation using ECOCLIMAP only and to in situ measurements of soil moisture, latent and sensible heat fluxes. The results show that the introduction of the HR products improved the timing of the evapotranspiration. The impact was the most visible on the crops having a growing season in summer (maize, sunflower), because the growth period is more sensitive to the climate.

Anticipating Climate Change Impacts on Army Installations

DTIC Science & Technology

2011-10-01

13 3.2 Recent technologically derived ecological characterizations ....................................... 14 3.2.1 USGS Gap Analysis Program... GAP ) ......................................................................................... 14 3.2.2 Hargrove/Hoffman potential multivariate... GAP national land cover map .................................................................................................. 14 5 A Hargrove

The thickness of cover sequences in the Western Desert of Iraq from a power spectrum analysis of gravity and magnetic data

NASA Astrophysics Data System (ADS)

Mousa, Ahmed; Mickus, Kevin; Al-Rahim, Ali

2017-05-01

The Western Desert of Iraq is part of the stable shelf region on the Arabian Plate where the subsurface structural makeup is relatively unknown due to the lack of cropping out rocks, deep drill holes and deep seismic refraction and reflection profiles. To remedy this situation, magnetic and gravity data were analyzed to determine the thickness of the Phanerozoic cover sequences. The 2-D power spectrum method was used to estimate the depth to density and magnetic susceptibility interfaces by using 0.5° square windows. Additionally, the gravity data were analyzed using isostatic residual and decompensative methods to isolate gravity anomalies due to upper crustal density sources. The decompensative gravity anomaly and the differentially reduced to the pole magnetic map indicate a series of mainly north-south and northwest-southeast trending maxima and minima anomalies related to Proterozoic basement lithologies and the varying thickness of cover sequences. The magnetic and gravity derived thickness of cover sequences maps indicate that these thicknesses range from 4.5 to 11.5 km. Both maps in general are in agreement but more detail in the cover thicknesses was determined by the gravity analysis. The gravity-based cover thickness maps indicates regions with shallower depths than the magnetic-based cover thickness t map which may be due to density differences between limestone and shale units within the Paleozoic sediments. The final thickness maps indicate that the Western Desert is a complicated region of basins and uplifts that are more complex than have been shown on previous structural maps of the Western Desert. These basins and uplifts may be related to Paleozoic compressional tectonic events and possibly to the opening of the Tethys Ocean. In addition, petroleum exploration could be extended to three basins outlined by our analysis within the relatively unexplored western portions of the Western Desert.

Towards Seamless Validation of Land Cover Data

NASA Astrophysics Data System (ADS)

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.

Sensitivity of thermal inertia calculations to variations in environmental factors. [in mapping of Earth's surface by remote sensing

NASA Technical Reports Server (NTRS)

Kahle, A. B.; Alley, R. E.; Schieldge, J. P.

1984-01-01

The sensitivity of thermal inertia (TI) calculations to errors in the measurement or parameterization of a number of environmental factors is considered here. The factors include effects of radiative transfer in the atmosphere, surface albedo and emissivity, variations in surface turbulent heat flux density, cloud cover, vegetative cover, and topography. The error analysis is based upon data from the Heat Capacity Mapping Mission (HCMM) satellite for July 1978 at three separate test sites in the deserts of the western United States. Results show that typical errors in atmospheric radiative transfer, cloud cover, and vegetative cover can individually cause root-mean-square (RMS) errors of about 10 percent (with atmospheric effects sometimes as large as 30-40 percent) in HCMM-derived thermal inertia images of 20,000-200,000 pixels.

MODIS land cover and LAI collection 4 product quality across nine states in the western hemisphere.

Treesearch

Warren B. Cohen; Thomas K. Maiersperger; David P. Turner; William D. Ritts; Dirk Pflugmacher; Robert E. Kennedy; Alan Kirschbaum; Steven W. Running; Marcos Costa; Stith T. Gower

2006-01-01

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS...

Simulating landscape change in the Olympic Peninsula using spatial ecological and socioeconomic data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flamm, R.O.; Gottfried, R.; Lee, R.G.

1994-06-01

Ecological and socioeconomic data were integrated to study landscape change for the Dungeness River basin in the Olympic Peninsula, Washington State. A multinomial logit procedure was used to evaluate twenty-two maps representing various data themes to derive transition probabilities of land cover change. Probabilities of forest disturbance were greater on private land than public. Between 1975 and 1988, forest cover increased, grassy/brushy covers decreased, and the number of forest patches increased about 30%. Simulations were run to estimate future land cover. These results were represented as frequency distributions for proportion cover and patch characteristics.

VEGETATION COVER ANALYSIS OF HAZARDOUS WASTE SITES IN UTAH AND ARIZONA USING HYPERSPECTRAL REMOTE SENSING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Serrato, M.; Jungho, I.; Jensen, J.

2012-01-17

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using threemore » different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.« less

A New, Large-scale Map of Interstellar Reddening Derived from H I Emission

NASA Astrophysics Data System (ADS)

Lenz, Daniel; Hensley, Brandon S.; Doré, Olivier

2017-09-01

We present a new map of interstellar reddening, covering the 39% of the sky with low H I column densities ({N}{{H}{{I}}}< 4× {10}20 cm-2 or E(B-V)≈ 45 mmag) at 16\\buildrel{ \\prime}\\over{.} 1 resolution, based on all-sky observations of Galactic H I emission by the HI4PI Survey. In this low-column-density regime, we derive a characteristic value of {N}{{H}{{I}}}/E(B-V)=8.8 × {10}21 {{cm}}2 {{mag}}-1 for gas with | {v}{LSR}| < 90 km s-1 and find no significant reddening associated with gas at higher velocities. We compare our H I-based reddening map with the Schlegel et al. (SFD) reddening map and find them consistent to within a scatter of ≃ 5 mmag. Further, the differences between our map and the SFD map are in excellent agreement with the low-resolution (4\\buildrel{\\circ}\\over{.} 5) corrections to the SFD map derived by Peek and Graves based on observed reddening toward passive galaxies. We therefore argue that our H I-based map provides the most accurate interstellar reddening estimates in the low-column-density regime to date. Our reddening map is made publicly available at doi.org/10.7910/DVN/AFJNWJ.

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

NASA Astrophysics Data System (ADS)

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 continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error." International Journal of Digital Earth: 1-22. Shimada, M., et al. (2014). "New global forest/non-forest maps from ALOS PALSAR data (2007-2010)." Remote Sensing of Environment 155: 13-31.

A global dataset of crowdsourced land cover and land use reference data.

PubMed

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-06-13

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

A global dataset of crowdsourced land cover and land use reference data

PubMed Central

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F.; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-01-01

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general. PMID:28608851

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

NASA Astrophysics Data System (ADS)

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 hydrology in the LMB, plus improve water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change (e.g., from dam building and other LULC change).

Application of satellite and GIS technologies for land-cover and land-use mapping at the rural-urban fringe - A case study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Treitz, P.M.; Howarth, P.J.; Gong, Peng

1992-04-01

SPOT HRV multispectral and panchromatic data were recorded and coregistered for a portion of the rural-urban fringe of Toronto, Canada. A two-stage digital analysis algorithm incorporating a spectral-class frequency-based contextual classification of eight land-cover and land-use classes resulted in an overall Kappa coefficient of 82.2 percent for training-area data and a Kappa coefficient of 70.3 percent for test-area data. A matrix-overlay analysis was then performed within the geographic information system (GIS) to combine the land-cover and land-use classes generated from the SPOT digital classification with zoning information for the area. The map that was produced has an estimated interpretation accuracymore » of 78 percent. Global Positioning System (GPS) data provided a positional reference for new road networks. These networks, in addition to the new land-cover and land-use map derived from the SPOT HRV data, provide an up-to-date synthesis of change conditions in the area. 51 refs.« less

Rapid-response tools and datasets for post-fire remediation: Linking remote sensing and process-based hydrological models

Treesearch

M. E. Miller; William Elliot; M. Billmire; Pete Robichaud; K. A. Endsley

2016-01-01

Post-wildfire flooding and erosion can threaten lives, property and natural resources. Increased peak flows and sediment delivery due to the loss of surface vegetation cover and fire-induced changes in soil properties are of great concern to public safety. Burn severity maps derived from remote sensing data reflect fire-induced changes in vegetative cover and soil...

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

NASA Astrophysics Data System (ADS)

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 best Landsat images. Such an approach does not provide timely results, and cloud cover reduces the utility of map outputs. In a second study, I develop a method to exhaustively mine the recently opened Landsat archive for cloud-free observations and automatically map forest cover loss for Sumatra and Kalimantan for the 2000-2005 interval. In a comparison with a reference dataset consisting of 64 Landsat sample blocks, I show that my method, using per pixel time-series, provides more accurate forest cover loss maps for multiyear intervals than approaches using image composites. In a third study, I disaggregate Landsat-mapped forest cover loss, mapped over a multiyear interval, by year using annual forest cover loss maps generated from coarse spatial, high temporal resolution MODIS imagery. I further disaggregate and analyze forest cover loss by forest land use, and provinces. Forest cover loss trends show high spatial and temporal variability. These results underline the importance of annual mapping for the quantification of forest cover loss in Indonesia, specifically in the light of the developing Reducing Emissions from Deforestation and Forest Degradation in Developing Countries policy framework (REDD). All three studies highlight the advances in quantifying forest cover loss in the humid tropics made by integrating coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data. The three methods presented can be combined into an integrated monitoring strategy.

Resolution Enhancement of MODIS-derived Water Indices for Studying Persistent Flooding

NASA Astrophysics Data System (ADS)

Underwood, L. W.; Kalcic, M. T.; Fletcher, R. M.

2012-12-01

Monitoring coastal marshes for persistent flooding and salinity stress is a high priority issue in Louisiana. Remote sensing can identify environmental variables that can be indicators of marsh habitat conditions, and offer timely and relatively accurate information for aiding wetland vegetation management. Monitoring activity accuracy is often limited by mixed pixels which occur when areas represented by the pixel encompasses more than one cover type. Mixtures of marsh grasses and open water in 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data can impede flood area estimation. Flood mapping of such mixtures requires finer spatial resolution data to better represent the cover type composition within 250m MODIS pixel. Fusion of MODIS and Landsat can improve both spectral and temporal resolution of time series products to resolve rapid changes from forcing mechanisms like hurricane winds and storm surge. For this study, using a method for estimating sub-pixel values from a MODIS time series of a Normalized Difference Water Index (NDWI), using temporal weighting, was implemented to map persistent flooding in Louisiana coastal marshes. Ordinarily NDWI computed from daily 250m MODIS pixels represents a mixture of fragmented marshes and water. Here, sub-pixel NDWI values were derived for MODIS data using Landsat 30-m data. Each MODIS pixel was disaggregated into a mixture of the eight cover types according to the classified image pixels falling inside the MODIS pixel. The Landsat pixel means for each cover type inside a MODIS pixel were computed for the Landsat data preceding the MODIS image in time and for the Landsat data succeeding the MODIS image. The Landsat data were then weighted exponentially according to closeness in date to the MODIS data. The reconstructed MODIS data were produced by summing the product of fractional cover type with estimated NDWI values within each cover type. A new daily time series was produced using both the reconstructed 250-m MODIS, with enhanced features, and the approximated daily 30-m high-resolution image based on Landsat data. The algorithm was developed and tested over the Calcasieu-Sabine Basin, which was heavily inundated by storm surge from Hurricane Ike to study the extent and duration of flooding following the storm. Time series for 2000-2009, covering flooding events by Hurricane Rita in 2005 and Hurricane Ike in 2008, were derived. High resolution images were formed for all days in 2008 between the first cloud free Landsat scene and the last cloud-free Landsat scene. To refine and validate flooding maps, each time series was compared to Louisiana Coastwide Reference Monitoring System (CRMS) station water levels adjusted to marsh to optimize thresholds for MODIS-derived time series of NDWI. Seasonal fluctuations were adjusted by subtracting ten year average NDWI for marshes, excluding the hurricane events. Results from different NDWI indices and a combination of indices were compared. Flooding persistence that was mapped with higher-resolution data showed some improvement over the original MODIS time series estimates. The advantage of this novel technique is that improved mapping of extent and duration of inundation can be provided.

Resolution Enhancement of MODIS-Derived Water Indices for Studying Persistent Flooding

NASA Technical Reports Server (NTRS)

Underwood, L. W.; Kalcic, Maria; Fletcher, Rose

2012-01-01

Monitoring coastal marshes for persistent flooding and salinity stress is a high priority issue in Louisiana. Remote sensing can identify environmental variables that can be indicators of marsh habitat conditions, and offer timely and relatively accurate information for aiding wetland vegetation management. Monitoring activity accuracy is often limited by mixed pixels which occur when areas represented by the pixel encompasses more than one cover type. Mixtures of marsh grasses and open water in 250m Moderate Resolution Imaging Spectroradiometer (MODIS) data can impede flood area estimation. Flood mapping of such mixtures requires finer spatial resolution data to better represent the cover type composition within 250m MODIS pixel. Fusion of MODIS and Landsat can improve both spectral and temporal resolution of time series products to resolve rapid changes from forcing mechanisms like hurricane winds and storm surge. For this study, using a method for estimating sub-pixel values from a MODIS time series of a Normalized Difference Water Index (NDWI), using temporal weighting, was implemented to map persistent flooding in Louisiana coastal marshes. Ordinarily NDWI computed from daily 250m MODIS pixels represents a mixture of fragmented marshes and water. Here, sub-pixel NDWI values were derived for MODIS data using Landsat 30-m data. Each MODIS pixel was disaggregated into a mixture of the eight cover types according to the classified image pixels falling inside the MODIS pixel. The Landsat pixel means for each cover type inside a MODIS pixel were computed for the Landsat data preceding the MODIS image in time and for the Landsat data succeeding the MODIS image. The Landsat data were then weighted exponentially according to closeness in date to the MODIS data. The reconstructed MODIS data were produced by summing the product of fractional cover type with estimated NDWI values within each cover type. A new daily time series was produced using both the reconstructed 250-m MODIS, with enhanced features, and the approximated daily 30-m high-resolution image based on Landsat data. The algorithm was developed and tested over the Calcasieu-Sabine Basin, which was heavily inundated by storm surge from Hurricane Ike to study the extent and duration of flooding following the storm. Time series for 2000-2009, covering flooding events by Hurricane Rita in 2005 and Hurricane Ike in 2008, were derived. High resolution images were formed for all days in 2008 between the first cloud free Landsat scene and the last cloud-free Landsat scene. To refine and validate flooding maps, each time series was compared to Louisiana Coastwide Reference Monitoring System (CRMS) station water levels adjusted to marsh to optimize thresholds for MODIS-derived time series of NDWI. Seasonal fluctuations were adjusted by subtracting ten year average NDWI for marshes, excluding the hurricane events. Results from different NDWI indices and a combination of indices were compared. Flooding persistence that was mapped with higher-resolution data showed some improvement over the original MODIS time series estimates. The advantage of this novel technique is that improved mapping of extent and duration of inundation can be provided.

Evaluating the Effectiveness of Flood Control Strategies in Contrasting Urban Watersheds and Implications for Houston's Future Flood Vulnerability

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2016-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Linking land use changes to surface water quality variability in Lake Victoria: some insights from remote sensing

NASA Astrophysics Data System (ADS)

Mugo, R. M.; Limaye, A. S.; Nyaga, J. W.; Farah, H.; Wahome, A.; Flores, A.

2016-12-01

The water quality of inland lakes is largely influenced by land use and land cover changes within the lake's catchment. In Africa, some of the major land use changes are driven by a number of factors, which include urbanization, intensification of agricultural practices, unsustainable farm management practices, deforestation, land fragmentation and degradation. Often, the impacts of these factors are observable on changes in the land cover, and eventually in the hydrological systems. When the natural vegetation cover is reduced or changed, the surface water flow patterns, water and nutrient retention capacities are also changed. This can lead to high nutrient inputs into lakes, leading to eutrophication, siltation and infestation of floating aquatic vegetation. To assess the relationship between land use and land cover changes in part of the Lake Victoria Basin, a series of land cover maps were derived from Landsat imagery. Changes in land cover were identified through change maps and statistics. Further, the surface water chlorophyll-a concentration and turbidity were derived from MODIS-Aqua data for Lake Victoria. Chlrophyll-a and turbidity are good proxy indicators of nutrient inputs and siltation respectively. The trends in chlorophyll-a and turbidity concentrations were analyzed and compared to the land cover changes over time. Certain land cover changes related to agriculture and urban development were clearly identifiable. While these changes might not be solely responsible for variability in chlrophyll-a and turbidity concentrations in the lake, they are potentially contributing factors to this problem. This work illustrates the importance of addressing watershed degradation while seeking to solve water quality related problems.

Postfire soil burn severity mapping with hyperspectral image unmixing

USGS Publications Warehouse

Robichaud, P.R.; Lewis, S.A.; Laes, D.Y.M.; Hudak, A.T.; Kokaly, R.F.; Zamudio, J.A.

2007-01-01

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r2 = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r2 = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes. ?? 2006 Elsevier Inc. All rights reserved.

The relationship of field burn severity measures to satellite-derived Burned Area Reflectance Classification (BARC) maps

Treesearch

Andrew Hudak; Penelope Morgan; Carter Stone; Pete Robichaud; Terrie Jain; Jess Clark

2004-01-01

Preliminary results are presented from ongoing research on spatial variability of fire effects on soils and vegetation from the Black Mountain Two and Cooney Ridge wildfires, which burned in western Montana during the 2003 fire season. Extensive field fractional cover data were sampled to assess the efficacy of quantitative satellite image-derived indicators of burn...

Calibration and Validation of Tundra Plant Functional Type Fractional Cover Mapping

NASA Astrophysics Data System (ADS)

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.

Genetic map of artichoke × wild cardoon: toward a consensus map for Cynara cardunculus.

PubMed

Sonnante, Gabriella; Gatto, Angela; Morgese, Anita; Montemurro, Francesco; Sarli, Giulio; Blanco, Emanuela; Pignone, Domenico

2011-11-01

An integrated consensus linkage map is proposed for globe artichoke. Maternal and paternal genetic maps were constructed on the basis of an F(1) progeny derived from crossing an artichoke genotype (Mola) with its progenitor, the wild cardoon (Tolfa), using EST-derived SSRs, genomic SSRs, AFLPs, ten genes, and two morphological traits. For most genes, mainly belonging to the chlorogenic acid pathway, new markers were developed. Five of these were SNP markers analyzed through high-resolution melt technology. From the maternal (Mola) and paternal (Tolfa) maps, an integrated map was obtained, containing 337 molecular and one morphological markers ordered in 17 linkage groups (LGs), linked between Mola and Tolfa. The integrated map covers 1,488.8 cM, with an average distance of 4.4 cM between markers. The map was aligned with already existing maps for artichoke, and 12 LGs were linked via 31 bridge markers. LG numbering has been proposed. A total of 124 EST-SSRs and two genes were mapped here for the first time, providing a framework for the construction of a functional map in artichoke. The establishment of a consensus map represents a necessary condition to plan a complete sequencing of the globe artichoke genome.

Roles of Fog and Topography in Redwood Forest Hydrology

NASA Astrophysics Data System (ADS)

Francis, E. J.; Asner, G. P.

2017-12-01

Spatial variability of water in forests is a function of both climatic gradients that control water inputs and topo-edaphic variation that determines the flows of water belowground, as well as interactions of climate with topography. Coastal redwood forests are hydrologically unique because they are influenced by coastal low clouds, or fog, that is advected onto land by a strong coastal-to-inland temperature difference. Where fog intersects the land surface, annual water inputs from summer fog drip can be greater than that of winter rainfall. In this study, we take advantage of mapped spatial gradients in forest canopy water storage, topography, and fog cover in California to better understand the roles and interactions of fog and topography in the hydrology of redwood forests. We test a conceptual model of redwood forest hydrology with measurements of canopy water content derived from high-resolution airborne imaging spectroscopy, topographic variables derived from high-resolution LiDAR data, and fog cover maps derived from NASA MODIS data. Landscape-level results provide insight into hydrological processes within redwood forests, and cross-site analyses shed light on their generality.

Potential of remotely-sensed data for mapping sediment connectivity pathways and their seasonal changes in dryland environments

NASA Astrophysics Data System (ADS)

Foerster, Saskia; Wilczok, Charlotte; Brosinsky, Arlena; Kroll, Anja; Segl, Karl; Francke, Till

2014-05-01

Many drylands are characterized by strong erosion in headwater catchments, where connectivity processes play an important role in the redistribution of water and sediments. Sediment connectivity relates to the physical transfer of sediment through a drainage basin (Bracken and Croke 2007). The identification of sediment source areas and the way they connect to the channel network are essential to environmental management (Reid et al. 2007), especially where high erosion and sediment delivery rates occur. Vegetation cover and its spatial and temporal pattern is one of the main factors affecting sediment connectivity. This is particularly true for patchy vegetation covers typical for dryland environments. While many connectivity studies are based on field-derived data, the potential of remotely-sensed data for sediment connectivity analyses has not yet been fully exploited. Recent advances in remote sensing allow for quantitative, spatially explicit, catchment-wide derivation of surface information to be used in connectivity analyses. These advances include a continuous increase in spatial image resolution to comprise processes at the plot to hillslope to catchment scale, an increase in the temporal resolution to cover seasonal and long-term changes and an increase in the spectral resolution enabling the discrimination of dry and green vegetation fractions from soil surfaces in heterogeneous dryland landscapes. The utilization of remotely-sensed data for connectivity studies raises questions on what type of information is required, how scale of sediment flux and image resolution match, how the connectivity information can be incorporated into water and sediment transport models and how this improves model predictions. The objective of this study is to demonstrate the potential of remotely-sensed data for mapping sediment connectivity pathways and their seasonal change at the example of a mesoscale dryland catchment in the Spanish Pyrenees. Here, sediment connectivity pathways have been mapped for two adjacent sub-catchments (approx. 70 km²) of the Isábena River in different seasons using a quantitative connectivity index based on fractional vegetation cover and topography data. Fractional cover of green and dry vegetation, bare soil and rock were derived by applying a Multiple Endmember Spectral Mixture Analysis approach applied to a hyperspectral image dataset. Sediment connectivity was mapped using the Index of Connectivity (Borselli et al. 2008), in which the effect of land cover on runoff and sediment fluxes is expressed by a spatially distributed weighing factor (in this study, the cover and management factor of the RUSLE). The resulting connectivity maps show that areas behave very differently with regard to connectivity, depending on the land cover but also on the spatial distribution of vegetation abundances and topographic barriers. Most parts of the catchment show higher connectivity values in summer than in spring. The studied sub-catchments show a slightly different connectivity behaviour reflecting the different land cover proportions and their spatial configuration. Future work includes the incorporation of sediment connectivity information into a hydrological model (WASA-SED, Mueller et al. 2010) to better reflect connectivity processes and testing the sensitivity of the model to different input data.

Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models

NASA Astrophysics Data System (ADS)

Akgun, Aykut; Dag, Serhat; Bulut, Fikri

2008-05-01

Landslides are very common natural problems in the Black Sea Region of Turkey due to the steep topography, improper use of land cover and adverse climatic conditions for landslides. In the western part of region, many studies have been carried out especially in the last decade for landslide susceptibility mapping using different evaluation methods such as deterministic approach, landslide distribution, qualitative, statistical and distribution-free analyses. The purpose of this study is to produce landslide susceptibility maps of a landslide-prone area (Findikli district, Rize) located at the eastern part of the Black Sea Region of Turkey by likelihood frequency ratio (LRM) model and weighted linear combination (WLC) model and to compare the results obtained. For this purpose, landslide inventory map of the area were prepared for the years of 1983 and 1995 by detailed field surveys and aerial-photography studies. Slope angle, slope aspect, lithology, distance from drainage lines, distance from roads and the land-cover of the study area are considered as the landslide-conditioning parameters. The differences between the susceptibility maps derived by the LRM and the WLC models are relatively minor when broad-based classifications are taken into account. However, the WLC map showed more details but the other map produced by LRM model produced weak results. The reason for this result is considered to be the fact that the majority of pixels in the LRM map have high values than the WLC-derived susceptibility map. In order to validate the two susceptibility maps, both of them were compared with the landslide inventory map. Although the landslides do not exist in the very high susceptibility class of the both maps, 79% of the landslides fall into the high and very high susceptibility zones of the WLC map while this is 49% for the LRM map. This shows that the WLC model exhibited higher performance than the LRM model.

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

NASA Astrophysics Data System (ADS)

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.

Mapping Surface Temperatures on a Debris-Covered Glacier with an Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Kraaijenbrink, Philip D. A.; Shea, Joseph M.; Litt, Maxime; Steiner, Jakob F.; Treichler, Désirée; Koch, Inka; Immerzeel, Walter W.

2018-05-01

A mantel of debris cover often accumulates across the surface of glaciers in active mountain ranges with exceptionally steep terrain, such as the Andes, Himalaya and New Zealand Alps. Such a supraglacial debris layer has a major influence on a glacier's surface energy budget, enhancing radiation absorption and melt when the layer is thin, but insulating the ice when thicker than a few cm. Information on spatially distributed debris surface temperature has the potential to provide insight into the properties of the debris, its effects on the ice below and its influence on the near-surface boundary layer. Here, we deploy an unmanned aerial vehicle (UAV) equipped with a thermal infrared sensor on three separate missions over one day to map changing surface temperatures across the debris-covered Lirung Glacier in the Central Himalaya. We present a methodology to georeference and process the acquired thermal imagery, and correct for emissivity and sensor bias. Derived UAV surface temperatures are compared with distributed simultaneous in situ temperature measurements as well as with Landsat 8 thermal satellite imagery. Results show that the UAV-derived surface temperatures vary greatly both spatially and temporally, with -1.4±1.8, 11.0 ±5.2 and 15.3±4.7 °C for the three flights (mean±sd), respectively. The range in surface temperatures over the glacier during the morning is very large with almost 50 °C. Ground-based measurements are generally in agreement with the UAV imagery, but considerable deviations are present that are likely due to differences in measurement technique and approach, and validation is difficult as a result. The difference in spatial and temporal variability captured by the UAV as compared with much coarser satellite imagery is striking and it shows that satellite derived temperature maps should be interpreted with care. We conclude that UAVs provide a suitable means to acquire surface temperature maps of debris-covered glacier surfaces at high spatial and temporal resolution, but that there are caveats with regard to absolute temperature measurement.

Improving the Accuracy of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Kumar, Sujay; Chien, Janety Y. L.; Riggs, George A.

2012-01-01

The Air Force Weather Agency (AFWA) -- NASA blended snow-cover product, called ANSA, utilizes Earth Observing System standard snow products from the Moderate- Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE with SWE values calculated from snow depths reported at 1500 National Climatic Data Center (NCDC) co-op stations in the Lower Great Lakes Basin. Compared to station data, the ANSA significantly underestimates SWE in densely-forested areas. We use two methods to remove some of the bias observed in forested areas to reduce the root-mean-square error (RMSE) between the ANSA- and station-derived SWE. First, we calculated a 5- year mean ANSA-derived SWE for the winters of 2005-06 through 2009-10, and developed a five-year mean bias-corrected SWE map for each month. For most of the months studied during the five-year period, the 5-year bias correction improved the agreement between the ANSA-derived and station-derived SWE. However, anomalous months such as when there was very little snow on the ground compared to the 5-year mean, or months in which the snow was much greater than the 5-year mean, showed poorer results (as expected). We also used a 7-day running mean (7DRM) bias correction method using days just prior to the day in question to correct the ANSA data. This method was more effective in reducing the RMSE between the ANSA- and co-op-derived SWE values, and in capturing the effects of anomalous snow conditions.

Using satellite data in map design and production

USGS Publications Warehouse

Hutchinson, John A.

2002-01-01

Satellite image maps have been produced by the U.S. Geological Survey (USGS) since shortly after the launch of the first Landsat satellite in 1972. Over the years, the use of image data to design and produce maps has developed from a manual and photographic process to one that incorporates geographic information systems, desktop publishing, and digital prepress techniques. At the same time, the content of most image-based maps produced by the USGS has shifted from raw image data to land cover or other information layers derived from satellite imagery, often portrayed in combination with shaded relief.

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

PubMed Central

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 mapping of managed clearings in urbanizing landscapes. Our findings also demonstrate the need to more carefully consider managed clearings and their critical ecological functions in landscape- to regional-scale studies of urbanizing ecosystems. PMID:29432442

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

PubMed

Singh, Kunwar K; 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 mapping of managed clearings in urbanizing landscapes. Our findings also demonstrate the need to more carefully consider managed clearings and their critical ecological functions in landscape- to regional-scale studies of urbanizing ecosystems.

Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

NASA Astrophysics Data System (ADS)

Johansen, Kasper; Grove, James; Denham, Robert; Phinn, Stuart

2013-01-01

Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R2=0.60, n=41), ground vegetation cover (R=0.43, n=41), bank width/height ratio (R=0.41, n=41), and valley confinement (producer's accuracy=100%, n=9). Cross-validation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012.

MODIS Collection 6 Data at the National Snow and Ice Data Center (NSIDC)

NASA Astrophysics Data System (ADS)

Fowler, D. K.; Steiker, A. E.; Johnston, T.; Haran, T. M.; Fowler, C.; Wyatt, P.

2015-12-01

For over 15 years, the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) has archived and distributed snow and sea ice products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Earth Observing System (EOS) Aqua and Terra satellites. Collection 6 represents the next revision to NSIDC's MODIS archive, mainly affecting the snow-cover products. Collection 6 specifically addresses the needs of the MODIS science community by targeting the scenarios that have historically confounded snow detection and introduced errors into the snow-cover and fractional snow-cover maps even though MODIS snow-cover maps are typically 90 percent accurate or better under good observing conditions, Collection 6 uses revised algorithms to discriminate between snow and clouds, resolve uncertainties along the edges of snow-covered regions, and detect summer snow cover in mountains. Furthermore, Collection 6 applies modified and additional snow detection screens and new Quality Assessment protocols that enhance the overall accuracy of the snow maps compared with Collection 5. Collection 6 also introduces several new MODIS snow products, including a daily Climate Modelling Grid (CMG) cloud gap-filled (CGF) snow-cover map which generates cloud-free maps by using the most recent clear observations.. The MODIS Collection 6 sea ice extent and ice surface temperature algorithms and products are much the same as Collection 5; however, Collection 6 updates to algorithm inputs—in particular, the L1B calibrated radiances, land and water mask, and cloud mask products—have improved the sea ice outputs. The MODIS sea ice products are currently available at NSIDC, and the snow cover products are soon to follow in 2016 NSIDC offers a variety of methods for obtaining these data. Users can download data directly from an online archive or use the NASA Reverb Search & Order Tool to perform spatial, temporal, and parameter subsetting, reformatting, and re-projection of the data.

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

NASA Astrophysics Data System (ADS)

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.

Twenty-four year record of Northern Hemisphere snow cover derived from passive microwave remote sensing

NASA Astrophysics Data System (ADS)

Armstrong, Richard L.; Brodzik, Mary Jo

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. It is now possible to monitor the global fluctuation of snow cover over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a smiliar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible statellite data and the visible data typically show higher monthly variability. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as on into the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Trends in annual averages are similar, decreasing at rates of approximately 2% per decade. The only region where the passive microwave data consistently indicate snow and the visible data do not is over the Tibetan Plateau and surrounding mountain areas. In the effort to determine the accuracy of the microwave algorithm over this region we are acquiring surface snow observations through a collaborative study with CAREERI/Lanzhou. In order to provide an optimal snow cover product in the future, we are developing a procedure that blends snow extent maps derived from MODIS data with snow water equivalent maps derived from both SSM/I and AMSR.

Summit-to-sea mapping and change detection using satellite imagery: tools for conservation and management of coral reefs.

PubMed

Shapiro, A C; Rohmann, S O

2005-05-01

Continuous summit-to-sea maps showing both land features and shallow-water coral reefs have been completed in Puerto Rico and the U.S. Virgin Islands, using circa 2000 Landsat 7 Enhanced Thematic Mapper (ETM+) Imagery. Continuous land/sea terrain was mapped by merging Digital Elevation Models (DEM) with satellite-derived bathymetry. Benthic habitat characterizations were created by unsupervised classifications of Landsat imagery clustered using field data, and produced maps with an estimated overall accuracy of>75% (Tau coefficient >0.65). These were merged with Geocover-LC (land use/land cover) data to create continuous land/ sea cover maps. Image pairs from different dates were analyzed using Principle Components Analysis (PCA) in order to detect areas of change in the marine environment over two different time intervals: 2000 to 2001, and 1991 to 2003. This activity demonstrates the capabilities of Landsat imagery to produce continuous summit-to-sea maps, as well as detect certain changes in the shallow-water marine environment, providing a valuable tool for efficient coastal zone monitoring and effective management and conservation.

EnviroAtlas -- Memphis, TN (2012) -- One Meter Resolution Urban Land Cover Data Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas ). The Memphis, TN EnviroAtlas One Meter-scale Urban Land Cover (MULC) dataset comprises 2,733 km2 around the city of Memphis, surrounding towns, and rural areas. These leaf-on LC data and maps were derived from 1-m pixel, four-band (red, green, blue, and near-infrared) aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) on four dates in 2012: June 15, June 18, June 21 and June 23, and one date in 2013: July 12. Three separate LiDAR (Light Detection and Ranging) data sets collected on February 19, 2009 00e2?? August 2, 2010, December 1-2, 2011 and January 23-24, 2012 were integrated for Shelby Co., TN, Crittenden Co., AR, and DeSoto Co, MS. Five MULC classes were mapped directly from the NAIP and LiDAR data: Water, Impervious, Soil, Trees, and Grass/Herbaceous. Agriculture was derived from USDA Common Land Unit (CLU) data. Woody and emergent wetlands were copied from existing National Wetlands Inventory (NWI) data. Analysis of a random sampling of 612 photo-interpreted land cover reference points yielded an overall users accuracy of 86.9%. 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-u

EnviroAtlas -- Memphis, TN (2012) -- One Meter Resolution Urban Land Cover Data

EPA Pesticide Factsheets

The Memphis, TN EnviroAtlas One Meter-scale Urban Land Cover (MULC) dataset comprises 2,733 km2 around the city of Memphis, surrounding towns, and rural areas. These leaf-on LC data and maps were derived from 1-m pixel, four-band (red, green, blue, and near-infrared) aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) on four dates in 2012: June 15, June 18, June 21 and June 23, and one date in 2013: July 12. Three separate LiDAR (Light Detection and Ranging) data sets collected on February 19, 2009 00e2?? August 2, 2010, December 1-2, 2011 and January 23-24, 2012 were integrated for Shelby Co., TN, Crittenden Co., AR, and DeSoto Co, MS. Five MULC classes were mapped directly from the NAIP and LiDAR data: Water, Impervious, Soil, Trees, and Grass/Herbaceous. Agriculture was derived from USDA Common Land Unit (CLU) data. Woody and emergent wetlands were copied from existing National Wetlands Inventory (NWI) data. Analysis of a random sampling of 612 photo-interpreted land cover reference points yielded an overall users accuracy of 86.9%. 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 do

Measuring phenological variability from satellite imagery

USGS Publications Warehouse

Reed, Bradley C.; Brown, Jesslyn F.; Vanderzee, D.; Loveland, Thomas R.; Merchant, James W.; Ohlen, Donald O.

1994-01-01

Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large- area land cover mapping and monitoring. The utility of re- motely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.

A Case Study in the Effectiveness of Marine Protected Areas (MPAs): the Islands of Bonaire and Curacao, Dutch Caribbean

NASA Astrophysics Data System (ADS)

Relles, Noelle J.

The islands of Bonaire and Curacao, Dutch Caribbean, were both mapped along their leeward coasts for dominant coral community and other benthic cover in the early 1980s. This mapping effort offers a unique baseline for comparing changes in the benthic community of the two islands since that time, particularly given the marked differences between the two islands. Bonaire is well-protected and completely surrounded by a marine protected area (MPA), which includes two no-diving marine reserves; additionally, Bonaire's population is only around 15,000. In contrast, the island of Curacao is home to 140,000 inhabitants and marine protection is limited, with a reef area of 600 ha established as a "paper" park (i.e., little enforcement). Video transects collected by SCUBA over the reefs were collected on Bonaire in January of 2008; when compared to data from 1985, coral cover had declined in the shallowest portion of the reef (< 5 m) and was mostly the result of declines in Acropora spp., whereas head corals increased. Transects closest to the no-diving marine reserves showed higher coral cover and diversity than transects located farther from the reserves. Satellite remote sensing techniques were used to create landscape-scale reef maps along the leeward coasts of both islands, which could differentiate areas of high hard coral cover (> 20%), predominantly sand (> 50%) and areas where hard coral and sand were mixed with soft corals, sea whips and marine plants. These modern maps (2007-09) were groundtruthed using the video data collected on Bonaire for accuracy and then compared to the early 1980s maps of the reefs on both islands. Bonaire experienced declines in coral cover overall and the remaining coral was increasingly patchy; however, changes in patch characteristics were not significant over the time period, but status as a marine reserve and the sheltering of the shoreline did appear to buffer against coral loss. Surprisingly, the island of Curacao did not experience a decline in total coral cover, but did become increasingly patchy, significantly more so than Bonaire. The Curacao Underwater Park afforded no additional protection against coral loss or fragmentation than an adjacent unprotected area of reef. The difference between the two islands in coral loss versus fragmentation has the potential for a unique natural experiment to study the effects of habitat fragmentation in the absence of overall habitat loss at the landscape scale. The Bonaire National Marine Park could benefit by restricting visitors to its most frequented dive sites by increasing the cost of entry into a tiered pay system, thus generating more income for education and management of the park, as well as deterring some divers from these overused sites. Satellite remote sensing-derived maps are useful for rapid reef mapping and can be utilized for comparison to ancillary maps created by more traditional methods. Satellite-derived maps can only distinguish benthic habitats coarsely (3-4 habitat classes) and are only as reliable as their source data, they benefit greatly from fieldwork to determine depth, geographic location, and benthic habitat cover in real time.

Integrating Statistical and Expert Knowledge to Develop Phenoregions for the Continental United States

NASA Astrophysics Data System (ADS)

Betancourt, J. L.; Biondi, F.; Bradford, J. B.; Foster, J. R.; Betancourt, J. L.; Foster, J. R.; Biondi, F.; Bradford, J. B.; Henebry, G. M.; Post, E.; Koenig, W.; Hoffman, F. M.; de Beurs, K.; Hoffman, F. M.; Kumar, J.; Hargrove, W. W.; Norman, S. P.; Brooks, B. G.

2016-12-01

Vegetated ecosystems exhibit unique phenological behavior over the course of a year, suggesting that remotely sensed land surface phenology may be useful for characterizing land cover and ecoregions. However, phenology is also strongly influenced by temperature and water stress; insect, fire, and weather disturbances; and climate change over seasonal, interannual, decadal and longer time scales. Normalized difference vegetation index (NDVI), a remotely sensed measure of greenness, provides a useful proxy for land surface phenology. We used NDVI for the conterminous United States (CONUS) derived from the Moderate Resolution Spectroradiometer (MODIS) every eight days at 250 m resolution for the period 2000-2015 to develop phenological signatures of emergent ecological regimes called phenoregions. We employed a "Big Data" classification approach on a supercomputer, specifically applying an unsupervised data mining technique, to this large collection of NDVI measurements to develop annual maps of phenoregions. This technique produces a prescribed number of prototypical phenological states to which every location belongs in any year. To reduce the impact of short-term disturbances, we derived a single map of the mode of annual phenological states for the CONUS, assigning each map cell to the state with the largest integrated NDVI in cases where multiple states tie for the highest frequency of occurrence. Since the data mining technique is unsupervised, individual phenoregions are not associated with an ecologically understandable label. To add automated supervision to the process, we applied the method of Mapcurves, developed by Hargrove and Hoffman, to associate individual phenoregions with labeled polygons in expert-derived maps of biomes, land cover, and ecoregions. We will present the phenoregions methodology and resulting maps for the CONUS, describe the "label-stealing" technique for ascribing biome characteristics to phenoregions, and introduce a new polar plotting scheme for processing NDVI data by localized seasonality.

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

NASA Astrophysics Data System (ADS)

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, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from http://maps.elie.ucl.ac.be/CCI/viewer/download.php (Land Cover Maps - v2.0.7; see details in Sect. 5). The PFT map translation protocol and an example in 2000 can be downloaded from https://doi.org/10.5281/zenodo.834229. The annual ESA CCI PFT maps from 1992 to 2015 at 0.5° × 0.5° resolution can also be downloaded from https://doi.org/10.5281/zenodo.1048163.

