Filters: Tags: landcover (X)
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Digital Landcover Dataset for the Southwestern United States - Arizona/New Mexico Plateau eco-region
Multi-season satellite imagery (Landsat ETM+) from 1999-2001 were used in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) to model natural and semi-natural vegetation. The minimum mapping unit for this dataset is approximately 1 acre. Landcover classes are drawn from NatureServe's Ecological System concept, with 109 of the 125 total classes mapped at the system level. For the majority of classes, a decision tree classifier was used to discriminate landcover types, while a minority of classes (e.g. urban classes, sand dunes, burn scars, etc.) were mapped using other techniques. Twenty mapping areas, each characterized by similar ecological and spectral characteristics,...
Tags: landcover,
vegetation cover
This dataset contains generalized landcover data for the Prairie Farm Rehabilitation Administration (PFRA) determined agricultural extent of Manitoba and Saskatchewan. This dataset was subset into two shapefiles. The other half of this dataset covers PFRA agricultural areas of Alberta and British Columbia. The Landcover Generalization process was undertaken to solve rendering problems of the original vectorized landcover data due to its unwieldy/overwhelming size. LANDSAT 7 imagery used in the process was collected during the WGTPP. This landcover imagery has a 30 meter resolution and is stored in over 1,100 vectorized 1:50,000 map sheet tiles. The data requires over seven gigabytes of disc space. If the user wishes...
LandPro2009 is ARC's landuse/landcover GIS database for the 21-county Atlanta Region (Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Fulton, Gwinnett, Henry, Rockdale, the EPA non-attainment (8hr standard) counties of Carroll, Coweta, Barrow, Bartow, Forsyth, Hall, Newton, Paulding, Spalding and Walton and Dawson which will become a part of the 2010 Urbanized Area). LandPro2009 was created by on-screen photo-interpretation and digitizing of ortho-rectified aerial photography at a scale of 1:14,000. The primary source for this GIS database was 2009 true color imagery with 1.64-foot pixel resolution, provided by Aerials Express, Inc.
The Gulf Coast Prairie Landscape Conservation Cooperative needed seamless landcover data for the south-central United States. This information is essential for developing computer modeling tools related to the conservation of many terrestrial species and determining the quality of vegetation to assess current and desired conditions.
Categories: Data,
Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2012,
Conservation NGOs,
DATA ANALYSIS AND VISUALIZATION,
DATA ANALYSIS AND VISUALIZATION,
Data Acquisition and Development,
This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 75.5%. A series of post-modeling steps brought the final number of land cover classes to 28.
Categories: Data;
Types: Citation;
Tags: Birds,
CMR,
Charles M. Russell National Wildlife Refuge,
Data Visualization & Tools,
Landcover,
The dataset includes Land Use/Land Cover types throughout the Chenier Eco-Region in Southwest Louisiana. Using the 2015 National Aerial Imagery Program (NAIP) dataset (1m) as the basemap, E-Cognition image objects were derived from the multiresolution segmentation algorithm at 75 and 250 segments. Attempts to refine the data training methods using E-cognition, to extrapolate automating categories of this information to the entire map resulted with exceedingly low accuracy. Therefore, a raster was produced by piecing together several data resources, which provide reliable data for specific LandUse/LandCover (LULC) categories. This process involved stitching together more reliable sources for specific categories to...
Types: Citation;
Tags: Ecology,
Landcover,
Landuse,
USGS Science Data Catalog (SDC),
coastal Louisiana,
A new regional dataset was produced using decision tree classifier and other techniques to model landcover. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to derive rule sets for the various landcover classes. Eleven mapping areas, each characterized by similar ecological and spectral characteristics, were modeled independently of one another. An internal validation for modeled classes was performed on a withheld 20% of the sample data to assess model performance. Results of the validation will be presented in the final report and are not available at this time. Mapping area models were mosaicked to...
This dataset is the third (circa 2013) in a series of three 1-kilometer land use land cover (LULC) time-periods datasets (1975, 2000, and 2013) aids in monitoring change in West Africa’s land resources. To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification” (Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Gambia,
Land Cover,
Land Use,
biota,
land cover,
Generally, the mapping of land cover is done by adopting or developing a land cover classification system, delineating areas of relative homogeneity (basic cartographic objects), then labeling these areas using categories defined by the classification system. More detailed attributes of the individual areas are added as more information becomes available, and a process of validating both polygon pattern and labels is applied for editing and revising the map. This is done in an iterative fashion, with the results from one step causing re-evaluation of results from another step. In its coarse filter approach to conservation biology (e.g., Jenkins 1985, Noss 1987), gap analysis relies on maps of dominant natural land...
