20230919 20014600 1.0 FALSE 002 nlcd_2021_impervious_20230630_VA_NCAPNEP_poly_3cats_wm -9321008.402800 -8364507.038300 4100412.199200 4795102.071100 1 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_Web_Mercator_Auxiliary_Sphere <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.2.0'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> 150000000 5000 20240715 08324100 20240715 08324100 dataset U.S. Geological Survey Customer Services Representative 605/594-6151 605/594-6589 47914 252nd Street Sioux Falls SD 57198-0001 US custserv@usgs.gov 20240715 ArcGIS Metadata 1.0 GS ScienceBase U.S. Geological Survey 1-888-275-8747 Denver Federal Center, Building 810, Mail Stop 302 Denver CO 80225 US sciencebase@usgs.gov None ERDAS Imagine 2018 .img ESRI ArcGIS Pro software and supporting operating systems. 1012 https://doi.org/10.5066/P9JZ7AO3 https://www.mrlc.gov/data https://www.mrlc.gov/data-services-page https://doi.org/10.5066/P9JZ7AO3 File Geodatabase Feature Class nlcd_2021_impervious_20230630_VA_NCAPNEP_poly_3cats_wm 2023-06-30 U.S. Geological Survey Jon Dewitz U.S. Geological Survey Sioux Falls, SD remote-sensing image None None A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. NLCD 2021 adds an additional year to the map products produced for NLCD 2019, with a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. The overall accuracy of the 2019 Level I land cover was 91%. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2021 operational mapping (see https://doi.org/10.1080/15481603.2023.2181143 for the latest accuracy assessment publication). Questions about the NLCD 2021 land cover product can be directed to the NLCD 2021 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.</SPAN></P></DIV></DIV></DIV> Virginia and a portion of North Carolina that is within the Albemarle-Pamlico National Estuary Partnership (APNEP) management boundary, was extracted from the National Land Cover Dataset (NLCD) 2021 Impervious layer for analysis and display in the Wetland Condition and Assessment Tool (WetCAT). U.S. Geological Survey U.S. Geological Survey Customer Service Representative 605/594-6151 605/594-6589 47914 252nd Street Sioux Falls SD 57198-0001 US custserv@usgs.gov Common Geographic Areas United States NGDA Portfolio Themes Land Use Land Cover Theme National Geospatial Data Asset NGDA USGS Thesaurus digital spatial data GIS U.S. Geological Survey (USGS) Image processing Land cover ISO 19115 Topic Category biota imageryBaseMapsEarthCover U.S. Department of Commerce, 1995, (Countries, dependencies, areas of special sovereignty, and their principal administrative divisions, Federal Information Processing Standard 10-4): Washington, D.C., National Institute of Standards and Technology US United States U.S. digital spatial data GIS US United States United States biota imageryBaseMapsEarthCover Land Use Land Cover Theme U.S. U.S. Geological Survey (USGS) Image processing National Geospatial Data Asset NGDA Land cover Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. None. Please see 'Distribution Info' for details. None N/A <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations.</SPAN></P></DIV></DIV></DIV> Overall Methodology Design for the United States National Land Cover Database 2016 Products 2019-12-11 Zhe Zhu Collin Homer Danny Howard George Xian Congcong Li Suming Jin Limin Yang Patrick Danielson MDPI AG n/a Jon Dewitz publication Remote Sensing vol. 11, issue 24 ppg. 2971 https://doi.org/10.3390/rs11242971 National Land Cover Database 2019: A New Strategy for Creating Clean Leaf-On and Leaf-Off Landsat Composite Images 2023-02-21 Brian Granneman Patrick Danielson Catherine Costello American Association for the Advancement of Science (AAAS) n/a Zhe Zhu Kelcy Smith Suming Jin Jon Dewitz publication Journal of Remote Sensing vol. 3 https://doi.org/10.34133/remotesensing.0022 National Land Cover Database 2019: A Comprehensive Strategy for Creating the 1986–2019 Forest Disturbance Product 2023-02-16 Jon Dewitz Zhe Zhu Congcong Li American Association for the Advancement of Science (AAAS) n/a Leila Gass Brian Granneman Patrick Danielson Suming Jin Adam Case Daniel Sorenson Catherine Costello Md Rakibul Islam Shogib publication Journal of Remote Sensing vol. 3 