VizieR Online Data Catalog: Herschel Multi-tiered Extragalactic Survey (Oliver+, 2012)

NASA Astrophysics Data System (ADS)

Oliver, S. J.; Bock, J.; Altieri, B.; Amblard, A.; Arumugam, V.; Aussel, H.; Babbedge, T.; Beelen, A.; Bethermin, M.; Blain, A.; Boselli, A.; Bridge, C.; Brisbin, D.; Buat, V.; Burgarella, D.; Castro-Rodriguez, N.; Cava, A.; Chanial, P.; Cirasuolo, M.; Clements, D. L.; Conley, A.; Conversi, L.; Cooray, A.; Dowell, C. D.; Dubois, E. N.; Dwek, E.; Dye, S.; Eales, S.; Elbaz, D.; Farrah, D.; Feltre, A.; Ferrero, P.; Fiolet, N.; Fox, M.; Franceschini, A.; Gear, W.; Giovannoli, E.; Glenn, J.; Gong, Y.; Gonzalez Solares, E. A.; Griffin, M.; Halpern, M.; Harwit, M.; Hatziminaoglou, E.; Heinis, S.; Hurley, P.; Hwang, H. S.; Hyde, A.; Ibar, E.; Ilbert, O.; Isaak, K.; Ivison, R. J.; Lagache, G.; Le Floc'h, E.; Levenson, L.; Faro, B. L.; Lu, N.; Madden, S.; Maffei, B.; Magdis, G.; Mainetti, G.; Marchetti, L.; Marsden, G.; Marshall, J.; Mortier, A. M. J.; Nguyen, H. T.; O'Halloran, B.; Omont, A.; Page, M. J.; Panuzzo, P.; Papageorgiou, A.; Patel, H.; Pearson, C. P.; Perez-Fournon, I.; Pohlen, M.; Rawlings, J. I.; Raymond, G.; Rigopoulou, D.; Riguccini, L.; Rizzo, D.; Rodighier!, O. G.; Ros Eboom, I. G.; Rowan-Robinson, M.; Sanchez Portal, M.; Schulz, B.; Scott, D.; Seymour, N.; Shupe, D. L.; Smith, A. J.; Stevens, J. A.; Symeonidis, M.; Trichas, M.; Tugwell, K. E.; Vaccari, M.; Valtchanov, I.; Vieira, J. D.; Viero, M.; Vigroux, L.; Wang, L.; Ward, R.; Wardlow, J.; Wright, G.; Xu, C. K.; Zemcov, M.

2017-03-01

SPIRE maps (250, 350 and 500 microns) and PACS maps (100 and 160 microns) covering an area of more than 385 square degrees in the sky resulting from observations taken as part of HerMES (KPGTsoliver1), a Herschel Key Project whose main objective was to chart the formation and evolution of infrared galaxies throughout cosmic history, measuring the bolometric emission of infrared galaxies and their clustering properties. The associated catalogues extracted from these maps include over 1,200,000 entries representing over 340,000 galaxies. They consist of 'blind extraction' catalogues containing photometric information derived directly from these maps, 'band merged' catalogues extracted at SPIRE 250 micron positions plus 'cross-identification' catalogues based on prior Spitzer MIPS 24 micron source positions. The latest data releases contain also information derived from the complementary Herschel programmes HeLMS (GT2mviero1) and HeRS (OT2mviero2). (4 data files).

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

USGS Publications Warehouse

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.

Incorporating Land-Use Mapping Uncertainty in Remote Sensing Based Calibration of Land-Use Change Models

NASA Astrophysics Data System (ADS)

Cockx, K.; Van de Voorde, T.; Canters, F.; Poelmans, L.; Uljee, I.; Engelen, G.; de Jong, K.; Karssenberg, D.; van der Kwast, J.

2013-05-01

Building urban growth models typically involves a process of historic calibration based on historic time series of land-use maps, usually obtained from satellite imagery. Both the remote sensing data analysis to infer land use and the subsequent modelling of land-use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Our research aims to quantify and reduce these uncertainties by means of a particle filter data assimilation approach that incorporates uncertainty in land-use mapping and land-use model parameter assessment into the calibration process. This paper focuses on part of this work, more in particular the modelling of uncertainties associated with the impervious surface cover estimation and urban land-use classification adopted in the land-use mapping approach. Both stages are submitted to a Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The approach was applied on the central part of the Flanders region (Belgium), using a time-series of Landsat/SPOT-HRV data covering the years 1987, 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original classification, it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, incorporating uncertainty in the land-use change model calibration through particle filter data assimilation is proposed to address the uncertainty observed in the derived land-use maps and to reduce uncertainty in future land-use predictions.

Vegetation, plant biomass, and net primary productivity patterns in the Canadian Arctic

NASA Astrophysics Data System (ADS)

Gould, W. A.; Raynolds, M.; Walker, D. A.

2003-01-01

We have developed maps of dominant vegetation types, plant functional types, percent vegetation cover, aboveground plant biomass, and above and belowground annual net primary productivity for Canada north of the northern limit of trees. The area mapped covers 2.5 million km2 including glaciers. Ice-free land covers 2.3 million km2 and represents 42% of all ice-free land in the Circumpolar Arctic. The maps combine information on climate, soils, geology, hydrology, remotely sensed vegetation classifications, previous vegetation studies, and regional expertise to define polygons drawn using photo-interpretation of a 1:4,000,000 scale advanced very high resolution radiometer (AVHRR) color infrared image basemap. Polygons are linked to vegetation description, associated properties, and descriptive literature through a series of lookup tables in a graphic information systems (GIS) database developed as a component of the Circumpolar Arctic Vegetation Map (CAVM) project. Polygons are classified into 20 landcover types including 17 vegetation types. Half of the region is sparsely vegetated (<50% vegetation cover), primarily in the High Arctic (bioclimatic subzones A-C). Whereas most (86%) of the estimated aboveground plant biomass (1.5 × 1015 g) and 87% of the estimated above and belowground annual net primary productivity (2.28 × 1014 g yr-1) are concentrated in the Low Arctic (subzones D and E). The maps present more explicit spatial patterns of vegetation and ecosystem attributes than have been previously available, the GIS database is useful in summarizing ecosystem properties and can be easily updated and integrated into circumpolar mapping efforts, and the derived estimates fall within the range of current published estimates.

Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960-2005.

PubMed

Dewan, Ashraf M; Yamaguchi, Yasushi

2009-03-01

This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images such as SPOT, IRS, IKONOS and field data. The overall accuracy of land cover change maps, generated from Landsat and IRS-1D data, ranged from 85% to 90%. The analysis indicated that the urban expansion of Dhaka Metropolitan resulted in the considerable reduction of wetlands, cultivated land, vegetation and water bodies. The maps showed that between 1960 and 2005 built-up areas increased approximately 15,924 ha, while agricultural land decreased 7,614 ha, vegetation decreased 2,336 ha, wetland/lowland decreased 6,385 ha, and water bodies decreased about 864 ha. The amount of urban land increased from 11% (in 1960) to 344% in 2005. Similarly, the growth of landfill/bare soils category was about 256% in the same period. Much of the city's rapid growth in population has been accommodated in informal settlements with little attempt being made to limit the risk of environmental impairments. The study quantified the patterns of land use/cover change for the last 45 years for Dhaka Metropolitan that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.

Mapping vegetation cover and biomass on the Qinghai-Tibet-Plateau using hyperspectral measurements and multispectral satellite images

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Lehnert, Lukas W.; Wang, Yun; Reudenbach, Christoph; Nauss, Thomas; Bendix, Jörg

2016-04-01

Pastoralism is the dominant land-use on the Qinghai-Tibet-Plateau (QTP) providing the major economic resource for the local population. However, the pastures are highly supposed to be affected by ongoing degradation whose extent is still disputed. This study uses hyperspectral in situ measurements and multispectral satellite images to assess vegetation cover and above ground biomass (AGB) as proxies of pasture degradation on a regional scale. Using Random Forests in conjunction with recursive feature selection as modeling tool, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate vegetation cover and AGB. To regionalize pasture degradation proxies, the transferability of the locally derived models to high resolution multispectral satellite data is assessed. For this purpose, 1183 hyperspectral measurements and vegetation records were sampled at 18 locations on the QTP. AGB was determined on 25 0.5x0.5m plots. Proxies for pasture degradation were derived from the spectra by calculating narrow-band indices (NBI). Using the NBI as predictor variables vegetation cover and AGB were modeled. Models were calculated using the hyperspectral data as well as the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used. In contrast, errors in AGB estimations were considerably higher. Only small differences in accuracy were observed between the models based on hyper- compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite data, the results still show a high potential to map pasture degradation proxies on the QTP even for larger scales.

Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data.

PubMed

Fallati, Luca; Savini, Alessandra; Sterlacchini, Simone; Galli, Paolo

2017-08-01

The Maldives islands in recent decades have experienced dramatic land-use change. Uninhabited islands were turned into new resort islands; evergreen tropical forests were cut, to be replaced by fields and new built-up areas. All these changes happened without a proper monitoring and urban planning strategy from the Maldivian government due to the lack of national land-use and land-cover (LULC) data. This study aimed to realize the first land-use map of the entire Maldives archipelago and to detect land-use and land-cover change (LULCC) using high-resolution satellite images and socioeconomic data. Due to the peculiar geographic and environmental features of the archipelago, the land-use map was obtained by visual interpretation and manual digitization of land-use patches. The images used, dated 2011, were obtained from Digital Globe's WorldView 1 and WorldView 2 satellites. Nine land-use classes and 18 subclasses were identified and mapped. During a field survey, ground control points were collected to test the geographic and thematic accuracy of the land-use map. The final product's overall accuracy was 85%. Once the accuracy of the map had been checked, LULCC maps were created using images from the early 2000s derived from Google Earth historical imagery. Post-classification comparison of the classified maps showed that growth of built-up and agricultural areas resulted in decreases in forest land and shrubland. The LULCC maps also revealed an increase in land reclamation inside lagoons near inhabited islands, resulting in environmental impacts on fragile reef habitat. The LULC map of the Republic of the Maldives produced in this study can be used by government authorities to make sustainable land-use planning decisions and to provide better management of land use and land cover.

EnviroAtlas -- Green Bay, Wisconsin -- One Meter Resolution Urban Land Cover Data (2010)

EPA Pesticide Factsheets

The Green Bay, WI one meter-scale urban land cover (LC) dataset comprises 936 km2 around the city of Green Bay, surrounding towns, tribal lands and rural areas in Brown and Outagamie Counties. These leaf-on LC data and maps were derived from 1-m pixel, four-band (red, green, blue, and near-infrared) aerial photography acquired from the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) on three dates in 2010: July 3, July 25, and August 5. LiDAR data collected on November 18, 2010 was integrated for the Brown County portion. Eight land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). Wetlands were copied from the best available existing wetlands data. Analysis of a random sampling of 566 photo-interpreted land cover reference points yielded an overall accuracy of 91.3%. 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 b

Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

NASA Astrophysics Data System (ADS)

Zhu, Zhe; Gallant, Alisa L.; Woodcock, Curtis E.; Pengra, Bruce; Olofsson, Pontus; Loveland, Thomas R.; Jin, Suming; Dahal, Devendra; Yang, Limin; Auch, Roger F.

2016-12-01

The U.S. Geological Survey's Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4-8. Training data were selected from map products of the U.S. Geological Survey's Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening "Function of mask" (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).

Global rates of habitat loss and implications for amphibian conservation

USGS Publications Warehouse

Gallant, Alisa L.; Klaver, R.W.; Casper, G.S.; Lannoo, M.J.

2007-01-01

A large number of factors are known to affect amphibian population viability, but most authors agree that the principal causes of amphibian declines are habitat loss, alteration, and fragmentation. We provide a global assessment of land use dynamics in the context of amphibian distributions. We accomplished this by compiling global maps of amphibian species richness and recent rates of change in land cover, land use, and human population growth. The amphibian map was developed using a combination of published literature and digital databases. We used an ecoregion framework to help interpret species distributions across environmental, rather than political, boundaries. We mapped rates of land cover and use change with statistics from the World Resources Institute, refined with a global digital dataset on land cover derived from satellite data. Temporal maps of human population were developed from the World Resources Institute database and other published sources. Our resultant map of amphibian species richness illustrates that amphibians are distributed in an uneven pattern around the globe, preferring terrestrial and freshwater habitats in ecoregions that are warm and moist. Spatiotemporal patterns of human population show that, prior to the 20th century, population growth and spread was slower, most extensive in the temperate ecoregions, and largely exclusive of major regions of high amphibian richness. Since the beginning of the 20th century, human population growth has been exponential and has occurred largely in the subtropical and tropical ecoregions favored by amphibians. Population growth has been accompanied by broad-scale changes in land cover and land use, typically in support of agriculture. We merged information on land cover, land use, and human population growth to generate a composite map showing the rates at which humans have been changing the world. When compared with the map of amphibian species richness, we found that many of the regions of the earth supporting the richest assemblages of amphibians are currently undergoing the highest rates of landscape modification.

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2009-10-01

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

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

NASA Technical Reports Server (NTRS)

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.

Updating Landsat-derived land-cover maps using change detection and masking techniques

NASA Technical Reports Server (NTRS)

Likens, W.; Maw, K.

1982-01-01

The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.

BOREAS AFM-12 1-km AVHRR Seasonal Land Cover Classification

NASA Technical Reports Server (NTRS)

Steyaert, Lou; Hall, Forrest G.; Newcomer, Jeffrey A. (Editor); Knapp, David E. (Editor); Loveland, Thomas R.; Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-12 team's efforts focused on regional scale Surface Vegetation and Atmosphere (SVAT) modeling to improve parameterization of the heterogeneous BOREAS landscape for use in larger scale Global Circulation Models (GCMs). This regional land cover data set was developed as part of a multitemporal one-kilometer Advanced Very High Resolution Radiometer (AVHRR) land cover analysis approach that 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. This land cover classification was derived 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). This regional data set was developed for use by BOREAS investigators, especially those involved in simulation modeling, remote sensing algorithm development, and aircraft flux studies. Based on regional field data verification, this multitemporal one-kilometer AVHRR land cover mapping approach was effective in characterizing the biome-level land cover structure, embedded spatially heterogeneous landscape patterns, and other types of key land cover information of interest to BOREAS modelers.The land cover mosaics in this classification include: (1) wet conifer mosaic (low, medium, and high tree stand density), (2) mixed coniferous-deciduous forest (80% coniferous, codominant, and 80% deciduous), (3) recent visible bum, vegetation regeneration, or rock outcrops-bare ground-sparsely vegetated slow regeneration bum (four classes), (4) open water and grassland marshes, and (5) general agricultural land use/ grasslands (three classes). This land cover mapping approach did not detect small subpixel-scale landscape features such as fens, bogs, and small water bodies. Field observations and comparisons with Landsat Thematic Mapper (TM) suggest a minimum effective resolution of these land cover classes in the range of three to four kilometers, in part, because of the daily to monthly compositing process. In general, potential accuracy limitations are mitigated by the use of conservative parameterization rules such as aggregation of predominant land cover classes within minimum horizontal grid cell sizes of ten kilometers. The AFM-12 one-kilometer AVHRR seasonal land cover classification data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Mapping snow depth in open alpine terrain from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Marti, R.; Gascoin, S.; Berthier, E.; de Pinel, M.; Houet, T.; Laffly, D.

2016-07-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.

Automated Glacier Mapping using Object Based Image Analysis. Case Studies from Nepal, the European Alps and Norway

NASA Astrophysics Data System (ADS)

Vatle, S. S.

2015-12-01

Frequent and up-to-date glacier outlines are needed for many applications of glaciology, not only glacier area change analysis, but also for masks in volume or velocity analysis, for the estimation of water resources and as model input data. Remote sensing offers a good option for creating glacier outlines over large areas, but manual correction is frequently necessary, especially in areas containing supraglacial debris. We show three different workflows for mapping clean ice and debris-covered ice within Object Based Image Analysis (OBIA). By working at the object level as opposed to the pixel level, OBIA facilitates using contextual, spatial and hierarchical information when assigning classes, and additionally permits the handling of multiple data sources. Our first example shows mapping debris-covered ice in the Manaslu Himalaya, Nepal. SAR Coherence data is used in combination with optical and topographic data to classify debris-covered ice, obtaining an accuracy of 91%. Our second example shows using a high-resolution LiDAR derived DEM over the Hohe Tauern National Park in Austria. Breaks in surface morphology are used in creating image objects; debris-covered ice is then classified using a combination of spectral, thermal and topographic properties. Lastly, we show a completely automated workflow for mapping glacier ice in Norway. The NDSI and NIR/SWIR band ratio are used to map clean ice over the entire country but the thresholds are calculated automatically based on a histogram of each image subset. This means that in theory any Landsat scene can be inputted and the clean ice can be automatically extracted. Debris-covered ice can be included semi-automatically using contextual and morphological information.

Use of landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping

USGS Publications Warehouse

Maxwell, S.K.; Craig, M.E.

2008-01-01

Failure of the Scan Line Corrector (SLC) on the Landsat ETM+ sensor has had a major impact on many applications that rely on continuous medium resolution imagery to meet their objectives. The United States Department of Agriculture (USDA) Cropland Data Layer (CDL) program uses Landsat imagery as the primary source of data to produce crop-specific maps for 20 states in the USA. A new method has been developed to fill the image gaps resulting from the SLC failure to support the needs of Landsat users who require coincident spectral data, such as for crop type mapping and monitoring. We tested the new gap-filled method for a CDL crop type mapping project in eastern Nebraska. Scan line gaps were simulated on two Landsat 5 images (spring and late summer 2003) and then gap-filled using landscape boundary models, or segment models, that were derived from 1992 and 2002 Landsat images (used in the gap-fill process). Various date combinations of original and gap-filled images were used to derive crop maps using a supervised classification process. Overall kappa values were slightly higher for crop maps derived from SLC-off gap-filled images compared to crop maps derived from the original imagery (0.3–1.3% higher). Although the age of the segment model used to derive the SLC-off gap-filled product did not negatively impact the overall agreement, differences in individual cover type agreement did increase (−0.8%–1.6% using the 2002 segment model to −5.0–5.1% using the 1992 segment model). Classification agreement also decreased for most of the classes as the size of the segment used in the gap-fill process increased.

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

NASA Astrophysics Data System (ADS)

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.

Mapping dominant annual land cover from 2009 to 2013 across Victoria, Australia using satellite imagery

PubMed Central

Sheffield, Kathryn; Morse-McNabb, Elizabeth; Clark, Rob; Robson, Susan; Lewis, Hayden

2015-01-01

There is a demand for regularly updated, broad-scale, accurate land cover information in Victoria from multiple stakeholders. This paper documents the methods used to generate an annual dominant land cover (DLC) map for Victoria, Australia from 2009 to 2013. Vegetation phenology parameters derived from an annual time series of the Moderate Resolution Imaging Spectroradiometer Vegetation Indices 16-day 250 m (MOD13Q1) product were used to generate annual DLC maps, using a three-tiered hierarchical classification scheme. Classification accuracy at the broadest (primary) class level was over 91% for all years, while it ranged from 72 to 81% at the secondary class level. The most detailed class level (tertiary) had accuracy levels ranging from 61 to 68%. The approach used was able to accommodate variable climatic conditions, which had substantial impacts on vegetation growth patterns and agricultural production across the state between both regions and years. The production of an annual dataset with complete spatial coverage for Victoria provides a reliable base data set with an accuracy that is fit-for-purpose for many applications. PMID:26602009

Automated detection of ice cliffs within supraglacial debris cover

NASA Astrophysics Data System (ADS)

Herreid, Sam; Pellicciotti, Francesca

2018-05-01

Ice cliffs within a supraglacial debris cover have been identified as a source for high ablation relative to the surrounding debris-covered area. Due to their small relative size and steep orientation, ice cliffs are difficult to detect using nadir-looking space borne sensors. The method presented here uses surface slopes calculated from digital elevation model (DEM) data to map ice cliff geometry and produce an ice cliff probability map. Surface slope thresholds, which can be sensitive to geographic location and/or data quality, are selected automatically. The method also attempts to include area at the (often narrowing) ends of ice cliffs which could otherwise be neglected due to signal saturation in surface slope data. The method was calibrated in the eastern Alaska Range, Alaska, USA, against a control ice cliff dataset derived from high-resolution visible and thermal data. Using the same input parameter set that performed best in Alaska, the method was tested against ice cliffs manually mapped in the Khumbu Himal, Nepal. Our results suggest the method can accommodate different glaciological settings and different DEM data sources without a data intensive (high-resolution, multi-data source) recalibration.

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

NASA Astrophysics Data System (ADS)

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 (<30%), which are areas of concern for environmental monitoring. At finer spatial resolutions, the inclusion of SAR data actually reduced accuracies. Overall, the RFE was able to produce the most accurate model (R2 = 0.8, RMSE = 8.9, at the 120 m pixel scale). For mapping savannah woody cover at the 30 m pixel scale, we suggest that monitoring methodologies 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 use statistics for West Virginia, Part I

USGS Publications Warehouse

Erwin, Robert B.; ,; ,

1979-01-01

The West Virginia Geological and Economic Survey and the United States Geological Survey have completed a cooperative program to provide land-use and land-cover maps and data for the State. This program begins to satisfy a longstanding need for a consistent level of detail, standardization in categorization, and scale of compilation for land-use and land-cover maps and data. The statistical information contained in this Bulletin provides land-use acreage tabulations for the first 20 counties that have been completed. Statistics are being compiled for the remaining counties and will be published shortly. This information has been derived from the recently completed Land-Use Map of West Virginia (on open file at the West Virginia Geological and Economic Survey - Environmental Section). In addition to land-use acreage, we have also included land-use percent. All statistics throughout this Bulletin are in the same format for ease of comparison.

SAR For REDD+ in the Mai Ndombe District (DRC)

NASA Astrophysics Data System (ADS)

Haarpaintner, Jorg

2016-08-01

The overall goal of the project "SAR for REDD" is to provide cloud-penetrating satellite synthetic aperture radar (SAR) pre-processing and analysing capabilities and tools to support operational tropical forest monitoring in REDD countries and primarily in Africa. The project's end-user is the Observatoir Satellitale des Forêts d'Afrique Centrale (OSFAC).This paper presents an overall summary of the project and shows first results of the satellite products, that will be delivered to the user in addition to software tools to enhance the user's own technical capacity.The products shown here are SAR mosaics and derived forest-land cover maps based on C-band Sentinel-1A data for 2015, ALOS-PALSAR data for the period 2007-2010 and ALOS-2 PALSAR-2 for 2015. In addition, a forest cover change map from 2007 to 2010 based on ALOS PALSAR has been produced and is compared to results from the Global Forest Cover project [1].

Application of remote sensing to estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Kiefer, R. W.

1980-01-01

A variety of remote sensing data sources and interpretation techniques has been tested in a 6136 hectare watershed with agricultural, forest and urban land cover to determine the relative utility of alternative aerial photographic data sources for gathering the desired land use/land cover data. The principal photographic data sources are high altitude 9 x 9 inch color infrared photos at 1:120,000 and 1:60,000 and multi-date medium altitude color and color infrared photos at 1:60,000. Principal data for estimating soil erosion potential include precipitation, soil, slope, crop, crop practice, and land use/land cover data derived from topographic maps, soil maps, and remote sensing. A computer-based geographic information system organized on a one-hectare grid cell basis is used to store and quantify the information collected using different data sources and interpretation techniques. Research results are compared with traditional Universal Soil Loss Equation field survey methods.

Global Survey of Anthropogenic Neighborhood Threats to Conservation of Grass-Shrub and Forest Vegetation

EPA Science Inventory

The ecological functions of natural vegetation are threatened when it is subsumed in anthropogenic landscapes. We report the first comparative global survey of anthropogenic landscape threats to forest and grass-shrub vegetation. Using a global land-cover map derived from remote...

MHC class I-associated peptides derive from selective regions of the human genome.

PubMed

Pearson, Hillary; Daouda, Tariq; Granados, Diana Paola; Durette, Chantal; Bonneil, Eric; Courcelles, Mathieu; Rodenbrock, Anja; Laverdure, Jean-Philippe; Côté, Caroline; Mader, Sylvie; Lemieux, Sébastien; Thibault, Pierre; Perreault, Claude

2016-12-01

MHC class I-associated peptides (MAPs) define the immune self for CD8+ T lymphocytes and are key targets of cancer immunosurveillance. Here, the goals of our work were to determine whether the entire set of protein-coding genes could generate MAPs and whether specific features influence the ability of discrete genes to generate MAPs. Using proteogenomics, we have identified 25,270 MAPs isolated from the B lymphocytes of 18 individuals who collectively expressed 27 high-frequency HLA-A,B allotypes. The entire MAP repertoire presented by these 27 allotypes covered only 10% of the exomic sequences expressed in B lymphocytes. Indeed, 41% of expressed protein-coding genes generated no MAPs, while 59% of genes generated up to 64 MAPs, often derived from adjacent regions and presented by different allotypes. We next identified several features of transcripts and proteins associated with efficient MAP production. From these data, we built a logistic regression model that predicts with good accuracy whether a gene generates MAPs. Our results show preferential selection of MAPs from a limited repertoire of proteins with distinctive features. The notion that the MHC class I immunopeptidome presents only a small fraction of the protein-coding genome for monitoring by the immune system has profound implications in autoimmunity and cancer immunology.

MHC class I–associated peptides derive from selective regions of the human genome

PubMed Central

Pearson, Hillary; Granados, Diana Paola; Durette, Chantal; Bonneil, Eric; Courcelles, Mathieu; Rodenbrock, Anja; Laverdure, Jean-Philippe; Côté, Caroline; Thibault, Pierre

2016-01-01

MHC class I–associated peptides (MAPs) define the immune self for CD8+ T lymphocytes and are key targets of cancer immunosurveillance. Here, the goals of our work were to determine whether the entire set of protein-coding genes could generate MAPs and whether specific features influence the ability of discrete genes to generate MAPs. Using proteogenomics, we have identified 25,270 MAPs isolated from the B lymphocytes of 18 individuals who collectively expressed 27 high-frequency HLA-A,B allotypes. The entire MAP repertoire presented by these 27 allotypes covered only 10% of the exomic sequences expressed in B lymphocytes. Indeed, 41% of expressed protein-coding genes generated no MAPs, while 59% of genes generated up to 64 MAPs, often derived from adjacent regions and presented by different allotypes. We next identified several features of transcripts and proteins associated with efficient MAP production. From these data, we built a logistic regression model that predicts with good accuracy whether a gene generates MAPs. Our results show preferential selection of MAPs from a limited repertoire of proteins with distinctive features. The notion that the MHC class I immunopeptidome presents only a small fraction of the protein-coding genome for monitoring by the immune system has profound implications in autoimmunity and cancer immunology. PMID:27841757

Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vinvent V.; DiGirolamo, Nicolo; Bayr, Klaus J.; Houser, Paul (Technical Monitor)

2001-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. These products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to the currently available operation products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set is generated for long-term climates studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the product. The snow product suite starts with a 500-m resolution swath snow-cover map which is gridded to the Integerized Sinusoidal Grid to produce daily and eight-day composite tile products. The sequence then proceeds to a climate-modeling grid product at 5-km spatial resolution, with both daily and eight-day composite products. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented. Near-term enhancements include daily snow albedo and fractional snow cover.

Utilizing Multiple Datasets for Snow Cover Mapping

NASA Technical Reports Server (NTRS)

Tait, Andrew B.; Hall, Dorothy K.; Foster, James L.; Armstrong, Richard L.

1999-01-01

Snow-cover maps generated from surface data are based on direct measurements, however they are prone to interpolation errors where climate stations are sparsely distributed. Snow cover is clearly discernable using satellite-attained optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Passive microwave (PM) data is unaffected by clouds, however, the snow-cover signature is significantly affected by melting snow and the microwaves may be transparent to thin snow (less than 3cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies. This paper describes a method that combines ground and satellite data to produce a Multiple-Dataset Snow-Cover Product (MDSCP). Comparisons with current snow-cover products show that the MDSCP draws together the advantages of each of its component products while minimizing their potential errors. Improved estimates of the snow-covered area are derived through the addition of two snow-cover classes ("thin or patchy" and "high elevation" snow cover) and from the analysis of the climate station data within each class. The compatibility of this method for use with Moderate Resolution Imaging Spectroradiometer (MODIS) data, which will be available in 2000, is also discussed. With the assimilation of these data, the resolution of the MDSCP would be improved both spatially and temporally and the analysis would become completely automated.

High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

NASA Astrophysics Data System (ADS)

Alsharrah, Saad A.; Bruce, David A.; Bouabid, Rachid; Somenahalli, Sekhar; Corcoran, Paul A.

2015-10-01

The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard; Caldwell, Megan K.

2014-01-01

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 and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

Arctic multiyear ice classification and summer ice cover using passive microwave satellite data

NASA Technical Reports Server (NTRS)

Comiso, J. C.

1990-01-01

Passive microwave data collected by Nimbus 7 were used to classify and monitor the Arctic multilayer sea ice cover. Sea ice concentration maps during several summer minima are analyzed to obtain estimates of ice floes that survived summer, and the results are compared with multiyear-ice concentrations derived from these data by using an algorithm that assumes a certain emissivity for multiyear ice. The multiyear ice cover inferred from the winter data was found to be about 25 to 40 percent less than the summer ice-cover minimum, indicating that the multiyear ice cover in winter is inadequately represented by the passive microwave winter data and that a significant fraction of the Arctic multiyear ice floes exhibits a first-year ice signature.

Time series evapotranspiration maps at a regional scale: A methodology, evaluation, and their use in water resources management

NASA Astrophysics Data System (ADS)

Gowda, P. H.

2016-12-01

Evapotranspiration (ET) is an important process in ecosystems' water budget and closely linked to its productivity. Therefore, regional scale daily time series ET maps developed at high and medium resolutions have large utility in studying the carbon-energy-water nexus and managing water resources. There are efforts to develop such datasets on a regional to global scale but often faced with the limitations of spatial-temporal resolution tradeoffs in satellite remote sensing technology. In this study, we developed frameworks for generating high and medium resolution daily ET maps from Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) data, respectively. For developing high resolution (30-m) daily time series ET maps with Landsat TM data, the series version of Two Source Energy Balance (TSEB) model was used to compute sensible and latent heat fluxes of soil and canopy separately. Landsat 5 (2000-2011) and Landsat 8 (2013-2014) imageries for row 28/35 and 27/36 covering central Oklahoma was used. MODIS data (2001-2014) covering Oklahoma and Texas Panhandle was used to develop medium resolution (250-m), time series daily ET maps with SEBS (Surface Energy Balance System) model. An extensive network of weather stations managed by Texas High Plains ET Network and Oklahoma Mesonet was used to generate spatially interpolated inputs of air temperature, relative humidity, wind speed, solar radiation, pressure, and reference ET. A linear interpolation sub-model was used to estimate the daily ET between the image acquisition days. Accuracy assessment of daily ET maps were done against eddy covariance data from two grassland sites at El Reno, OK. Statistical results indicated good performance by modeling frameworks developed for deriving time series ET maps. Results indicated that the proposed ET mapping framework is suitable for deriving daily time series ET maps at regional scale with Landsat and MODIS data.

Information system for preserving culture heritage in areas affected by heavy industry and mining

NASA Astrophysics Data System (ADS)

Pacina, Jan; Kopecký, Jiří; Bedrníková, Lenka; Handrychová, Barbora; Švarcová, Martina; Holá, Markéta; Pončíková, Edita

2014-05-01

The natural development of the Ústí region (North-West Bohemia, the Czech Republic) has been affected by the human activity during the past hundred years. The heavy industrialization and the brown coal mining have completely changed the land-use in the region. The open-pit coal mines are completely destroying the surrounding landscape, including settlement, communications, hydrological network and the over-all natural development of the region. The other factor affecting the natural development of the landscape, land-use and settlement was the political situation in 1945 (end of the 2nd World War) when the borderland was depopulated. All these factors caused vanishing of more than two hundreds of colonies, villages and towns during this period of time. The task of this project is to prepare and offer for public use a comprehensive information system preserving the cultural heritage in the form of processed old maps, aerial imagery, land-use and georelief reconstructions, local studies, text and photo documents covering the extinct landscape and settlement. Wide range of various maps was used for this area - Müller's map of Bohemia (ca. 1720) followed by the 1st, 2nd and 3rd Military survey of Habsburg empire (1792, 1894, 1938), maps of Stabile cadaster (ca. 1840) and State map derived in the scale 1:5000 (1953, 1972, 1981). All the maps were processed, georeferenced, hand digitized and are further used as base layers for visualization and analysis. The historical aerial imagery was processed in standard ways of photogrammetry and is covering the year 1938, 1953 and the current state. The other important task covered by this project is the georelief reconstruction. We use the old maps and aerial imagery to reconstruct the complete time-line of the georelief development. This time-line is covering the period since 1938 until now. The derived digital terrain models and further on analyzed and printed on a 3D printer. Other reconstruction task are performed using the processed old maps - here we are studying the land-use change, settlement development and the industrialization and brown coal mining effect on the hydrological network structure. The processed data (old maps, aerial photographs, land-use and georelief reconstructions) are published as a web-mapping application built using the ArcGIS API for Flex technology. The application is offering visualization and overlay tools so the user can perform basic landscape and land-use development analyses. The resulting information system will consist of three parts - the web-mapping application, database containing the text and photo information about the vanished towns and villages (spatially linked to the web-mapping application) and other local studies performed on single sites in the region. The local studies are focused on application of data collection methods as UAV (Unmanned Aerial Vehicle), KAP (Kite Aerial Photography) and LIDAR.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

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

PubMed

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

PubMed Central

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. PMID:23418469

Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

USGS Publications Warehouse

Chastain, R.A.; Struckhoff, M.A.; He, H.S.; Larsen, D.R.

2008-01-01

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project

USGS Publications Warehouse

Hansen, M.C.; Egorov, Alexey; Roy, David P.; Potapov, P.; Ju, J.; Turubanova, S.; Kommareddy, I.; Loveland, Thomas R.

2011-01-01

Vegetation Continuous Field (VCF) layers of 30 m percent tree cover, bare ground, other vegetation and probability of water were derived for the conterminous United States (CONUS) using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data sets from the Web-Enabled Landsat Data (WELD) project. Turnkey approaches to land cover characterization were enabled due to the systematic WELD Landsat processing, including conversion of digital numbers to calibrated top of atmosphere reflectance and brightness temperature, cloud masking, reprojection into a continental map projection and temporal compositing. Annual, seasonal and monthly WELD composites for 2008 were used as spectral inputs to a bagged regression and classification tree procedure using a large training data set derived from very high spatial resolution imagery and available ancillary data. The results illustrate the ability to perform Landsat land cover characterizations at continental scales that are internally consistent while retaining local spatial and thematic detail.

Quantifying landscape pattern and assessing the land cover changes in Piatra Craiului National Park and Bucegi Natural Park, Romania, using satellite imagery and landscape metrics.

PubMed

Vorovencii, Iosif

2015-11-01

Protected areas of Romania have enjoyed particular importance after 1989, but, at the same time, they were subject to different anthropogenic and natural pressures which resulted in the occurrence of land cover changes. These changes have generally led to landscape degradation inside and at the borders of the protected areas. In this article, 12 landscape metrics were used in order to quantify landscape pattern and assess land cover changes in two protected areas, Piatra Craiului National Park (PCNP) and Bucegi Natural Park (BNP). The landscape metrics were obtained from land cover maps derived from Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper Plus (ETM+) images from 1987, 1993, 2000, 2009 and 2010. Three land cover classes were analysed in PCNP and five land cover map classes in BNP. The results show a landscape fragmentation trend for both parks, affecting different types of land covers. Between 1987 and 2010, in PCNP fragmentation was, in principle, the result not only of anthropogenic activities such as forest cuttings and illegal logging but also of natural causes. In BNP, between 1987 and 2009, the fragmentation affected the pasture which resulted in the occurrence of bare land and rocky areas because of the erosion on the Bucegi Plateau.

A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data

USGS Publications Warehouse

Pengra, Bruce; Long, Jordan; Dahal, Devendra; Stehman, Stephen V.; Loveland, Thomas R.

2015-01-01

The methodology for selection, creation, and application of a global remote sensing validation dataset using high resolution commercial satellite data is presented. High resolution data are obtained for a stratified random sample of 500 primary sampling units (5 km  ×  5 km sample blocks), where the stratification based on Köppen climate classes is used to distribute the sample globally among biomes. The high resolution data are classified to categorical land cover maps using an analyst mediated classification workflow. Our initial application of these data is to evaluate a global 30 m Landsat-derived, continuous field tree cover product. For this application, the categorical reference classification produced at 2 m resolution is converted to percent tree cover per 30 m pixel (secondary sampling unit)for comparison to Landsat-derived estimates of tree cover. We provide example results (based on a subsample of 25 sample blocks in South America) illustrating basic analyses of agreement that can be produced from these reference data. Commercial high resolution data availability and data quality are shown to provide a viable means of validating continuous field tree cover. When completed, the reference classifications for the full sample of 500 blocks will be released for public use.

Subsurface Investigation of the Neogene Mygdonian Basin, Greece Using Magnetic Data

NASA Astrophysics Data System (ADS)

Ibraheem, Ismael M.; Gurk, Marcus; Tougiannidis, Nikolaos; Tezkan, Bülent

2018-02-01

A high-resolution ground and marine magnetic survey was executed to determine the structure of the subsurface and the thickness of the sedimentary cover in the Mygdonian Basin. A spacing of approximately 250 m or 500 m between measurement stations was selected to cover an area of 15 km × 22 km. Edge detectors such as total horizontal derivative (THDR), analytic signal (AS), tilt derivative (TDR), enhanced total horizontal gradient of tilt derivative (ETHDR) were applied to map the subsurface structure. Depth was estimated by power spectrum analysis, tilt derivative, source parameter imaging (SPI), and 2D-forward modeling techniques. Spectral analysis and SPI suggest a depth to the basement ranging from near surface to 600 m. For some selected locations, depth was also calculated using the TDR technique suggesting depths from 160 to 400 m. 2D forward magnetic modeling using existing boreholes as constraints was carried out along four selected profiles and confirmed the presence of alternative horsts and grabens formed by parallel normal faults. The dominant structural trends inferred from THDR, AS, TDR, and ETHDR are N-S, NW-SE, NE-SW and E-W. This corresponds with the known structural trends in the area. Finally, a detailed structural map showing the magnetic blocks and the structural architecture of the Mygdonian Basin was drawn up by collating all of the results.

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

NASA Technical Reports Server (NTRS)

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.

Georectification and snow classification of webcam images: potential for complementing satellite-derrived snow maps over Switzerland

NASA Astrophysics Data System (ADS)

Dizerens, Céline; Hüsler, Fabia; Wunderle, Stefan

2016-04-01

The spatial and temporal variability of snow cover has a significant impact on climate and environment and is of great socio-economic importance for the European Alps. Satellite remote sensing data is widely used to study snow cover variability and can provide spatially comprehensive information on snow cover extent. However, cloud cover strongly impedes the surface view and hence limits the number of useful snow observations. Outdoor webcam images not only offer unique potential for complementing satellite-derived snow retrieval under cloudy conditions but could also serve as a reference for improved validation of satellite-based approaches. Thousands of webcams are currently connected to the Internet and deliver freely available images with high temporal and spatial resolutions. To exploit the untapped potential of these webcams, a semi-automatic procedure was developed to generate snow cover maps based on webcam images. We used daily webcam images of the Swiss alpine region to apply, improve, and extend existing approaches dealing with the positioning of photographs within a terrain model, appropriate georectification, and the automatic snow classification of such photographs. In this presentation, we provide an overview of the implemented procedure and demonstrate how our registration approach automatically resolves the orientation of a webcam by using a high-resolution digital elevation model and the webcam's position. This allows snow-classified pixels of webcam images to be related to their real-world coordinates. We present several examples of resulting snow cover maps, which have the same resolution as the digital elevation model and indicate whether each grid cell is snow-covered, snow-free, or not visible from webcams' positions. The procedure is expected to work under almost any weather condition and demonstrates the feasibility of using webcams for the retrieval of high-resolution snow cover information.

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

NASA Astrophysics Data System (ADS)

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.

Tailoring Earth Observation To Ranchers For Improved Land Management And Profitability: The VegMachine Online Project

NASA Astrophysics Data System (ADS)

Scarth, P.; Trevithick, B.; Beutel, T.

2016-12-01

VegMachine Online is a freely available browser application that allows ranchers across Australia to view and interact with satellite derived ground cover state and change maps on their property and extract this information in a graphical format using interactive tools. It supports the delivery and communication of a massive earth observation data set in an accessible, producer friendly way . Around 250,000 Landsat TM, ETM and OLI images were acquired across Australia, converted to terrain corrected surface reflectance and masked for cloud, cloud shadow, terrain shadow and water. More than 2500 field sites across the Australian rangelands were used to derive endmembers used in a constrained unmixing approach to estimate the per-pixel proportion of bare, green and non-green vegetation for all images. A seasonal metoid compositing method was used to produce national fractional cover virtual mosaics for each three month period since 1988. The time series of green fraction is used to estimate the persistent green due to tree and shrub canopies, and this estimate is used to correct the fractional cover to ground cover for our mixed tree-grass rangeland systems. Finally, deciles are produced for key metrics every season to track a pixels relativity to the entire time series. These data are delivered through time series enabled web mapping services and customised web processing services that enable the full time series over any spatial extent to be interrogated in seconds via a RESTful interface. These services interface with a front end browser application that provides product visualization for any date in the time series, tools to draw or import polygon boundaries, plot time series ground cover comparisons, look at the effect of historical rainfall and tools to run the revised universal soil loss equation in web time to assess the effect of proposed changes in cover retention. VegMachine Online is already being used by ranchers monitoring paddock condition, organisations supporting land management initiatives in Great Barrier Reef catchments, by students developing tools to understand land condition and degradation and the underlying data and APIs are supporting several other land condition mapping tools.

