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The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
This data release contains three 10-meter resolution GeoTIFFs representing 10-meter (35-foot), 30-meter (100-foot) and 90-meter (300-foot) riparian buffer zones along shorelines, rivers, streams, and other lotic (flowing) water features. The layers are binary, where the value of each cell represents the presence or absence of the buffer zone. In addition, the data release contains shapefile layers that document the extent of corrections that were made to the data to address errors in the stream network (see processing steps section for more details). The methodology combines various fine-scale input layers, including a 1:24k stream network and Chesapeake Bay 1-meter resolution Land Use/Land Cover to approximate...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Chesapeake,
Delaware,
District of Columbia,
Ecology,
Forestry,
This dataset provides high-resolution, species-specific land cover maps for the Hawaiian island of Lāna'i based on 2020 WorldView-2 satellite imagery. Machine learning models were trained on extensive ground control polygons and points. The land cover maps capture the distribution and diversity of vegetation with high accuracy to support conservation planning and monitoring. This data release consists of two child items, one containing the field and expert collected ground control data used to train our models, and another consisting of resulting land cover maps for the island of Lāna‘i. The research effort that generated these input data, and products are carefully described in the associated manuscript Berio Fortini...
This data set consists of ground control points used for independent pixel-level model validation (ground_control_points.gpkg): This dataset consists of 295 points distributed across the 15 vegetation classes on the island of Lāna‘i. The points were randomly generated from the final species-specific land cover classification map and stratified by class to ensure representation across all classes. The dataset provides species-specific land cover labels for the points, with the spatial location corresponding to the pixel coordinate location on the 2m resolution land cover map. Comparing modeled class assignments to these expert-validated classes enables an independent accuracy assessment supplemental to the polygon-based...
This dataset provides high-resolution, species-specific land cover maps for the Hawaiian island of Lāna'i based on 2020 WorldView-2 satellite imagery. Machine learning models were trained on extensive ground control polygons and points. The land cover maps capture the distribution and diversity of vegetation with high accuracy to support conservation planning and monitoring. This data release consists of two child items, one containing the field and expert collected ground control data used to train our models, and another consisting of resulting land cover maps for the island of Lāna‘i. The research effort that generated these input data, and products are carefully described in the associated manuscript Berio Fortini...
This raster integrates the species-specific and community classifications using a hierarchical approach based on classification certainty. A 0.66 probability threshold was applied, with pixels assigned the finest species-specific class as long as the probability exceeded the threshold. Pixels below the threshold were assigned to the broader community class meeting the threshold. This approach displays the most detailed class possible given a minimum confidence, providing a map that balances specificity and certainty. Please note that to reduce the inherent 'salt and pepper' noise in the final land cover classification map, we applied a 3x3 pixel moving window majority filter to the final classification results.
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
This tabular data set represents the percent of land cover classes in the riparian zone from the 2019 edition of the National Land Cover databases for the years 2001, 2004, 2006, 2008, 2011, 2013, 2016 and 2019 compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. These databases can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data is the "National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021)" databases for the years 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019 produced by the...
This raster depicts the distribution of broader community-level vegetation classes across Lāna‘i. To generate this map, the species-specific class probabilities were summed to get total probability of membership in each defined community class. Each pixel was then assigned to the community class with the highest probability. This generalized map allows for an assessment of vegetation patterns at a coarser categorical level across the island. Please note that to reduce the inherent 'salt and pepper' noise in the final land cover classification map, we applied a 3x3 pixel moving window majority filter to the final classification results.
This raster stack contains 15 probability layers representing the pixel-level predicted probability of membership in each species-specific vegetation class from 0 to 1. These probability layers can be used to generate class membership uncertainty maps or probabilistic class cover maps from the model outputs. They provide additional information beyond the discrete categorial land cover assignments.
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Conservation,
Hawaiian Islands,
Hawai‘i,
Invasive species,
Land cover,
These data were created to describe the causes of land cover change that occurred in the Lower Rio Grande (LRG) Valley and Alluvial Floodplain ecoregions of Texas for the time intervals of 2001 to 2006 and 2006 to 2011. The study area covers approximately 600,000 hectares at the southernmost tip of Texas and is one of the fastest growing regions in the United States. Some of the largest cities in the area include Brownsville and Harlingen, Texas. Two raster maps showing the causes of land change were created at a 30-meter resolution using automated and manual photo interpretation techniques. There were 26 categories of land change causes (for example, urban expansion or surficial mining) identified across the LRG...
Categories: Data;
Tags: LRG,
Land Use Change,
Lower Rio Grande,
Lower Rio Grande Alluvial Floodplain,
Lower Rio Grande Valley,
The National Land Cover Database (NLCD), a product suite produced through the Multi-resolution Land Characteristics (MRLC) consortium, is an operational land cover monitoring program. The release of NLCD2019 extends the database to 18 years. We collected land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate...
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
This data set consists of ground control polygons used for model training and evaluation (ground_control_polygons.gpkg): This dataset consists of refined vegetation polygons digitized across the island of Lāna‘i representing the 15 land cover classes of interest. High-resolution aerial imagery and extensive field experience were used to iteratively collect and improve the polygons through expert review and interpretation. The polygons were divided into a 250m grid overlaying the island to balance sample size and spatial resolution while reducing spatial autocorrelation, resulting in 1,754 smaller polygons. These polygon data served as the primary dataset used to train, validate, and evaluate the classification models...
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
Categories: Data,
Data Release - Revised;
Tags: GIS,
Image processing,
Land Use Land Cover Theme,
Land cover,
NGDA,
This dataset provides high-resolution, species-specific land cover maps for the Hawaiian island of Lāna'i based on 2020 WorldView-2 satellite imagery. Machine learning models were trained on extensive ground control polygons and points. The land cover maps capture the distribution and diversity of vegetation with high accuracy to support conservation planning and monitoring. This data release consists of two child items, one containing the field and expert collected ground control data used to train our models, and another consisting of resulting land cover maps for the island of Lāna‘i. The research effort that generated these input data, and products are carefully described in the associated manuscript Berio Fortini...
This raster depicts the distribution of 15 species-specific vegetation classes across the island of Lāna‘i at 2m resolution. It represents the final selected neural network model predictions with expert-adjusted posterior probabilities. Each pixel is assigned to the most likely species-specific class based on the model. Overall and class-specific accuracy assessments indicate this map has generally over 95% accuracy. It provides detailed species-level vegetation mapping to support conservation planning and monitoring. Please note that to reduce the inherent 'salt and pepper' noise in the final land cover classification map, we applied a 3x3 pixel moving window majority filter to the final classification results.
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of...
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