https://doi.org/10.34133/remotesensing.0021 A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies 2018-12-01 ISPRS Journal of Photogrammetry and Remote Sensing 146: 108-123. Yang, L., et al. publication https://doi.org/10.1016/j.isprsjprs.2018.09.006 Esri ArcGIS 13.2.2.49743 -130.2328 -63.6722 21.7423 52.851 ground condition 2001-01-01 2021-01-01 Corner Coordinates (center of pixel, projection meters) Upper Left Corner: -2493045 meters(X), 3310005 meters(Y) Lower Right Corner: 2342655 meters(X), 177285 meters(Y) 1 -83.732043 -75.139645 39.510775 34.531629 See https://www.mrlc.gov/data for the full list of products available. This NLCD product is the version dated June 30, 2023. A formal accuracy assessment has not been conducted for NLCD 2021 Land Cover, NLCD 2021 Land Cover Change, or NLCD 2021 Impervious Surface products. A 2019 accuracy assessment publication can be found here: James Wickham, Stephen V. Stehman, Daniel G. Sorenson, Leila Gass & Jon A. Dewitz (2023) Thematic accuracy assessment of the NLCD 2019 land cover for the conterminous United States, GIScience & Remote Sensing, 60:1, DOI: 10.1080/15481603.2023.2181143. This document and the described land cover map are considered "provisional" until a formal accuracy assessment is completed. The U.S. Geological Survey can make no guarantee as to the accuracy or completeness of this information, and it is provided with the understanding that it is not guaranteed to be correct or complete. Conclusions drawn from this information are the responsibility of the user. Unknown N/A N/A This data provides locations and technical specifications of legacy and current versions of the United States Wind Turbines database. United States Wind Turbine Database Wind turbines 2021-01-01 Hoen et al vector digital data https://doi.org/10.5066/F7TX3DN0 observed 2021-01-01 The solar installations dataset is an edited version of the Solar Photovoltaic Generating Units dataset. A global inventory of solar photovoltaic generating units - dataset Solar installations 2021-01-01 Kruitwagen et al vector digital data https://doi.org/10.5281/zenodo.5005867 observed 2021-01-01 Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), Landsat Operational Land Imager (OLI), Landsat Analysis Ready Data (ARD) Landsat—Earth Observation Satellites Landsat Products 2020-04-08 U.S. Geological Survey publication https://www.usgs.gov/core-science-systems/nli/landsat/landsat-5?qt-science_support_page_related_con=0#qt-science_support_page_related_con https://doi.org/10.3133/fs20153081 ground condition 1984-01-01 2021-01-01 NLCD 2019 Impervious surface product The National Land Cover Database (NLCD) NLCD 2019 Impervious surface 2019-01-01 MRLC consortium U.S. Geological Survey raster digital data https://www.mrlc.gov observed 2019-01-01 Digital Elevation Model (DEM) LF 2020 Update Elevation products DEM 2022-01-31 USGS 3D Elevation Program (3DEP) LANDFIRE raster digital data https://landfire.gov/elevation.php https://landfire.gov/index.php ground condition 2020-01-01 NLCD 2019 Impervious descriptor product The National Land Cover Database (NLCD) NLCD 2019 Impervious descriptor 2019-01-01 U.S. Geological Survey MRLC consortium raster digital data https://www.mrlc.gov observed 2019-01-01 Initial impervious footprint - To generate an initial impervious footprint, three U-net models were trained on the multiclass-classification task of predicting “urban” and “roads”. The model was trained with 120,000 training and 40,000 validation 256X256 pixel Landsat image chips covering the entire extent of CONUS. The model inputs are consistent with what was used to generate the urban intensity U-net models; the only difference was the target mask the models were trained to predict. These models mapped all NLCD impervious footprint pixels to two classes (“urban” and “roads”); this was used to generate the impervious extent. 2021-01-01 2021 impervious product - The percent imperviousness values (1-100%) for the impervious change pixels were extracted from the unmasked impervious layer. Values for previously published urban remained the same except for areas that were 40% or more greater in value, in the unmasked impervious layer. 