Construction of a High-Density Genetic Map from RNA-Seq Data for an Arabidopsis Bay-0 × Shahdara RIL Population

PubMed Central

Serin, Elise A. R.; Snoek, L. B.; Nijveen, Harm; Willems, Leo A. J.; Jiménez-Gómez, Jose M.; Hilhorst, Henk W. M.; Ligterink, Wilco

2017-01-01

High-density genetic maps are essential for high resolution mapping of quantitative traits. Here, we present a new genetic map for an Arabidopsis Bayreuth × Shahdara recombinant inbred line (RIL) population, built on RNA-seq data. RNA-seq analysis on 160 RILs of this population identified 30,049 single-nucleotide polymorphisms (SNPs) covering the whole genome. Based on a 100-kbp window SNP binning method, 1059 bin-markers were identified, physically anchored on the genome. The total length of the RNA-seq genetic map spans 471.70 centimorgans (cM) with an average marker distance of 0.45 cM and a maximum marker distance of 4.81 cM. This high resolution genotyping revealed new recombination breakpoints in the population. To highlight the advantages of such high-density map, we compared it to two publicly available genetic maps for the same population, comprising 69 PCR-based markers and 497 gene expression markers derived from microarray data, respectively. In this study, we show that SNP markers can effectively be derived from RNA-seq data. The new RNA-seq map closes many existing gaps in marker coverage, saturating the previously available genetic maps. Quantitative trait locus (QTL) analysis for published phenotypes using the available genetic maps showed increased QTL mapping resolution and reduced QTL confidence interval using the RNA-seq map. The new high-density map is a valuable resource that facilitates the identification of candidate genes and map-based cloning approaches. PMID:29259624

Evaluation and time series analysis of mountain snow from MODIS and VIIRS fractional snow cover products

NASA Astrophysics Data System (ADS)

Bormann, K.; Rittger, K.; Painter, T. H.

2016-12-01

The continuation of large-scale snow cover records into the future is crucial for monitoring the impacts of global pressures such as climate change and weather variability on the cryosphere. With daily MODIS records since 2000 from a now ageing MODIS constellation (Terra & Aqua) and daily VIIRS records since 2012 from the Suomi-NPP platform, the consistency of information between the two optical sensors must be understood. First, we evaluated snow cover maps derived from both MODIS and VIIRS retrievals with coincident cloud-free Landsat 8 OLI maps across a range of locations. We found that both MODIS and VIIRS snow cover maps show similar errors when evaluated with Landsat OLI retrievals. Preliminary results also show a general agreement in regional snowline between the two sensors that is maintained during the spring snowline retreat where the proportion of mixed pixels is increased. The agreement between sensors supports the future use of VIIRS snow cover maps to continue the long-term record beyond the lifetime of MODIS. Second, we use snowline elevation to quantify large scale snow cover variability and to monitor potential changes in the rain/snow transition zone where climate change pressures may be enhanced. Despite the large inter-annual variability that is often observed in snow metrics, we expect that over the 16-year time series we will see a rise in seasonal elevation of the snowline and consequently an increasing rain/snow transition boundary in mountain environments. These results form the basis for global snowline elevation monitoring using optical remote sensing data and highlight regional differences in snowline elevation dynamics. The long-term variability in observed snowline elevation provides a recent climatology of mountain snowpack across several regions that will likely to be of interest to those interested in climate change impacts in mountain environments. This work will also be of interest to existing users of MODSCAG and VIIRSCAG snow cover products and those working in remote sensing of the mountain snowpack.

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

NASA Technical Reports Server (NTRS)

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 composition was noted in the change detection image.

A PRELIMINARY ASSESSMENT OF THE MONTREAL PROCESS INDICATORS OF FOREST FRAGMENTATION FOR THE UNITED STATES

EPA Science Inventory

As part of the 2003 U.S. Report on Sustainable Forests, four metrics of forest fragmentation patch size, edge amount, inter-patch contrast - were measured within 142,602 non overlapping 56.25 km2 analysis units on land-cover maps derived from satellite imagery for the 48 contermi...

Forest/non-forest stratification in Georgia with Landsat Thematic Mapper data

Treesearch

William H. Cooke

2000-01-01

Geographically accurate Forest Inventory and Analysis (FIA) data may be useful for training, classification, and accuracy assessment of Landsat Thematic Mapper (TM) data. Minimum expectation for maps derived from Landsat data is accurate discrimination of several land cover classes. Landsat TM costs have decreased dramatically, but acquiring cloud-free scenes at...

The Use of Satellite Observations in Ice Cover Simulations

DTIC Science & Technology

1992-01-01

Io rmotions have been used to map upper-level winds over polar diagnose the origins of a large area of reduced ice ,,ncfl.’c regions (Turner and...was motivated by the availability of coverage in the Arctic. Also shown are November-April s-ver!,_- the multiyear ice concentrations derived from

Landsat-Derived, Time-Series Remote Sensing Analysis of Fire Regime, Microclimate, and Urbanization's Influence on Biodiversity in the Santa Monica Mountain Coastal Range

NASA Astrophysics Data System (ADS)

Ma, J.; Dmochowski, J. E.

2016-12-01

Southern California's Santa Monica Mountain coastal range hosts chaparral and coastal sage scrub ecosystems with distinct, local variations in their fire regime, microclimate, and proximity to urbanization. The high biodiversity combined with ongoing human impact make monitoring the ecological and land cover changes crucial. Due to their extensive, continuous temporal coverage and high spatial resolution, Landsat data are well suited to this purpose. Landsat-derived time-series NDVI data and classification maps have been compiled to identify regions most sensitive to change in order to determine the effects of fire regime, geography, and urbanization on vegetative changes; and assess the encroachment of non-native grasses. Spatial analysis of the classification maps identified the factors more conducive to land-cover changes as native shrubs were replaced with non-native grasses. Understanding the dynamics that govern semi-arid resilience, overall greening, and fire regime is important to predicting and managing large scale ecosystem changes as pressures from global climate change and urbanization intensify.

Mapping Land Cover and Land Use Changes in the Congo Basin Forests with Optical Satellite Remote Sensing: a Pilot Project Exploring Methodologies that Improve Spatial Resolution and Map Accuracy

NASA Astrophysics Data System (ADS)

Molinario, G.; Baraldi, A.; Altstatt, A. L.; Nackoney, J.

2011-12-01

The University of Maryland has been a USAID Central Africa Rregional Program for the Environment (CARPE) cross-cutting partner for many years, providing remote sensing derived information on forest cover and forest cover changes in support of CARPE's objectives of diminishing forest degradation, loss and biodiversity loss as a result of poor or inexistent land use planning strategies. Together with South Dakota State University, Congo Basin-wide maps have been provided that map forest cover loss at a maximum of 60m resolution, using Landsat imagery and higher resolution imagery for algorithm training and validation. However, to better meet the needs within the CARPE Landscapes, which call for higher resolution, more accurate land cover change maps, UMD has been exploring the use of the SIAM automatic spectral -rule classifier together with pan-sharpened Landsat data (15m resolution) and Very High Resolution imagery from various sources. The pilot project is being developed in collaboration with the African Wildlife Foundation in the Maringa Lopori Wamba CARPE Landscape. If successful in the future this methodology will make the creation of high resolution change maps faster and easier, making it accessible to other entities in the Congo Basin that need accurate land cover and land use change maps in order, for example, to create sustainable land use plans, conserve biodiversity and resources and prepare Reducing Emissions from forest Degradation and Deforestation (REDD) Measurement, Reporting and Verification (MRV) projects. The paper describes the need for higher resolution land cover change maps that focus on forest change dynamics such as the cycling between primary forests, secondary forest, agriculture and other expanding and intensifying land uses in the Maringa Lopori Wamba CARPE Landscape in the Equateur Province of the Democratic Republic of Congo. The Methodology uses the SIAM remote sensing imagery automatic spectral rule classifier, together with pan-sharpened Landsat imagery with 15m resolution and Very High Resolution imagery from different sensors, obtained from the Department of Defense database that was recently opened to NASA and its Earth Observation partners. Particular emphasis is placed on the detection of agricultural fields and their expansion in primary forests or intensification in secondary forests and fallow fields, as this is the primary driver of deforestation in this area. Fields in this area area also of very small size and irregular shapes, often partly obscured by neighboring forest canopy, hence the technical challenge of correctly detecting them and tracking them through time. Finally, the potential for use of this methodology in other regions where information on land cover changes is needed for land use sustainability planning, is also addressed.

Quantitative analysis of terrain units mapped in the northern quarter of Venus from Venera 15/16 data

NASA Technical Reports Server (NTRS)

Schaber, G. G.

1991-01-01

The contacts between 34 geological/geomorphic terrain units in the northern quarter of Venus mapped from Venera 15/16 data were digitized and converted to a Sinusoidal Equal-Area projection. The result was then registered with a merged Pioneer Venus/Venera 15/16 altimetric database, root mean square (rms) slope values, and radar reflectivity values derived from Pioneer Venus. The resulting information includes comparisons among individual terrain units and terrain groups to which they are assigned in regard to percentage of map area covered, elevation, rms slopes, distribution of suspected craters greater than 10 km in diameter.

An Autosomal Genetic Linkage Map of the Sheep Genome

PubMed Central

Crawford, A. M.; Dodds, K. G.; Ede, A. J.; Pierson, C. A.; Montgomery, G. W.; Garmonsway, H. G.; Beattie, A. E.; Davies, K.; Maddox, J. F.; Kappes, S. W.; Stone, R. T.; Nguyen, T. C.; Penty, J. M.; Lord, E. A.; Broom, J. E.; Buitkamp, J.; Schwaiger, W.; Epplen, J. T.; Matthew, P.; Matthews, M. E.; Hulme, D. J.; Beh, K. J.; McGraw, R. A.; Beattie, C. W.

1995-01-01

We report the first extensive ovine genetic linkage map covering 2070 cM of the sheep genome. The map was generated from the linkage analysis of 246 polymorphic markers, in nine three-generation fullsib pedigrees, which make up the AgResearch International Mapping Flock. We have exploited many markers from cattle so that valuable comparisons between these two ruminant linkage maps can be made. The markers, used in the segregation analyses, comprised 86 anonymous microsatellite markers derived from the sheep genome, 126 anonymous microsatellites from cattle, one from deer, and 33 polymorphic markers of various types associated with known genes. The maximum number of informative meioses within the mapping flock was 222. The average number of informative meioses per marker was 140 (range 18-209). Linkage groups have been assigned to all 26 sheep autosomes. PMID:7498748

Updating the New Zealand Glacier Inventory

NASA Astrophysics Data System (ADS)

Baumann, S. C.; Anderson, B.; Mackintosh, A.; Lorrey, A.; Chinn, T.; Collier, C.; Rack, W.; Purdie, H.

2017-12-01

The last complete glacier inventory of New Zealand dates from the year 1978 (North Island 1988) and was manually constructed from oblique aerial photographs and geodetic maps (Chinn 2001). The inventory has been partly updated by Gjermundsen et al. (2011) for the year 2002 (40% of total area) and by Sirguey & More (2010) for the year 2009 (32% of total area), both using ASTER satellite imagery. We used Landsat 8 OLI/TIRS satellite data from February/March 2016 to map the total glaciated area. Clean and debris-covered ice were mapped semi-automatically. The band ratio approach was used for clean ice (ratio: red/SWIR). We mapped debris-covered ice using a supervised classification (maximum likelihood). Manual post processing was necessary due to misclassifications (e.g. lakes, clouds) or mapping in shadowed areas. It was also necessary to manually combine the clean and debris-covered parts into single glaciers. Additional input data for the post processing were Sentinel 2 images from the same time period, orthophotos from Land Information New Zealand (resolution: 0.75 m, date: Nov 2014), and the 1978/88 outlines from the GLIMS database (http://www.glims.org/). As the Sentinel 2 data were more heavily cloud covered compared to the Landsat 8 images, they were only used for post processing and not for the classification itself. Initial results show that New Zealand glaciers covered an area of about 1050 km² in 2016, a reduction of 16% since 1978. Approximately 17% of glacier area was covered in surface debris. The glaciers in the central Southern Alps around Mt Cook reduced in area by 24%. Glaciers in the North Island of New Zealand reduced by 71% since 1988, and only 2 km² of ice cover remained in 2016. Chinn, TJH (2001). "Distribution of the glacial water resources of New Zealand." Journal of Hydrology (NZ) 40(2): 139-187 Gjermundsen, EF, Mathieu, R, Kääb, A, Chinn, TJH, Fitzharris, B & Hagen, JO (2011). "Assessment of multispectral glacier mapping methods and derivation of glacier area changes, 1978-2002, in the central Southern Alps, New Zealand, from ASTER satellite data, field survey and existing inventory data." Journal of Glaciology 57(204): 667-683 Sirguey, P & More, B (2010). GLIMS Glacier Database. Boulder, NSIDC

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

USGS Publications Warehouse

Kokaly, Raymond F.; Despain, Don G.; Clark, Roger N.; Livo, K. Eric

2003-01-01

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).

Glacier Surface Lowering and Stagnation in the Manaslu Region of Nepal

NASA Astrophysics Data System (ADS)

Robson, B. A.; Nuth, C.; Nielsen, P. R.; Hendrickx, M.; Dahl, S. O.

2015-12-01

Frequent and up-to-date glacier outlines are needed for many applications of glaciology, not only glacier area change analysis, but also for masks in volume or velocity analysis, for the estimation of water resources and as model input data. Remote sensing offers a good option for creating glacier outlines over large areas, but manual correction is frequently necessary, especially in areas containing supraglacial debris. We show three different workflows for mapping clean ice and debris-covered ice within Object Based Image Analysis (OBIA). By working at the object level as opposed to the pixel level, OBIA facilitates using contextual, spatial and hierarchical information when assigning classes, and additionally permits the handling of multiple data sources. Our first example shows mapping debris-covered ice in the Manaslu Himalaya, Nepal. SAR Coherence data is used in combination with optical and topographic data to classify debris-covered ice, obtaining an accuracy of 91%. Our second example shows using a high-resolution LiDAR derived DEM over the Hohe Tauern National Park in Austria. Breaks in surface morphology are used in creating image objects; debris-covered ice is then classified using a combination of spectral, thermal and topographic properties. Lastly, we show a completely automated workflow for mapping glacier ice in Norway. The NDSI and NIR/SWIR band ratio are used to map clean ice over the entire country but the thresholds are calculated automatically based on a histogram of each image subset. This means that in theory any Landsat scene can be inputted and the clean ice can be automatically extracted. Debris-covered ice can be included semi-automatically using contextual and morphological information.

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

NASA Astrophysics Data System (ADS)

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 < 0.05). Interestingly, the relationship between PWCC and L8 band 4 surface reflectance was weaker (r2 = 0.46, p < 0.05) than that for DNs. This suggests that the scene model used in L8 atmospheric correction may not account well for within-pixel 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.

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

NASA Astrophysics Data System (ADS)

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 itself.

Mekong Land Cover Dasboard: Regional Land Cover Mointoring Systems

NASA Astrophysics Data System (ADS)

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-quality regional land cover maps on a regular basis that are consistent and continuous across the landscape. The system is being designed to facilitate improved policy, planning, and decision making by a wide range of users (such as government agencies, local community groups, non-profit organizations, and the private sector).

Forest Dragon-3: Decadal Trends of Northeastern Forests in China from Earth Observation Synergy

NASA Astrophysics Data System (ADS)

Schmullius, C.; Balling, J.; Schratz, P.; Thiel, C.; Santoro, M.; Wegmuller, U.; Li, Z.; Yong, P.

2016-08-01

In Forest DRAGON 3, synergy of Earth Observation products to derive information of decadal trends of forest in northeast China was investigated. Following up the results of Forest-DRAGON 1 and 2, Growing Stock Volume (GSV) products from different years were investigated to derive information on vegetational in north- east China. The BIOMASAR maps of 2005 and 2010, produced within the previous DRAGON projects, set the base for all analyses. We took a closer look at scale problems regarding GSV derivation, which are introduced by differing landcover within one pixel, to investigate differences throughout pixel classes with varying landcover class percentages. We developed an approach to select pixels containing forest only with the aim of undertaking a detailed analysis on retrieved GSV values for such pixels for the years 2005 and 2010. Using existing land cover products at different scales, the plausibility of changes in the BIOMASAR maps were checked.

Mapping global surface water inundation dynamics using synergistic information from SMAP, AMSR2 and Landsat

NASA Astrophysics Data System (ADS)

Du, J.; Kimball, J. S.; Galantowicz, J. F.; Kim, S.; Chan, S.; Reichle, R. H.; Jones, L. A.; Watts, J. D.

2017-12-01

A method to monitor global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fwLBand) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency Tb observations from AMSR2. The resulting fwLBand global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The fwLBand annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly fwLBand averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable fwLBand performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) fwLBand results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m fwLBand retrievals showed favourable classification accuracy for water (commission error 31.84%; omission error 28.08%) and land (commission error 0.82%; omission error 0.99%) and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new fwLBand algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics, potentially benefiting hydrological monitoring, flood assessments, and global climate and carbon modeling.

A 3D convolutional neural network approach to land cover classification using LiDAR and multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Xu, Z.; Guan, K.; Peng, B.; Casler, N. P.; Wang, S. W.

2017-12-01

Landscape has complex three-dimensional features. These 3D features are difficult to extract using conventional methods. Small-footprint LiDAR provides an ideal way for capturing these features. Existing approaches, however, have been relegated to raster or metric-based (two-dimensional) feature extraction from the upper or bottom layer, and thus are not suitable for resolving morphological and intensity features that could be important to fine-scale land cover mapping. Therefore, this research combines airborne LiDAR and multi-temporal Landsat imagery to classify land cover types of Williamson County, Illinois that has diverse and mixed landscape features. Specifically, we applied a 3D convolutional neural network (CNN) method to extract features from LiDAR point clouds by (1) creating occupancy grid, intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into a 3D CNN feature extractor for many epochs of learning. The learned features (e.g., morphological features, intensity features, etc) were combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. We used photo interpretation for training and testing data generation. The classification results show that our approach outperforms traditional methods using LiDAR derived feature maps, and promises to serve as an effective methodology for creating high-quality land cover maps through fusion of complementary types of remote sensing data.

Intra- and interspecific variation in tropical tree and liana phenology derived from Unmanned Aerial Vehicle images

NASA Astrophysics Data System (ADS)

Bohlman, S.; Park, J.; Muller-Landau, H. C.; Rifai, S. W.; Dandois, J. P.

2017-12-01

Phenology is a critical driver of ecosystem processes. There is strong evidence that phenology is shifting in temperate ecosystems in response to climate change, but tropical tree and liana phenology remains poorly quantified and understood. A key challenge is that tropical forests contain hundreds of plant species with a wide variety of phenological patterns. Satellite-based observations, an important source of phenology data in northern latitudes, are hindered by frequent cloud cover in the tropics. To quantify phenology over a large number of individuals and species, we collected bi-weekly images from unmanned aerial vehicles (UAVs) in the well-studied 50-ha forest inventory plot on Barro Colorado Island, Panama. Between October 2014 and December 2015 and again in May 2015, we collected a total of 35 sets of UAV images, each with continuous coverage of the 50-ha plot, where every tree ≥ 1 cm DBH is mapped. Spectral, texture, and image information was extracted from the UAV images for individual tree crowns, which was then used as inputs for a machine learning algorithm to predict percent leaf and branch cover. We obtained the species identities of 2000 crowns in the images via field mapping. The objectives of this study are to (1) determined if machine learning algorithms, applied to UAV images, can effectively quantify changes in leaf cover, which we term "deciduousness; (2) determine how liana cover effects deciduousness and (3) test how well UAV-derived deciduousness patterns match satellite-derived temporal patterns. Machine learning algorithms trained on a variety of image parameters could effectively determine leaf cover, despite variation in lighting and viewing angles. Crowns with higher liana cover have less overall deciduousness (tree + liana together) than crowns with lower liana cover. Individual crown deciduousness, summed over all crowns measured in the 50-ha plot, showed a similar seasonal pattern as MODIS EVI composited over 10 years. However, MODIS EVI phenology was "greened" up earlier than UAV-based deciduousness, perhaps reflecting the new late dry season leaf flush that increases EVI but not overall leaf cover. We discuss how the potential mechanisms that explain variation among species and between trees and lianas and the consequences for these variation for ecosystem processes and modeling.

Mapping snow depth from stereo satellite imagery

NASA Astrophysics Data System (ADS)

Gascoin, S.; Marti, R.; Berthier, E.; Houet, T.; de Pinel, M.; Laffly, D.

2016-12-01

To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km²) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a -0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is -0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km²). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of -0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available. Based on this method we have initiated a multi-year survey of the peak snow depth in the Bassiès catchment.

Topographic soil wetness index derived from combined Alaska-British Columbia datasets for the Gulf of Alaska region

NASA Astrophysics Data System (ADS)

D'Amore, D. V.; Biles, F. E.

2016-12-01

The flow of water is often highlighted as a priority in land management planning and assessments related to climate change. Improved measurement and modeling of soil moisture is required to develop predictive estimates for plant distributions, soil moisture, and snowpack, which all play important roles in ecosystem planning in the face of climate change. Drainage indexes are commonly derived from GIS tools with digital elevation models. Soil moisture classes derived from these tools are useful digital proxies for ecosystem functions associated with the concentration of water on the landscape. We developed a spatially explicit topographically derived soil wetness index (TWI) across the perhumid coastal temperate rainforest (PCTR) of Alaska and British Columbia. Developing applicable drainage indexes in complex terrain and across broad areas required careful application of the appropriate DEM, caution with artifacts in GIS covers and mapping realistic zones of wetlands with the indicator. The large spatial extent of the model has facilitated the mapping of forest habitat and the development of water table depth mapping in the region. A key element of the TWI is the merging of elevation datasets across the US-Canada border where major rivers transect the international boundary. The unified TWI allows for seemless mapping across the international border and unified ecological applications. A python program combined with the unified DEM allows end users to quickly apply the TWI to all areas of the PCTR. This common platform can facilitate model comparison and improvements to local soil moisture conditions, generation of streamflow, and ecological site conditions. In this presentation we highlight the application of the TWI for mapping risk factors related to forest decline and the development of a regional water table depth map. Improved soil moisture maps are critical for deriving spatial models of changes in soil moisture for both plant growth and streamflow across future climate conditions.

Defining landscape resistance values in least-cost connectivity models for the invasive grey squirrel: a comparison of approaches using expert-opinion and habitat suitability modelling.

PubMed

Stevenson-Holt, Claire D; Watts, Kevin; Bellamy, Chloe C; Nevin, Owen T; Ramsey, Andrew D

2014-01-01

Least-cost models are widely used to study the functional connectivity of habitat within a varied landscape matrix. A critical step in the process is identifying resistance values for each land cover based upon the facilitating or impeding impact on species movement. Ideally resistance values would be parameterised with empirical data, but due to a shortage of such information, expert-opinion is often used. However, the use of expert-opinion is seen as subjective, human-centric and unreliable. This study derived resistance values from grey squirrel habitat suitability models (HSM) in order to compare the utility and validity of this approach with more traditional, expert-led methods. Models were built and tested with MaxEnt, using squirrel presence records and a categorical land cover map for Cumbria, UK. Predictions on the likelihood of squirrel occurrence within each land cover type were inverted, providing resistance values which were used to parameterise a least-cost model. The resulting habitat networks were measured and compared to those derived from a least-cost model built with previously collated information from experts. The expert-derived and HSM-inferred least-cost networks differ in precision. The HSM-informed networks were smaller and more fragmented because of the higher resistance values attributed to most habitats. These results are discussed in relation to the applicability of both approaches for conservation and management objectives, providing guidance to researchers and practitioners attempting to apply and interpret a least-cost approach to mapping ecological networks.

Fracture mapping and strip mine inventory in the Midwest by using ERTS-1 imagery

NASA Technical Reports Server (NTRS)

Wier, C. W.; Wobber, F. J.; Russell, O. R.; Amato, R. V.

1973-01-01

Analysis of the ERTS-1 imagery and high-altitude infrared photography indicates that useful fracture data can be obtained in Indiana and Illinois despite a glacial till cover. ERTS MSS bands 5 and 7 have proven most useful for fracture mapping in coal-bearing rocks in this region. Preliminary results suggest a reasonable correlation between image-detected fractures and mine roof-fall accidents. Information related to surface mined land, such as disturbed area, water bodies, and kind of reclamation, has been derived from the analysis of ERTS imagery.

Optimized extreme learning machine for urban land cover classification using hyperspectral imagery

NASA Astrophysics Data System (ADS)

Su, Hongjun; Tian, Shufang; Cai, Yue; Sheng, Yehua; Chen, Chen; Najafian, Maryam

2017-12-01

This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.

Basic forest cover mapping using digitized remote sensor data and automated data processing techniques

NASA Technical Reports Server (NTRS)

Coggeshall, M. E.; Hoffer, R. M.

1973-01-01

Remote sensing equipment and automatic data processing techniques were employed as aids in the institution of improved forest resource management methods. On the basis of automatically calculated statistics derived from manually selected training samples, the feature selection processor of LARSYS selected, upon consideration of various groups of the four available spectral regions, a series of channel combinations whose automatic classification performances (for six cover types, including both deciduous and coniferous forest) were tested, analyzed, and further compared with automatic classification results obtained from digitized color infrared photography.

Remote Sensing-based Models of Soil Vulnerability to Compaction and Erosion from Off-highway Vehicles

NASA Astrophysics Data System (ADS)

Villarreal, M. L.; Webb, R. H.; Norman, L.; Psillas, J.; Rosenberg, A.; Carmichael, S.; Petrakis, R.; Sparks, P.

2014-12-01

Intensive off-road vehicle use for immigration, smuggling, and security of the United States-Mexico border has prompted concerns about long-term human impacts on sensitive desert ecosystems. To help managers identify areas susceptible to soil erosion from vehicle disturbances, we developed a series of erosion potential models based on factors from the Revised Universal Soil Loss Equation (RUSLE), with particular focus on the management factor (P-factor) and vegetation cover (C-factor). To better express the vulnerability of soils to human disturbances, a soil compaction index (applied as the P-factor) was calculated as the difference in saturated hydrologic conductivity (Ks) between disturbed and undisturbed soils, which was then scaled up to remote sensing-based maps of vehicle tracks and digital soils maps. The C-factor was improved using a satellite-based vegetation index, which was better correlated with estimated ground cover (r2 = 0.77) than data derived from regional land cover maps (r2 = 0.06). RUSLE factors were normalized to give equal weight to all contributing factors, which provided more management-specific information on vulnerable areas where vehicle compaction of sensitive soils intersects with steep slopes and low vegetation cover. Resulting spatial data on vulnerability and erosion potential provide land managers with information to identify critically disturbed areas and potential restoration sites where off-road driving should be restricted to reduce further degradation.

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

PubMed

Zhang, Jinshui; Yuan, Zhoumiqi; Shuai, Guanyuan; Pan, Yaozhong; Zhu, Xiufang

2017-04-26

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.

Forest fire risk zonation mapping using remote sensing technology

NASA Astrophysics Data System (ADS)

Chandra, Sunil; Arora, M. K.

2006-12-01

Forest fires cause major losses to forest cover and disturb the ecological balance in our region. Rise in temperature during summer season causing increased dryness, increased activity of human beings in the forest areas, and the type of forest cover in the Garhwal Himalayas are some of the reasons that lead to forest fires. Therefore, generation of forest fire risk maps becomes necessary so that preventive measures can be taken at appropriate time. These risk maps shall indicate the zonation of the areas which are in very high, high, medium and low risk zones with regard to forest fire in the region. In this paper, an attempt has been made to generate the forest fire risk maps based on remote sensing data and other geographical variables responsible for the occurrence of fire. These include altitude, temperature and soil variations. Key thematic data layers pertaining to these variables have been generated using various techniques. A rule-based approach has been used and implemented in GIS environment to estimate fuel load and fuel index leading to the derivation of fire risk zonation index and subsequently to fire risk zonation maps. The fire risk maps thus generated have been validated on the ground for forest types as well as for forest fire risk areas. These maps would help the state forest departments in prioritizing their strategy for combating forest fires particularly during the fire seasons.

Susceptibility and triggering scenarios at a regional scale for shallow landslides

NASA Astrophysics Data System (ADS)

Gullà, G.; Antronico, L.; Iaquinta, P.; Terranova, O.

2008-07-01

The work aims at identifying susceptible areas and pluviometric triggering scenarios at a regional scale in Calabria (Italy), with reference to shallow landsliding events. The proposed methodology follows a statistical approach and uses a database linked to a GIS that has been created to support the various steps of spatial data management and manipulation. The shallow landslide predisposing factors taken into account are derived from (i) the 40-m digital terrain model of the region, an ˜ 15,075 km 2 extension; (ii) outcropping lithology; (iii) soils; and (iv) land use. More precisely, a map of the slopes has been drawn from the digital terrain model. Two kinds of covers [prevalently coarse-grained (CG cover) or fine-grained (FG cover)] were identified, referring to the geotechnical characteristics of geomaterial covers and to the lithology map; soilscapes were drawn from soil maps; and finally, the land use map was employed without any prior processing. Subsequently, the inventory maps of some shallow landsliding events, totaling more than 30,000 instabilities of the past and detected by field surveys and photo aerial restitution, were employed to calibrate the relative importance of these predisposing factors. The use of single factors (first level analysis) therefore provides three different susceptibility maps. Second level analysis, however, enables better location of areas susceptible to shallow landsliding events by crossing the single susceptibility maps. On the basis of the susceptibility map obtained by the second level analysis, five different classes of susceptibility to shallow landsliding events have been outlined over the regional territory: 8.9% of the regional territory shows very high susceptibility, 14.3% high susceptibility, 15% moderate susceptibility, 3.6% low susceptibility, and finally, about 58% very low susceptibility. Finally, the maps of two significant shallow landsliding events of the past and their related rainfalls have been utilized to identify the relevant pluviometric triggering scenarios. By using 205 daily rainfall series, different triggering pluviometric scenarios have been identified with reference to CG and FG covers: a value of 365 mm of the total rainfall of the event and/or 170 mm/d of the rainfall maximum intensity and a value of 325 mm of the total rainfall of the event and/or 158 mm/d of the rainfall maximum intensity are able to trigger shallow landsliding events for CG and FG covers, respectively. The results obtained from this study can help administrative authorities to plan future development activities and mitigation measures in shallow landslide-prone areas. In addition, the proposed methodology can be useful in managing emergency situations at a regional scale for shallow landsliding events triggered by intense rainfalls; through this approach, the susceptibility and the pluviometric triggering scenario maps will be improved by means of finer calibration of the involved factors.

Estimating number and size of forest patches from FIA plot data

Treesearch

Mark D. Nelson; Andrew J. Lister; Mark H. Hansen

2009-01-01

Forest inventory and analysis (FIA) annual plot data provide for estimates of forest area, type, volume, growth, and other attributes. Estimates of forest landscape metrics, such as those describing abundance, size, and shape of forest patches, however, typically are not derived from FIA plot data but from satellite image-based land cover maps. Associating image-based...

Mapping technological and biophysical capacities of watersheds to regulate floods

USGS Publications Warehouse

Mogollón, Beatriz; Villamagna, Amy M.; Frimpong, Emmanuel A.; Angermeier, Paul

2016-01-01

Flood regulation is a widely valued and studied service provided by watersheds. Flood regulation benefits people directly by decreasing the socio-economic costs of flooding and indirectly by its positive impacts on cultural (e.g., fishing) and provisioning (e.g., water supply) ecosystem services. Like other regulating ecosystem services (e.g., pollination, water purification), flood regulation is often enhanced or replaced by technology, but the relative efficacy of natural versus technological features in controlling floods has scarcely been examined. In an effort to assess flood regulation capacity for selected urban watersheds in the southeastern United States, we: (1) used long-term flood records to assess relative influence of technological and biophysical indicators on flood magnitude and duration, (2) compared the widely used runoff curve number (RCN) approach for assessing the biophysical capacity to regulate floods to an alternative approach that acknowledges land cover and soil properties separately, and (3) mapped technological and biophysical flood regulation capacities based on indicator importance-values derived for flood magnitude and duration. We found that watersheds with high biophysical (via the alternative approach) and technological capacities lengthened the duration and lowered the peak of floods. We found the RCN approach yielded results opposite that expected, possibly because it confounds soil and land cover processes, particularly in urban landscapes, while our alternative approach coherently separates these processes. Mapping biophysical (via the alternative approach) and technological capacities revealed great differences among watersheds. Our study improves on previous mapping of flood regulation by (1) incorporating technological capacity, (2) providing high spatial resolution (i.e., 10-m pixel) maps of watershed capacities, and (3) deriving importance-values for selected landscape indicators. By accounting for technology that enhances or replaces natural flood regulation, our approach enables watershed managers to make more informed choices in their flood-control investments.

Land cover mapping of Greater Mesoamerica using MODIS data

USGS Publications Warehouse

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.

Updated NASA Satellite Flood Map of Southeastern Texas (ALOS-2 Data)

NASA Image and Video Library

2017-08-31

The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory in Pasadena, California, used synthetic aperture radar imagery from the Japan Aerospace Exploration Agency's (JAXA) ALOS-2 satellite to create this Flood Proxy Map depicting areas of Southeastern Texas that are likely flooded as a result of Hurricane Harvey (shown by light blue pixels). The map is derived images taken before (July 30, 2017) and after (Aug. 27, 2017) Hurricane Harvey made landfall. The map covers an area of 220 by 400 miles (350 by 640 kilometers). Each pixel measures about 55 yards (50 meters) across. Local ground observations provided anecdotal preliminary validation. The results are also cross-validated with ARIA Sentinel-1 flood proxy map v0.2. The map should be used as guidance, and may be less reliable over urban areas. ALOS-2 data were accessed through the International Charter. https://photojournal.jpl.nasa.gov/catalog/PIA21931

An autosomal genetic linkage map of the sheep genome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crawford, A.M.; Ede, A.J.; Pierson, C.A.

1995-06-01

We report the first extensive ovine genetic linkage map covering 2070 cM of the sheep genome. The map was generated from the linkage analysis of 246 polymorphic markers, in nine three-generation full-sib pedigrees, which make up the AgResearch International Mapping Flock. We have exploited many markers from cattle so that valuable comparisons between these two ruminant linkage maps can be made. The markers, used in the segregation analyses, comprised 86 anonymous microsatellite markers derived from the sheep genome, 126 anonymous microsatellites from cattle, one from deer, and 33 polymorphic markers of various types associated with known genes. The maximum numbermore » of informative meioses within the mapping flock was 22. The average number of informative meioses per marker was 140 (range 18-209). Linkage groups have been assigned to all 26 sheep autosomes. 102 refs., 8 figs., 5 tabs.« less

Environmental Controls on Above-Ground Biomass in the Taita Hills, Kenya

NASA Astrophysics Data System (ADS)

Adhikari, H.; Heiskanen, J.; Siljander, M.; Maeda, E. E.; Heikinheimo, V.; Pellikka, P.

2016-12-01

Tropical forests are globally significant ecosystems which maintain high biodiversity and provide valuable ecosystem services, including carbon sink, climate change mitigation and adaptation. This ecosystem has been severely degraded for decades. However, the magnitude and spatial patterns of the above ground biomass (AGB) in the tropical forest-agriculture landscapes is highly variable, even under the same climatic condition and land use. This work aims 1) to generate wall-to-wall map of AGB density for the Taita Hills in Kenya based on field measurements and airborne laser scanning (ALS) and 2) to examine environmental controls on AGB using geospatial data sets on topography, soils, climate and land use, and statistical modelling. The study area (67000 ha) is located in the northernmost part of the Eastern Arc Mountains of Kenya and Tanzania, and the highest hilltops reach over 2200 m in elevation. Most of the forest area has been cleared for croplands and agroforestry, and hills are surrounded by the semi-arid scrublands and dry savannah at an elevation of 600-900 m a.s.l. As a result, the current land cover is a mosaic of various types of land cover and land use. The field measurements were carried out in total of 216 plots in 2013-2015 for AGB computations and ALS flights were conducted in 2014-2015. AGB map at 30 m x 30 m resolution was implemented using multiple linear regression based on ALS variables derived from the point cloud, namely canopy cover and 25 percentile height of ALS returns (R2 = 0.88). Boosted regression trees (BRT) was used for examining the relationship between AGB and explanatory variables, which were derived from ALS-based high resolution DEM (2 m resolution), soil database, downscaled climate data and land cover/use maps based on satellite image analysis. The results of these analyses will be presented in the conference.

Assessment of the Thematic Accuracy of Land Cover Maps

NASA Astrophysics Data System (ADS)

Höhle, J.

2015-08-01

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 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 such as user's and producer's accuracy, and kappa coefficient. In addition, confidence intervals were computed for several accuracy measures. The achieved accuracies and confidence intervals are thoroughly analysed and recommendations are derived from the gained experiences. Reliable reference values are obtained using stereovision, false-colour image pairs, and positioning to the checkpoints with 3D coordinates. The influence of the training areas on the results is studied. Cross validation has been tested with a few reference points in order to derive approximate accuracy measures. The two classification 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 of the confidence interval of six classes was 14% of the user's accuracy.

Digital Bedrock Compilation: A Geodatabase Covering Forest Service Lands in California

NASA Astrophysics Data System (ADS)

Elder, D.; de La Fuente, J. A.; Reichert, M.

2010-12-01

This digital database contains bedrock geologic mapping for Forest Service lands within California. This compilation began in 2004 and the first version was completed in 2005. Second publication of this geodatabase was completed in 2010 and filled major gaps in the southern Sierra Nevada and Modoc/Medicine Lake/Warner Mountains areas. This digital map database was compiled from previously published and unpublished geologic mapping, with source mapping and review from California Geological Survey, the U.S. Geological Survey and others. Much of the source data was itself compilation mapping. This geodatabase is huge, containing ~107,000 polygons and ~ 280,000 arcs. Mapping was compiled from more than one thousand individual sources and covers over 41,000,000 acres (~166,000 km2). It was compiled from source maps at various scales - from ~ 1:4,000 to 1:250,000 and represents the best available geologic mapping at largest scale possible. An estimated 70-80% of the source information was digitized from geologic mapping at 1:62,500 scale or better. Forest Service ACT2 Enterprise Team compiled the bedrock mapping and developed a geodatabase to store this information. This geodatabase supports feature classes for polygons (e.g, map units), lines (e.g., contacts, boundaries, faults and structural lines) and points (e.g., orientation data, structural symbology). Lookup tables provide detailed information for feature class items. Lookup/type tables contain legal values and hierarchical groupings for geologic ages and lithologies. Type tables link coded values with descriptions for line and point attributes, such as line type, line location and point type. This digital mapping is at the core of many quantitative analyses and derivative map products. Queries of the database are used to produce maps and to quantify rock types of interest. These include the following: (1) ultramafic rocks - where hazards from naturally occurring asbestos are high, (2) granitic rocks - increased erosion hazards, (3) limestone, chert, sedimentary rocks - paleontological resources (Potential Fossil Yield Classification maps), (4) calcareous rocks (cave resources, water chemistry), and (5) lava flows - lava tubes (more caves). Map unit groupings (e.g., belts, terranes, tectonic & geomorphic provinces) can also be derived from the geodatabase. Digital geologic mapping was used in ground water modeling to predict effects of tunneling through the San Bernardino Mountains. Bedrock mapping is used in models that characterize watershed sediment regimes and quantify anthropogenic influences. When combined with digital geomorphology mapping, this geodatabase helps to assess landslide hazards.

Globally scalable generation of high-resolution land cover from multispectral imagery

NASA Astrophysics Data System (ADS)

Stutts, S. Craig; Raskob, Benjamin L.; Wenger, Eric J.

2017-05-01

We present an automated method of generating high resolution ( 2 meter) land cover using a pattern recognition neural network trained on spatial and spectral features obtained from over 9000 WorldView multispectral images (MSI) in six distinct world regions. At this resolution, the network can classify small-scale objects such as individual buildings, roads, and irrigation ponds. This paper focuses on three key areas. First, we describe our land cover generation process, which involves the co-registration and aggregation of multiple spatially overlapping MSI, post-aggregation processing, and the registration of land cover to OpenStreetMap (OSM) road vectors using feature correspondence. Second, we discuss the generation of land cover derivative products and their impact in the areas of region reduction and object detection. Finally, we discuss the process of globally scaling land cover generation using cloud computing via Amazon Web Services (AWS).