The Missouri Resource Assessment Partnership (MoRAP) of the University of Missouri, in conjunction with the Oklahoma Biological Survey of the University of Oklahoma, produced a vegetation and landcover GIS data layer for the eastern portions of Oklahoma. This effort was accomplished with direction and funding from the Oklahoma Department of Wildlife Conservation and state and federal partners (particularly the Gulf Coast Prairie and Great Plains Landscape Conservation Cooperatives of the U. S. Fish and Wildlife Service). The legend for the layer is based on NatureServe’s Ecological System Classification, with finer thematic units derived from land cover and abiotic modifiers of the System unit. Data for development...
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Ecological Systems,
Landcover,
Oklahoma,
Subsystems,
Vegetation
CDF-FRAP compiled the "best available" land cover data into a single data layer, to support the various analyses required for the 2002 Forest and Range Assessment. Typically the most current and detailed data were collected for various regions of the state or for unique mapping efforts (farmland, wetlands, riparian vegetation). Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common California Wildlife Wabitat Relationships (CWHR) system classification. Data sources had unique scale/resolution, multi-source data provided as 100m GRID. The original 1/2002 data used to support the Asessment is also available...
Tags: landcover,
south coast
High resolution vegetation polygons mapped by the National Park Service. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation primarily reflects conditions that existed during 1994 and 1995. Several sets of aerial photography were utilized for this project: 1) NOAA 1:24,000 March 1994 Natural Color Prints (Leaf Off) covering...
CDF-FRAP compiled the "best available" land cover data into a single data layer, to support the various analyses required for the 2002 Forest and Range Assessment. Typically the most current and detailed data were collected for various regions of the state or for unique mapping efforts (farmland, wetlands, riparian vegetation). Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common California Wildlife Wabitat Relationships (CWHR) system classification. Data sources had unique scale/resolution, multi-source data provided as 100m GRID. The original 1/2002 data used to support the Asessment is also available...
Tags: landcover,
sierra nevada
CDF-FRAP compiled the "best available" land cover data into a single data layer, to support the various analyses required for the 2002 Forest and Range Assessment. Typically the most current and detailed data were collected for various regions of the state or for unique mapping efforts (farmland, wetlands, riparian vegetation). Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common California Wildlife Wabitat Relationships (CWHR) system classification. Data sources had unique scale/resolution, multi-source data provided as 100m GRID. The original 1/2002 data used to support the Asessment is also available...
This dataset contains generalized landcover data for the Prairie Farm Rehabilitation Administration (PFRA) agricultural extent of Alberta and British Columbia. This dataset was subset into two shapefiles. The other half of this dataset covers PFRA agricultural areas of Manitoba and Saskatchewan. The Landcover Generalization process was undertaken to solve rendering problems of the original vectorized landcover data due to its unwieldy/overwhelming size. LANDSAT 7 imagery used in the process was collected during the WGTPP. This landcover imagery has a 30 meter resolution and is stored in over 1,100 vectorized 1:50,000 map sheet tiles. The data requires over seven gigabytes of disc space. If the user wishes to view...
CDF-FRAP compiled the "best available" land cover data into a single data layer, to support the various analyses required for the 2002 Forest and Range Assessment. Typically the most current and detailed data were collected for various regions of the state or for unique mapping efforts (farmland, wetlands, riparian vegetation). Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common California Wildlife Wabitat Relationships (CWHR) system classification. Data sources had unique scale/resolution, multi-source data provided as 100m GRID. The original 1/2002 data used to support the Asessment is also available...
Tags: landcover,
san joaquin
This raster file represents classification and mapping results for priority area 1 of the Cody Region and Yellowstone National Park land cover remote sensing project. Extensive field collected reference data describing the range of plant communities and habitat types comprising the Bighorn Basin have been analyzed to produce a classification of land cover types based on the Wyoming Game and Fish Department (WGFD) Wildlife Observation System (WOS). Corresponding land cover classes were subsequently spatially modeled using a non-parametric Classification and Regression Tree (CART) algorithm that integrated both spectral data from Landsat Thematic Mapper satellite imagery and a variety of ancillary environmental data...
Categories: Data;
Types: Downloadable;
Tags: Absaroka,
Bighorn Basin,
Land Cover,
Land Use,
Landcover,
The dataset includes Land Use/Land Cover types throughout the Chenier Eco-Region in Southwest Louisiana. Using the 2015 NAIP dataset (1m) as the basemap, E-Cognition image objects were derived from the multiresolution segmentation algorithm at 75 and 250 segments. Attempts to refine the data training methods using E-cognition, to extrapolate automating categories of this information to the entire map resulted with exceedingly low accuracy. Therefore, a raster was produced by piecing together several data resources, which provide reliable data for specific LULC categories. This process involved stitching together more reliable sources for specific categories to apply to higher resolution (75) segmentation product....
Types: Citation;
Tags: Landcover,
Landuse,
USGS Science Data Catalog (SDC),
coastal Louisiana,
environment,
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal,...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Acadia National Park,
ArcGIS Pro,
Arcpy,
Autoclassification,
Automation,
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal,...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Acadia National Park,
ArcGIS Pro,
Arcpy,
Autoclassification,
Automation,
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