2021-01-01 Impervious Change Pixels - The initial 2021 impervious change pixels were created by comparing the 2021 urban footprint with the 2019 published urban descriptor and extracting the difference. These change pixels were manually edited for omission and commission errors. Ancillary data were then added to the change pixels to create the final 2021 impervious change pixels. These ancillary data consisted of solar installations, wind turbines, and roads. The solar installations dataset is an edited version of the Solar Photovoltaic Generating Units dataset produced by Kruitwagen et al (2021) (https://doi.org/10.5281/zenodo.5005867). The U.S. Wind Turbine Database from Hoen et al (2021) (https://doi.org/10.5066/F7TX3DN0) was used without edits. NavStreets road datasets were used in previous versions of NLCD but an updated version was not available to the USGS. New subdivision roads from the 2021 urban footprint and a small number of manually drawn roads were added to the 2021 impervious change pixels. 2021-01-01 Unmasked Impervious - To produce the unmasked impervious layer a multilayered perceptron neural network (MLP) was deployed across CONUS. The MLP was trained to perform the regression task of predicting the 1-100 impervious fractional cover. To sample data to train the network, we broke CONUS into a grid comprised of 256x256 pixel regions of interest (ROIs) and sampled from that grid all ROIs with at least 40% impervious cover according to NLCD 2019 impervious fractional cover, which gave us samples from large impervious areas. From those ROIs, we then sampled 66 million training and 16 million validation data points with an even distribution across each impervious intensity (1-100). Those training points were then randomly split into 4 subsets, each corresponding to one of the following respective years: 2011, 2013, 2016, 2019. We used those points to query surface reflectance values from leaf-on composite and leaf-off synthetic imagery (see metadata for NLCD 2021 land cover), elevation data, and spatial urban intensity probabilities. The spatial urban intensity probabilities were generated by an ensemble of U-net models that were trained to predict the 4 urban intensity classes as defined by the NLCD product legend (open space, low intensity, medium intensity, high intensity). Two U-net models were trained using all ROIs in the CONUS 256x256 pixel grid. Inputs to these models included leaf-on composite and leaf-off synthetic imagery, and elevation data. To create the final training and validation datasets we randomly split the CONUS grid into to 2 equal sets: A and B. Using the ROIs from set A we queried the input features from the years 2011 and 2016 and from the ROIs in set B we queried input features from the years 2013 and 2019. These U-net models do not act as the final impervious predictors but instead as spatial feature generators. The spatial features learned by these convolutional neural networks were then fed into the pixel-based MLP, as spatial probabilities of urban intensity, to boost its predicting power. The U-nets were trained using categorical focal Jaccard loss and monitored with the Jaccard Index metric (IOU). The impervious fractional cover regression model (MLP) was trained using mean squared error as a loss function and monitored with mean absolute error as the metric. 2021-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov 2021 impervious extent - The final impervious change pixels were added to that 2019 impervious descriptor file to create the new 2021 impervious descriptor file. This file maps the extent of all impervious for the 2021 NLCD. 2021-01-01 2 17481 30.000000 25330 30.000000 1 1 0 1084275.000000 1447095.000000 1084275.000000 1971525.000000 1844175.000000 1971525.000000 1844175.000000 1447095.000000 1464225.000000 1709310.000000 0 EPSG 6.18.3(9.3.1.2) Band_1 711.000000 1.000000 32 nlcd_2021_impervious_20230630_VA_NCAPNEP_poly_3cats_wm Product showing the attributes for the impervious cover throughout CONUS National Land Cover Database Feature Class 0 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. impervCat impervCat String 255 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. Shape_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. Impervious Surface Attributes Attributes defined by USGS and ESRI. Simple FALSE 0 TRUE FALSE