Baseline map of carbon emissions from deforestation in tropical regions.

PubMed

Harris, Nancy L; Brown, Sandra; Hagen, Stephen C; Saatchi, Sassan S; Petrova, Silvia; Salas, William; Hansen, Matthew C; Potapov, Peter V; Lotsch, Alexander

2012-06-22

Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

Use of digital land-cover data from the Landsat satellite in estimating streamflow characteristics in the Cumberland Plateau of Tennessee

USGS Publications Warehouse

Hollyday, E.F.; Hansen, G.R.

1983-01-01

Streamflow may be estimated with regression equations that relate streamflow characteristics to characteristics of the drainage basin. A statistical experiment was performed to compare the accuracy of equations using basin characteristics derived from maps and climatological records (control group equations) with the accuracy of equations using basin characteristics derived from Landsat data as well as maps and climatological records (experimental group equations). Results show that when the equations in both groups are arranged into six flow categories, there is no substantial difference in accuracy between control group equations and experimental group equations for this particular site where drainage area accounts for more than 90 percent of the variance in all streamflow characteristics (except low flows and most annual peak logarithms). (USGS)

Baseline Map of Carbon Emissions from Deforestation in Tropical Regions

NASA Astrophysics Data System (ADS)

Harris, Nancy L.; Brown, Sandra; Hagen, Stephen C.; Saatchi, Sassan S.; Petrova, Silvia; Salas, William; Hansen, Matthew C.; Potapov, Peter V.; Lotsch, Alexander

2012-06-01

Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

A new global 1-km dataset of percentage tree cover derived from remote sensing

USGS Publications Warehouse

DeFries, R.S.; Hansen, M.C.; Townshend, J.R.G.; Janetos, A.C.; Loveland, Thomas R.

2000-01-01

Accurate assessment of the spatial extent of forest cover is a crucial requirement for quantifying the sources and sinks of carbon from the terrestrial biosphere. In the more immediate context of the United Nations Framework Convention on Climate Change, implementation of the Kyoto Protocol calls for estimates of carbon stocks for a baseline year as well as for subsequent years. Data sources from country level statistics and other ground-based information are based on varying definitions of 'forest' and are consequently problematic for obtaining spatially and temporally consistent carbon stock estimates. By combining two datasets previously derived from the Advanced Very High Resolution Radiometer (AVHRR) at 1 km spatial resolution, we have generated a prototype global map depicting percentage tree cover and associated proportions of trees with different leaf longevity (evergreen and deciduous) and leaf type (broadleaf and needleleaf). The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial datasets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks. The percentage tree cover dataset is available through the Global Land Cover Facility at the University of Maryland at http://glcf.umiacs.umd.edu.

High-resolution land cover classification using low resolution global data

NASA Astrophysics Data System (ADS)

Carlotto, Mark J.

2013-05-01

A fusion approach is described that combines texture features from high-resolution panchromatic imagery with land cover statistics derived from co-registered low-resolution global databases to obtain high-resolution land cover maps. The method does not require training data or any human intervention. We use an MxN Gabor filter bank consisting of M=16 oriented bandpass filters (0-180°) at N resolutions (3-24 meters/pixel). The size range of these spatial filters is consistent with the typical scale of manmade objects and patterns of cultural activity in imagery. Clustering reduces the complexity of the data by combining pixels that have similar texture into clusters (regions). Texture classification assigns a vector of class likelihoods to each cluster based on its textural properties. Classification is unsupervised and accomplished using a bank of texture anomaly detectors. Class likelihoods are modulated by land cover statistics derived from lower resolution global data over the scene. Preliminary results from a number of Quickbird scenes show our approach is able to classify general land cover features such as roads, built up area, forests, open areas, and bodies of water over a wide range of scenes.

BOREAS RSS-8 Snow Maps Derived from Landsat TM Imagery

NASA Technical Reports Server (NTRS)

Hall, Dorothy; Chang, Alfred T. C.; Foster, James L.; Chien, Janeet Y. L.; Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Remote Sensing Science (RSS)-8 team utilized Landsat Thematic Mapper (TM) images to perform mapping of snow extent over the Southern Study Area (SSA). This data set consists of two Landsat TM images that were used to determine the snow-covered pixels over the BOREAS SSA on 18 Jan 1993 and on 06 Feb 1994. The data are stored in binary image format files. The RSS-08 snow map data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Surface mineral maps of Afghanistan derived from HyMap imaging spectrometer data, version 2

USGS Publications Warehouse

Kokaly, Raymond F.; King, Trude V.V.; Hoefen, Todd M.

2013-01-01

This report presents a new version of surface mineral maps derived from HyMap imaging spectrometer data collected over Afghanistan in the fall of 2007. This report also describes the processing steps applied to the imaging spectrometer data. The 218 individual flight lines composing the Afghanistan dataset, covering more than 438,000 square kilometers, were georeferenced to a mosaic of orthorectified Landsat images. The HyMap data were converted from radiance to reflectance using a radiative transfer program in combination with ground-calibration sites and a network of cross-cutting calibration flight lines. The U.S. Geological Survey Material Identification and Characterization Algorithm (MICA) was used to generate two thematic maps of surface minerals: a map of iron-bearing minerals and other materials, which have their primary absorption features at the shorter wavelengths of the reflected solar wavelength range, and a map of carbonates, phyllosilicates, sulfates, altered minerals, and other materials, which have their primary absorption features at the longer wavelengths of the reflected solar wavelength range. In contrast to the original version, version 2 of these maps is provided at full resolution of 23-meter pixel size. The thematic maps, MICA summary images, and the material fit and depth images are distributed in digital files linked to this report, in a format readable by remote sensing software and Geographic Information Systems (GIS). The digital files can be downloaded from http://pubs.usgs.gov/ds/787/downloads/.

Specifications for updating USGS land use and land cover maps

USGS Publications Warehouse

Milazzo, Valerie A.

1983-01-01

To meet the increasing demands for up-to-date land use and land cover information, a primary goal of the U.S. Geological Survey's (USGS) national land use and land cover mapping program is to provide for periodic updating of maps and data in a timely and uniform manner. The technical specifications for updating existing USGS land use and land cover maps that are presented here cover both the interpretive aspects of detecting and identifying land use and land cover changes and the cartographic aspects of mapping and presenting the change data in conventional map format. They provide the map compiler with the procedures and techniques necessary to then use these change data to update existing land use and land cover maps in a manner that is both standardized and repeatable. Included are specifications for the acquisition of remotely sensed source materials, selection of compilation map bases, handling of data base corrections, editing and quality control operations, generation of map update products for USGS open file, and the reproduction and distribution of open file materials. These specifications are planned to become part of the National Mapping Division's Technical Instructions.

A map of dust reddening to 4.5 kpc from Pan-STARRS1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schlafly, E. F.; Rix, H.-W.; Martin, N. F.

2014-07-01

We present a map of the dust reddening to 4.5 kpc derived from Pan-STARRS1 stellar photometry. The map covers almost the entire sky north of declination –30° at a resolution of 7'-14', and is based on the estimated distances and reddenings to more than 500 million stars. The technique is designed to map dust in the Galactic plane, where many other techniques are stymied by the presence of multiple dust clouds at different distances along each line of sight. This reddening-based dust map agrees closely with the Schlegel et al. (SFD) far-infrared emission-based dust map away from the Galactic plane,more » and the most prominent differences between the two maps stem from known limitations of SFD in the plane. We also compare the map with Planck, finding likewise good agreement in general at high latitudes. The use of optical data from Pan-STARRS1 yields reddening uncertainty as low as 25 mmag E(B – V).« less

Arizona land use experiment

NASA Technical Reports Server (NTRS)

Winikka, C. C.; Schumann, H. H.

1975-01-01

Utilization of new sources of statewide remote sensing data, taken from high-altitude aircraft and from spacecraft is discussed along with incorporation of information extracted from these sources into on-going land and resources management programs in Arizona. Statewide cartographic applications of remote sensor data taken by NASA high-altitude aircraft include the development of a statewide semi-analytic control network, the production of nearly 1900 orthophotoquads (image maps) that are coincident in scale and area with the U.S. Geological Survey (USGS) 7. 5 minute topographic quadrangle map series, and satellite image maps of Arizona produced from LANDSAt multispectral scanner imagery. These cartographic products are utilized for a wide variety of experimental and operational earth resources applications. Applications of the imagery, image maps, and derived information discussed include: soils and geologic mapping projects, water resources investigations, land use inventories, environmental impact studies, highway route locations and mapping, vegetation cover mapping, wildlife habitat studies, power plant siting studies, statewide delineation of irrigation cropland, position determination of drilling sites, pictorial geographic bases for thematic mapping, and court exhibits.

Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia-Sudan)

NASA Astrophysics Data System (ADS)

Hassaballah, Khalid; Mohamed, Yasir; Uhlenbrook, Stefan; Biro, Khalid

2017-10-01

Understanding the land use and land cover changes (LULCCs) and their implication on surface hydrology of the Dinder and Rahad basins (D&R, approximately 77 504 km2) is vital for the management and utilization of water resources in the basins. Although there are many studies on LULCC in the Blue Nile Basin, specific studies on LULCC in the D&R are still missing. Hence, its impact on streamflow is unknown. The objective of this paper is to understand the LULCC in the Dinder and Rahad and its implications on streamflow response using satellite data and hydrological modelling. The hydrological model has been derived by different sets of land use and land cover maps from 1972, 1986, 1998 and 2011. Catchment topography, land cover and soil maps are derived from satellite images and serve to estimate model parameters. Results of LULCC detection between 1972 and 2011 indicate a significant decrease in woodland and an increase in cropland. Woodland decreased from 42 to 14 % and from 35 to 14 % for Dinder and Rahad, respectively. Cropland increased from 14 to 47 % and from 18 to 68 % in Dinder and Rahad, respectively. The model results indicate that streamflow is affected by LULCC in both the Dinder and the Rahad rivers. The effect of LULCC on streamflow is significant during 1986 and 2011. This could be attributed to the severe drought during the mid-1980s and the recent large expansion in cropland.

Evaluating Satellite and Supercomputing Technologies for Improved Coastal Ecosystem Assessments

NASA Astrophysics Data System (ADS)

McCarthy, Matthew James

Water quality and wetlands represent two vital elements of a healthy coastal ecosystem. Both experienced substantial declines in the U.S. during the 20th century. Overall coastal wetland cover decreased over 50% in the 20th century due to coastal development and water pollution. Management and legislative efforts have successfully addressed some of the problems and threats, but recent research indicates that the diffuse impacts of climate change and non-point source pollution may be the primary drivers of current and future water-quality and wetland stress. In order to respond to these pervasive threats, traditional management approaches need to adopt modern technological tools for more synoptic, frequent and fine-scale monitoring and assessment. In this dissertation, I explored some of the applications possible with new, commercial satellite imagery to better assess the status of coastal ecosystems. Large-scale land-cover change influences the quality of adjacent coastal water. Satellite imagery has been used to derive land-cover maps since the 1960's. It provides multiple data points with which to evaluate the effects of land-cover change on water quality. The objective of the first chapter of this research was to determine how 40 years of land-cover change in the Tampa Bay watershed (6,500 km2) may have affected turbidity and chlorophyll concentration - two proxies for coastal water quality. Land cover classes were evaluated along with precipitation and wind stress as explanatory variables. Results varied between analyses for the entire estuary and those of segments within the bay. Changes in developed land percent cover best explained the turbidity and chlorophyll-concentration time series for the entire bay (R2 > 0.75, p < 0.02). The paucity of official land-cover maps (i.e. five maps) restricted the temporal resolution of the assessments. Furthermore, most estuaries along the Gulf of Mexico do not have forty years of water-quality time series with which to perform evaluations against land-cover change. Ocean-color satellite imagery was used to derive proxies for coastal water with near-daily satellite observations since 2000. The goal of chapter two was to identify drivers of turbidity variability for 11 National Estuary Program water bodies along the Gulf of Mexico. Land cover assessments could not be used as an explanatory variable because of the low temporal resolution (i.e. approximately one map per five-year period). Ocean color metrics were evaluated against atmospheric, meteorological, and oceanographic variables including precipitation, wind speed, U and V wind vectors, river discharge, and water level over weekly, monthly, seasonal and annual time steps. Climate indices like the North Atlantic Oscillation and El Nino Southern Oscillation index were also examined as possible drivers of long-term changes. Extreme turbidity events were defined by the 90th and 95th percentile observations over each time step. Wind speed, river discharge and El Nino best explained variability in turbidity time-series and extreme events (R2 > 0.2, p < 0.05), but this varied substantially between time steps and estuaries. The background land cover analyses conducted for coastal water quality studies showed that there are substantial discrepancies between the wetland extent estimates mapped by local, state and federal agencies. The third chapter of my research sought to examine these differences and evaluate the accuracy and precision of wetland maps using high spatial-resolution (i.e. two-meter) WorldView-2 satellite imagery. Ground validation data showed that wetlands mapped at two study sites in Tampa Bay were more accurately identified by WorldView-2 than by Landsat imagery (30-meter resolution). When compared to maps produced separately by the National Oceanic and Atmospheric Administration, Southwest Florida Water Management District, and National Wetland Inventory, we found that these historical land cover products overestimated by 2-10 times the actual extent of wetlands as identified in the WorldView-2 maps. We could find no study that had utilized more than six of these commercial images for a given project. Part of the problem is cost of the images, but there is also the cost of processing the images, which is typically done one at a time and with substantial human interaction. Chapter four explains an approach to automate the preprocessing and classification of imagery to detect wetlands within the Tampa Bay watershed (6,500 km2). Software scripts in Python, Matlab and Linux were used to ingest 130 WorldView-2 images and to generate maps that included wetlands, uplands, water, and bare and developed land. These maps proved to be more accurate at identifying forested wetland (78%) than those by NOAA, SWFWMD, and NWI (45-65%) based on ground validation data. Typical processing methods would have required 4-5 months to complete this work, but this protocol completed the 130 images in under 24 hours. Chapter five of the dissertation reviews coastal management case studies that have used satellite technologies. The objective was to illustrate the utility of this technology. The management sectors reviewed included coral reefs, wetlands, water quality, public health, and fisheries and aquaculture.

Fuel models and fire potential from satellite and surface observations

USGS Publications Warehouse

Burgan, R.E.; Klaver, R.W.; Klarer, J.M.

1998-01-01

A national 1-km resolution fire danger fuel model map was derived through use of previously mapped land cover classes and ecoregions, and extensive ground sample data, then refined through review by fire managers familiar with various portions of the U.S. The fuel model map will be used in the next generation fire danger rating system for the U.S., but it also made possible immediate development of a satellite and ground based fire potential index map. The inputs and algorithm of the fire potential index are presented, along with a case study of the correlation between the fire potential index and fire occurrence in California and Nevada. Application of the fire potential index in the Mediterranean ecosystems of Spain, Chile, and Mexico will be tested.

Global Maps of Lunar Neutron Fluxes from the LEND Instrument

NASA Technical Reports Server (NTRS)

Litvak, M. L.; Mitrofanov, I. G.; Sanin, A.; Malakhov, A.; Boynton, W. V.; Chin, G.; Droege, G.; Evans, L. G.; Garvin, J.; Golovin, D. V.;

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Landslides Mapped from LIDAR Imagery, Kitsap County, Washington

USGS Publications Warehouse

McKenna, Jonathan P.; Lidke, David J.; Coe, Jeffrey A.

2008-01-01

Landslides are a recurring problem on hillslopes throughout the Puget Lowland, Washington, but can be difficult to identify in the densely forested terrain. However, digital terrain models of the bare-earth surface derived from LIght Detection And Ranging (LIDAR) data express topographic details sufficiently well to identify landslides. Landslides and escarpments were mapped using LIDAR imagery and field checked (when permissible and accessible) throughout Kitsap County. We relied almost entirely on derivatives of LIDAR data for our mapping, including topographic-contour, slope, and hill-shaded relief maps. Each mapped landslide was assigned a level of 'high' or 'moderate' confidence based on the LIDAR characteristics and on field observations. A total of 231 landslides were identified representing 0.8 percent of the land area of Kitsap County. Shallow debris topples along the coastal bluffs and large (>10,000 m2) landslide complexes are the most common types of landslides. The smallest deposit mapped covers an area of 252 m2, while the largest covers 0.5 km2. Previous mapping efforts that relied solely on field and photogrammetric methods identified only 57 percent of the landslides mapped by LIDAR (61 percent high confidence and 39 percent moderate confidence), although nine landslides previously identified were not mapped during this study. The remaining 43 percent identified using LIDAR have 13 percent high confidence and 87 percent moderate confidence. Coastal areas are especially susceptible to landsliding; 67 percent of the landslide area that we mapped lies within 500 meters of the present coastline. The remaining 33 percent are located along drainages farther inland. The LIDAR data we used for mapping have some limitations including (1) rounding of the interface area between low slope surfaces and vertical faces (that is, along the edges of steep escarpments) which results in scarps being mapped too far headward (one or two meters), (2) incorrect laser-distance measurements resulting in inaccurate elevation values, (3) removal of valid ground elevations, (4) false ground roughness, and (5) faceted surface texture. Several of these limitations are introduced by algorithms in the processing software that are designed to remove non-ground elevations from LIDAR data. Despite these limitations, the algorithm-enhanced LIDAR imagery does effectively 'remove' vegetation that obscures many landslides, and is therefore a valuable tool for landslide inventories and investigations in heavily vegetated regions such as the Puget Lowland.

Analysis of urban area land cover using SEASAT Synthetic Aperture Radar data

NASA Technical Reports Server (NTRS)

Henderson, F. M. (Principal Investigator)

1980-01-01

Digitally processed SEASAT synthetic aperture raar (SAR) imagery of the Denver, Colorado urban area was examined to explore the potential of SAR data for mapping urban land cover and the compatability of SAR derived land cover classes with the United States Geological Survey classification system. The imagery is examined at three different scales to determine the effect of image enlargement on accuracy and level of detail extractable. At each scale the value of employing a simplistic preprocessing smoothing algorithm to improve image interpretation is addressed. A visual interpretation approach and an automated machine/visual approach are employed to evaluate the feasibility of producing a semiautomated land cover classification from SAR data. Confusion matrices of omission and commission errors are employed to define classification accuracies for each interpretation approach and image scale.

Methods Used in EnviroAtlas to Assess Urban Natural ...

EPA Pesticide Factsheets

Previous studies have positively correlated human exposures to natural features with health promoting outcomes such as increased physical activity, improved cognitive function, increased social engagement, and reduced ambient air pollution. When using remotely-sensed data to investigate these relationships, researchers must first identify an appropriate spatial resolution to characterize exposures. However, metric development has often been limited by the lack of fine-scale land cover data, especially across multiple communities. As a result, researchers commonly use coarse resolution imagery. EnviroAtlas, a U.S. Environmental Protection Agency web-based ecosystem services mapping tool, has developed 1-meter resolution land cover data across 16 diverse U.S. Census Urban Areas using aerial photography and supplemental data. Research maps derived from these foundational data include percent tree cover along busy roads, percent tree cover and green space along walkable streets, and percent natural vegetation bordering water bodies. EnviroAtlas has also developed multiple smoothed “heat maps” of proximity to specific types of features at every 1m point; these include total green space, tree cover, and water within 50m, 500m, and 1,000m buffers; walking distance to the nearest park entrance; and intersection density as an indicator of neighborhood walkability.EnviroAtlas variables are available to external researchers, public health professionals and planners t

Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake

PubMed Central

Pareeth, Sajid; Salmaso, Nico; Adrian, Rita; Neteler, Markus

2016-01-01

Availability of remotely sensed multi-spectral images since the 1980’s, which cover three decades of voluminous data could help researchers to study the changing dynamics of bio-physical characteristics of land and water. In this study, we introduce a new methodology to develop homogenised Lake Surface Water Temperature (LSWT) from multiple polar orbiting satellites. Precisely, we developed homogenised 1 km daily LSWT maps covering the last 30 years (1986 to 2015) combining data from 13 satellites. We used a split-window technique to derive LSWT from brightness temperatures and a modified diurnal temperature cycle model to homogenise data which were acquired between 8:00 to 17:00 UTC. Gaps in the temporal LSWT data due to the presence of clouds were filled by applying Harmonic ANalysis of Time Series (HANTS). The satellite derived LSWT maps were validated based on long-term monthly in-situ bulk temperature measurements in Lake Garda, the largest lake in Italy. We found the satellite derived homogenised LSWT being significantly correlated to in-situ data. The new LSWT time series showed a significant annual rate of increase of 0.020 °C yr−1 (*P < 0.05), and of 0.036 °C yr−1 (***P < 0.001) during summer. PMID:27502177

How are the wetlands over tropical basins impacted by the extreme hydrological events?

NASA Astrophysics Data System (ADS)

Al-Bitar, A.; Parrens, M.; Frappart, F.; Papa, F.; Kerr, Y. H.; Cretaux, J. F.; Wigneron, J. P.

2016-12-01

Wetlands play a crucial role in tropical basins and still many questions remain unanswered on how extreme events (like El-Nino) impacts them. Answering these questions is challenging as monitoring of inland water surfaces via remote sensing over tropical areas is a difficult task because of impact of vegetation and cloud cover. Several microwave based products have been elaborated to monitor these surfaces (Papa et al. 2010). In this study we combine the use of L-band microwave brightness temperatures and altimetric data from SARAL/ALTIKA to derive water storage maps at relatively high (7days) temporal frequency. The area of interest concerns the Amazon, Congo and GBH basins A first order radiative model is used to derive surface water over land from the brightness temperature measured by ESA SMOS mission at coarse resolution (25 km x 25 km) and 7-days frequency. An initial investigation of the use of the SMAP mission for the same purpose will be also presented. The product is compared to the static land cover map such as ESA CCI and the International Geosphere-Biosphere Program (IGBP) and also dynamic maps from SWAPS. It is then combined to the altimetric data to derive water storage maps. The water surfaces and water storage products are then compared to precipitation data from GPM TRMM datasets, ground water storage change from GRACE and river discharge data from field data. The amplitudes and time shifts of the signals is compared based on the sub-basin definition from Hydroshed database. The dataset is then divided into years of strong and weak El-Nino signal and the anomaly is between the two dataset is compared. The results show a strong influence of EL-Nino on the time shift of the different components showing that the hydrological regime of wetlands is highly impacted by these extreme events. This can have dramatic impacts on the ecosystem as the wetlands are vulnerable with a high biodiversity.

Discerning spatial and temporal LAI and clear-sky FAPAR variability during summer at the Toolik Lake vegetation monitoring grid (North Slope, Alaska)

NASA Astrophysics Data System (ADS)

Heim, B.; Beamish, A. L.; Walker, D. A.; Epstein, H. E.; Sachs, T.; Chabrillat, S.; Buchhorn, M.; Prakash, A.

2016-12-01

Ground data for the validation of satellite-derived terrestrial Essential Climate Variables (ECVs) at high latitudes are sparse. Also for regional model evaluation (e.g. climate models, land surface models, permafrost models), we lack accurate ranges of terrestrial ground data and face the problem of a large mismatch in scale. Within the German research programs `Regional Climate Change' (REKLIM) and the Environmental Mapping and Analysis Program (EnMAP), we conducted a study on ground data representativeness for vegetation-related variables within a monitoring grid at the Toolik Lake Long-Term Ecological Research station; the Toolik Lake station lies in the Kuparuk River watershed on the North Slope of the Brooks Mountain Range in Alaska. The Toolik Lake grid covers an area of 1 km2 containing Eight five grid points spaced 100 meters apart. Moist acidic tussock tundra is the most dominant vegetation type within the grid. Eight five permanent 1 m2 plots were also established to be representative of the individual gridpoints. Researchers from the University of Alaska Fairbanks have undertaken assessments at these plots, including Leaf Area Index (LAI) and field spectrometry to derive the Normalized Difference Vegetation Index (NDVI). During summer 2016, we conducted field spectrometry and LAI measurements at selected plots during early, peak and late summer. We experimentally measured LAI on more spatially extensive Elementary Sampling Units (ESUs) to investigate the spatial representativeness of the permanent 1 m2 plots and to map ESUs for various tundra types. LAI measurements are potentially influenced by landscape-inherent microtopography, sparse vascular plant cover, and dead woody matter. From field spectrometer measurements, we derived a clear-sky mid-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). We will present the first data analyses comparing FAPAR and LAI, and maps of biophysically-focused ESUs for evaluation of the use of remote sensing data to estimate these ecosystem properties.

Agricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests

NASA Astrophysics Data System (ADS)

Vogels, M. F. A.; de Jong, S. M.; Sterk, G.; Addink, E. A.

2017-02-01

Land-use and land-cover (LULC) conversions have an important impact on land degradation, erosion and water availability. Information on historical land cover (change) is crucial for studying and modelling land- and ecosystem degradation. During the past decades major LULC conversions occurred in Africa, Southeast Asia and South America as a consequence of a growing population and economy. Most distinct is the conversion of natural vegetation into cropland. Historical LULC information can be derived from satellite imagery, but these only date back until approximately 1972. Before the emergence of satellite imagery, landscapes were monitored by black-and-white (B&W) aerial photography. This photography is often visually interpreted, which is a very time-consuming approach. This study presents an innovative, semi-automated method to map cropland acreage from B&W photography. Cropland acreage was mapped on two study sites in Ethiopia and in The Netherlands. For this purpose we used Geographic Object-Based Image Analysis (GEOBIA) and a Random Forest classification on a set of variables comprising texture, shape, slope, neighbour and spectral information. Overall mapping accuracies attained are 90% and 96% for the two study areas respectively. This mapping method increases the timeline at which historical cropland expansion can be mapped purely from brightness information in B&W photography up to the 1930s, which is beneficial for regions where historical land-use statistics are mostly absent.

A comparison of FIA plot data derived from image pixels and image objects

Treesearch

Charles E. Werstak

2012-01-01

The use of Forest Inventory and Analysis (FIA) plot data for producing continuous and thematic maps of forest attributes (e.g., forest type, canopy cover, volume, and biomass) at the regional level from satellite imagery can be challenging due to differences in scale. Specifically, classification errors that may result from assumptions made between what the field data...

Tree Cover Mapping Tool—Documentation and user manual

USGS Publications Warehouse

Cotillon, Suzanne E.; Mathis, Melissa L.

2016-06-02

The Tree Cover Mapping (TCM) tool was developed by scientists at the U.S. Geological Survey Earth Resources Observation and Science Center to allow a user to quickly map tree cover density over large areas using visual interpretation of high resolution imagery within a geographic information system interface. The TCM tool uses a systematic sample grid to produce maps of tree cover. The TCM tool allows the user to define sampling parameters to estimate tree cover within each sample unit. This mapping method generated the first on-farm tree cover maps of vast regions of Niger and Burkina Faso. The approach contributes to implementing integrated landscape management to scale up re-greening and restore degraded land in the drylands of Africa. The TCM tool is easy to operate, practical, and can be adapted to many other applications such as crop mapping, settlements mapping, or other features. This user manual provides step-by-step instructions for installing and using the tool, and creating tree cover maps. Familiarity with ArcMap tools and concepts is helpful for using the tool.

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

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3768 and 3668, Imam-Saheb (215), Rustaq (216), Baghlan (221), and Taloqan (222) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3468, Chak Wardak Syahgerd (509) and Kabul (510) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

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

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3570, Tagab-E-Munjan (505) and Asmar-Kamdesh (506) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3564, Chahriaq (Joand) (405) and Gurziwan (406) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3364, Pasa-Band (417) and Kejran (418) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

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

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3462, Herat (409) and Chesht-Sharif (410) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3262, Farah (421) and Hokumat-E-Pur-Chaman (422) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3362, Shin-Dand (415) and Tulak (416) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3670, Jam-Kashem (223) and Zebak (224) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3466, Lal-Sarjangal (507) and Bamyan (508) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3566, Sang-Charak (501) and Sayghan-O-Kamard (502) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3568, Polekhomri (503) and Charikar (504) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3566, Sang-Charak (501) and Sayghan-O-Kamard (502) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

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

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3764 and 3664, Jalajin (117), Kham-Ab (118), Char Shangho (123), and Sheberghan (124) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3570, Tagab-E-Munjan (505) and Asmar-Kamdesh (506) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3364, Pasa-Band (417) and Kejran (418) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3466, Lal-Sarjangal (507) and Bamyan (508) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3168 and 3268, Yahya-Wona (703), Wersek (704), Khayr-Kot (521), and Urgon (522) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

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

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3266, Ourzgan (519) and Moqur (520) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3368 and Part of Quadrangle 3370, Ghazni (515), Gardez (516), and Part of Jaji-Maydan (517) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3468, Chak Wardak-Syahgerd (509) and Kabul (510) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3470 and the Northern Edge of Quadrangle 3370, Jalal-Abad (511), Chaghasaray (512), and Northernmost Jaji-Maydan (517) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3060 and 2960, Qala-I-Fath (608), Malek-Sayh-Koh (613), and Gozar-E-Sah (614) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3670, Jarm-Keshem (223) and Zebak (224) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3164, Lashkargah (605) and Kandahar (606) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3772, 3774, 3672, and 3674, Gaz-Khan (313), Sarhad (314), Kol-I-Chaqmaqtin (315), Khandud (319), Deh-Ghulaman (320), and Ertfah (321) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3564, Chahriaq (Joand) (405) and Gurziwan (406) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3260 and 3160, Dasht-E-Chahe-Mazar (419), Anardara (420), Asparan (601), and Kang (602) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3462, Herat (409) and Chesht-Sharif (410) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3464, Shahrak (411) and Kasi (412) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3362, Shin-Dand (415) and Tulak (416) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3162, Chakhansur (603) and Kotalak (604) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3064, 3066, 2964, and 2966, Laki-Bander (611), Jahangir-Naweran (612), Sreh-Chena (707), Shah-Esmail (617), Reg-Alaqadari (618), and Samandkhan-Karez (713) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3166, Jaldak (701) and Maruf-Nawa (702) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

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

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3366, Gizab (513) and Nawer (514) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3666 and 3766, Balkh (219), Mazar-I-Sharif (220), Qarqin (213), and Hazara Toghai (214) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3264, Nawzad-Musa-Qala (423) and Dehrawat (424) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3264, Nawzad-Musa-Qala (423) and Dehrawat (424) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3870 and 3770, Maymayk (211), Jamarj-I-Bala (212), Faydz-Abad (217), and Parkhaw (218) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3062 and 2962, Charburjak (609), Khanneshin (610), Gawdezereh (615), and Galachah (616) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3262, Farah (421) and Hokumat-E-Pur-Chaman (422) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3560 and 3562, Sir Band (402), Khawja-Jir (403), and Bala-Murghab (404) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangle 3166, Jaldak (701) and Maruf-Nawa (702) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Natural-Color-Image Map of Quadrangles 3460 and 3360, Kol-I-Namaksar (407), Ghuryan (408), Kawir-I-Naizar (413), and Kohe-Mahmudo-Esmailjan (414) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a natural-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The natural colors were generated using calibrated red-, green-, and blue-wavelength Landsat image data, which were correlated with red, green, and blue values of corresponding picture elements in MODIS (Moderate Resolution Imaging Spectrometer) 'true color' mosaics of Afghanistan. These mosaics have been published on http://www.truecolorearth.com and modified to match more closely the Munsell colors of sampled surfaces. Peak elevations are derived from Shuttle Radar Topography Mission (SRTM) digital data, averaged over a pixel representing an area of 85 m2, and they are slightly lower than the highest corresponding local point. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR

NASA Astrophysics Data System (ADS)

Chen, Yang; Zhu, Xuan; Yebra, Marta; Harris, Sarah; Tapper, Nigel

2016-10-01

Fuel structural characteristics affect fire behavior including fire intensity, spread rate, flame structure, and duration, therefore, quantifying forest fuel structure has significance in understanding fire behavior as well as providing information for fire management activities (e.g., planned burns, suppression, fuel hazard assessment, and fuel treatment). This paper presents a method of forest fuel strata classification with an integration between terrestrial light detection and ranging (LiDAR) data and geographic information system for automatically assessing forest fuel structural characteristics (e.g., fuel horizontal continuity and vertical arrangement). The accuracy of fuel description derived from terrestrial LiDAR scanning (TLS) data was assessed by field measured surface fuel depth and fuel percentage covers at distinct vertical layers. The comparison of TLS-derived depth and percentage cover at surface fuel layer with the field measurements produced root mean square error values of 1.1 cm and 5.4%, respectively. TLS-derived percentage cover explained 92% of the variation in percentage cover at all fuel layers of the entire dataset. The outcome indicated TLS-derived fuel characteristics are strongly consistent with field measured values. TLS can be used to efficiently and consistently classify forest vertical layers to provide more precise information for forest fuel hazard assessment and surface fuel load estimation in order to assist forest fuels management and fire-related operational activities. It can also be beneficial for mapping forest habitat, wildlife conservation, and ecosystem management.

Shrub Abundance Mapping in Arctic Tundra with Misr

NASA Astrophysics Data System (ADS)

Duchesne, R.; Chopping, M. J.; Wang, Z.; Schaaf, C.; Tape, K. D.

2013-12-01

Over the last 60 years an increase in shrub abundance has been observed in the Arctic tundra in connection with a rapid surface warming trend. Rapid shrub expansion may have consequences in terms of ecosystem structure and function, albedo, and feedbacks to climate; however, its rate is not yet known. The goal of this research effort is thus to map large scale changes in Arctic tundra vegetation by exploiting the structural signal in moderate resolution satellite remote sensing images from NASA's Multiangle Imaging SpectroRadiometer (MISR), mapped onto a 250m Albers Conic Equal Area grid. We present here large area shrub mapping supported by reference data collated using extensive field inventory data and high resolution panchromatic imagery. MISR Level 1B2 Terrain radiance scenes from the Terra satellite from 15 June-31 July, 2000 - 2010 were converted to surface bidirectional reflectance factors (BRF) using MISR Toolkit routines and the MISR 1 km LAND product BRFs. The red band data in all available cameras were used to invert the RossThick-LiSparse-Reciprocal BRDF model to retrieve kernel weights, model-fitting RMSE, and Weights of Determination. The reference database was constructed using aerial survey, three field campaigns (field inventory for shrub count, cover, mean radius and height), and high resolution imagery. Tall shrub number, mean crown radius, cover, and mean height estimates were obtained from QuickBird and GeoEye panchromatic image chips using the CANAPI algorithm, and calibrated using field-based estimates, thus extending the database to over eight hundred locations. Tall shrub fractional cover maps for the North Slope of Alaska were constructed using the bootstrap forest machine learning algorithm that exploits the surface information provided by MISR. The reference database was divided into two datasets for training and validation. The model derived used a set of 19 independent variables(the three kernel weights, ratios and interaction terms; white and black sky albedos; and blue, green, red, and NIR nadir camera BRFs), to grow a forest of decision trees. The final estimate is the average of the predicted values from each tree. Observations not used in constructing the trees were used in validation. The model was applied with a large volume of MISR data and the resulting fractional cover estimates were combined into annual maps using a compositing algorithm that flags results affected by cloud, cloud shadow, surface water, extreme outliers, topographic shading, and burned areas. The maps show that shrub cover is lower on the north slope in comparison to southern part, as expected, however, a preliminary assessment of the fractional cover change over the last decade, achieved by averaging fractional cover values for 2000-2002 and 2008-2010 and then calculating the change between the two periods, revealed that there are large areas for which we cannot determine the sign of the change with high confidence, as the precision of our estimate is close to the magnitude of the cover values. Additional research is thus required to reliably map shrub cover in this environment at annual intervals.

Genic Microsatellite Markers in Brassica rapa: Development, Characterization, Mapping, and Their Utility in Other Cultivated and Wild Brassica Relatives

PubMed Central

Ramchiary, Nirala; Nguyen, Van Dan; Li, Xiaonan; Hong, Chang Pyo; Dhandapani, Vignesh; Choi, Su Ryun; Yu, Ge; Piao, Zhong Yun; Lim, Yong Pyo

2011-01-01

Genic microsatellite markers, also known as functional markers, are preferred over anonymous markers as they reveal the variation in transcribed genes among individuals. In this study, we developed a total of 707 expressed sequence tag-derived simple sequence repeat markers (EST-SSRs) and used for development of a high-density integrated map using four individual mapping populations of B. rapa. This map contains a total of 1426 markers, consisting of 306 EST-SSRs, 153 intron polymorphic markers, 395 bacterial artificial chromosome-derived SSRs (BAC-SSRs), and 572 public SSRs and other markers covering a total distance of 1245.9 cM of the B. rapa genome. Analysis of allelic diversity in 24 B. rapa germplasm using 234 mapped EST-SSR markers showed amplification of 2 alleles by majority of EST-SSRs, although amplification of alleles ranging from 2 to 8 was found. Transferability analysis of 167 EST-SSRs in 35 species belonging to cultivated and wild brassica relatives showed 42.51% (Sysimprium leteum) to 100% (B. carinata, B. juncea, and B. napus) amplification. Our newly developed EST-SSRs and high-density linkage map based on highly transferable genic markers would facilitate the molecular mapping of quantitative trait loci and the positional cloning of specific genes, in addition to marker-assisted selection and comparative genomic studies of B. rapa with other related species. PMID:21768136

The iPot Project: improved potato monitoring in Belgium using remote sensing and crop growth modelling

NASA Astrophysics Data System (ADS)

Piccard, Isabelle; Nackaerts, Kris; Gobin, Anne; Goffart, Jean-Pierre; Planchon, Viviane; Curnel, Yannick; Tychon, Bernard; Wellens, Joost; Cools, Romain; Cattoor, Nele

2015-04-01

Belgian potato processors, traders and packers are increasingly working with potato contracts. The close follow up of contracted parcels on the land as well as from above is becoming an important tool to improve the quantity and quality of the potato crop and reduce risks in order to plan the storage, packaging or processing and as such to strengthen the competitiveness of the Belgian potato chain in a global market. At the same time, precision agriculture continues to gain importance and progress. Farmers are obligated to invest in new technologies. Between mid-May and the end of June 2014 potato fields in Gembloux were monitored from emergence till canopy closure. UAV images (RGB) and digital (hemispherical) photographs were taken at ten-daily intervals. Crop emergence maps show the time (date) and degree of crop emergence and crop closure (in terms of % cover). For three UAV flights during the growing season RGB images at 3 cm resolution were processed using a K-means clustering algorithm to classify the crop according to its greenness. Based on the greenness %cover and daily cover growth were derived for 5x5m pixels and 25x25m pixels. The latter resolution allowed for comparison with high resolution satellite imagery. Vegetation indices such as %Cover and LAI were calculated with the Cyclopes algorithm (INRA-EMMAH) from high resolution satellite images (DMC/Deimos, 22m pixel size). DMC based cover maps showed similar patterns as compared with the UAV-based cover maps, and allows for further applications of the data in crop management. Today the use of geo-information by the (private) agricultural sector in Belgium is rather limited, notwithstanding the great benefits this type of information may offer, as recognized by the sector. The iPot project, financed by the Belgian Science Policy Office (BELSPO), aims to provide the Belgian potato sector, represented by Belgapom, with near real time information on field condition (weather-soil) and crop development and with early yield estimates, derived from a combination of satellite images and crop growth models. An intuitive web based geo-information platform is being developed to allow both the Belgian potato industry and the potato research centres to access, analyse and combine the data with their own field observations in close collaboration with the farmers, for improved decision-making.

Alaska Interim Land Cover Mapping Program; final report

USGS Publications Warehouse

Fitzpatrick-Lins, Katherine; Doughty, E.F.; Shasby, Mark; Benjamin, Susan

1989-01-01

In 1985, the U.S. Geological Survey initiated a research project to develop an interim land cover data base for Alaska as an alternative to the nationwide Land Use and Land Cover Mapping Program. The Alaska Interim Land Cover Mapping Program was subsequently created to develop methods for producing a series of land cover maps that utilized the existing Landsat digital land cover classifications produced by and for the major land management agencies for mapping the vegetation of Alaska. The program was successful in producing digital land cover classifications and statistical summaries using a common statewide classification and in reformatting these data to produce l:250,000-scale quadrangle-based maps directly from the Scitex laser plotter. A Federal and State agency review of these products found considerable user support for the maps. Presently the Geological Survey is committed to digital processing of six to eight quadrangles each year.

Facilitating the exploitation of ERTS-1 imagery using snow enhancement techniques. [geological fault maps of Massachusetts and Connecticut

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Amato, R. V.; Leshendok, T.

1973-01-01

The author has identified the following significant results. The applications of ERTS-1 imagery for geological fracture mapping regardless of season has been repeatedly confirmed. The enhancement provided by a differential cover of snow increases the number and length of fracture-lineaments which can be detected with ERTS-1 data and accelerates the fracture mapping process for a variety of practical applications. The geological mapping benefits of the program will be realized in geographic areas where data are most needed - complex glaciated terrain and areas of deep residual soils. ERTS-1 derived fracture-lineament maps which provide detail well in excess of existing geological maps are not available in the Massachusetts-Connecticut area. The large quantity of new data provided by ERTS-1 may accelerate and improve field mapping now in progress in the area. Numerous other user groups have requested data on the techniques. This represents a major change in operating philosophy for groups who to data judged that snow obscured geological detail.

Land use and land cover change in the Greater Yellowstone Ecosystem: 1975-1995

USGS Publications Warehouse

Parmenter, A.W.; Hansen, A.; Kennedy, R.E.; Cohen, W.; Langner, U.; Lawrence, R.; Maxwell, B.; Gallant, Alisa; Aspinall, R.

2003-01-01

Shifts in the demographic and economic character of the Greater Yellowstone Ecosystem (GYE) are driving patterns of land cover and land use change in the region. Such changes may have important consequences for ecosystem functioning. The objective of this paper is to quantify the trajectories and rates of change in land cover and use across the GYE for the period 1975-1995 using satellite imagery. Spectral and geographic variables were used as inputs to classification tree regression analysis (CART) to find "rules" which defined land use and land cover classes on the landscape. The resulting CART functions were used to map land cover and land use across seven Landsat TM scenes for 1995. We then used a thresholding technique to identify locations that differed in spectral properties between the 1995 and 1985 time periods. These "changed" locations were classified using CART functions derived from spectral and geographic data from 1985. This was similarly done for the year 1975 based on Landsat MSS data. Differences between the 1975, 1985, and 1995 maps were considered change in land cover and use. We calibrated and tested the accuracy of our models using data acquired through manual interpretation of aerial photos. Elevation and vegetative indices derived from the remotely sensed satellite imagery explained the most variance in the land use and land cover classes (-i.e., defined the "rules" most often). Overall accuracies from our study were good, ranging from 94% at the coarsest level of detail to 74% at the finest. The largest changes over the study period were the increases in burned, urban, and mixed conifer-herbaceous classes and decreases in woody deciduous, mixed woody deciduous-herbaceous, and conifer habitats. These changes have important implications for ecological function and biodiversity. The expansion of mixed conifer classes may increase fuel loads and enhance risk to the growing number of rural homes. The reduction of woody deciduous cover types is likely reducing population sizes for the numerous plant and animal species that specialize on this habitat type. Some of these species are also negatively influenced by the increase of rural homes in and near woody deciduous habitats.

Facilitating the exploitation of ERTS imagery using snow enhancement techniques

NASA Technical Reports Server (NTRS)

Wobber, F. J. (Principal Investigator); Martin, K. R.; Amato, R. V.

1973-01-01

The author has identified the following significant results. Detection and analysis of fracture systems can be more effectively conducted utilizing snow cover as an enhancement tool. From analysis within the Great Barrington Test Site it appears that the use of aeromagnetic data effectively supplements lineament data acquired using ERTS imagery. Coincidence of lineaments derived from aeromagnetics with lineaments interpreted from ERTS imagery apparently indicate the presence of mineralized fracture systems and dikes. Utilizing both tools can increase the speed and efficiency of mineral exploration and geological mapping in areas where bedrock is obscured by a thick unconsolidated sediment cover.

A Simple Sequence Repeat- and Single-Nucleotide Polymorphism-Based Genetic Linkage Map of the Brown Planthopper, Nilaparvata lugens

PubMed Central

Jairin, Jirapong; Kobayashi, Tetsuya; Yamagata, Yoshiyuki; Sanada-Morimura, Sachiyo; Mori, Kazuki; Tashiro, Kosuke; Kuhara, Satoru; Kuwazaki, Seigo; Urio, Masahiro; Suetsugu, Yoshitaka; Yamamoto, Kimiko; Matsumura, Masaya; Yasui, Hideshi

2013-01-01

In this study, we developed the first genetic linkage map for the major rice insect pest, the brown planthopper (BPH, Nilaparvata lugens). The linkage map was constructed by integrating linkage data from two backcross populations derived from three inbred BPH strains. The consensus map consists of 474 simple sequence repeats, 43 single-nucleotide polymorphisms, and 1 sequence-tagged site, for a total of 518 markers at 472 unique positions in 17 linkage groups. The linkage groups cover 1093.9 cM, with an average distance of 2.3 cM between loci. The average number of marker loci per linkage group was 27.8. The sex-linkage group was identified by exploiting X-linked and Y-specific markers. Our linkage map and the newly developed markers used to create it constitute an essential resource and a useful framework for future genetic analyses in BPH. PMID:23204257

Extent of Texas Flooding Shown in New NASA Map

NASA Image and Video Library

2017-08-30

The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory in Pasadena, California, created this Flood Proxy Map depicting areas of Southeastern Texas that are likely flooded as a result of Hurricane Harvey, shown by light blue pixels. The map is derived from synthetic aperture radar amplitude images from the Japan Aerospace Exploration Agency's (JAXA) ALOS-2 PALSAR-2 satellite, taken before (July 30, 2017) and after (August 27, 2017) Hurricane Harvey made landfall. The map covers an area of 135 square miles (350 square kilometers). Each pixel measures about 538 square feet (50 square meters). Local ground observations provided anecdotal preliminary validation. This flood proxy map should be used as guidance to identify areas that are likely flooded, and may be less reliable over urban areas. ALOS-2 data were accessed through the International Charter. https://photojournal.jpl.nasa.gov/catalog/PIA21928

Topographic mapping of the Moon

USGS Publications Warehouse

Wu, S.S.C.

1985-01-01

Contour maps of the Moon have been compiled by photogrammetric methods that use stereoscopic combinations of all available metric photographs from the Apollo 15, 16, and 17 missions. The maps utilize the same format as the existing NASA shaded-relief Lunar Planning Charts (LOC-1, -2, -3, and -4), which have a scale of 1:2 750 000. The map contour interval is 500m. A control net derived from Apollo photographs by Doyle and others was used for the compilation. Contour lines and elevations are referred to the new topographic datum of the Moon, which is defined in terms of spherical harmonics from the lunar gravity field. Compilation of all four LOC charts was completed on analytical plotters from 566 stereo models of Apollo metric photographs that cover approximately 20% of the Moon. This is the first step toward compiling a global topographic map of the Moon at a scale of 1:5 000 000. ?? 1985 D. Reidel Publishing Company.

Physical and Radiative Characteristic and Long-term Variability of the Okhotsk Sea Ice Cover

NASA Technical Reports Server (NTRS)

Nishio, Fumihiko; Comiso, Josefino C.; Gersten, Robert; Nakayama, Masashige; Ukita, Jinro; Gasiewski, Al; Stanko, Boba; Naoki, Kazuhiro

2008-01-01

Much of what we know about the large scale characteristics of the Okhotsk Sea ice cover has been provided by ice concentration maps derived from passive microwave data. To understand what satellite data represent in a highly divergent and rapidly changing environment like the Okhotsk Sea, we take advantage of concurrent satellite, aircraft, and ship data acquired on 7 February and characterized the sea ice cover at different scales from meters to hundreds of kilometers. Through comparative analysis of surface features using co-registered data from visible, infrared and microwave channels we evaluated the general radiative and physical characteristics of the ice cover as well as quantify the distribution of different ice types in the region. Ice concentration maps from AMSR-E using the standard sets of channels, and also only the 89 GHz channel for optimal resolution, are compared with aircraft and high resolution visible data and while the standard set provides consistent results, the 89 GHz provides the means to observe mesoscale patterns and some unique features of the ice cover. Analysis of MODIS data reveals that thick ice types represents about 37% of the ice cover indicating that young and new ice types represent a large fraction of the ice cover that averages about 90% ice concentration according to passive microwave data. These results are used to interpret historical data that indicate that the Okhotsk Sea ice extent and area are declining at a rapid rate of about -9% and -12 % per decade, respectively.

InSAR Maps of Deformation Covering Raft River, Idaho from 2007 to 2010

DOE Data Explorer

Reinisch, Elena C. (ORCID:0000000252211921)

2007-03-11

This dataset contains maps of deformation covering Raft River, Idaho from 2007 to 2010 calculated from interferometric synthetic aperture radar data. This dataset is used in the study entitled "Inferring geothermal reservoir processes at the Raft River Geothermal Field, Idaho, USA through modeling InSAR-measured surface deformation" by F. Liu, et al. This dataset was derived from raw SAR data from the Envisat satellite missions operated by the European Space Agency (ESA) that are copyrighted by ESA and were provided through the WInSAR consortium at the UNAVCO facility. All pair directories use the image acquired on 3/11/2007 as a reference image. To view specific information for each grd file, please use the GMT command "grdinfo" - e.g., for grd file In20070311_20071111/drho_utm.grd, use terminal command: grdinfo In20070311_20071111/drho_utm.grd

Vesta surface thermal properties map

USGS Publications Warehouse

Capria, Maria Teresa; Tosi, F.; De Santis, Maria Cristina; Capaccioni, F.; Ammannito, E.; Frigeri, A.; Zambon, F; Fonte, S.; Palomba, E.; Turrini, D.; Titus, T.N.; Schroder, S.E.; Toplis, M.J.; Liu, J.Y.; Combe, J.-P.; Raymond, C.A.; Russell, C.T.

2014-01-01

The first ever regional thermal properties map of Vesta has been derived from the temperatures retrieved by infrared data by the mission Dawn. The low average value of thermal inertia, 30 ± 10 J m−2 s−0.5 K−1, indicates a surface covered by a fine regolith. A range of thermal inertia values suggesting terrains with different physical properties has been determined. The lower thermal inertia of the regions north of the equator suggests that they are covered by an older, more processed surface. A few specific areas have higher than average thermal inertia values, indicative of a more compact material. The highest thermal inertia value has been determined on the Marcia crater, known for its pitted terrain and the presence of hydroxyl in the ejecta. Our results suggest that this type of terrain can be the result of soil compaction following the degassing of a local subsurface reservoir of volatiles.

A method to derive vegetation distribution maps for pollen dispersion models using birch as an example

NASA Astrophysics Data System (ADS)

Pauling, A.; Rotach, M. W.; Gehrig, R.; Clot, B.

2012-09-01

Detailed knowledge of the spatial distribution of sources is a crucial prerequisite for the application of pollen dispersion models such as, for example, COSMO-ART (COnsortium for Small-scale MOdeling - Aerosols and Reactive Trace gases). However, this input is not available for the allergy-relevant species such as hazel, alder, birch, grass or ragweed. Hence, plant distribution datasets need to be derived from suitable sources. We present an approach to produce such a dataset from existing sources using birch as an example. The basic idea is to construct a birch dataset using a region with good data coverage for calibration and then to extrapolate this relationship to a larger area by using land use classes. We use the Swiss forest inventory (1 km resolution) in combination with a 74-category land use dataset that covers the non-forested areas of Switzerland as well (resolution 100 m). Then we assign birch density categories of 0%, 0.1%, 0.5% and 2.5% to each of the 74 land use categories. The combination of this derived dataset with the birch distribution from the forest inventory yields a fairly accurate birch distribution encompassing entire Switzerland. The land use categories of the Global Land Cover 2000 (GLC2000; Global Land Cover 2000 database, 2003, European Commission, Joint Research Centre; resolution 1 km) are then calibrated with the Swiss dataset in order to derive a Europe-wide birch distribution dataset and aggregated onto the 7 km COSMO-ART grid. This procedure thus assumes that a certain GLC2000 land use category has the same birch density wherever it may occur in Europe. In order to reduce the strict application of this crucial assumption, the birch density distribution as obtained from the previous steps is weighted using the mean Seasonal Pollen Index (SPI; yearly sums of daily pollen concentrations). For future improvement, region-specific birch densities for the GLC2000 categories could be integrated into the mapping procedure.

Satellite Snow-Cover Mapping: A Brief Review

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.

1995-01-01

Satellite snow mapping has been accomplished since 1966, initially using data from the reflective part of the electromagnetic spectrum, and now also employing data from the microwave part of the spectrum. Visible and near-infrared sensors can provide excellent spatial resolution from space enabling detailed snow mapping. When digital elevation models are also used, snow mapping can provide realistic measurements of snow extent even in mountainous areas. Passive-microwave satellite data permit global snow cover to be mapped on a near-daily basis and estimates of snow depth to be made, but with relatively poor spatial resolution (approximately 25 km). Dense forest cover limits both techniques and optical remote sensing is limited further by cloudcover conditions. Satellite remote sensing of snow cover with imaging radars is still in the early stages of research, but shows promise at least for mapping wet or melting snow using C-band (5.3 GHz) synthetic aperture radar (SAR) data. Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning with the launch of the first EOS platform in 1998. Digital maps will be produced that will provide daily, and maximum weekly global snow, sea ice and lake ice cover at 1-km spatial resolution. Statistics will be generated on the extent and persistence of snow or ice cover in each pixel for each weekly map, cloudcover permitting. It will also be possible to generate snow- and ice-cover maps using MODIS data at 250- and 500-m resolution, and to study and map snow and ice characteristics such as albedo. been under development. Passive-microwave data offer the potential for determining not only snow cover, but snow water equivalent, depth and wetness under all sky conditions. A number of algorithms have been developed to utilize passive-microwave brightness temperatures to provide information on snow cover and water equivalent. The variability of vegetative Algorithms are being developed to map global snow and ice cover using Earth Algorithms to map global snow cover using passive-microwave data have also cover and of snow grain size, globally, limits the utility of a single algorithm to map global snow cover.

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

NASA Astrophysics Data System (ADS)

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.

Three-dimensional habitat structure and landscape genetics: a step forward in estimating functional connectivity.

PubMed

Milanesi, P; Holderegger, R; Bollmann, K; Gugerli, F; Zellweger, F

2017-02-01

Estimating connectivity among fragmented habitat patches is crucial for evaluating the functionality of ecological networks. However, current estimates of landscape resistance to animal movement and dispersal lack landscape-level data on local habitat structure. Here, we used a landscape genetics approach to show that high-fidelity habitat structure maps derived from Light Detection and Ranging (LiDAR) data critically improve functional connectivity estimates compared to conventional land cover data. We related pairwise genetic distances of 128 Capercaillie (Tetrao urogallus) genotypes to least-cost path distances at multiple scales derived from land cover data. Resulting β values of linear mixed effects models ranged from 0.372 to 0.495, while those derived from LiDAR ranged from 0.558 to 0.758. The identification and conservation of functional ecological networks suffering from habitat fragmentation and homogenization will thus benefit from the growing availability of detailed and contiguous data on three-dimensional habitat structure and associated habitat quality. © 2016 by the Ecological Society of America.

The first genetic map of pigeon pea based on diversity arrays technology (DArT) markers.

PubMed

Yang, Shi Ying; Saxena, Rachit K; Kulwal, Pawan L; Ash, Gavin J; Dubey, Anuja; Harper, John D I; Upadhyaya, Hari D; Gothalwal, Ragini; Kilian, Andrzej; Varshney, Rajeev K

2011-04-01

With an objective to develop a genetic map in pigeon pea (Cajanus spp.), a total of 554 diversity arrays technology (DArT) markers showed polymorphism in a pigeon pea F(2) mapping population of 72 progenies derived from an interspecific cross of ICP 28 (Cajanus cajan) and ICPW 94 (Cajanus scarabaeoides). Approximately 13% of markers did not conform to expected segregation ratio. The total number of DArT marker loci segregating in Mendelian manner was 405 with 73.1% (P > 0.001) of DArT markers having unique segregation patterns. Two groups of genetic maps were generated using DArT markers. While the maternal genetic linkage map had 122 unique DArT maternal marker loci, the paternal genetic linkage map has a total of 172 unique DArT paternal marker loci. The length of these two maps covered 270.0 cM and 451.6 cM, respectively. These are the first genetic linkage maps developed for pigeon pea, and this is the first report of genetic mapping in any grain legume using diversity arrays technology.

A Genetic Linkage Map of the Male Goat Genome

PubMed Central

Vaiman, D.; Schibler, L.; Bourgeois, F.; Oustry, A.; Amigues, Y.; Cribiu, E. P.

1996-01-01

This paper presents a first genetic linkage map of the goat genome. Primers derived from the flanking sequences of 612 bovine, ovine and goat microsatellite markers were gathered and tested for amplification with goat DNA under standardized PCR conditions. This screen made it possible to choose a set of 55 polymorphic markers that can be used in the three species and to define a panel of 223 microsatellites suitable for the goat. Twelve half-sib paternal goat families were then used to build a linkage map of the goat genome. The linkage analysis made it possible to construct a meiotic map covering 2300 cM, i.e., >80% of the total estimated length of the goat genome. Moreover, eight cosmids containing microsatellites were mapped by fluorescence in situ hybridization in goat and sheep. Together with 11 microsatellite-containing cosmids previously mapped in cattle (and supposing conservation of the banding pattern between this species and the goat) and data from the sheep map, these results made the orientation of 15 linkage groups possible. Furthermore, 12 coding sequences were mapped either genetically or physically, providing useful data for comparative mapping. PMID:8878693

Genetic linkage map of the interspecific grape rootstock cross Ramsey (Vitis champinii) x Riparia Gloire (Vitis riparia).

PubMed

Lowe, K M; Walker, M A

2006-05-01

The first genetic linkage map of grape derived from rootstock parents was constructed using 188 progeny from a cross of Ramsey (Vitis champinii) x Riparia Gloire (V. riparia). Of 354 simple sequence repeat markers tested, 205 were polymorphic for at least one parent, and 57.6% were fully informative. Maps of Ramsey, Riparia Gloire, and the F1 population were created using JoinMap software, following a pseudotestcross strategy. The set of 205 SSRs allowed for the identification of all 19 Vitis linkage groups (2n=38), with a total combined map length of 1,304.7 cM, averaging 6.8 cM between markers. The maternal map consists of 172 markers aligned into 19 linkage groups (1,244.9 cM) while 126 markers on the paternal map cover 18 linkage groups (1,095.5 cM). The expected genome coverage is over 92%. Segregation distortion occurred in the Ramsey, Riparia Gloire, and consensus maps for 10, 13, and 16% of the markers, respectively. These distorted markers clustered primarily on the linkage groups 3, 5, 14 and 17. No genome-wide difference in recombination rate was observed between Ramsey and Riparia Gloire based on 315 common marker intervals. Fifty-four new Vitis-EST-derived SSR markers were mapped, and were distributed evenly across the genome on 16 of the 19 linkage groups. These dense linkage maps of two phenotypically diverse North American Vitis species are valuable tools for studying the genetics of many rootstock traits including nematode resistance, lime and salt tolerance, and ability to induce vigor.

Using Landsat satellite data to support pesticide exposure assessment in California

USGS Publications Warehouse

Maxwell, Susan K.; Airola, Matthew; Nuckols, John R.

2010-01-01

We found the combination of Landsat 5 and 7 image data would clearly benefit pesticide exposure assessment in this region by 1) providing information on crop field conditions at or near the time when pesticides are applied, and 2) providing information for validating the CDWR map. The Landsat image time-series was useful for identifying idle, single-, and multi-cropped fields. Landsat data will be limited during the winter months due to cloud cover, and for years prior to the Landsat 7 launch (1999) when only one satellite was operational at any given time. We suggest additional research to determine the feasibility of integrating CDWR land use maps and Landsat data to derive crop maps in locations and time periods where maps are not available, which will allow for substantial improvements to chemical exposure estimation.

High-resolution forest mapping for behavioural studies in the Nature Reserve ‘Les Nouragues’, French Guiana

PubMed Central

Ringler, Max; Mangione, Rosanna; Pašukonis, Andrius; Rainer, Gerhard; Gyimesi, Kristin; Felling, Julia; Kronaus, Hannes; Réjou-Méchain, Maxime; Chave, Jérôme; Reiter, Karl; Ringler, Eva

2015-01-01

For animals with spatially complex behaviours at relatively small scales, the resolution of a global positioning system (GPS) receiver location is often below the resolution needed to correctly map animals’ spatial behaviour. Natural conditions such as canopy cover, canyons or clouds can further degrade GPS receiver reception. Here we present a detailed, high-resolution map of a 4.6 ha Neotropical river island and a 8.3 ha mainland plot with the location of every tree >5 cm DBH and all structures on the forest floor, which are relevant to our study species, the territorial frog Allobates femoralis (Dendrobatidae). The map was derived using distance- and compass-based survey techniques, rooted on dGPS reference points, and incorporates altitudinal information based on a LiDAR survey of the area. PMID:27053943

High-resolution mapping of Martian water ice clouds using Mars Express OMEGA observations - Derivation of the diurnal cloud life cycle

NASA Astrophysics Data System (ADS)

Szantai, Andre; Audouard, Joachim; Madeleine, Jean-Baptiste; Forget, Francois; Pottier, Alizée; Millour, Ehouarn; Gondet, Brigitte; Langevin, Yves; Bibring, Jean-Pierre

2016-10-01

The mapping in space and time of water ice clouds can help to explain the Martian water cycle and atmospheric circulation. For this purpose, an ice cloud index (ICI) corresponding to the depth of a water ice absorption band at 3.4 microns is derived from a series of OMEGA images (spectels) covering 5 Martian years. The ICI values for the corresponding pixels are then binned on a high-resolution regular grid (1° longitude x 1° latitude x 5° Ls x 1 h local time) and averaged. Inside each bin, the cloud cover is calculated by dividing the number of pixels considered as cloudy (after comparison to a threshold) to the number of all (valid) pixelsWe compare the maps of clouds obtained around local time 14:00 with collocated TES cloud observations (which were only obtained around this time of the day). A good agreement is found.Averaged ICI compared to the water ice column variable from the Martian Climate Database (MCD) show a correct correlation (~0.5) , which increases when values limited to the tropics only are compared.The number of gridpoints containing ICI values is small ( ~1%), but by taking several neighbor gridpoints and over longer periods, we can observe a cloud life cycle during daytime. An example in the the tropics, around the northern summer solstice, shows a decrease of cloudiness in the morning followed by an increase in the afternoon.

Land-Cover Trends of the Sierra Nevada Ecoregion, 1973-2000

USGS Publications Warehouse

Raumann, Christian G.; Soulard, Christopher E.

2007-01-01

The U.S. Geological Survey has developed and is implementing the Land Cover Trends project to estimate and describe the temporal and spatial distribution and variability of contemporary land-use and land-cover change in the United States. As part of the Land Cover Trends project, the purpose of this study was to assess land-use/land-cover change in the Sierra Nevada ecoregion for the period 1973 to 2000 using a probability sampling technique and satellite imagery. We randomly selected 36 100-km2 sample blocks to derive thematic images of land-use/land-cover for five dates of Landsat imagery (1973, 1980, 1986, 1992, 2000). We visually interpreted as many as 11 land-use/land-cover classes using a 60-meter minimum mapping unit from the five dates of imagery yielding four periods for analysis. Change-detection results from post-classification comparison of our mapped data showed that landscape disturbance from fire was the dominant change from 1973-2000. The second most-common change was forest disturbance resulting from harvest of timber resources by way of clear-cutting. The rates of forest regeneration from temporary fire and harvest disturbances coincided with the rates of disturbance from the previous period. Relatively minor landscape changes were caused by new development and reservoir drawdown. Multiple linear regression analysis suggests that land ownership and the proportion of forest and developed cover types were significant determinants of the likelihood of direct human-induced change occurring in sampling units. Driving forces of change include land ownership, land management such as fire suppression policy, and demand for natural resources.

Comparison of LiDAR-derived data and high resolution true color imagery for extracting urban forest cover

Treesearch

Aaron E. Maxwell; Adam C. Riley; Paul Kinder

2013-01-01

Remote sensing has many applications in forestry. Light detection and ranging (LiDAR) and high resolution aerial photography have been investigated as means to extract forest data, such as biomass, timber volume, stand dynamics, and gap characteristics. LiDAR return intensity data are often overlooked as a source of input raster data for thematic map creation. We...

Medical technology advances from space research

NASA Technical Reports Server (NTRS)

Pool, S. L.

1972-01-01

Details of medical research and development programs, particularly an integrated medical laboratory, as derived from space technology are given. The program covers digital biotelemetry systems, automatic visual field mapping equipment, sponge electrode caps for clinical electroencephalograms, and advanced respiratory analysis equipment. The possibility of using the medical laboratory in ground based remote areas and regional health care facilities, as well as long duration space missions is discussed.

Geospatial Modelling Approach for Interlinking of Rivers: A Case Study of Vamsadhara and Nagavali River Systems in Srikakulam, Andhra Pradesh

NASA Astrophysics Data System (ADS)

Swathi Lakshmi, A.; Saran, S.; Srivastav, S. K.; Krishna Murthy, Y. V. N.

2014-11-01

India is prone to several natural disasters such as floods, droughts, cyclones, landslides and earthquakes on account of its geoclimatic conditions. But the most frequent and prominent disasters are floods and droughts. So to reduce the impact of floods and droughts in India, interlinking of rivers is one of the best solutions to transfer the surplus flood waters to deficit/drought prone areas. Geospatial modelling provides a holistic approach to generate probable interlinking routes of rivers based on existing geoinformatics tools and technologies. In the present study, SRTM DEM and AWiFS datasets coupled with land-use/land -cover, geomorphology, soil and interpolated rainfall surface maps have been used to identify the potential routes in geospatial domain for interlinking of Vamsadhara and Nagavali River Systems in Srikakulam district, Andhra Pradesh. The first order derivatives are derived from DEM and road, railway and drainage networks have been delineated using the satellite data. The inundation map has been prepared using AWiFS derived Normalized Difference Water Index (NDWI). The Drought prone areas were delineated on the satellite image as per the records declared by Revenue Department, Srikakulam. Majority Rule Based (MRB) aggregation technique is performed to optimize the resolution of obtained data in order to retain the spatial variability of the classes. Analytical Hierarchy Process (AHP) based Multi-Criteria Decision Making (MCDM) is implemented to obtain the prioritization of parameters like geomorphology, soil, DEM, slope, and land use/land-cover. A likelihood grid has been generated and all the thematic layers are overlaid to identify the potential grids for routing optimization. To give a better routing map, impedance map has been generated and several other constraints are considered. The implementation of canal construction needs extra cost in some areas. The developed routing map is published into OGC WMS services using open source GeoServer and proposed routing service can be visualized over Bhuvan portal (http://www.bhuvan.nrsc.gov.in/).Thus the obtained routing map of proposed canals focuses on transferring the surplus waters to drought prone areas to solve the problem of water scarcity, to properly utilize the flood waters for irrigational purposes and also help in recharging of groundwater. Similar methodology can be adopted in other interlinking of river systems.

Vegetation and terrain mapping in Alaska using Landsat MSS and digital terrain data

USGS Publications Warehouse

Shasby, Mark; Carneggie, David M.

1986-01-01

During the past 5 years, the U.S. Geological Survey's (USGS) Earth Resources Observation Systems (EROS) Data Center Field Office in Anchorage, Alaska has worked cooperatively with Federal and State resource management agencies to produce land-cover and terrain maps for 245 million acres of Alaska. The need for current land-cover information in Alaska comes principally from the mandates of the Alaska National Interest Lands Conservation Act (ANILCA), December 1980, which requires major land management agencies to prepare comprehensive management plans. The land-cover mapping projects integrate digital Landsat data, terrain data, aerial photographs, and field data. The resultant land-cover and terrain maps and associated data bases are used for resource assessment, management, and planning by many Alaskan agencies including the U.S. Fish and Wildlife Service, U.S. Forest Service, Bureau of Land Management, and Alaska Department of Natural Resources. Applications addressed through use of the digital land-cover and terrain data bases range from comprehensive refuge planning to multiphased sampling procedures designed to inventory vegetation statewide. The land-cover mapping programs in Alaska demonstrate the operational utility of digital Landsat data and have resulted in a new land-cover mapping program by the USGS National Mapping Division to compile 1:250,000-scale land-cover maps in Alaska using a common statewide land-cover map legend.

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Hall, Dorothy K.; Eylander, John B.; Riggs, George A.; Nghiem, Son V.; Tedesco, Marco; Kim, Edward; Montesano, Paul M.; Kelly, Richard E. J.; Casey, Kimberly A.;

Mapping land cover through time with the Rapid Land Cover Mapper—Documentation and user manual

USGS Publications Warehouse

Cotillon, Suzanne E.; Mathis, Melissa L.

2017-02-15

The Rapid Land Cover Mapper is an Esri ArcGIS® Desktop add-in, which was created as an alternative to automated or semiautomated mapping methods. Based on a manual photo interpretation technique, the tool facilitates mapping over large areas and through time, and produces time-series raster maps and associated statistics that characterize the changing landscapes. The Rapid Land Cover Mapper add-in can be used with any imagery source to map various themes (for instance, land cover, soils, or forest) at any chosen mapping resolution. The user manual contains all essential information for the user to make full use of the Rapid Land Cover Mapper add-in. This manual includes a description of the add-in functions and capabilities, and step-by-step procedures for using the add-in. The Rapid Land Cover Mapper add-in was successfully used by the U.S. Geological Survey West Africa Land Use Dynamics team to accurately map land use and land cover in 17 West African countries through time (1975, 2000, and 2013).

Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information

NASA Astrophysics Data System (ADS)

Mishra, Varun Narayan; Prasad, Rajendra; Kumar, Pradeep; Srivastava, Prashant K.; Rai, Praveen Kumar

2017-10-01

Updated and accurate information of rice-growing areas is vital for food security and investigating the environmental impact of rice ecosystems. The intent of this work is to explore the feasibility of dual-polarimetric C-band Radar Imaging Satellite-1 (RISAT-1) data in delineating rice crop fields from other land cover features. A two polarization combination of RISAT-1 backscatter, namely ratio (HH/HV) and difference (HH-HV), significantly enhanced the backscatter difference between rice and nonrice categories. With these inputs, a QUEST decision tree (DT) classifier is successfully employed to extract the spatial distribution of rice crop areas. The results showed the optimal polarization combination to be HH along with HH/HV and HH-HV for rice crop mapping with an accuracy of 88.57%. Results were further compared with a Landsat-8 operational land imager (OLI) optical sensor-derived rice crop map. Spatial agreement of almost 90% was achieved between outputs produced from Landsat-8 OLI and RISAT-1 data. The simplicity of the approach used in this work may serve as an effective tool for rice crop mapping.

Genotyping-by-sequencing enables linkage mapping in three octoploid cultivated strawberry families

PubMed Central

Salinas, Natalia; Tennessen, Jacob A.; Zurn, Jason D.; Sargent, Daniel James; Hancock, James; Bassil, Nahla V.

2017-01-01

Genotyping-by-sequencing (GBS) was used to survey genome-wide single-nucleotide polymorphisms (SNPs) in three biparental strawberry (Fragaria × ananassa) populations with the goal of evaluating this technique in a species with a complex octoploid genome. GBS sequence data were aligned to the F. vesca ‘Fvb’ reference genome in order to call SNPs. Numbers of polymorphic SNPs per population ranged from 1,163 to 3,190. Linkage maps consisting of 30–65 linkage groups were produced from the SNP sets derived from each parent. The linkage groups covered 99% of the Fvb reference genome, with three to seven linkage groups from a given parent aligned to any particular chromosome. A phylogenetic analysis performed using the POLiMAPS pipeline revealed linkage groups that were most similar to ancestral species F. vesca for each chromosome. Linkage groups that were most similar to a second ancestral species, F. iinumae, were only resolved for Fvb 4. The quantity of missing data and heterogeneity in genome coverage inherent in GBS complicated the analysis, but POLiMAPS resolved F. × ananassa chromosomal regions derived from diploid ancestor F. vesca. PMID:28875078

Computer generated maps from digital satellite data - A case study in Florida

NASA Technical Reports Server (NTRS)

Arvanitis, L. G.; Reich, R. M.; Newburne, R.

1981-01-01

Ground cover maps are important tools to a wide array of users. Over the past three decades, much progress has been made in supplementing planimetric and topographic maps with ground cover details obtained from aerial photographs. The present investigation evaluates the feasibility of using computer maps of ground cover from satellite input tapes. Attention is given to the selection of test sites, a satellite data processing system, a multispectral image analyzer, general purpose computer-generated maps, the preliminary evaluation of computer maps, a test for areal correspondence, the preparation of overlays and acreage estimation of land cover types on the Landsat computer maps. There is every indication to suggest that digital multispectral image processing systems based on Landsat input data will play an increasingly important role in pattern recognition and mapping land cover in the years to come.

Preliminary integrated geologic map databases for the United States: Digital data for the geology of southeast Alaska

USGS Publications Warehouse

Gehrels, George E.; Berg, Henry C.

2006-01-01

The growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO export files and as ArcView shape files. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set of 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.

Digital Data for the reconnaissance geologic map for the Kuskokwim Bay Region of Southwest Alaska

USGS Publications Warehouse

Wilson, Frederic H.; Hults, Chad P.; Mohadjer, Solmaz; Coonrad, Warren L.; Shew, Nora B.; Labay, Keith A.

2008-01-01

INTRODUCTION The growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO export files and as ArcView shape files. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.

Contribution of Lake-Effect Snow to the Catskill Mountains Snowpack

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Digirolamo, Nicolo E.; Frei, Allan

2017-01-01

Meltwater from snow that falls in the Catskill Mountains in southern New York contributes to reservoirs that supply drinking water to approximately nine million people in New York City. Using the NOAA National Ice Centers Interactive Multisensor Snow and Ice Mapping System (IMS) 4km snow maps, we have identified at least 32 lake-effect (LE) storms emanating from Lake Erie andor Lake Ontario that deposited snow in the CatskillDelaware Watershed in the Catskill Mountains of southern New York State between 2004 and 2017. This represents a large underestimate of the contribution of LE snow to the Catskills snowpack because many of the LE snowstorms are not visible in the IMS snow maps when they travel over snow-covered terrain. Most of the LE snowstorms that we identified originate from Lake Ontario but quite a few originate from both Erie and Ontario, and a few from Lake Erie alone. Using satellite, meteorological and reanalysis data we identify conditions that contributed to LE snowfall in the Catskills. Clear skies following some of the storms permitted measurement of the extent of snow cover in the watershed using multiple satellite sensors. IMS maps tend to overestimate the extent of snow compared to MODerate resolution Imaging Spectroradiometer (MODIS) and Landsat-derived snow-cover extent maps. Using this combination of satellite and meteorological data, we can begin to quantify the important contribution of LE snow to the Catskills Mountain snowpack. Changes that are predicted in LE snowfall from the Great Lakes could impact the distribution of rain vs snow in the Catskills which may affect future reservoir operations in the NYC Water Supply System.

Watershed boundaries and digital elevation model of Oklahoma derived from 1:100,000-scale digital topographic maps

USGS Publications Warehouse

Cederstrand, J.R.; Rea, A.H.

1995-01-01

This document provides a general description of the procedures used to develop the data sets included on this compact disc. This compact disc contains watershed boundaries for Oklahoma, a digital elevation model, and other data sets derived from the digital elevation model. The digital elevation model was produced using the ANUDEM software package, written by Michael Hutchinson and licensed from the Centre for Resource and Environmental Studies at The Australian National University. Elevation data (hypsography) and streams (hydrography) from digital versions of the U.S. Geological Survey 1:100,000-scale topographic maps were used by the ANUDEM package to produce a hydrologically conditioned digital elevation model with a 60-meter cell size. This digital elevation model is well suited for drainage-basin delineation using automated techniques. Additional data sets include flow-direction, flow-accumulation, and shaded-relief grids, all derived from the digital elevation model, and the hydrography data set used in producing the digital elevation model. The watershed boundaries derived from the digital elevation model have been edited to be consistent with contours and streams from the U.S. Geological Survey 1:100,000-scale topographic maps. The watershed data set includes boundaries for 11-digit Hydrologic Unit Codes (watersheds) within Oklahoma, and 8-digit Hydrologic Unit Codes (cataloging units) outside Oklahoma. Cataloging-unit boundaries based on 1:250,000-scale maps outside Oklahoma for the Arkansas, Red, and White River basins are included. The other data sets cover Oklahoma, and where available, portions of 1:100,000-scale quadrangles adjoining Oklahoma.

From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Lehnert, Lukas W.; Wang, Yun; Reudenbach, Christoph; Nauss, Thomas; Bendix, Jörg

2017-03-01

Though the relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. Due to the enormous spatial extent, remote sensing provides the only possibility to investigate pasture degradation via frequently used proxies such as vegetation cover and aboveground biomass (AGB). However, unified remote sensing approaches are still lacking. This study tests the applicability of hyper- and multispectral in situ measurements to map vegetation cover and AGB on regional scales. Using machine learning techniques, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate pasture degradation proxies. To regionalize pasture degradation proxies, the transferability of the locally derived ML-models to high resolution multispectral satellite data is assessed. 1183 hyperspectral measurements and vegetation records were performed at 18 locations on the QTP. Random Forests models with recursive feature selection were trained to estimate vegetation cover and AGB using narrow-band indices (NBI) as predictors. Separate models were calculated using NBI from hyperspectral data as well as from the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used (cross validated R2 = 0.89). In contrast, errors in AGB estimations were considerably higher (cross validated R2 = 0.32). Only small differences in accuracy were observed between the models based on hyperspectral compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite data, the results still show a high potential to map pasture degradation proxies on the QTP. Thus, this study presents robust methodology to remotely detect and monitor pasture degradation at high spatial resolutions.

Evaluating the Synergistic Use of Low-Altitude AVIRIS and AIRSAR Data for Land Cover Mapping in Northeast Yellowstone National Park

NASA Technical Reports Server (NTRS)

Berglund, Judith; Spruce, Joseph

2001-01-01

Current land cover maps are needed by Yellowstone National Park (YNP) managers to assist them in protecting and preserving native flora and fauna. Synergistic use of hyperspectral and radar imagery offers great promise for mapping habitat in terms of cover type composition and structure. In response, a study was conducted to assess the utility of combining low-altitude AVIRIS and AIRSAR data for mapping land cover in a portion of northeast YNP. Land cover maps were produced from individual AVIRIS and AIRSAR data sets, as well as from a hybrid data stack of selected AVIRIS and AIRSAR data bands. The three resulting classifications were compared to field survey data and aerial photography to assess apparent benefits of hyperspectral/SAR data fusion for land cover mapping. Preliminary results will be presented.

On the merging of optical and SAR satellite imagery for surface water mapping applications

NASA Astrophysics Data System (ADS)

Markert, Kel N.; Chishtie, Farrukh; Anderson, Eric R.; Saah, David; Griffin, Robert E.

2018-06-01

Optical and Synthetic Aperture Radar (SAR) imagery from satellite platforms provide a means to discretely map surface water; however, the application of the two data sources in tandem has been inhibited by inconsistent data availability, the distinct physical properties that optical and SAR instruments sense, and dissimilar data delivery platforms. In this paper, we describe a preliminary methodology for merging optical and SAR data into a common data space. We apply our approach over a portion of the Mekong Basin, a region with highly variable surface water cover and persistent cloud cover, for surface water applications requiring dense time series analysis. The methods include the derivation of a representative index from both sensors that transforms data from disparate physical units (reflectance and backscatter) to a comparable dimensionless space applying a consistent water extraction approach to both datasets. The merging of optical and SAR data allows for increased observations in cloud prone regions that can be used to gain additional insight into surface water dynamics or flood mapping applications. This preliminary methodology shows promise for a common optical-SAR water extraction; however, data ranges and thresholding values can vary depending on data source, yielding classification errors in the resulting surface water maps. We discuss some potential future approaches to address these inconsistencies.

Rapid Crop Cover Mapping for the Conterminous United States.

PubMed

Dahal, Devendra; Wylie, Bruce; Howard, Danny

2018-06-05

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a 'two model mapping' approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one 'crop type model' to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of 'other' crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1 st of September.

Interacting Boson Model and nucleons

NASA Astrophysics Data System (ADS)

Otsuka, Takaharu

2012-10-01

An overview on the recent development of the microscopic derivation of the Interacting Boson Model is presented with some remarks not found elsewhere. The OAI mapping is reviewed very briefly, including the basic correspondence from nucleon-pair to boson. The new fermionboson mapping method is introduced, where intrinsic states of nucleons and bosons for a wide variation of shapes play an important role. Nucleon intrinsic states are obtained from mean field models, which is Skyrme model in examples to be shown. This method generates IBM-2 Hamiltonian which can describe and predict various situations of quadrupole collective states, including U(5), SU(3), O(6) and E(5) limits. The method is extended so that rotational response (cranking) can be handled, which enables us to describe rotational bands of strongly deformed nuclei. Thus, we have obtained a unified framework for the microscopic derivation of the IBM covering all known situations of quadrupole collectivity at low energy.

Remote Sensing of Vegetation Species Diversity: The Utility of Integrated Airborne Hyperspectral and Lidar Data

NASA Astrophysics Data System (ADS)

Krause, Keith Stuart

The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts are underway to measure, monitor, and protect habitats that contain high species diversity. Remote sensing technology shows extreme value for monitoring species diversity by mapping ecosystems and using those land cover maps or other derived data as proxies to species number and distribution. The National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) consists of remote sensing instruments such as an imaging spectrometer, a full-waveform lidar, and a high-resolution color camera. AOP collected data over the Ordway-Swisher Biological Station (OSBS) in May 2014. A majority of the OSBS site is covered by the Sandhill ecosystem, which contains a very high diversity of vegetation species and is a native habitat for several threatened fauna species. The research presented here investigates ways to analyze the AOP data to map ecosystems at the OSBS site. The research attempts to leverage the high spatial resolution data and study the variability of the data within a ground plot scale along with integrating data from the different sensors. Mathematical features are derived from the data and brought into a decision tree classification algorithm (rpart), in order to create an ecosystem map for the site. The hyperspectral and lidar features serve as proxies for chemical, functional, and structural differences in the vegetation types for each of the ecosystems. K-folds cross validation shows a training accuracy of 91%, a validation accuracy of 78%, and a 66% accuracy using independent ground validation. The results presented here represent an important contribution to utilizing integrated hyperspectral and lidar remote sensing data for ecosystem mapping, by relating the spatial variability of the data within a ground plot scale to a collection of vegetation types that make up a given ecosystem.

Genetic mapping of resistance to Fusarium oxysporum f. sp. tulipae in tulip.

PubMed

Tang, Nan; van der Lee, Theo; Shahin, Arwa; Holdinga, Maarten; Bijman, Paul; Caser, Matteo; Visser, Richard G F; van Tuyl, Jaap M; Arens, Paul

Fusarium oxysporum is a major problem in the production of tulip bulbs. Breeding for resistant cultivars through a conventional approach is a slow process due to the long life cycle of tulip. Until now, marker-assisted selection (MAS) has been hampered by the large genome size and the absence of a genetic map. This study is aimed at construction of the first genetic map for tulip and at the identification of loci associated with resistance to F. oxysporum . A cross-pollinated population of 125 individuals segregating for Fusarium resistance was obtained from Tulipa gesneriana "Kees Nelis" and T. fosteriana "Cantata." Fusarium resistance of the mapping population was evaluated through a soil infection test in two consecutive years, and a spot inoculation test in which a green fluorescent protein tagged Fusarium strain was used for inoculation. The genetic maps have been constructed for the parents separately. The genetic map of "Kees Nelis" comprised 342 markers on 27 linkage groups covering 1707 cM, while the map of "Cantata" comprised 300 markers on 21 linkage groups covering 1201 cM. Median distance between markers was 3.9 cM for "Kees Nelis" and 3.1 cM for "Cantata." Six putative quantitative trait loci (QTLs) for Fusarium resistance were identified, derived from both parents. QTL2, QTL3, and QTL6 were significant in all disease tests. For the flanking markers of the QTLs, phenotypic means of the two allelic groups, segregating from a parent for such a marker, were significantly different. These markers will be useful for the development of MAS in tulip breeding.

FIP bias in a sigmoidal active region

NASA Astrophysics Data System (ADS)

Baker, D.; Brooks, D. H.; Démoulin, P.; van Driel-Gesztelyi, Lidia; Green, L. M.; Steed, K.; Carlyle, J.

2014-01-01

We investigate first ionization potential (FIP) bias levels in an anemone active region (AR) - coronal hole (CH) complex using an abundance map derived from Hinode/EIS spectra. The detailed, spatially resolved abundance map has a large field of view covering 359'' × 485''. Plasma with high FIP bias, or coronal abundances, is concentrated at the footpoints of the AR loops whereas the surrounding CH has a low FIP bias, ~1, i.e. photospheric abundances. A channel of low FIP bias is located along the AR's main polarity inversion line containing a filament where ongoing flux cancellation is observed, indicating a bald patch magnetic topology characteristic of a sigmoid/flux rope configuration.

Central atlantic regional ecological test site: A prototype regional environmental information system, volume 1. [Pennsylvania, Delaware, Maryland, Virginia, and the District of Columbia

NASA Technical Reports Server (NTRS)

Alexander, R. H. (Principal Investigator)

1979-01-01

The author has identified the following significant results. LANDSAT data showed the test region in 1972 to be 9% urban and built-up land, 38% agriculture, 50% forest, 3% nonforested wetlands, and less than 1% barren land, exclusive of water-covered areas. A comprehensive user evaluation revealed greatest demand for high-altitude aerial photography and the detailed maps and data products that can be derived from the metropolitan areas agencies, found relatively little use for LANDSAT imagery at 1:250,000 scale and corresponding manually interpreted land use maps.

LANDSAT landcover information applied to regional planning decisions. [Prince Edward County, Virginia

NASA Technical Reports Server (NTRS)

Dixon, C. M.

1981-01-01

Land cover information derived from LANDSAT is being utilized by Piedmont Planning District Commission located in the State of Virginia. Progress to date is reported on a level one land cover classification map being produced with nine categories. The nine categories of classification are defined. The computer compatible tape selection is presented. Two unsupervised classifications were done, with 50 and 70 classes respectively. Twenty-eight spectral classes were developed using the supervised technique, employing actual ground truth training sites. The accuracy of the unsupervised classifications are estimated through comparison with local county statistics and with an actual pixel count of LANDSAT information compared to ground truth.

Physical and Radiative Characteristics and Long Term Variability of the Okhotsk Sea Ice Cover

NASA Technical Reports Server (NTRS)

Nishio, Fumihiko; Comiso, Josefino C.; Gersten, Robert; Nakayama, Masashige; Ukita, Jinro; Gasiewski, Al; Stanko, Boba; Naoki, Kazuhiro

2007-01-01

Much of what we know about the large scale characteristics of the Okhotsk Sea ice cover comes from ice concentration maps derived from passive microwave data. To understand what these satellite data represents in a highly divergent and rapidly changing environment like the Okhotsk Sea, we analyzed concurrent satellite, aircraft, and ship data and characterized the sea ice cover at different scales from meters to tens of kilometers. Through comparative analysis of surface features using co-registered data from visible, infrared and microwave channels we evaluated how the general radiative and physical characteristics of the ice cover changes as well as quantify the distribution of different ice types in the region. Ice concentration maps from AMSR-E using the standard sets of channels, and also only the 89 GHz channel for optimal resolution, are compared with aircraft and high resolution visible data and while the standard set provides consistent results, the 89 GHz provides the means to observe mesoscale patterns and some unique features of the ice cover. Analysis of MODIS data reveals that thick ice types represents about 37% of the ice cover indicating that young and new ice represent a large fraction of the lice cover that averages about 90% ice concentration, according to passive microwave data. A rapid decline of -9% and -12 % per decade is observed suggesting warming signals but further studies are required because of aforementioned characteristics and because the length of the ice season is decreasing by only 2 to 4 days per decade.

MODIS Snow-Cover Products

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; DiGirolamo, Nicole E.; Bayr, Klaus J.; Houser, Paul R. (Technical Monitor)

2002-01-01

On December 18, 1999, the Terra satellite was launched with a complement of five instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS). Many geophysical products are derived from MODIS data including global snow-cover products. MODIS snow and ice products have been available through the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) since September 13, 2000. MODIS snow-cover products represent potential improvement to or enhancement of the currently-available operational products mainly because the MODIS products are global and 500-m resolution, and have the capability to separate most snow and clouds. Also the snow-mapping algorithms are automated which means that a consistent data set may be generated for long-term climate studies that require snow-cover information. Extensive quality assurance (QA) information is stored with the products. The MODIS snow product suite begins with a 500-m resolution, 2330-km swath snow-cover map which is then gridded to an integerized sinusoidal grid to produce daily and 8-day composite tile products. The sequence proceeds to a climate-modeling grid (CMG) product at about 5.6-km spatial resolution, with both daily and 8-day composite products. Each pixel of the CMG contains fraction of snow cover from 40 - 100%. Measured errors of commission in the CMG are low, for example, on the continent of Australia in the spring, they vary from 0.02 - 0.10%. Near-term enhancements include daily snow albedo and fractional snow cover. A case study from March 6, 2000, involving MODIS data and field and aircraft measurements, is presented to show some early validation work.

Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015

USGS Publications Warehouse

Boyte, Stephen; Wylie, Bruce K.

2016-01-01

Cheatgrass (Bromus tectorum L.) dramatically changes shrub steppe ecosystems in the Northern Great Basin, United States.Current-season cheatgrass location and percent cover are difficult to estimate rapidly.We explain the development of a near-real-time cheatgrass percent cover dataset and map in the Northern Great Basin for the current year (2015), display the current year’s map, provide analysis of the map, and provide a website link to download the map (as a PDF) and the associated dataset.The near-real-time cheatgrass percent cover dataset and map were consistent with non-expedited, historical cheatgrass percent cover datasets and maps.Having cheatgrass maps available mid-summer can help land managers, policy makers, and Geographic Information Systems personnel as they work to protect socially relevant areas such as critical wildlife habitats.

GlobCorine- A Joint EEA-ESA Project for Operational Land Cover and Land Use Mapping at Pan-European Scale

NASA Astrophysics Data System (ADS)

Bontemps, S.; Defourny, P.; Van Bogaert, E.; Weber, J. L.; Arino, O.

2010-12-01

Regular and global land cover mapping contributes to evaluating the impact of human activities on the environment. Jointly supported by the European Space Agency and the European Environmental Agency, the GlobCorine project builds on the GlobCover findings and aims at making the full use of the MERIS time series for frequent land cover monitoring. The GlobCover automated classification approach has been tuned to the pan-European continent and adjusted towards a classification compatible with the Corine typology. The GlobCorine 2005 land cover map has been achieved, validated and made available to a broad- level stakeholder community from the ESA website. A first version of the GlobCorine 2009 map has also been produced, demonstrating the possibility for an operational production of frequent and updated global land cover maps.

LANDSAT-4 Science Characterization Early Results. Volume 4: Applications. [agriculture, soils land use, geology, hydrology, wetlands, water quality, biomass identification, and snow mapping

NASA Technical Reports Server (NTRS)

Barker, J. L. (Editor)

1985-01-01

The excellent quality of TM data allows researchers to proceed directly with applications analyses, without spending a significant amount of time applying various corrections to the data. The early results derived of TM data are discussed for the following applications: agriculture, land cover/land use, soils, geology, hydrology, wetlands biomass, water quality, and snow.

A preliminary assessment of Montreal process indicators of forest fragmentation for the United States

Treesearch

Kurt H. Riitters; James D. Wickham; John W. Coulston

2004-01-01

Abstract. As part of the U.S. 2003 National Report on Sustainable Forests, four metrics of forest fragmentation – patch size, edge amount, inter-patch distance, and patch contrast – were measured within 137 744 non-overlapping 5625 ha analysis units on land-cover maps derived from satellite imagery for the 48 conterminous States. The perimeter of a...

Soil erosion modelling for NSW coastal catchments using RUSLE in a GIS environment

NASA Astrophysics Data System (ADS)

Yang, Xihua; Chapman, Greg

2006-10-01

In this study, hillslope erosion risk has been estimated for all eastern New South Wales (NSW) catchments, Australia using Revised Universal Soil Loss Equation (RUSLE) in a geographic information system (GIS) environment. Rainfall-runoff erosivity (R) factor was interpolated from NSW rainfall-erosivity contour (isoerodent) data. Soil erodibility (K) factor was based on the soil regolith stability and sediment yield classification. The classification was derived from soil landscape and related soil map data. The slope length and steepness (LS) factor was derived from high resolution digital elevation model (DEM). A fully-automated program using Arc Macro Language (AML) produced RUSLE-based LS factor grids for all coastal catchments. The outputs are comparable to the range of LS values summarised in the literature. Cover and management (C) factor and conservation support-practices (P) factor were set to one. They are intended to be allocated according to land use, ground cover and erosion control provisions for particular land management actions. The resulting erosion risk map, with pixel size of 25-m, provides unprecedented resolution of relative expected sheet and rill erosion across all NSW costal catchments and can be adapted for a range of erosion control purposes such as bushfire hazard reduction and comprehensive costal assessment.

Mapping Wetlands of Dongting Lake in China Using Landsat and SENTINEL-1 Time Series at 30M

NASA Astrophysics Data System (ADS)

Xing, L.; Tang, X.; Wang, H.; Fan, W.; Gao, X.

2018-04-01

Mapping and monitoring wetlands of Dongting lake using optical sensor data has been limited by cloud cover, and open access Sentinal-1 C-band data could provide cloud-free SAR images with both have high spatial and temporal resolution, which offer new opportunities for monitoring wetlands. In this study, we combined optical data and SAR data to map wetland of Dongting Lake reserves in 2016. Firstly, we generated two monthly composited Landsat land surface reflectance, NDVI, NDWI, TC-Wetness time series and Sentinel-1 (backscattering coefficient for VH and VV) time series. Secondly, we derived surface water body with two monthly frequencies based on the threshold method using the Sentinel-1 time series. Then the permanent water and seasonal water were separated by the submergence ratio. Other land cover types were identified based on SVM classifier using Landsat time series. Results showed that (1) the overall accuracies and kappa coefficients were above 86.6 % and 0.8. (3) Natural wetlands including permanent water body (14.8 %), seasonal water body (34.6 %), and permanent marshes (10.9 %) were the main land cover types, accounting for 60.3 % of the three wetland reserves. Human-made wetlands, such as rice fields, accounted 34.3 % of the total area. Generally, this study proposed a new flowchart for wetlands mapping in Dongting lake by combining multi-source remote sensing data, and the use of the two-monthly composited optical time series effectively made up the missing data due to the clouds and increased the possibility of precise wetlands classification.

Mapping erodibility in dust source regions based on geomorphology, meteorology, and remote sensing

NASA Astrophysics Data System (ADS)

Parajuli, Sagar Prasad; Yang, Zong-Liang; Kocurek, Gary

2014-09-01

Mineral dust in the atmosphere has implications for Earth's radiation budget, biogeochemical cycles, hydrological cycles, human health, and visibility. Currently, the simulated vertical mass flux of dust differs greatly among the existing dust models. While most of the models utilize an erodibility factor to characterize dust sources, this factor is assumed to be static, without sufficient characterization of the highly heterogeneous and dynamic nature of dust source regions. We present a high-resolution land cover map of the Middle East and North Africa (MENA) in which the terrain is classified by visually examining satellite images obtained from Google Earth Professional and Environmental Systems Research Institute Basemap. We show that the correlation between surface wind speed and Moderate Resolution Imaging Spectroradiometer deep blue aerosol optical depth (AOD) can be used as a proxy for erodibility, which satisfactorily represents the spatiotemporal distribution of soil-derived dust sources. This method also identifies agricultural dust sources and eliminates the satellite-observed dust component that arises from long-range transport, pollution, and biomass burning. The erodible land cover of the MENA region is grouped into nine categories: (1) bedrock: with sediment, (2) sand deposit, (3) sand deposit: on bedrock, (4) sand deposit: stabilized, (5) agricultural and urban area, (6) fluvial system, (7) stony surface, (8) playa/sabkha, and (9) savanna/grassland. Our results indicate that erodibility is linked to the land cover type and has regional variation. An improved land cover map, which explicitly accounts for sediment supply, availability, and transport capacity, may be necessary to represent the highly dynamic nature of dust sources in climate models.

Assessment of runoff response to landscape changes in the San Pedro subbasin (Nayarit, Mexico) using remote sensing data and GIS.

PubMed

Hernández-Guzmán, Rafael; Ruiz-Luna, Arturo; Berlanga-Robles, César Alejandro

2008-10-01

Results on runoff estimates as a response to land-use and land-cover changes are presented. We used remote sensing and GIS techniques with rainfall time-series data, spatial ancillary information, and the curve-number method (NRCS-CN) to assess the runoff response in the San Pedro subbasin. Thematic maps with eight land-cover classes derived from satellite imagery classification (1973, 1990, and 2000) and hydrologic soil-group maps were used as the input for the runoff calculation. About 20% to 25% of the subbasin landscape has changed since 1973, mainly as consequence of the growth of agriculture. Forest is the main cover, although further analyses indicate that forest is degrading from good to poor conditions when evaluated as a function of the spectral response. Soils with low infiltration rates, classified as the hydrological soil-group "C", were dominant in the area (52%). The overlaying of all the hydrological soil groups with the land-use map produced a total of 43 hydro-group and land-use categories for which runoff was calculated using the curve-number method. Estimates of total runoff volumes (26 x 10(6) m3) were similar for the three dates analyzed in spite of landscape changes, but there were temporal variations among the hydro-group and land-use categories as a consequence. Changes are causing the rise of covers with high runoff potential and the increase of runoff depth is expected, but it can be reversed by different management of subbasin hydro-groups and land-use units.

Fusion of pan-tropical biomass maps using weighted averaging and regional calibration data

NASA Astrophysics Data System (ADS)

Ge, Yong; Avitabile, Valerio; Heuvelink, Gerard B. M.; Wang, Jianghao; Herold, Martin

2014-09-01

Biomass is a key environmental variable that influences many biosphere-atmosphere interactions. Recently, a number of biomass maps at national, regional and global scales have been produced using different approaches with a variety of input data, such as from field observations, remotely sensed imagery and other spatial datasets. However, the accuracy of these maps varies regionally and is largely unknown. This research proposes a fusion method to increase the accuracy of regional biomass estimates by using higher-quality calibration data. In this fusion method, the biases in the source maps were first adjusted to correct for over- and underestimation by comparison with the calibration data. Next, the biomass maps were combined linearly using weights derived from the variance-covariance matrix associated with the accuracies of the source maps. Because each map may have different biases and accuracies for different land use types, the biases and fusion weights were computed for each of the main land cover types separately. The conceptual arguments are substantiated by a case study conducted in East Africa. Evaluation analysis shows that fusing multiple source biomass maps may produce a more accurate map than when only one biomass map or unweighted averaging is used.

KINETIC TOMOGRAPHY. I. A METHOD FOR MAPPING THE MILKY WAY’S INTERSTELLAR MEDIUM IN FOUR DIMENSIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tchernyshyov, Kirill; Peek, J. E. G.

2017-01-01

We have developed a method for deriving the distribution of the Milky Way’s interstellar medium as a function of longitude, latitude, distance, and line-of-sight velocity. This method takes as input maps of reddening as a function of longitude, latitude, distance, and maps of line emission as a function of longitude, latitude, and line-of-sight velocity. We have applied this method to data sets covering much of the Galactic plane. The output of this method correctly reproduces the line-of-sight velocities of high-mass star-forming regions with known distances from Reid et al. and qualitatively agrees with results from the Milky Way kinematics literature.more » These maps will be useful for measuring flows of gas around the Milky Way’s spiral arms and into and out of giant molecular clouds.« less

Land Cover Indicators for U.S. National Climate Assessments

NASA Astrophysics Data System (ADS)

Channan, S.; Thomson, A. M.; Collins, K. M.; Sexton, J. O.; Torrens, P.; Emanuel, W. R.

2014-12-01

Land is a critical resource for human habitat and for the vast majority of human activities. Many natural resources are derived from terrestrial ecosystems or otherwise extracted from the landscape. Terrestrial biodiversity depends on land attributes as do people's perceptions of the value of land, including its value for recreation or tourism. Furthermore, land surface properties and processes affect weather and climate, and land cover change and land management affect emissions of greenhouse gases. Thus, land cover with its close association with climate is so pervasive that a land cover indicator is of fundamental importance to U.S. national climate assessments and related research. Moderate resolution remote sensing products (MODIS) were used to provide systematic data on annual distributions of land cover over the period 2001-2012. Selected Landsat observations and data products further characterize land cover at higher resolution. Here we will present the prototype for a suite of land cover indicators including land cover maps as well as charts depicting attributes such as composition by land cover class, statistical indicators of landscape characteristics, and tabular data summaries indispensable for communicating the status and trends of U.S. land cover at national, regional and state levels.

Comparison of different landform classification methods for digital landform and soil mapping of the Iranian loess plateau

NASA Astrophysics Data System (ADS)

Hoffmeister, Dirk; Kramm, Tanja; Curdt, Constanze; Maleki, Sedigheh; Khormali, Farhad; Kehl, Martin

2016-04-01

The Iranian loess plateau is covered by loess deposits, up to 70 m thick. Tectonic uplift triggered deep erosion and valley incision into the loess and underlying marine deposits. Soil development strongly relates to the aspect of these incised slopes, because on northern slopes vegetation protects the soil surface against erosion and facilitates formation and preservation of a Cambisol, whereas on south-facing slopes soils were probably eroded and weakly developed Entisols formed. While the whole area is intensively stocked with sheep and goat, rain-fed cropping of winter wheat is practiced on the valley floors. Most time of the year, the soil surface is unprotected against rainfall, which is one of the factors promoting soil erosion and serious flooding. However, little information is available on soil distribution, plant cover and the geomorphological evolution of the plateau, as well as on potentials and problems in land use. Thus, digital landform and soil mapping is needed. As a requirement of digital landform and soil mapping, four different landform classification methods were compared and evaluated. These geomorphometric classifications were run on two different scales. On the whole area an ASTER GDEM and SRTM dataset (30 m pixel resolution) was used. Likewise, two high-resolution digital elevation models were derived from Pléiades satellite stereo-imagery (< 1m pixel resolution, 10 by 10 km). The high-resolution information of this dataset was aggregated to datasets of 5 and 10 m scale. The applied classification methods are the Geomorphons approach, an object-based image approach, the topographical position index and a mainly slope based approach. The accuracy of the classification was checked with a location related image dataset obtained in a field survey (n ~ 150) in September 2015. The accuracy of the DEMs was compared to measured DGPS trenches and map-based elevation data. The overall derived accuracy of the landform classification based on the high-resolution DEM with a resolution of 5 m is approximately 70% and on a 10 m resolution >58%. For the 30 m resolution datasets is the achieved accuracy approximately 40%, as several small scale features are not recognizable in this resolution. Thus, for an accurate differentiation between different important landform types, high-resolution datasets are necessary for this strongly shaped area. One major problem of this approach are the different classes derived by each method and the various class annotations. The result of this evaluation will be regarded for the derivation of landform and soil maps.

Topographic Map of Quadrangle 3768 and 3668, Imam-Saheb (215), Rustaq (216), Baghlan (221), and Taloqan (222) Quadrangles, Afghanistan

USGS Publications Warehouse

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 report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3368 and Part of Quadrangle 3370, Ghazni (515), Gardez (516), and Jaji-Maydan (517) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3666 and 3766, Balkh (219), Mazar-I-Sharif (220), Qarqin (213), and Hazara Toghai (214) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3560 and 3562, Sir-Band (402), Khawja-Jir (403), and Bala-Murghab (404) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3770 and 3870, Maymayk (211), Jamarj-I-Bala (212), Faydz-Abad (217), and Parkhaw (218) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3764 and 3664, Jalajin (117), Kham-Ab (118), Char Shangho (123), and Sheberghan (124) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3260 and 3160, Dasht-E-Chahe-Mazar (419), Anardara (420), Asparan (601), and Kang (602) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangle 3470 and the Northern Edge of 3370, Jalal-Abad (511), Chaghasaray (512), and Northernmost Jaji-Maydan (517) Quadrangles, Afg

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3772, 3774, 3672, and 3674, Gaz-Khan (313), Sarhad (314), Kol-I-Chaqmaqtin (315), Khandud (319), Deh-Ghulaman (320), and Erftah (321) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3062 and 2962, Charburjak (609), Khanneshin (610), Gawdezereh (615), and Galachah (616) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3168 and 3268, Yahya-Wona (703), Wersek (704), Khayr-Kot (521), and Urgon (522) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 2964, 2966, 3064, and 3066, Shah-Esmail (617), Reg-Alaqadari (618), Samandkhan-Karez (713), Laki-Bander (611), Jahangir-Naweran (612), and Sreh-Chena (707) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3060 and 2960, Qala-I-Fath (608), Malek-Sayh-Koh (613), and Gozar-E-Sah (614) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

Topographic Map of Quadrangles 3460 and 3360, Kol-I-Namaksar (407), Ghuryan (408), Kawir-I-Naizar (413), and Kohe-Mahmudo-Esmailjan (414) Quadrangles, Afghanistan

USGS Publications Warehouse

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 in cooperation with the AGS and AGCHO.

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

USGS Publications Warehouse

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 improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).

An assessment of forest cover trends in South and North Korea, from 1980 to 2010.

PubMed

Engler, Robin; Teplyakov, Victor; Adams, Jonathan M

2014-01-01

It is generally believed that forest cover in North Korea has undergone a substantial decrease since 1980, while in South Korea, forest cover has remained relatively static during that same period of time. The United Nations Food and Agriculture Organization (FAO) Forest Resources Assessments--based on the reported forest inventories from North and South Korea--suggest a major forest cover decrease in North Korea, but only a slight decrease in South Korea during the last 30 years. In this study, we seek to check and validate those assessments by comparing them to independently derived forest cover maps compiled for three time intervals between 1990 and 2010, as well as to provide a spatially explicit view of forest cover change in the Korean Peninsula since the 1990s. We extracted tree cover data for the Korean Peninsula from existing global datasets derived from satellite imagery. Our estimates, while qualitatively supporting the FAO results, show that North Korea has lost a large number of densely forested areas, and thus in this sense has suffered heavier forest loss than the FAO assessment suggests. Given the limited time interval studied in our assessment, the overall forest loss from North Korea during the whole span of time since 1980 may have been even heavier than in our estimate. For South Korea, our results indicate that the forest cover has remained relatively stable at the national level, but that important variability in forest cover evolution exists at the regional level: While the northern and western provinces show an overall decrease in forested areas, large areas in the southeastern part of the country have increased their forest cover.

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

NASA Astrophysics Data System (ADS)

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.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

Global Forest Canopy Height Maps Validation and Calibration for The Potential of Forest Biomass Estimation in The Southern United States

NASA Astrophysics Data System (ADS)

Ku, N. W.; Popescu, S. C.

2015-12-01

In the past few years, three global forest canopy height maps have been released. Lefsky (2010) first utilized the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate a global forest canopy height map in 2010. Simard et al. (2011) integrated GLAS data and other ancillary variables, such as MODIS, Shuttle Radar Topography Mission (STRM), and climatic data, to generate another global forest canopy height map in 2011. Los et al. (2012) also used GLAS data to create a vegetation height map in 2012.Several studies attempted to compare these global height maps to other sources of data., Bolton et al. (2013) concluded that Simard's forest canopy height map has strong agreement with airborne lidar derived heights. Los map is a coarse spatial resolution vegetation height map with a 0.5 decimal degrees horizontal resolution, around 50 km in the US, which is not feasible for the purpose of our research. Thus, Simard's global forest canopy height map is the primary map for this research study. The main objectives of this research were to validate and calibrate Simard's map with airborne lidar data and other ancillary variables in the southern United States. The airborne lidar data was collected between 2010 and 2012 from: (1) NASA LiDAR, Hyperspectral & Thermal Image (G-LiHT) program; (2) National Ecological Observatory Network's (NEON) prototype data sharing program; (3) NSF Open Topography Facility; and (4) the Department of Ecosystem Science and Management at Texas A&M University. The airborne lidar study areas also cover a wide variety of vegetation types across the southern US. The airborne lidar data is post-processed to generate lidar-derived metrics and assigned to four different classes of point cloud data. The four classes of point cloud data are the data with ground points, above 1 m, above 3 m, and above 5 m. The root mean square error (RMSE) and coefficient of determination (R2) are used for examining the discrepancies of the canopy heights between the airborne lidar-derived metrics and global forest canopy height map, and the regression and random forest approaches are used to calibrate the global forest canopy height map. In summary, the research shows a calibrated forest canopy height map of the southern US.

Design and analysis for thematic map accuracy assessment: Fundamental principles

Treesearch

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...

Estimating vegetation vulnerability to detect areas prone to land degradation in the Mediterranean basin

NASA Astrophysics Data System (ADS)

Imbrenda, Vito; Coluzzi, Rosa; D'Emilio, Mariagrazia; Lanfredi, Maria; Simoniello, Tiziana

2013-04-01

Vegetation is one of the key components to study land degradation vulnerability because of the complex interactions and feedbacks that link it to soil. In the Mediterranean region, degradation phenomena are due to a mix of predisposing factors (thin soil horizons, low soil organic matter, increasing aridity, etc.) and bad management practices (overgrazing, deforestation, intensification of agriculture, tourism development). In particular, in areas threatened by degradation processes but still covered by vegetation, large scale soil condition evaluation is a hard task and the detection of stressed vegetation can be useful to identify on-going soil degradation phenomena and to reduce their impacts through interventions for recovery/rehabilitation. In this context the use of satellite time series can increase the efficacy and completeness of the land degradation assessment, providing precious information to understand vegetation dynamics. In order to estimate vulnerability levels in Basilicata (a Mediterranean region of Southern Italy) in the framework of PRO-LAND project (PO-FESR Basilicata 2007-2013), we crossed information on potential vegetation vulnerability with information on photosynthetic activity dynamics. Potential vegetation vulnerability represents the vulnerability related to the type of present cover in terms of fire risk, erosion protection, drought resistance and plant cover distribution. It was derived from an updated land cover map by separately analyzing each factor, and then by combining them to obtain concise information on the possible degradation exposure. The analysis of photosynthetic activity dynamics provides information on the status of vegetation, that is fundamental to discriminate the different vulnerability levels within the same land cover, i.e. the same potential vulnerability. For such a purpose, we analyzed a time series (2000-2010) of a satellite vegetation index (MODIS NDVI) with 250m resolution, available as 16-day composite from the NASA LP DAAC dataset. Vegetation activity trends were estimated and then normalized to the starting conditions to obtain the percentage variation (NDVI-PV) for the considered period. Information on the potential vulnerability and vegetation activity dynamics were classified into indexes and combined to obtain the final map of the actual vegetation vulnerability and to identify on-going degradation phenomena and priority sites within areas already compromised. As for the investigated area, this map shows a composite picture in which only a few values of high vulnerability are scattered along areas where medium-high vulnerability values generally prevail. Here, we singled out two kind of areas: one largely devoted to intensive agriculture, and other one mostly characterized by bare soils and sparse vegetation. On the contrary, a large part of natural and seminatural vegetation located along the Apennine chain does not show critical vulnerability values. By comparing the vegetation vulnerability map with the vulnerability map due to anthropic factors (pressure induced by agricultural and grazing activities, estimated by indicators derived from census data), we found correlation, confirming the anthropogenic cause of vulnerability and therefore the major role held by soil management in areas mainly devoted to intensive farming.

Land cover mapping for development planning in Eastern and Southern Africa

NASA Astrophysics Data System (ADS)

Oduor, P.; Flores Cordova, A. I.; Wakhayanga, J. A.; Kiema, J.; Farah, H.; Mugo, R. M.; Wahome, A.; Limaye, A. S.; Irwin, D.

2016-12-01

Africa continues to experience intensification of land use, driven by competition for resources and a growing population. Land cover maps are some of the fundamental datasets required by numerous stakeholders to inform a number of development decisions. For instance, they can be integrated with other datasets to create value added products such as vulnerability impact assessment maps, and natural capital accounting products. In addition, land cover maps are used as inputs into Greenhouse Gas (GHG) inventories to inform the Agriculture, Forestry and other Land Use (AFOLU) sector. However, the processes and methodologies of creating land cover maps consistent with international and national land cover classification schemes can be challenging, especially in developing countries where skills, hardware and software resources can be limiting. To meet this need, SERVIR Eastern and Southern Africa developed methodologies and stakeholder engagement processes that led to a successful initiative in which land cover maps for 9 countries (Malawi, Rwanda, Namibia, Botswana, Lesotho, Ethiopia, Uganda, Zambia and Tanzania) were developed, using 2 major classification schemes. The first sets of maps were developed based on an internationally acceptable classification system, while the second sets of maps were based on a nationally defined classification system. The mapping process benefited from reviews from national experts and also from technical advisory groups. The maps have found diverse uses, among them the definition of the Forest Reference Levels in Zambia. In Ethiopia, the maps have been endorsed by the national mapping agency as part of national data. The data for Rwanda is being used to inform the Natural Capital Accounting process, through the WAVES program, a World Bank Initiative. This work illustrates the methodologies and stakeholder engagement processes that brought success to this land cover mapping initiative.

Land cover mapping of North and Central America—Global Land Cover 2000

USGS Publications Warehouse

Latifovic, Rasim; Zhu, Zhi-Liang

2004-01-01

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

High resolution linkage maps of the model organism Petunia reveal substantial synteny decay with the related genome of tomato.

PubMed

Bossolini, Eligio; Klahre, Ulrich; Brandenburg, Anna; Reinhardt, Didier; Kuhlemeier, Cris

2011-04-01

Two linkage maps were constructed for the model plant Petunia. Mapping populations were obtained by crossing the wild species Petunia axillaris subsp. axillaris with Petunia inflata, and Petunia axillaris subsp. parodii with Petunia exserta. Both maps cover the seven chromosomes of Petunia, and span 970 centimorgans (cM) and 700 cM of the genomes, respectively. In total, 207 markers were mapped. Of these, 28 are multilocus amplified fragment length polymorphism (AFLP) markers and 179 are gene-derived markers. For the first time we report on the development and mapping of 83 Petunia microsatellites. The two maps retain the same marker order, but display significant differences of recombination frequencies at orthologous mapping intervals. A complex pattern of genomic rearrangements was detected with the related genome of tomato (Solanum lycopersicum), indicating that synteny between Petunia and other Solanaceae crops has been considerably disrupted. The newly developed markers will facilitate the genetic characterization of mutants and ecological studies on genetic diversity and speciation within the genus Petunia. The maps will provide a powerful tool to link genetic and genomic information and will be useful to support sequence assembly of the Petunia genome.

Assessing hydrologic impacts of future Land Change scenarios in the San Pedro River (U.S./Mexico)

NASA Astrophysics Data System (ADS)

Kepner, W. G.; Burns, S.; Sidman, G.; Levick, L.; Goodrich, D. C.; Guertin, P.; Yee, W.; Scianni, M.

2012-12-01

An approach was developed to characterize the hydrologic impacts of urban expansion through time for the San Pedro River, a watershed of immense international importance that straddles the U.S./Mexico border. Future urban growth is a key driving force altering local and regional hydrology and is represented by decadal changes in housing density maps from 2010 to 2100 derived from the Integrated Climate and Land-Use Scenarios (ICLUS) database. ICLUS developed future housing density maps by adapting the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines to the conterminous United States. To characterize the hydrologic impacts of future growth, the housing density maps were reclassified to National Land Cover Database 2006 land cover classes and used to parameterize the Soil and Water Assessment Tool (SWAT) using the Automated Geospatial Watershed Assessment (AGWA) tool. The presentation will report 1) the methodology for adapting the ICLUS data for use in AGWA as an approach to evaluate basin-wide impacts of development on water-quantity and -quality, 2) initial results of the application of the methodology, and 3) discuss implications of the analysis.

Error, Power, and Blind Sentinels: The Statistics of Seagrass Monitoring

PubMed Central

Schultz, Stewart T.; Kruschel, Claudia; Bakran-Petricioli, Tatjana; Petricioli, Donat

2015-01-01

We derive statistical properties of standard methods for monitoring of habitat cover worldwide, and criticize them in the context of mandated seagrass monitoring programs, as exemplified by Posidonia oceanica in the Mediterranean Sea. We report the novel result that cartographic methods with non-trivial classification errors are generally incapable of reliably detecting habitat cover losses less than about 30 to 50%, and the field labor required to increase their precision can be orders of magnitude higher than that required to estimate habitat loss directly in a field campaign. We derive a universal utility threshold of classification error in habitat maps that represents the minimum habitat map accuracy above which direct methods are superior. Widespread government reliance on blind-sentinel methods for monitoring seafloor can obscure the gradual and currently ongoing losses of benthic resources until the time has long passed for meaningful management intervention. We find two classes of methods with very high statistical power for detecting small habitat cover losses: 1) fixed-plot direct methods, which are over 100 times as efficient as direct random-plot methods in a variable habitat mosaic; and 2) remote methods with very low classification error such as geospatial underwater videography, which is an emerging, low-cost, non-destructive method for documenting small changes at millimeter visual resolution. General adoption of these methods and their further development will require a fundamental cultural change in conservation and management bodies towards the recognition and promotion of requirements of minimal statistical power and precision in the development of international goals for monitoring these valuable resources and the ecological services they provide. PMID:26367863

Airborne Laser Swath Mapping: Improved Penetration of Dense Vegetation Opens New Applications

NASA Astrophysics Data System (ADS)

Carter, W. E.; Shrestha, R. L.; Slatton, K. C.

2009-12-01

Historically, mapping structures and terrain obscured by dense forests has been problematical, because shadows limit or prevent the use of airborne photogrammetric techniques, and ground surveying techniques are slow, labor intensive, and too costly for many applications. Airborne laser swath mapping (ALSM) units with pulse rates of a few thousand to a few tens of thousands of pulses per second typically resulted in 1 or 2 points per square meter of terrain, which worked reasonably well in sparse to moderately forested areas. For example, data collected with a 30 kHz laser, provided sufficient returns from the ground in areas covered with redwood, mixed hardwoods, and conifer forests, to create 1 to 2 meter resolution bare earth digital elevation models (DEM). These DEMs were useful in studies of forest covered landslides, terraces, and fault lines. However, in dense semi-tropical areas of Florida, with primary and secondary canopies that include dense brush such as palmetto, the DEMs were significantly degraded, and in many areas it was not possible to derive bare earth DEMs that were reliable in height to better than 0.5 to 1.0 meter. In 2007 the UF purchased a second generation Optech ALSM unit that has decimeter accuracy ranging with pulse rates of 100 to 125 kHz. Flying at 600 meters AGL, 60 meters per second, and using a scan angle of ± 20 degrees and scan rate of 40 Hz, results in about 5 laser pulses per square meter within a single swath. In April 2009 a UF team collected ALSM observations covering approximately 2000 acres at Caracol, Belize, to support archaeological studies of the ancient (650 to 900AD) Mayan city, which is largely covered with dense jungle. By overlapping adjacent swaths by 50%, and flying the project area twice with orthogonal flight lines, an accumulated data set containing approximately 20 pulses per square meter, with a distribution of incident angles was realized. The Caracol area has been under study for 25 years and traditional mapping techniques involved cutting pathways through the jungle, typically at 50 meter intervals, and using transits, electronic distance measuring instruments and total stations to map visible features. Without completely clearing the vegetation, it was difficult for ground surveyors to identify and map all of the pertinent features, and preliminary analysis suggest that the ALSM data display areas of previously unmapped mounded settlement, as well as subtle features in the terrain, including shallow agricultural terraces. The ability to map structures and terrain in areas covered with semi-tropical and tropical forests and jungles opens new opportunities for archaeological studies, and promises to impact geological and geophysical studies in these difficult to map regions as well.

Application of remotely sensed land-use information to improve estimates of streamflow characteristics, volume 8. [Maryland, Virginia, and Delaware

NASA Technical Reports Server (NTRS)

Pluhowski, E. J. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Land use data derived from high altitude photography and satellite imagery were studied for 49 basins in Delaware, and eastern Maryland and Virginia. Applying multiple regression techniques to a network of gaging stations monitoring runoff from 39 of the basins, demonstrated that land use data from high altitude photography provided an effective means of significantly improving estimates of stream flow. Forty stream flow characteristic equations for incorporating remotely sensed land use information, were compared with a control set of equations using map derived land cover. Significant improvement was detected in six equations where level 1 data was added and in five equations where level 2 information was utilized. Only four equations were improved significantly using land use data derived from LANDSAT imagery. Significant losses in accuracy due to the use of remotely sensed land use information were detected only in estimates of flood peaks. Losses in accuracy for flood peaks were probably due to land cover changes associated with temporal differences among the primary land use data sources.

Relationship Between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing and Stream Power in the Wind River Range, Wyoming

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; DiGirolamo, Nicolo E.; Riggs, George A.

2010-01-01

Earlier onset of springtime weather including earlier snowmelt has been documented in the western United States over at least the last 50 years. Because the majority (>70%) of the water supply in the western U.S. comes from snowmelt, analysis of the declining spring snowpack (and shrinking glaciers) has important implications for streamflow management. The amount of water in a snowpack influences stream discharge which can also influence erosion and sediment transport by changing stream power, or the rate at which a stream can do work such as move sediment and erode the stream bed. The focus of this work is the Wind River Range (WRR) in west-central Wyoming. Ten years of Moderate-Resolution Imaging Spectroradiometer (MODIS) snow-cover, cloud- gap-filled (CGF) map products and 30 years of discharge and meteorological station data are studied. Streamflow data from six streams in the WRR drainage basins show lower annual discharge and earlier snowmelt in the decade of the 2000s than in the previous three decades, though no trend of either lower streamflow or earlier snowmelt was observed using MODIS snow-cover maps within the decade of the 2000s. Results show a statistically-significant trend at the 95% confidence level (or higher) of increasing weekly maximum air temperature (for three out of the five meteorological stations studied) in the decade of the 1970s, and also for the 40-year study period. MODIS-derived snow cover (percent of basin covered) measured on 30 April explains over 89% of the variance in discharge for maximum monthly streamflow in the decade of the 2000s using Spearman rank correlation analysis. We also investigated stream power for Bull Lake Creek Above Bull Lake from 1970 to 2009; a statistically-significant end toward reduced stream power was found (significant at the 90% confidence level). Observed changes in streamflow and stream power may be related to increasing weekly maximum air temperature measured during the 40-year study period. The strong relationship between percent of basin covered and streamflow indicates that MODIS data is useful for predicting streamflow, leading to improved reservoir management

Relationship Between Satellite-Derived Snow Cover and Snowmelt-Runoff Timing and Stream Power in the Wind River Range, Wyoming

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; DiGirolano, Nocolo E.

2010-01-01

Earlier onset of springtime weather including earlier snowmelt has been documented in the western United States over at least the last 50 years. Because the majority (>70%) of the water supply in the western U.S. comes from snowmelt, analysis of the declining spring snowpack (and shrinking glaciers) has important implications for streamflow management. The amount of water in a snowpack influences stream discharge which can also influence erosion and sediment transport by changing stream power, or the rate at which a stream can do work such as move sediment and erode the stream bed. The focus of this work is the Wind River Range (WRR) in west-central Wyoming. Ten years of Moderate-Resolution Imaging Spectroradiometer (MODIS) snow-cover, cloud- gap-filled (CGF) map products and 30 years of discharge and meteorological station a are studied. Streamflow data from six streams in the WRR drainage basins show lower annual discharge and earlier snowmelt in the decade of the 2000s than in the previous three decades, though no trend of either lower streamflow or earlier snowmelt was observed using MODIS snow-cover maps within the decade of the 2000s. Results show a statistically-significant trend at the 95% confidence level (or higher) of increasing weekly maximum air temperature (for three out of the five meteorological stations studied) in the decade of the 1970s, and also for the 40-year study period. MODIS- derived snow cover (percent of basin covered) measured on 30 April explains over 89% of the variance in discharge for maximum monthly streamflow in the decade of the 2000s using Spearman rank correlation analysis. We also investigated stream power for Bull Lake Creek Above Bull Lake from 1970 to 2009; a statistically-significant trend toward reduced stream power was found (significant at the 90% confidence level). Observed changes in streamflow and stream power may be related to increasing weekly maximum air temperature measured during the 40-year study period. The strong relationship between percent of basin covered and streamflow indicates that MODIS data is useful for predicting streamflow, leading to improved reservoir management.

EVALUATING ECOREGIONS FOR SAMPLING AND MAPPING LAND-COVER PATTERNS

EPA Science Inventory

Ecoregional stratification has been proposed for sampling and mapping land- cover composition and pattern over time. Using a wall-to-wall land-cover map of the United States, we evaluated geographic scales of variance for 17 landscape pattern indices, and compared stratification ...

Large scale IRAM 30 m CO-observations in the giant molecular cloud complex W43

NASA Astrophysics Data System (ADS)

Carlhoff, P.; Nguyen Luong, Q.; Schilke, P.; Motte, F.; Schneider, N.; Beuther, H.; Bontemps, S.; Heitsch, F.; Hill, T.; Kramer, C.; Ossenkopf, V.; Schuller, F.; Simon, R.; Wyrowski, F.

2013-12-01

We aim to fully describe the distribution and location of dense molecular clouds in the giant molecular cloud complex W43. It was previously identified as one of the most massive star-forming regions in our Galaxy. To trace the moderately dense molecular clouds in the W43 region, we initiated W43-HERO, a large program using the IRAM 30 m telescope, which covers a wide dynamic range of scales from 0.3 to 140 pc. We obtained on-the-fly-maps in 13CO (2-1) and C18O (2-1) with a high spectral resolution of 0.1 km s-1 and a spatial resolution of 12''. These maps cover an area of ~1.5 square degrees and include the two main clouds of W43 and the lower density gas surrounding them. A comparison to Galactic models and previous distance calculations confirms the location of W43 near the tangential point of the Scutum arm at approximately 6 kpc from the Sun. The resulting intensity cubes of the observed region are separated into subcubes, which are centered on single clouds and then analyzed in detail. The optical depth, excitation temperature, and H2 column density maps are derived out of the 13CO and C18O data. These results are then compared to those derived from Herschel dust maps. The mass of a typical cloud is several 104 M⊙ while the total mass in the dense molecular gas (>102 cm-3) in W43 is found to be ~1.9 × 106 M⊙. Probability distribution functions obtained from column density maps derived from molecular line data and Herschel imaging show a log-normal distribution for low column densities and a power-law tail for high densities. A flatter slope for the molecular line data probability distribution function may imply that those selectively show the gravitationally collapsing gas. Appendices are available in electronic form at http://www.aanda.orgThe final datacubes (13CO and C18O) for the entire survey are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/560/A24

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

USGS Publications Warehouse

Maxwell, S.K.; Meliker, J.R.; Goovaerts, P.

2010-01-01

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Derived crop management data for the LandCarbon Project

USGS Publications Warehouse

Schmidt, Gail; Liu, Shu-Guang; Oeding, Jennifer

2011-01-01

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

High-resolution mapping of forest carbon stocks in the Colombian Amazon

NASA Astrophysics Data System (ADS)

Asner, G. P.; Clark, J. K.; Mascaro, J.; Galindo García, G. A.; Chadwick, K. D.; Navarrete Encinales, D. A.; Paez-Acosta, G.; Cabrera Montenegro, E.; Kennedy-Bowdoin, T.; Duque, Á.; Balaji, A.; von Hildebrand, P.; Maatoug, L.; Bernal, J. F. Phillips; Yepes Quintero, A. P.; Knapp, D. E.; García Dávila, M. C.; Jacobson, J.; Ordóñez, M. F.

2012-07-01

High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40%) of the Colombian Amazon - a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

High-resolution Mapping of Forest Carbon Stocks in the Colombian Amazon

NASA Astrophysics Data System (ADS)

Asner, G. P.; Clark, J. K.; Mascaro, J.; Galindo García, G. A.; Chadwick, K. D.; Navarrete Encinales, D. A.; Paez-Acosta, G.; Cabrera Montenegro, E.; Kennedy-Bowdoin, T.; Duque, Á.; Balaji, A.; von Hildebrand, P.; Maatoug, L.; Bernal, J. F. Phillips; Knapp, D. E.; García Dávila, M. C.; Jacobson, J.; Ordóñez, M. F.

2012-03-01

High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or Light Detection and Ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (>40 %) of the Colombian Amazon - a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon mapping samples had 14.6 % uncertainty at 1 ha resolution, and regional maps based on stratification and regression approaches had 25.6 % and 29.6 % uncertainty, respectively, in any given hectare. High-resolution approaches with reported local-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision-makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

Derivation of a northern-hemispheric biomass map for use in global carbon cycle models

NASA Astrophysics Data System (ADS)

Thurner, Martin; Beer, Christian; Santoro, Maurizio; Carvalhais, Nuno; Wutzler, Thomas; Schepaschenko, Dmitry; Shvidenko, Anatoly; Kompter, Elisabeth; Levick, Shaun; Schmullius, Christiane

2013-04-01

Quantifying the state and the change of the World's forests is crucial because of their ecological, social and economic value. Concerning their ecological importance, forests provide important feedbacks on the global carbon, energy and water cycles. In addition to their influence on albedo and evapotranspiration, they have the potential to sequester atmospheric carbon dioxide and thus to mitigate global warming. The current state and inter-annual variability of forest carbon stocks remain relatively unexplored, but remote sensing can serve to overcome this shortcoming. While for the tropics wall-to-wall estimates of above-ground biomass have been recently published, up to now there was a lack of similar products covering boreal and temperate forests. Recently, estimates of forest growing stock volume (GSV) were derived from ENVISAT ASAR C-band data for latitudes above 30° N. Utilizing a wood density and a biomass compartment database, a forest carbon density map covering North-America, Europe and Asia with 0.01° resolution could be derived out of this dataset. Allometric functions between stem, branches, root and foliage biomass were fitted and applied for different leaf types (broadleaf, needleleaf deciduous, needleleaf evergreen forest). Additionally, this method enabled uncertainty estimation of the resulting carbon density map. Intercomparisons with inventory-based biomass products in Russia, Europe and the USA proved the high accuracy of this approach at a regional scale (r2 = 0.70 - 0.90). Based on the final biomass map, the forest carbon stocks and densities (excluding understorey vegetation) for three biomes were estimated across three continents. While 40.7 ± 15.7 Gt of carbon were found to be stored in boreal forests, temperate broadleaf/mixed forests and temperate conifer forests contain 24.5 ± 9.4 Gt(C) and 14.5 ± 4.8 Gt(C), respectively. In terms of carbon density, most of the carbon per area is stored in temperate conifer (62.1 ± 20.7 Mg(C)/ha(Forest)) and broadleaf/mixed forests (58.0 ± 22.1 Mg(C)/ha(Forest)), whereas boreal forests have a carbon density of only 40.0 ± 15.4 Mg(C)/ha(Forest). While European forest carbon stocks are relatively small, the carbon density is higher compared to the other continents. The derived biomass map substantially improves the knowledge on the current carbon stocks of the northern-hemispheric boreal and temperate forests, serving as a new benchmark for spatially explicit and consistent biomass mapping with moderate spatial resolution. This product can be of great value for global carbon cycle models as well as national carbon monitoring systems. Further investigations concentrate on improving biomass parameterizations and representations in such kind of models. The presented map will help to improve the simulation of biomass spatial patterns and variability and enables identifying the dominant influential factors like climatic conditions and disturbances.

Assessing map accuracy in a remotely sensed, ecoregion-scale cover map

USGS Publications Warehouse

Edwards, T.C.; Moisen, Gretchen G.; Cutler, D.R.

1998-01-01

Landscape- and ecoregion-based conservation efforts increasingly use a spatial component to organize data for analysis and interpretation. A challenge particular to remotely sensed cover maps generated from these efforts is how best to assess the accuracy of the cover maps, especially when they can exceed 1000 s/km2 in size. Here we develop and describe a methodological approach for assessing the accuracy of large-area cover maps, using as a test case the 21.9 million ha cover map developed for Utah Gap Analysis. As part of our design process, we first reviewed the effect of intracluster correlation and a simple cost function on the relative efficiency of cluster sample designs to simple random designs. Our design ultimately combined clustered and subsampled field data stratified by ecological modeling unit and accessibility (hereafter a mixed design). We next outline estimation formulas for simple map accuracy measures under our mixed design and report results for eight major cover types and the three ecoregions mapped as part of the Utah Gap Analysis. Overall accuracy of the map was 83.2% (SE=1.4). Within ecoregions, accuracy ranged from 78.9% to 85.0%. Accuracy by cover type varied, ranging from a low of 50.4% for barren to a high of 90.6% for man modified. In addition, we examined gains in efficiency of our mixed design compared with a simple random sample approach. In regard to precision, our mixed design was more precise than a simple random design, given fixed sample costs. We close with a discussion of the logistical constraints facing attempts to assess the accuracy of large-area, remotely sensed cover maps.

Rapid crop cover mapping for the conterminous United States

USGS Publications Warehouse

Dahal, Devendra; Wylie, Bruce K.; Howard, Daniel

2018-01-01

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a ‘two model mapping’ approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250 m for the current year by the month of September. In this approach, we divided the CCM model into one ‘crop type model’ to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of ‘other’ crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.

Use of high resolution Airborne Laser Scanning data for landslide interpretation under mixed forest and tropical rainforest: case study in Barcelonnette, France and Cameron Highlands, Malaysia

NASA Astrophysics Data System (ADS)

Azahari Razak, Khamarrul; Straatsma, Menno; van Westen, Cees; Malet, Jean-Philippe; de Jong, Steven M.

2010-05-01

Airborne Laser Scanning (ALS) is the state of the art technology for topographic mapping over a wide variety of spatial and temporal scales. It is also a promising technique for identification and mapping of landslides in a forested mountainous landscape. This technology demonstrates the ability to pass through the gaps between forest foliage and record the terrain height under vegetation cover. To date, most of the images either derived from satellite imagery, aerial-photograph or synthetic aperture radar are not appropriate for visual interpretation of landslide features that are covered by dense vegetation. However, it is a necessity to carefully map the landslides in order to understand its processes. This is essential for landslide hazard and risk assessment. This research demonstrates the capabilities of high resolution ALS data to recognize and identify different types of landslides in mixed forest in Barcelonnette, France and tropical rainforest in Cameron Highlands, Malaysia. ALS measurements over the 100-years old forest in Bois Noir catchment were carried out in 2007 and 2009. Both ALS dataset were captured using a Riegl laser scanner. First and last pulse with density of one point per meter square was derived from 2007 ALS dataset, whereas multiple return (of up to five returns) pulse was derived from July 2009 ALS dataset, which consists of 60 points per meter square over forested terrain. Generally, this catchment is highly affected by shallow landslides which mostly occur beneath dense vegetation. It is located in the dry intra-Alpine zone and represented by the climatic of the South French Alps. In the Cameron Highlands, first and last pulse data was captured in 2004 which covers an area of up to 300 kilometres square. Here, the Optech laser scanner was used under the Malaysian national pilot study which has slightly low point density. With precipitation intensity of up to 3000 mm per year over rugged topography and elevations up to 2800 m a.s.l., mapping the landslides under tropical rainforest which are highly vegetated and rapidly re-vegetated still remains a challenge. With the advancement of point clouds processing algorithm, high resolution Digital Terrain Models (DTMs) are becoming a very valuable data source for the production of landslide related maps. In this study, two filtering algorithms, which are based on least square interpolation and progressive TIN densification, are used to extract the bare earth surface. Quantitative and qualitative assessment that was carried out under ISPRS Working Group III/3 shown that those algorithms performed well in terms of discontinuity preservation, vegetation on the slope and high outlier influence in the point clouds. Hence, they are capable to extract ground points under difficult scenarios, especially for application under rugged forested terrain. The optimal terrain information has been exploited from ALS point clouds, particularly to preserve important landslide characteristics and to filter out unnecessary features. Morphological characteristics and geometric signatures of landslides are taken into consideration for the derivation of high-quality digital terrain model. Furthermore, ALS-derived DTMs are investigated at different spatial scales for suitable hillslopes morphology representation. Hence, appropriate 2D and 3D visualization methods are presented in such a way to help the image interpreters to detect landslides and classify them according to type, movement mechanism and activity status in forested mountainous terrain.

Bi-scale analysis of multitemporal land cover fractions for wetland vegetation mapping

NASA Astrophysics Data System (ADS)

Michishita, Ryo; Jiang, Zhiben; Gong, Peng; Xu, Bing

2012-08-01

Land cover fractions (LCFs) derived through spectral mixture analysis are useful in understanding sub-pixel information. However, few studies have been conducted on the analysis of time-series LCFs. Although multi-scale comparisons of spectral index, hard classification, and land surface temperature images have received attention, rarely have these approaches been applied to LCFs. This study compared the LCFs derived through Multiple Endmember Spectral Mixture Analysis (MESMA) using the time-series Landsat Thematic Mapper (TM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired in the Poyang Lake area, China between 2004 and 2005. Specifically, we aimed to: (1) propose an approach for optimal endmember (EM) selection in time-series MESMA; (2) understand the trends in time-series LCFs derived from the TM and MODIS data; and (3) examine the trends in the correlation between the bi-scale LCFs derived from the time-series TM and MODIS data. Our results indicated: (1) the EM spectra chosen according to the proposed hierarchical three-step approach (overall, seasonal, and individual) accurately modeled the both the TM and MODIS images; (2) green vegetation (GV) and NPV/soil/impervious surface (N/S/I) classes followed sine curve trends in the overall area, while the two water classes displayed the water level change pattern in the areas primarily covered with wetland vegetation; and (3) GV, N/S/I, and bright water classes indicated a moderately high agreement between the TM and MODIS LCFs in the whole area (adjusted R2 ⩾ 0.6). However, low levels of correlations were found in the areas primarily dominated by wetland vegetation for all land cover classes.

Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice and snow, and other materials: The USGS tricorder algorithm

NASA Technical Reports Server (NTRS)

Clark, Roger N.; Swayze, Gregg A.

1995-01-01

One of the challenges of Imaging Spectroscopy is the identification, mapping and abundance determination of materials, whether mineral, vegetable, or liquid, given enough spectral range, spectral resolution, signal to noise, and spatial resolution. Many materials show diagnostic absorption features in the visual and near infrared region (0.4 to 2.5 micrometers) of the spectrum. This region is covered by the modern imaging spectrometers such as AVIRIS. The challenge is to identify the materials from absorption bands in their spectra, and determine what specific analyses must be done to derive particular parameters of interest, ranging from simply identifying its presence to deriving its abundance, or determining specific chemistry of the material. Recently, a new analysis algorithm was developed that uses a digital spectral library of known materials and a fast, modified-least-squares method of determining if a single spectral feature for a given material is present. Clark et al. made another advance in the mapping algorithm: simultaneously mapping multiple minerals using multiple spectral features. This was done by a modified-least-squares fit of spectral features, from data in a digital spectral library, to corresponding spectral features in the image data. This version has now been superseded by a more comprehensive spectral analysis system called Tricorder.

The Earth Observation Data for Habitat Monitoring (EODHaM) system

NASA Astrophysics Data System (ADS)

Lucas, Richard; Blonda, Palma; Bunting, Peter; Jones, Gwawr; Inglada, Jordi; Arias, Marcela; Kosmidou, Vasiliki; Petrou, Zisis I.; Manakos, Ioannis; Adamo, Maria; Charnock, Rebecca; Tarantino, Cristina; Mücher, Caspar A.; Jongman, Rob H. G.; Kramer, Henk; Arvor, Damien; Honrado, Joāo Pradinho; Mairota, Paola

2015-05-01

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.

Quantifying Vegetation Composition, Structure and Dynamics in Selected Australian Ecosystems: Science to Management

NASA Astrophysics Data System (ADS)

Phinn, S. R.; Scarth, P.; Armston, J.; Witte, C.; Danaher, T.; Flood, N.; Gill, T.; Lucas, R.

2011-12-01

Management of Australian ecosystems is carried out by state governments using information derived from satellite image data. The state of Queensland covers approximately 1.8 x 10^6 km^2 and uses satellite remote sensing and field survey programs to support legislated environmental monitoring, management and compliance activities.This poster outlines how the Joint Remote Sensing Research Program(JRSRP)delivered satellite image based data sets to address these activities by mapping foliage projective cover, vegetation height and biomass. Foliage projective cover (FPC), the vertically projected percentage cover of photosynthetic foliage of all strata, is produced from Landsat TM/ETM data using 88 scenes and over 1700 field sites. The JRSRP enabled government staff to be seconded to a university research group to work on the project, and the university provided postdoctoral and graduate student support. The JRSRP activities focussed on geometric and topographic corrections, BRDF corrections and time-series based approaches for correcting the archive of field survey and Landsat TM/ETM+ images. This has now progressed to a program using the entire Landsat TM/ETM+ archive on an annual basis and annual state-wide field survey data. The Landsat TM/ETM+ calibrations have been a critical input to the Landsat program's global vicarious calibration activities. Vegetation height is a critical parameter required for a range of state-wide activities and can be mapped accurately from field plots to regional areas using airborne Lidar. To develop statewide height estimates, an approach was developed using Icesat and existing vegetation community maps. By aggregating the spaceborne Icesat full waveform data within the mapped vegetation structure polygons it was possible to retrieve vegetation vertical structure information continuously across the landscape. This was used to derive mean canopy and understorey height, depth and density across Queensland, which was validated using airborne lidar data provided by the JRSRP. Biomass mapping is emerging as a critical environmental parameter for local, state and national agencies in Australia. Staff from JRSRP developed an approach with University of Aberystwyth in Wales, through JAXA's Kyoto and Carbon initiative, for acquiring ALOS PALSAR L-band image data, conducting geometric and radiometric corrections, and normalising for significant scene to scene differences in soil and vegetation moisture content. This pre-processing of 31 image strip time-series generated state-wide mosaics for Queensland that were then used with 1815 field survey sites collected across the state to produce a state-wide biomass estimation model for L-HV data, providing estimates for both remnant and non-remnant forests, with saturation at 263 Mg.Ha^-1 for 20% estimation error. The Joint Remote Sensing Research Program has enabled a sound approach to research and development for validated operational applications.

Hyperspectral surface materials map of quadrangle 3464, Shahrak (411) and Kasi (412) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3264, Naw Zad-Musa Qala (423) and Dihrawud (424) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3164, Lashkar Gah (605) and Kandahar (606) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3162, Chakhansur (603) and Kotalak (604) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3568, Pul-e Khumri (503) and Charikar (504) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3166, Jaldak (701) and Maruf-Nawa (702) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Giles, Stuart A.; Johnson, Michaela R.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3366, Gizab (513) and Nawer (514) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3362, Shindand (415) and Tulak (416) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Giles, Stuart A.; Johnson, Michaela R.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3262, Farah (421) and Hokumat-e-pur-Chaman (422) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3564, Jowand (405) and Gurziwan (406) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3364, Pasaband (417) and Markaz-e Kajiran (418) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3368, Ghazni (515) and Gardez (516) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3770, Faizabad (217) and Parkhaw (218) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Giles, Stuart A.; Johnson, Michaela R.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral Surface Materials Map of Quadrangle 3268, Khayr Kot (521) and Urgun (522) Quadrangles, Afghanistan, Showing Iron-bearing Minerals and Other Materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Giles, Stuart A.; Johnson, Michaela R.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3462, Herat (409) and Chishti Sharif (410) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3266, Uruzgan (519) and Moqur (520) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Giles, Stuart A.; Johnson, Michaela R.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3470, Jalalabad (511) and Chaghasaray (512) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Giles, Stuart A.; Johnson, Michaela R.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3566, Sangcharak (501) and Sayghan-o-Kamard (502) quadrangles, Afghanistan, showing iron-bearing minerals and other material

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Giles, Stuart A.; Johnson, Michaela R.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3570, Tagab-e-Munjan (505) and Asmar-Kamdesh (506) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3468, Chak-e Wardak-Siyahgird (509) and Kabul (510) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Giles, Stuart A.; Johnson, Michaela R.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3670, Jurm-Kishim (223) and Zebak (224) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Hyperspectral surface materials map of quadrangle 3562, Khawja-Jir (403) and Murghab (404) quadrangles, Afghanistan, showing iron-bearing minerals and other materials

USGS Publications Warehouse

King, Trude V.V.; Hoefen, Todd M.; Kokaly, Raymond F.; Livo, Keith E.; Johnson, Michaela R.; Giles, Stuart A.

2013-01-01

This map shows the spatial distribution of selected iron-bearing minerals and other materials derived from analysis of airborne HyMap™ imaging spectrometer (hyperspectral) data of Afghanistan collected in late 2007. This map is one in a series of U.S. Geological Survey/Afghanistan Geological Survey quadrangle maps covering Afghanistan. Flown at an altitude of 50,000 feet (15,240 meters (m)), the HyMap™ imaging spectrometer measured reflected sunlight in 128 channels, covering wavelengths between 0.4 and 2.5 μm. The data were georeferenced, atmospherically corrected and converted to apparent surface reflectance, empirically adjusted using ground-based reflectance measurements, and combined into a mosaic with 23-m pixel spacing. Variations in water vapor and dust content of the atmosphere, in solar angle, and in surface elevation complicated correction; therefore, some classification differences may be present between adjacent flight lines. The reflectance spectrum of each pixel of HyMap™ imaging spectrometer data was compared to the reference materials in a spectral library of minerals, vegetation, water, and other materials. Minerals occurring abundantly at the surface and those having unique spectral features were easily detected and discriminated, while minerals having slightly different compositions but similar spectral features were less easily discriminated; thus, some map classes consist of several minerals having similar spectra, such as “Goethite and jarosite.” A designation of “Not classified” was assigned to the pixel when there was no match with reference spectra.

Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis.

PubMed

Hamada, Yuki; Stow, Douglas A; Roberts, Dar A; Franklin, Janet; Kyriakidis, Phaedon C

2013-04-01

Arid and semi-arid shrublands have significant biological and economical values and have been experiencing dramatic changes due to human activities. In California, California sage scrub (CSS) is one of the most endangered plant communities in the US and requires close monitoring in order to conserve this important biological resource. We investigate the utility of remote-sensing approaches--object-based image analysis applied to pansharpened QuickBird imagery (QBPS/OBIA) and multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery (SPOT/MESMA)--for estimating fractional cover of true shrub, subshrub, herb, and bare ground within CSS communities of southern California. We also explore the effectiveness of life-form cover maps for assessing CSS conditions. Overall and combined shrub cover (i.e., true shrub and subshrub) were estimated more accurately using QBPS/OBIA (mean absolute error or MAE, 8.9 %) than SPOT/MESMA (MAE, 11.4 %). Life-form cover from QBPS/OBIA at a 25 × 25 m grid cell size seems most desirable for assessing CSS because of its higher accuracy and spatial detail in cover estimates and amenability to extracting other vegetation information (e.g., size, shape, and density of shrub patches). Maps derived from SPOT/MESMA at a 50 × 50 m scale are effective for retrospective analysis of life-form cover change because their comparable accuracies to QBPS/OBIA and availability of SPOT archives data dating back to the mid-1980s. The framework in this study can be applied to other physiognomically comparable shrubland communities.

Reconstructed historical land cover and biophysical parameters for studies of land-atmosphere interactions within the eastern United States

USGS Publications Warehouse

Steyaert, Louis T.; Knox, R.G.

2008-01-01

Over the past 350 years, the eastern half of the United States experienced extensive land cover changes. These began with land clearing in the 1600s, continued with widespread deforestation, wetland drainage, and intensive land use by 1920, and then evolved to the present-day landscape of forest regrowth, intensive agriculture, urban expansion, and landscape fragmentation. Such changes alter biophysical properties that are key determinants of land-atmosphere interactions (water, energy, and carbon exchanges). To understand the potential implications of these land use transformations, we developed and analyzed 20-km land cover and biophysical parameter data sets for the eastern United States at 1650, 1850, 1920, and 1992 time slices. Our approach combined potential vegetation, county-level census data, soils data, resource statistics, a Landsat-derived land cover classification, and published historical information on land cover and land use. We reconstructed land use intensity maps for each time slice and characterized the land cover condition. We combined these land use data with a mutually consistent set of biophysical parameter classes, to characterize the historical diversity and distribution of land surface properties. Time series maps of land surface albedo, leaf area index, a deciduousness index, canopy height, surface roughness, and potential saturated soils in 1650, 1850, 1920, and 1992 illustrate the profound effects of land use change on biophysical properties of the land surface. Although much of the eastern forest has returned, the average biophysical parameters for recent landscapes remain markedly different from those of earlier periods. Understanding the consequences of these historical changes will require land-atmosphere interactions modeling experiments.

Integrated approach using multi-platform sensors for enhanced high-resolution daily ice cover product

NASA Astrophysics Data System (ADS)

Bonev, George; Gladkova, Irina; Grossberg, Michael; Romanov, Peter; Helfrich, Sean

2016-09-01

The ultimate objective of this work is to improve characterization of the ice cover distribution in the polar areas, to improve sea ice mapping and to develop a new automated real-time high spatial resolution multi-sensor ice extent and ice edge product for use in operational applications. Despite a large number of currently available automated satellite-based sea ice extent datasets, analysts at the National Ice Center tend to rely on original satellite imagery (provided by satellite optical, passive microwave and active microwave sensors) mainly because the automated products derived from satellite optical data have gaps in the area coverage due to clouds and darkness, passive microwave products have poor spatial resolution, automated ice identifications based on radar data are not quite reliable due to a considerable difficulty in discriminating between the ice cover and rough ice-free ocean surface due to winds. We have developed a multisensor algorithm that first extracts maximum information on the sea ice cover from imaging instruments VIIRS and MODIS, including regions covered by thin, semitransparent clouds, then supplements the output by the microwave measurements and finally aggregates the results into a cloud gap free daily product. This ability to identify ice cover underneath thin clouds, which is usually masked out by traditional cloud detection algorithms, allows for expansion of the effective coverage of the sea ice maps and thus more accurate and detailed delineation of the ice edge. We have also developed a web-based monitoring system that allows comparison of our daily ice extent product with the several other independent operational daily products.

Continuity of MODIS and VIIRS Snow-Cover Maps during Snowmelt in the Catskill Mountains in New York

NASA Astrophysics Data System (ADS)

Hall, D. K.; Riggs, G. A., Jr.; Roman, M. O.; DiGirolamo, N. E.

2015-12-01

We investigate the local and regional differences and possible biases between the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible-Infrared Imager Radiometer Suite (VIIRS) snow-cover maps in the winter of 2012 during snowmelt conditions in the Catskill Mountains in New York using a time series of cloud-gap filled daily snow-cover maps. The MODIS Terra instrument has been providing daily global snow-cover maps since February 2000 (Riggs and Hall, 2015). Using the VIIRS instrument, launched in 2011, NASA snow products are being developed based on the heritage MODIS snow-mapping algorithms, and will soon be available to the science community. Continuity of the standard NASA MODIS and VIIRS snow-cover maps is essential to enable environmental-data records (EDR) to be developed for analysis of snow-cover trends using a consistent data record. For this work, we compare daily MODIS and VIIRS snow-cover maps of the Catskill Mountains from 29 February through 14 March 2012. The entire region was snow covered on 29 February and by 14 March the snow had melted; we therefore have a daily time series available to compare normalized difference snow index (NDSI), as an indicator of snow-cover fraction. The MODIS and VIIRS snow-cover maps have different spatial resolutions (500 m for MODIS and 375 m for VIIRS) and different nominal overpass times (10:30 AM for MODIS Terra and 2:30 PM for VIIRS) as well as different cloud masks. The results of this work will provide a quantitative assessment of the continuity of the snow-cover data records for use in development of an EDR of snow cover.http://modis-snow-ice.gsfc.nasa.gov/Riggs, G.A. and D.K. Hall, 2015: MODIS Snow Products User Guide to Collection 6, http://modis-snow-ice.gsfc.nasa.gov/?c=userguides

High-spatial-resolution mapping of precipitable water vapour using SAR interferograms, GPS observations and ERA-Interim reanalysis

NASA Astrophysics Data System (ADS)

Tang, Wei; Liao, Mingsheng; Zhang, Lu; Li, Wei; Yu, Weimin

2016-09-01

A high spatial and temporal resolution of the precipitable water vapour (PWV) in the atmosphere is a key requirement for the short-scale weather forecasting and climate research. The aim of this work is to derive temporally differenced maps of the spatial distribution of PWV by analysing the tropospheric delay "noise" in interferometric synthetic aperture radar (InSAR). Time series maps of differential PWV were obtained by processing a set of ENVISAT ASAR (Advanced Synthetic Aperture Radar) images covering the area of southern California, USA from 6 October 2007 to 29 November 2008. To get a more accurate PWV, the component of hydrostatic delay was calculated and subtracted by using ERA-Interim reanalysis products. In addition, the ERA-Interim was used to compute the conversion factors required to convert the zenith wet delay to water vapour. The InSAR-derived differential PWV maps were calibrated by means of the GPS PWV measurements over the study area. We validated our results against the measurements of PWV derived from the Medium Resolution Imaging Spectrometer (MERIS) which was located together with the ASAR sensor on board the ENVISAT satellite. Our comparative results show strong spatial correlations between the two data sets. The difference maps have Gaussian distributions with mean values close to zero and standard deviations below 2 mm. The advantage of the InSAR technique is that it provides water vapour distribution with a spatial resolution as fine as 20 m and an accuracy of ˜ 2 mm. Such high-spatial-resolution maps of PWV could lead to much greater accuracy in meteorological understanding and quantitative precipitation forecasts. With the launch of Sentinel-1A and Sentinel-1B satellites, every few days (6 days) new SAR images can be acquired with a wide swath up to 250 km, enabling a unique operational service for InSAR-based water vapour maps with unprecedented spatial and temporal resolution.

Suitable Site Selection of Small Dams Using Geo-Spatial Technique: a Case Study of Dadu Tehsil, Sindh

NASA Astrophysics Data System (ADS)

Khalil, Zahid

2016-07-01

Decision making about identifying suitable sites for any project by considering different parameters, is difficult. Using GIS and Multi-Criteria Analysis (MCA) can make it easy for those projects. This technology has proved to be an efficient and adequate in acquiring the desired information. In this study, GIS and MCA were employed to identify the suitable sites for small dams in Dadu Tehsil, Sindh. The GIS software is used to create all the spatial parameters for the analysis. The parameters that derived are slope, drainage density, rainfall, land use / land cover, soil groups, Curve Number (CN) and runoff index with a spatial resolution of 30m. The data used for deriving above layers include 30 meter resolution SRTM DEM, Landsat 8 imagery, and rainfall from National Centre of Environment Prediction (NCEP) and soil data from World Harmonized Soil Data (WHSD). Land use/Land cover map is derived from Landsat 8 using supervised classification. Slope, drainage network and watershed are delineated by terrain processing of DEM. The Soil Conservation Services (SCS) method is implemented to estimate the surface runoff from the rainfall. Prior to this, SCS-CN grid is developed by integrating the soil and land use/land cover raster. These layers with some technical and ecological constraints are assigned weights on the basis of suitability criteria. The pair wise comparison method, also known as Analytical Hierarchy Process (AHP) is took into account as MCA for assigning weights on each decision element. All the parameters and group of parameters are integrated using weighted overlay in GIS environment to produce suitable sites for the Dams. The resultant layer is then classified into four classes namely, best suitable, suitable, moderate and less suitable. This study reveals a contribution to decision making about suitable sites analysis for small dams using geo-spatial data with minimal amount of ground data. This suitability maps can be helpful for water resource management organizations in determination of feasible rainwater harvesting structures (RWH).

Preliminary integrated geologic map databases for the United States: Digital data for the reconnaissance bedrock geologic map for the northern Alaska peninsula area, southwest Alaska

USGS Publications Warehouse

,

2006-01-01

he growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO export files and as ArcView shape files. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.

Preliminary integrated geologic map databases for the United States: Digital data for the reconnaissance geologic map of the western Aleutian Islands, Alaska

USGS Publications Warehouse

,

2006-01-01

The growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO Exportfiles/ and as ArcView shape files. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.

Preliminary integrated geologic map databases for the United States: Digital data for the generalized bedrock geologic map, Yukon Flats region, east-central Alaska

USGS Publications Warehouse

Till, Alison B.; Dumoulin, Julie A.; Phillips, Jeffrey D.; Stanley, Richard G.; Crews, Jessie

2006-01-01

The growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO export files and as ArcView shape files. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.

Preliminary integrated geologic map databases for the United States: Digital data for the reconnaissance geologic map of the lower Yukon River region, Alaska

USGS Publications Warehouse

,

2006-01-01

The growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO export files and as ArcView shape files. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.

High-density linkage mapping revealed suppression of recombination at the sex determination locus in papaya.

PubMed Central

Ma, Hao; Moore, Paul H; Liu, Zhiyong; Kim, Minna S; Yu, Qingyi; Fitch, Maureen M M; Sekioka, Terry; Paterson, Andrew H; Ming, Ray

2004-01-01

A high-density genetic map of papaya (Carica papaya L.) was constructed using 54 F(2) plants derived from cultivars Kapoho and SunUp with 1501 markers, including 1498 amplified fragment length polymorphism (AFLP) markers, the papaya ringspot virus coat protein marker, morphological sex type, and fruit flesh color. These markers were mapped into 12 linkage groups at a LOD score of 5.0 and recombination frequency of 0.25. The 12 major linkage groups covered a total length of 3294.2 cM, with an average distance of 2.2 cM between adjacent markers. This map revealed severe suppression of recombination around the sex determination locus with a total of 225 markers cosegregating with sex types. The cytosine bases were highly methylated in this region on the basis of the distribution of methylation-sensitive and -insensitive markers. This high-density genetic map is essential for cloning of specific genes of interest such as the sex determination gene and for the integration of genetic and physical maps of papaya. PMID:15020433

Multibeam Sonar Mapping and Modeling of a Submerged Bryophyte Mat in Crater Lake, Oregon

USGS Publications Warehouse

Dartnell, Peter; Collier, Robert; Buktenica, Mark; Jessup, Steven; Girdner, Scott; Triezenberg, Peter

2008-01-01

Traditionally, multibeam data have been used to map sea floor or lake floor morphology as well as the distribution of surficial facies in order to characterize the geologic component of benthic habitats. In addition to using multibeam data for geologic studies, we want to determine if these data can also be used directly to map the distribution of biota. Multibeam bathymetry and acoustic backscatter data collected in Crater Lake, Oregon, in 2000 are used to map the distribution of a deep-water bryophyte mat, which will be extremely useful for understanding the overall ecology of the lake. To map the bryophyte's distribution, depth range, acoustic backscatter intensity, and derived bathymetric index grids are used as inputs into a hierarchical decision-tree classification model. Observations of the bryophyte mat from over 23 line kilometers of lake-floor video collected in the summer of 2006 are used as controls for the model. The resulting map matches well with ground-truth information and shows that the bryophyte mat covers most of the platform surrounding Wizard Island as well as on outcrops around the caldera wall.

Phreatophytic land-cover map of the northern and central Great Basin Ecoregion: California, Idaho, Nevada, Utah, Oregon, and Wyoming

USGS Publications Warehouse

Mathie, Amy M.; Welborn, Toby L.; Susong, David D.; Tumbusch, Mary L.

2011-01-01

Increasing water use and changing climate in the Great Basin of the western United States are likely affecting the distribution of phreatophytic vegetation in the region. Phreatophytic plant communities that depend on groundwater are susceptible to natural and anthropogenic changes to hydrologic flow systems. The purpose of this report is to document the methods used to create the accompanying map that delineates areas of the Great Basin that have the greatest potential to support phreatophytic vegetation. Several data sets were used to develop the data displayed on the map, including Shrub Map (a land-cover data set derived from the Regional Gap Analysis Program) and Gap Analysis Program (GAP) data sets for California and Wyoming. In addition, the analysis used the surface landforms from the U.S. Geological Survey (USGS) Global Ecosystems Mapping Project data to delineate regions of the study area based on topographic relief that are most favorable to support phreatophytic vegetation. Using spatial analysis techniques in a GIS, phreatophytic vegetation classes identified within Shrub Map and GAP were selected and compared to the spatial distribution of selected landforms in the study area to delineate areas of phreatophyte vegetation. Results were compared to more detailed studies conducted in selected areas. A general qualitative description of the data and the limitations of the base data determined that these results provide a regional overview but are not intended for localized studies or as a substitute for detailed field analysis. The map is intended as a decision-support aide for land managers to better understand, anticipate, and respond to ecosystem changes in the Great Basin.

Mapping urban land cover from space: Some observations for future progress

NASA Technical Reports Server (NTRS)

Gaydos, L.

1982-01-01

The multilevel classification system adopted by the USGS for operational mapping of land use and land cover at levels 1 and 2 is discussed and the successes and failures of mapping land cover from LANDSAT digital data are reviewed. Techniques used for image interpretation and their relationships to sensor parameters are examined. The requirements for mapping levels 2 and 3 classes are considered.

Commentary: A cautionary tale regarding use of the National Land Cover Dataset 1992

USGS Publications Warehouse

Thogmartin, Wayne E.; Gallant, Alisa L.; Knutson, Melinda G.; Fox, Timothy J.; Suarez, Manuel J.

2004-01-01

Digital land-cover data are among the most popular data sources used in ecological research and natural resource management. However, processes for accurate land-cover classification over large regions are still evolving. We identified inconsistencies in the National Land Cover Dataset 1992, the most current and available representation of land cover for the conterminous United States. We also report means to address these inconsistencies in a bird-habitat model. We used a Geographic Information System (GIS) to position a regular grid (or lattice) over the upper midwestern United States and summarized the proportion of individual land covers in each cell within the lattice. These proportions were then mapped back onto the lattice, and the resultant lattice was compared to satellite paths, state borders, and regional map classification units. We observed mapping inconsistencies at the borders between mapping regions, states, and Thematic Mapper (TM) mapping paths in the upper midwestern United States, particularly related to grass I and-herbaceous, emergent-herbaceous wetland, and small-grain land covers. We attributed these discrepancies to differences in image dates between mapping regions, suboptimal image dates for distinguishing certain land-cover types, lack of suitable ancillary data for improving discrimination for rare land covers, and possibly differences among image interpreters. To overcome these inconsistencies for the purpose of modeling regional populations of birds, we combined grassland-herbaceous and pasture-hay land-cover classes and excluded the use of emergent-herbaceous and small-grain land covers. We recommend that users of digital land-cover data conduct similar assessments for other regions before using these data for habitat evaluation. Further, caution is advised in using these data in the analysis of regional land-cover change because it is not likely that future digital land-cover maps will repeat the same problems, thus resulting in biased estimates of change.

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

USGS Publications Warehouse

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.

The way forward

USGS Publications Warehouse

Estes, John; Belward, Alan; Loveland, Thomas; Scepan, Joseph; Strahler, Alan H.; Townshend, John B.; Justice, Chris

1999-01-01

This paper focuses on the lessons hearned in the conduct of the lnternational Geosphere Biosphere Program's Data and Information System (rcnr-nts), global 1-km Land-Cover Mapping Project (n$cover). There is stiLL considerable fundamental research to be conducted dealing with the development and validation of thematic geospatial products derived from a combination of remotely sensed and ancillary data. Issues include database and data product development, classification legend definitions, processing and analysis techniques, and sampling strategies. A significant infrastructure is required to support an effort such as DISCover. The infrastructure put in place under the auspices of the IGBP-DIS serves as a model, and must be put in place to enable replication and development of projects such as Discover.

Digital Data for the reconnaissance geologic map for Prince William Sound and the Kenai Peninsula, Alaska

USGS Publications Warehouse

Wilson, Frederic H.; Hults, Chad P.; Labay, Keith A.; Shew, Nora B.

2007-01-01

The growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO export files and as ArcView shape files. The files named __geol contain geologic polygons and line (contact) attributes; files named __fold contain fold axes; files named __lin contain lineaments; and files named __dike contain dikes as lines. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.

Next Generation Mapping of Enological Traits in an F2 Interspecific Grapevine Hybrid Family

PubMed Central

Sun, Qi; Manns, David C.; Sacks, Gavin L.; Mansfield, Anna Katharine; Luby, James J.; Londo, Jason P.; Reisch, Bruce I.; Cadle-Davidson, Lance E.; Fennell, Anne Y.

2016-01-01

In winegrapes (Vitis spp.), fruit quality traits such as berry color, total soluble solids content (SS), malic acid content (MA), and yeast assimilable nitrogen (YAN) affect fermentation or wine quality, and are important traits in selecting new hybrid winegrape cultivars. Given the high genetic diversity and heterozygosity of Vitis species and their tendency to exhibit inbreeding depression, linkage map construction and quantitative trait locus (QTL) mapping has relied on F1 families with the use of simple sequence repeat (SSR) and other markers. This study presents the construction of a genetic map by single nucleotide polymorphisms identified through genotyping-by-sequencing (GBS) technology in an F2 mapping family of 424 progeny derived from a cross between the wild species V. riparia Michx. and the interspecific hybrid winegrape cultivar, ‘Seyval’. The resulting map has 1449 markers spanning 2424 cM in genetic length across 19 linkage groups, covering 95% of the genome with an average distance between markers of 1.67 cM. Compared to an SSR map previously developed for this F2 family, these results represent an improved map covering a greater portion of the genome with higher marker density. The accuracy of the map was validated using the well-studied trait berry color. QTL affecting YAN, MA and SS related traits were detected. A joint MA and SS QTL spans a region with candidate genes involved in the malate metabolism pathway. We present an analytical pipeline for calling intercross GBS markers and a high-density linkage map for a large F2 family of the highly heterozygous Vitis genus. This study serves as a model for further genetic investigations of the molecular basis of additional unique characters of North American hybrid wine cultivars and to enhance the breeding process by marker-assisted selection. The GBS protocols for identifying intercross markers developed in this study can be adapted for other heterozygous species. PMID:26974672

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application

PubMed Central

Maxwell, Susan K.

2010-01-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. PMID:21135917

Satellite Data Sets in the Polar Regions

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Busalacchi, Antonio J. (Technical Monitor)

2000-01-01

We have generated about two decades of consistently derived geophysical parameters in the polar regions. The key parameters are sea ice concentration, surface temperature, albedo, and cloud cover statistics. Sea ice concentrations were derived from the Scanning Multichannel Microwave Radiometer (SMMR) data and the Special Scanning Cl Microwave Imager (SSM/I) data from several platforms using the enhanced Bootstrap Algorithm for the period 1978 through 1999. The new algorithm reduces the errors associated with spatial and temporal variations in the emissivity and surface temperatures of sea ice. Also, bad data at ocean/land interfaces are identified and deleted in an unsupervised manner. Surface ice temperature, albedo and cloud cover statistics are derived simultaneously from the Advanced Very High Resolution Radiometer (AVHRR) data from 1981 through 1999 and mapped at a higher resolution but the same format as the ice concentration data. The technique makes use these co-registered ice concentration maps to enable cloud masking to be done separately for open ocean, sea ice and land areas. The effect of inversion is minimized by taking into consideration the expected changes in the effect of inversion with altitude, especially in the Antarctic. A technique for ice type regional classification has also been developed using multichannel cluster analysis and a neural network. This provide a means to identify large areas of thin ice, first year ice, and older ice types. The data sets have been shown to be coherent with each other and provide a powerful tool for in depth studies of the currently changing Arctic and Antarctic environment.

Advanced backcross QTL mapping of resistance to Fusarium head blight and plant morphological traits in a Triticum macha × T. aestivum population.

PubMed

Buerstmayr, Maria; Lemmens, Marc; Steiner, Barbara; Buerstmayr, Hermann

2011-07-01

While many reports on genetic analysis of Fusarium head blight (FHB) resistance in bread wheat have been published during the past decade, only limited information is available on FHB resistance derived from wheat relatives. In this contribution, we report on the genetic analysis of FHB resistance derived from Triticum macha (Georgian spelt wheat). As the origin of T. macha is in the Caucasian region, it is supposed that its FHB resistance differs from other well-investigated resistance sources. To introduce valuable alleles from the landrace T. macha into a modern genetic background, we adopted an advanced backcross QTL mapping scheme. A backcross-derived recombinant-inbred line population of 321 BC(2)F(3) lines was developed from a cross of T. macha with the Austrian winter wheat cultivar Furore. The population was evaluated for Fusarium resistance in seven field experiments during four seasons using artificial inoculations. A total of 300 lines of the population were genetically fingerprinted using SSR and AFLP markers. The resulting linkage map covered 33 linkage groups with 560 markers. Five novel FHB-resistance QTL, all descending from T. macha, were found on four chromosomes (2A, 2B, 5A, 5B). Several QTL for morphological and developmental traits were mapped in the same population, which partly overlapped with FHB-resistance QTL. Only the 2BL FHB-resistance QTL co-located with a plant height QTL. The largest-effect FHB-resistance QTL in this population mapped at the spelt-type locus on chromosome 5A and was associated with the wild-type allele q, but it is unclear whether q has a pleiotropic effect on FHB resistance or is closely linked to a nearby resistance QTL.

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

NASA Astrophysics Data System (ADS)

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.

Application of Landsat 5-TM and GIS data to elk habitat studies in northern Idaho

NASA Astrophysics Data System (ADS)

Hayes, Stephen Gordon

1999-12-01

An extensive geographic information system (GIS) database and a large radiotelemetry sample of elk (n = 153) were used to study habitat use and selection differences between cow and bull elk (Cervus elaphus) in the Coeur d'Alene Mountains of Idaho. Significant sex differences in 40 ha area use, and interactive effects of sex and season on selection of 40 ha areas from home ranges were found. In all seasons, bulls used habitats with more closed canopy forest, more hiding cover, and less shrub and graminoid cover, than cows. Cows selected areas with shrub and graminoid cover in winter and avoided areas with closed canopy forest and hiding cover in winter and summer seasons. Both sexes selected 40 ha areas of unfragmented hiding cover and closed canopy forest during the hunting season. Bulls also avoided areas with high open road densities during the rut and hunting season. These results support present elk management recommendations, but our observations of sexual segregation provide biologists with an opportunity to refine habitat management plans to target bulls and cows specifically. Furthermore, the results demonstrate that hiding cover and canopy closure can be accurately estimated from Landsat 5-TM imagery and GIS soil data at a scale and resolution to which elk respond. As a result, our habitat mapping methods can be applied to large areas of private and public land with consistent, cost-efficient results. Non-Lambertian correction models of Landsat 5-TM imagery were compared to an uncorrected image to determine if topographic normalization increased the accuracy of elk habitat maps of forest structure in northern Idaho. The non-Lambertian models produced elk habitat maps with overall and kappa statistic accuracies as much as 21.3% higher (p < 0.0192) than the uncorrected image. Log-linear models and power analysis were used to study the dependence of commission and omission error rates on topographic normalization, vegetation type, and solar incidence angle. Examination of Type I and Type II likelihood ratio test error rates indicated that topographic normalization increased accuracy in sapling/pole closed forest, clearcuts, open forest, and shrubfields. Non-Lambertian models that allowed the Minnaert constant (k) to vary as a function of solar incidence and vegetation type offered no improvement in accuracy over the non-Lambertian model with k estimated for each TM band. The bias of habitat use proportion estimates, derived from the most accurate map, was quantified and the applicability of the non-Lambertian model to elk habitat mapping is discussed.

Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image.

PubMed

Rapinel, Sébastien; Clément, Bernard; Magnanon, Sylvie; Sellin, Vanessa; Hubert-Moy, Laurence

2014-11-01

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers. Copyright © 2014 Elsevier Ltd. All rights reserved.

Global land cover mapping: a review and uncertainty analysis

USGS Publications Warehouse

Congalton, Russell G.; Gu, Jianyu; Yadav, Kamini; Thenkabail, Prasad S.; Ozdogan, Mutlu

2014-01-01

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

PubMed

Na, X D; Zang, S Y; Wu, C S; Li, W L

2015-11-01

Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area.

Digital data for the geology of the Southern Brooks Range, Alaska

USGS Publications Warehouse

Till, Alison B.; Dumoulin, Julie A.; Harris, Anita G.; Moore, Thomas E.; Bleick, Heather A.; Siwiec, Benjamin; Labay, Keith A.; Wilson, Frederic H.; Shew, Nora B.

2008-01-01

The growth in the use of Geographic Information Systems (GIS) has highlighted the need for digital geologic maps that have been attributed with information about age and lithology. Such maps can be conveniently used to generate derivative maps for manifold special purposes such as mineral-resource assessment, metallogenic studies, tectonic studies, and environmental research. This report is part of a series of integrated geologic map databases that cover the entire United States. Three national-scale geologic maps that portray most or all of the United States already exist; for the conterminous U.S., King and Beikman (1974a,b) compiled a map at a scale of 1:2,500,000, Beikman (1980) compiled a map for Alaska at 1:2,500,000 scale, and for the entire U.S., Reed and others (2005a,b) compiled a map at a scale of 1:5,000,000. A digital version of the King and Beikman map was published by Schruben and others (1994). Reed and Bush (2004) produced a digital version of the Reed and others (2005a) map for the conterminous U.S. The present series of maps is intended to provide the next step in increased detail. State geologic maps that range in scale from 1:100,000 to 1:1,000,000 are available for most of the country, and digital versions of these state maps are the basis of this product. The digital geologic maps presented here are in a standardized format as ARC/INFO export files and as ArcView shape files. The files named __geol contain geologic polygons and line (contact) attributes; files named __fold contain fold axes; files named __lin contain lineaments; and files named __dike contain dikes as lines. Data tables that relate the map units to detailed lithologic and age information accompany these GIS files. The map is delivered as a set 1:250,000-scale quadrangle files. To the best of our ability, these quadrangle files are edge-matched with respect to geology. When the maps are merged, the combined attribute tables can be used directly with the merged maps to make derivative maps.

Comparison of MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

NASA Astrophysics Data System (ADS)

Klein, A. G.; Thapa, S.

2017-12-01

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS. While it is speculated that MODIS and VIIRS snow cover products may differ because of their differing spatial resolutions and spectral coverage quantitative comparisons between their snow products are currently limited. Therefore this study intercompares MODIS and VIIRS snow products for the 2016 Hydrological Year over the Midwestern United States and southern Canada. Two hundred and forty-four swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI Snow Cover product at a snow cover fraction of 30% generated binary snow maps most comparable to the NOAA VIIRS binary snow product. Overall agreement between MODIS and VIIRS was found to be approximately 98%. This exceeds the VIIRS accuracy requirements of 90% probability of correct typing. Agreement was highest during the winter but lower during late fall and spring. Comparability was lowest over forest. MODIS and VIIRS often mapped snow/no-snow transition zones as cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicates that VIIRS is mapping more snow cover and less cloud cover compared to MODIS. This is evidenced by the average area of snow in MYD10L2 and VSCMO being 5.72% and 11.43%, no-snow 26.65% and 28.67%, and cloud 65.02% and 59.91%, respectively. Visual comparisons depict good qualitative agreement between snow cover area visible in MODIS and VIIRS false color imagery and mapped in their respective snow cover products. While VIIRS and MODIS have similar capacity to map snow cover, VIIRS has the potential to more accurately map snow cover area for the successive development of climate data records.

Landspotting: Social gaming to collect vast amounts of data for satellite validation

NASA Astrophysics Data System (ADS)

Fritz, S.; Purgathofer, P.; Kayali, F.; Fellner, M.; Wimmer, M.; Sturn, T.; Triebnig, G.; Krause, S.; Schindler, F.; Kollegger, M.; Perger, C.; Dürauer, M.; Haberl, W.; See, L.; McCallum, I.

2012-04-01

At present there is no single satellite-derived global land cover product that is accurate enough to provide reliable estimates of forest or cropland area to determine, e.g., how much additional land is available to grow biofuels or to tackle problems of food security. The Landspotting Project aims to improve the quality of this land cover information by vastly increasing the amount of in-situ validation data available for calibration and validation of satellite-derived land cover. The Geo-Wiki (Geo-Wiki.org) system currently allows users to compare three satellite derived land cover products and validate them using Google Earth. However, there is presently no incentive for anyone to provide this data so the amount of validation through Geo-Wiki has been limited. However, recent competitions have proven that incentive driven campaigns can rapidly create large amounts of input. The LandSpotting Project is taking a truly innovative approach through the development of the Landspotting game. The game engages users whilst simultaneously collecting a large amount of in-situ land cover information. The development of the game is informed by the current raft of successful social gaming that is available on the internet and as mobile applications, many of which are geo-spatial in nature. Games that are integrated within a social networking site such as Facebook illustrate the power to reach and continually engage a large number of individuals. The number of active Facebook users is estimated to be greater than 400 million, where 100 million are accessing Facebook from mobile devices. The Landspotting Game has similar game mechanics as the famous strategy game "Civilization" (i.e. build, harvest, research, war, diplomacy, etc.). When a player wishes to make a settlement, they must first classify the land cover over the area they wish to settle. As the game is played on the earth surface with Google Maps, we are able to record and store this land cover/land use classification geographically. Every player can play the game for free (i.e. a massive multiplayer online game). Furthermore, it is a social game on Facebook (e.g. invite friends, send friends messages, purchase gifts, help friends, post messages onto the wall, etc). The game is played in a web browser, therefore it runs everywhere (where Flash is supported) without requiring the user to install anything additional. At the same time, the Geo-Wiki system will be modified to use the acquired in-situ validation information to create new outputs: a hybrid land cover map, which takes the best information from each individual product to create a single integrated version; a database of validation points that will be freely available to the land cover user community; and a facility that allows users to create a specific targeted validation area, which will then be provided to the crowdsourcing community for validation. These outputs will turn Geo-Wiki into a valuable system for earth system scientists.

Mapping extent and change in surface mines within the United States for 2001 to 2006

USGS Publications Warehouse

Soulard, Christopher E.; Acevedo, William; Stehman, Stephen V.; Parker, Owen P.

2016-01-01

A complete, spatially explicit dataset illustrating the 21st century mining footprint for the conterminous United States does not exist. To address this need, we developed a semi-automated procedure to map the country's mining footprint (30-m pixel) and establish a baseline to monitor changes in mine extent over time. The process uses mine seed points derived from the U.S. Energy Information Administration (EIA), U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), and USGS National Land Cover Dataset (NLCD) and recodes patches of barren land that meet a “distance to seed” requirement and a patch area requirement before mapping a pixel as mining. Seed points derived from EIA coal points, an edited MRDS point file, and 1992 NLCD mine points were used in three separate efforts using different distance and patch area parameters for each. The three products were then merged to create a 2001 map of moderate-to-large mines in the United States, which was subsequently manually edited to reduce omission and commission errors. This process was replicated using NLCD 2006 barren pixels as a base layer to create a 2006 mine map and a 2001–2006 mine change map focusing on areas with surface mine expansion. In 2001, 8,324 km2 of surface mines were mapped. The footprint increased to 9,181 km2 in 2006, representing a 10·3% increase over 5 years. These methods exhibit merit as a timely approach to generate wall-to-wall, spatially explicit maps representing the recent extent of a wide range of surface mining activities across the country. 

Galactic reddening in 3D from stellar photometry - an improved map

NASA Astrophysics Data System (ADS)

Green, Gregory M.; Schlafly, Edward F.; Finkbeiner, Douglas; Rix, Hans-Walter; Martin, Nicolas; Burgett, William; Draper, Peter W.; Flewelling, Heather; Hodapp, Klaus; Kaiser, Nicholas; Kudritzki, Rolf-Peter; Magnier, Eugene A.; Metcalfe, Nigel; Tonry, John L.; Wainscoat, Richard; Waters, Christopher

2018-07-01

We present a new 3D map of interstellar dust reddening, covering three quarters of the sky (declinations of δ ≳ -30°) out to a distance of several kiloparsecs. The map is based on high-quality stellar photometry of 800 million stars from Pan-STARRS 1 and 2MASS. We divide the sky into sightlines containing a few hundred stars each, and then infer stellar distances and types, along with the line-of-sight dust distribution. Our new map incorporates a more accurate average extinction law and an additional 1.5 yr of Pan-STARRS 1 data, tracing dust to greater extinctions and at higher angular resolutions than our previous map. Out of the plane of the Galaxy, our map agrees well with 2D reddening maps derived from far-infrared dust emission. After accounting for a 25 per cent difference in scale, we find a mean scatter of ˜10 per cent between our map and the Planck far-infrared emission-based dust map, out to a depth of 0.8 mag in E(gP1 - rP1), with the level of agreement varying over the sky. Our map can be downloaded at http://argonaut.skymaps.info, or from the Harvard Dataverse (Green 2017).

Forgotten forests--issues and prospects in biome mapping using Seasonally Dry Tropical Forests as a case study.

PubMed

Särkinen, Tiina; Iganci, João R V; Linares-Palomino, Reynaldo; Simon, Marcelo F; Prado, Darién E

2011-11-24

South America is one of the most species diverse continents in the world. Within South America diversity is not distributed evenly at both local and continental scales and this has led to the recognition of various areas with unique species assemblages. Several schemes currently exist which divide the continental-level diversity into large species assemblages referred to as biomes. Here we review five currently available biome maps for South America, including the WWF Ecoregions, the Americas basemap, the Land Cover Map of South America, Morrone's Biogeographic regions of Latin America, and the Ecological Systems Map. The comparison is performed through a case study on the Seasonally Dry Tropical Forest (SDTF) biome using herbarium data of habitat specialist species. Current biome maps of South America perform poorly in depicting SDTF distribution. The poor performance of the maps can be attributed to two main factors: (1) poor spatial resolution, and (2) poor biome delimitation. Poor spatial resolution strongly limits the use of some of the maps in GIS applications, especially for areas with heterogeneous landscape such as the Andes. Whilst the Land Cover Map did not suffer from poor spatial resolution, it showed poor delimitation of biomes. The results highlight that delimiting structurally heterogeneous vegetation is difficult based on remote sensed data alone. A new refined working map of South American SDTF biome is proposed, derived using the Biome Distribution Modelling (BDM) approach where georeferenced herbarium data is used in conjunction with bioclimatic data. Georeferenced specimen data play potentially an important role in biome mapping. Our study shows that herbarium data could be used as a way of ground-truthing biome maps in silico. The results also illustrate that herbarium data can be used to model vegetation maps through predictive modelling. The BDM approach is a promising new method in biome mapping, and could be particularly useful for mapping poorly known, fragmented, or degraded vegetation. We wish to highlight that biome delimitation is not an exact science, and that transparency is needed on how biomes are used as study units in macroevolutionary and ecological research.

Forgotten forests - issues and prospects in biome mapping using Seasonally Dry Tropical Forests as a case study

PubMed Central

2011-01-01

Background South America is one of the most species diverse continents in the world. Within South America diversity is not distributed evenly at both local and continental scales and this has led to the recognition of various areas with unique species assemblages. Several schemes currently exist which divide the continental-level diversity into large species assemblages referred to as biomes. Here we review five currently available biome maps for South America, including the WWF Ecoregions, the Americas basemap, the Land Cover Map of South America, Morrone's Biogeographic regions of Latin America, and the Ecological Systems Map. The comparison is performed through a case study on the Seasonally Dry Tropical Forest (SDTF) biome using herbarium data of habitat specialist species. Results Current biome maps of South America perform poorly in depicting SDTF distribution. The poor performance of the maps can be attributed to two main factors: (1) poor spatial resolution, and (2) poor biome delimitation. Poor spatial resolution strongly limits the use of some of the maps in GIS applications, especially for areas with heterogeneous landscape such as the Andes. Whilst the Land Cover Map did not suffer from poor spatial resolution, it showed poor delimitation of biomes. The results highlight that delimiting structurally heterogeneous vegetation is difficult based on remote sensed data alone. A new refined working map of South American SDTF biome is proposed, derived using the Biome Distribution Modelling (BDM) approach where georeferenced herbarium data is used in conjunction with bioclimatic data. Conclusions Georeferenced specimen data play potentially an important role in biome mapping. Our study shows that herbarium data could be used as a way of ground-truthing biome maps in silico. The results also illustrate that herbarium data can be used to model vegetation maps through predictive modelling. The BDM approach is a promising new method in biome mapping, and could be particularly useful for mapping poorly known, fragmented, or degraded vegetation. We wish to highlight that biome delimitation is not an exact science, and that transparency is needed on how biomes are used as study units in macroevolutionary and ecological research. PMID:22115315

Clear-Sky Narrowband Albedo Datasets Derived from Modis Data

NASA Astrophysics Data System (ADS)

Chen, Y.; Minnis, P.; Sun-Mack, S.; Arduini, R. F.; Hong, G.

2013-12-01

Satellite remote sensing of clouds requires an accurate estimate of the clear-sky radiances for a given scene to detect clouds and aerosols and to retrieve their microphysical properties. Knowing the spatial and angular variability of clear-sky albedo is essential for predicting the clear-sky radiance at solar wavelengths. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the near-infrared (NIR; 1.24, 1.6 or 2.13 μm) and visible (VIS; 0.63 μm) channels available on the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS) to help identify clouds and retrieve their properties. Generally, clear-sky albedo for a given surface type is determined for conditions when the vegetation is either thriving or dormant and free of snow. The clear-sky albedos are derived using a radiative transfer parameterization of the impact of the atmosphere, including aerosols, on the observed reflectances. This paper presents the method of generating monthly clear-sky overhead albedo maps for both snow-free and snow-covered surfaces of these channels using one year of MODIS (Moderate Resolution Imaging Spectroradiometer) CERES products. Maps of 1.24 and 1.6 μm are being used as the background to help retrieve cloud properties (e.g., effective particle size, optical depth) in CERES cloud retrievals in both snow-free and snow-covered conditions.

A new map of global ecological land units—An ecophysiographic stratification approach

USGS Publications Warehouse

Sayre, Roger; Dangermond, Jack; Frye, Charlie; Vaughan, Randy; Aniello, Peter; Breyer, Sean P.; Cribbs, Douglas; Hopkins, Dabney; Nauman, Richard; Derrenbacher, William; Wright, Dawn J.; Brown, Clint; Convis, Charles; Smith, Jonathan H.; Benson, Laurence; Van Sistine, Darren; Warner, Harumi; Cress, Jill Janene; Danielson, Jeffrey J.; Hamann, Sharon L.; Cecere, Thomas; Reddy, Ashwan D.; Burton, Devon; Grosse, Andrea; True, Diane; Metzger, Marc; Hartmann, Jens; Moosdorf, Nils; Durr, Hans; Paganini, Marc; Defourny, Pierre; Arino, Olivier; Maynard, Simone; Anderson, Mark; Comer, Patrick

2014-01-01

In response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterized in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs. This subdivision of the Earth’s surface into relatively fine, ecological land areas is designed to be useful for various types of ecosystem research and management applications, including assessments of climate change impacts to ecosystems, economic and non-economic valuation of ecosystem services, and conservation planning.

Evaluating ecoregions for sampling and mapping land-cover patterns

Treesearch

Kurt H. Riitters; James D. Wickham; Timothy G. Wade

2006-01-01

Ecoregional stratification has been proposed for sampling and mapping land-cover composition and pattern over time. Using a wall-to-wall land-cover map of the United States, we evaluated geographic scales of variance for nine landscapelevel and eight forest pattern indices, and compared stratification by ecoregions, administrative units, and watersheds. Ecoregions...

State and evolution of the African rainforests between 1990 and 2010

PubMed Central

Mayaux, Philippe; Pekel, Jean-François; Desclée, Baudouin; Donnay, François; Lupi, Andrea; Achard, Frédéric; Clerici, Marco; Bodart, Catherine; Brink, Andreas; Nasi, Robert; Belward, Alan

2013-01-01

This paper presents a map of Africa's rainforests for 2005. Derived from moderate resolution imaging spectroradiometer data at a spatial resolution of 250 m and with an overall accuracy of 84%, this map provides new levels of spatial and thematic detail. The map is accompanied by measurements of deforestation between 1990, 2000 and 2010 for West Africa, Central Africa and Madagascar derived from a systematic sample of Landsat images—imagery from equivalent platforms is used to fill gaps in the Landsat record. Net deforestation is estimated at 0.28% yr−1 for the period 1990–2000 and 0.14% yr−1 for the period 2000–2010. West Africa and Madagascar exhibit a much higher deforestation rate than the Congo Basin, for example, three times higher for West Africa and nine times higher for Madagascar. Analysis of variance over the Congo Basin is then used to show that expanding agriculture and increasing fuelwood demands are key drivers of deforestation in the region, whereas well-controlled timber exploitation programmes have little or no direct influence on forest-cover reduction at present. Rural and urban population concentrations and fluxes are also identified as strong underlying causes of deforestation in this study. PMID:23878331

a Free and Open Source Tool to Assess the Accuracy of Land Cover Maps: Implementation and Application to Lombardy Region (italy)

NASA Astrophysics Data System (ADS)

Bratic, G.; Brovelli, M. A.; Molinari, M. E.

2018-04-01

The availability of thematic maps has significantly increased over the last few years. Validation of these maps is a key factor in assessing their suitability for different applications. The evaluation of the accuracy of classified data is carried out through a comparison with a reference dataset and the generation of a confusion matrix from which many quality indexes can be derived. In this work, an ad hoc free and open source Python tool was implemented to automatically compute all the matrix confusion-derived accuracy indexes proposed by literature. The tool was integrated into GRASS GIS environment and successfully applied to evaluate the quality of three high-resolution global datasets (GlobeLand30, Global Urban Footprint, Global Human Settlement Layer Built-Up Grid) in the Lombardy Region area (Italy). In addition to the most commonly used accuracy measures, e.g. overall accuracy and Kappa, the tool allowed to compute and investigate less known indexes such as the Ground Truth and the Classification Success Index. The promising tool will be further extended with spatial autocorrelation analysis functions and made available to researcher and user community.

Moving toward a Biomass Map of Boreal Eurasia based on ICESat GLAS, ASTER GDEM, and field measurements: Amount, Spatial distribution, and Statistical Uncertainties

NASA Astrophysics Data System (ADS)

Neigh, C. S.; Nelson, R. F.; Sun, G.; Ranson, J.; Montesano, P. M.; Margolis, H. A.

2011-12-01

The Eurasian boreal forest is the largest continuous forest in the world and contains a vast quantity of carbon stock that is currently vulnerable to loss from climate change. We develop and present an approach to map the spatial distribution of above ground biomass throughout this region. Our method combines satellite measurements from the Geoscience Laser Altimeter System (GLAS) that is carried on the Ice, Cloud and land Elevation Satellite ( ICESat), with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), and biomass field measurements collected from surveys from a number of different biomes throughout Boreal Eurasia. A slope model derived from the GDEM with quality control flags, and Moderate-resolution Imaging Spectroradiometer (MODIS) water mask was implemented to exclude poor quality GLAS shots and stratify measurements by MODIS International Geosphere Biosphere (IGBP) and World Wildlife Fund (WWF) ecozones. We derive equations from regional field measurements to estimate the spatial distribution of above ground biomass by land cover type within biome and present a map with uncertainties and limitations of this approach which can be used as a baseline for future studies.

Identifying landscape features associated with Rift Valley fever virus transmission, Ferlo region, Senegal, using very high spatial resolution satellite imagery.

PubMed

Soti, Valérie; Chevalier, Véronique; Maura, Jonathan; Bégué, Agnès; Lelong, Camille; Lancelot, Renaud; Thiongane, Yaya; Tran, Annelise

2013-03-01

Dynamics of most of vector-borne diseases are strongly linked to global and local environmental changes. Landscape changes are indicators of human activities or natural processes that are likely to modify the ecology of the diseases. Here, a landscape approach developed at a local scale is proposed for extracting mosquito favourable biotopes, and for testing ecological parameters when identifying risk areas of Rift Valley fever (RVF) transmission. The study was carried out around Barkedji village, Ferlo region, Senegal. In order to test whether pond characteristics may influence the density and the dispersal behaviour of RVF vectors, and thus the spatial variation in RVFV transmission, we used a very high spatial resolution remote sensing image (2.4 m resolution) provided by the Quickbird sensor to produce a detailed land-cover map of the study area. Based on knowledge of vector and disease ecology, seven landscape attributes were defined at the pond level and computed from the land-cover map. Then, the relationships between landscape attributes and RVF serologic incidence rates in small ruminants were analyzed through a beta-binomial regression. Finally, the best statistical model according to the Akaike Information Criterion corrected for small samples (AICC), was used to map areas at risk for RVF. Among the derived landscape variables, the vegetation density index (VDI) computed within a 500 m buffer around ponds was positively correlated with serologic incidence (p<0.001), suggesting that the risk of RVF transmission was higher in the vicinity of ponds surrounded by a dense vegetation cover. The final risk map of RVF transmission displays a heterogeneous spatial distribution, corroborating previous findings from the same area. Our results highlight the potential of very high spatial resolution remote sensing data for identifying environmental risk factors and mapping RVF risk areas at a local scale.

Identifying landscape features associated with Rift Valley fever virus transmission, Ferlo region, Senegal, using very high spatial resolution satellite imagery

PubMed Central

2013-01-01

Introduction Dynamics of most of vector-borne diseases are strongly linked to global and local environmental changes. Landscape changes are indicators of human activities or natural processes that are likely to modify the ecology of the diseases. Here, a landscape approach developed at a local scale is proposed for extracting mosquito favourable biotopes, and for testing ecological parameters when identifying risk areas of Rift Valley fever (RVF) transmission. The study was carried out around Barkedji village, Ferlo region, Senegal. Methods In order to test whether pond characteristics may influence the density and the dispersal behaviour of RVF vectors, and thus the spatial variation in RVFV transmission, we used a very high spatial resolution remote sensing image (2.4 m resolution) provided by the Quickbird sensor to produce a detailed land-cover map of the study area. Based on knowledge of vector and disease ecology, seven landscape attributes were defined at the pond level and computed from the land-cover map. Then, the relationships between landscape attributes and RVF serologic incidence rates in small ruminants were analyzed through a beta-binomial regression. Finally, the best statistical model according to the Akaike Information Criterion corrected for small samples (AICC), was used to map areas at risk for RVF. Results Among the derived landscape variables, the vegetation density index (VDI) computed within a 500 m buffer around ponds was positively correlated with serologic incidence (p<0.001), suggesting that the risk of RVF transmission was higher in the vicinity of ponds surrounded by a dense vegetation cover. The final risk map of RVF transmission displays a heterogeneous spatial distribution, corroborating previous findings from the same area. Conclusions Our results highlight the potential of very high spatial resolution remote sensing data for identifying environmental risk factors and mapping RVF risk areas at a local scale. PMID:23452759

GIS-based approach for quantifying landscape connectivity of Javan Hawk-Eagle habitat

NASA Astrophysics Data System (ADS)

Nurfatimah, C.; Syartinilia; Mulyani, Y. A.

2018-05-01

Javan Hawk-Eagle (Nisaetus bartelsi; JHE) is a law-protected endemic raptor which currently faced the decreased in number and size of habitat patches that will lead to patch isolation and species extinction. This study assessed the degree of connectivity between remnant habitat patches in central part of Java by utilizing Conefor Sensinode software as an additional tool for ArcGIS. The connectivity index was determined by three fractions which are infra, flux and connector. Using connectivity indices successfully identified 4 patches as core habitat, 9 patches as stepping-stone habitat and 6 patches as isolated habitat were derived from those connectivity indices. Those patches then being validated with land cover map derived from Landsat 8 of August 2014. 36% of core habitat covered by natural forest, meanwhile stepping stone habitat has 55% natural forest and isolated habitat covered by 59% natural forest. Isolated patches were caused by zero connectivity (PCcon = 0) and the patch size which too small to support viable JHE population. Yet, the condition of natural forest and the surrounding matrix landscape in isolated patches actually support the habitat need. Thus, it is very important to conduct the right conservation management system based on the condition of each patches.

Lithospheric magnetic field modelling of the African continent

NASA Astrophysics Data System (ADS)

Hemant, K.; Maus, S.

2003-04-01

New magnetic satellite missions in low-earth orbit are providing increasingly accurate maps of the lithospheric magnetic field. These maps can be used to infer the geological structure of regions hidden by Phanerozoic cover, taking into account our knowledge of crustal structure from surface geology and seismic methods. A GIS based modelling technique has been developed to model the various geological units of the continents using the UNESCO geological map of the world, supported by background geological information from various sources. Geological units of each region are assigned a susceptibility value based on laboratory values of the constituent rock types. Then, using the 3SMAC seismic crustal structure, a vertically integrated susceptibility (VIS) model is computed at each point of the region. Starting with this VIS model, the total field anomaly is computed at an altitude of 400 km and compared with the MF2 lithospheric magnetic field model derived from CHAMP data. The modelling results of the Precambrian units of the West African cratons agree well with MF2. The anomaly in the Central African cratonic region also correlates well, although part of it is unaccounted for as yet. Furthermore, the anomalies over the Tanzanian craton and surrounding region agree very well. Most of the regions around the South African cratons are hidden by Phanerozoic cover, yet the results above the Kaapvaal craton and the southern Zimbabwe craton around the Limpopo belt show good correspondence with the observed anomaly map. The results also suggest a probable extension of the Precambrian units below the sediments of younger age. In general, the lower crust is likely to be more mafic than presumed in our current understanding of Central Africa. Deviations in the magnitude of the anomalies in some regions are likely to be due to incomplete seismic information in those regions. Thus, the thickness of crustal layers derived from magnetic anomalies for these locations may help to constrain future geophysical models in the less explored regions of Africa.

Statewide LANDSAT inventory of California forests

NASA Technical Reports Server (NTRS)

Likens, W.; Peterson, D. (Principal Investigator)

1981-01-01

Six forest cover categories were mapped, along with 10 general land cover classes. To map the state's 100 million acres, 1.6 acre mapping units were utilized. Map products were created. Standing forest acreage for the state was computed to be 26.8 million acres.

Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

NASA Astrophysics Data System (ADS)

Gibril, Mohamed Barakat A.; Idrees, Mohammed Oludare; Yao, Kouame; Shafri, Helmi Zulhaidi Mohd

2018-01-01

The growing use of optimization for geographic object-based image analysis and the possibility to derive a wide range of information about the image in textual form makes machine learning (data mining) a versatile tool for information extraction from multiple data sources. This paper presents application of data mining for land-cover classification by fusing SPOT-6, RADARSAT-2, and derived dataset. First, the images and other derived indices (normalized difference vegetation index, normalized difference water index, and soil adjusted vegetation index) were combined and subjected to segmentation process with optimal segmentation parameters obtained using combination of spatial and Taguchi statistical optimization. The image objects, which carry all the attributes of the input datasets, were extracted and related to the target land-cover classes through data mining algorithms (decision tree) for classification. To evaluate the performance, the result was compared with two nonparametric classifiers: support vector machine (SVM) and random forest (RF). Furthermore, the decision tree classification result was evaluated against six unoptimized trials segmented using arbitrary parameter combinations. The result shows that the optimized process produces better land-use land-cover classification with overall classification accuracy of 91.79%, 87.25%, and 88.69% for SVM and RF, respectively, while the results of the six unoptimized classifications yield overall accuracy between 84.44% and 88.08%. Higher accuracy of the optimized data mining classification approach compared to the unoptimized results indicates that the optimization process has significant impact on the classification quality.

Global multiresolution models of surface wave propagation: comparing equivalently regularized Born and ray theoretical solutions

NASA Astrophysics Data System (ADS)

Boschi, Lapo

2006-10-01

I invert a large set of teleseismic phase-anomaly observations, to derive tomographic maps of fundamental-mode surface wave phase velocity, first via ray theory, then accounting for finite-frequency effects through scattering theory, in the far-field approximation and neglecting mode coupling. I make use of a multiple-resolution pixel parametrization which, in the assumption of sufficient data coverage, should be adequate to represent strongly oscillatory Fréchet kernels. The parametrization is finer over North America, a region particularly well covered by the data. For each surface-wave mode where phase-anomaly observations are available, I derive a wide spectrum of plausible, differently damped solutions; I then conduct a trade-off analysis, and select as optimal solution model the one associated with the point of maximum curvature on the trade-off curve. I repeat this exercise in both theoretical frameworks, to find that selected scattering and ray theoretical phase-velocity maps are coincident in pattern, and differ only slightly in amplitude.

False-Color-Image Map of Quadrangles 3768 and 3668, Imam-Saheb (215), Rustaq (216), Baghlan (221), and Taloqan (222) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3362, Shin-Dand (415) and Tulak (416) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3670, Jarm-Keshem (223) and Zebak (224) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3164, Lashkargah (605) and Kandahar (606) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3166, Jaldak (701) and Maruf-Nawa (702) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3366, Gizab (513) and Nawer (514) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3564, Chahriaq (Joand) (405) and Gurziwan (406) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3264, Nawzad-Musa-Qala (423) and Dehrawat (424) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3468, Chak Wardak-Syahgerd (509) and Kabul (510) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangles 3772, 3774, 3672, and 3674, Gaz-Khan (313), Sarhad (314), Kol-I-Chaqmaqtin (315), Khandud (319), Deh-Ghulaman (320), and Ertfah (321) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3470 and the Northern Edge of Quadrangle 3370, Jalal-Abad (511), Chaghasaray (512), and Northernmost Jaji-Maydan (517) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangles 3666 and 3766, Balkh (219), Mazar-I-Sharif (220), Qarqin (213), and Hazara Toghai (214) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3364, Pasa-Band (417) and Kejran (418) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3368 and Part of Quadrangle 3370, Ghazni (515), Gardez (516), and Part of Jaji-Maydan (517) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangles 3062 and 2962, Charburjak (609), Khanneshin (610), Gawdezereh (615), and Galachah (616) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3570, Tagab-E-Munjan (505) and Asmar-Kamdesh (506) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangles 3560 and 3562, Sir Band (402), Khawja-Jir (403), and Bala-Murghab (404) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangles 3764 and 3664, Jalajin (117), Kham-Ab (118), Char Shangho (123), and Sheberghan (124) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangles 3060 and 2960, Qala-I-Fath (608), Malek-Sayh-Koh (613), and Gozar-E-Sah (614) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3566, Sang-Charak (501) and Sayghan-O-Kamard (502) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3568, Polekhomri (503) and Charikar (504) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangles 3870 and 3770, Maymayk (211), Jamarj-I-Bala (212), Faydz-Abad (217), and Parkhaw (218) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3262, Farah (421) and Hokumat-E-Pur-Chaman (422) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangle 3162, Chakhansur (603) and Kotalak (604) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.

False-Color-Image Map of Quadrangles 3168 and 3268, Yahya-Wona (703), Wersek (704), Khayr-Kot (521), and Urgon (522) Quadrangles, Afghanistan

USGS Publications Warehouse

Davis, Philip A.; Turner, Kenzie J.

2007-01-01

This map is a false-color rendition created from Landsat 7 Enhanced Thematic Mapper Plus imagery collected between 1999 and 2002. The false colors were generated by applying an adaptive histogram equalization stretch to Landsat bands 7 (displayed in red), 4 (displayed in green), and 2 (displayed in blue). These three bands contain most of the spectral differences provided by Landsat imagery and, therefore, provide the most discrimination between surface materials. Landsat bands 4 and 7 are in the near-infrared and short-wave-infrared regions, respectively, where differences in absorption of sunlight by different surface materials are more pronounced than in visible wavelengths. 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). Cultural features were not derived from the Landsat base and consequently do not match it precisely. 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 (U.S. Geological Survey/Afghan Geological Survey) quadrangles covering Afghanistan. The maps for any given quadrangle have the same open-file report (OFR) 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 OFR numbers range in sequence from 1092 to 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 in cooperation with the AGS and AGCHO.