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STYLE="text-align:Left;"><DIV><DIV><P><SPAN>USGS NLCD Imperviousness 2001 Descriptor. 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 a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details. Disclaimer: This product is for informational purposes only and may not be suitable for legal, engineering, or surveying purposes. It does not represent an official survey and represents only the approximate relative location of features and boundaries. Mapping may not necessarily reflect on-the-ground conditions. This product and those involved in its production make no claims as to the accuracy or reliability of the data, and neither assumes, nor will accept liability for their use.</SPAN></P></DIV></DIV></DIV> USGS NLCD Imperviousness 2001 Descriptor. The goal of this project is to provide the Nation with complete, current and consistent public domain information on its land use and land cover. U.S. Geological Survey U.S. Geological Survey Customer Service Representative 605/594-6151 USGS EROS Sioux Falls SD 57198-0001 US custserv@usgs.gov Common Geographic Areas United States ISO 19115 Topic Category imageryBaseMapsEarthCover biota NGDA Portfolio Themes Land Use Land Cover Theme NGDA National Geospatial Data Asset USGS Thesaurus GIS Image processing Land cover digital spatial data U.S. Geological Survey (USGS) 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 United States U.S. US Land Use Land Cover Theme GIS imageryBaseMapsEarthCover Image processing Land cover biota NGDA digital spatial data United States United States U.S. US U.S. Geological Survey (USGS) National Geospatial Data Asset 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> 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 -130.2328 -63.6722 21.7423 52.851 ground condition 2001-01-01 2019-01-01 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) Version 6.2 (Build 9200) ; Esri ArcGIS 10.9.1.28388 1 -107.249941 -93.321062 36.706972 25.373245 See https://www.mrlc.gov/data for the full list of products available. This NLCD product is the version dated June 4, 2021. A formal accuracy assessment has not been conducted for NLCD 2019 Land Cover, NLCD 2019 Land Cover Change, or NLCD 2019 Impervious Surface products. A 2016 accuracy assessment publication can be found here: James Wickham, Stephen V. Stehman, Daniel G. Sorenson, Leila Gass, Jon A. Dewitz., Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States: Remote Sensing of Environment, Volume 257, 2021, 112357, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2021.112357. 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 Land Change Monitoring, Assessment, and Projection (LCMAP) is a U.S. Geological Survey (USGS) science initiative being implemented at the USGS Earth Resources Observation and Science (EROS) Center, that centers on structured, operational, ongoing, and timely collection and delivery of accurate and relevant data, information, and knowledge on land use, cover, and condition. Land Change Monitoring, Assessment, and Projection LCMAP 2020-01-01 U.S. Geological Survey (USGS) tabular digital data https://www.usgs.gov/core-science-systems/eros/lcmap/data-tools observed 1985-01-01 2019-01-01 Microsoft has made 124,885,597 footprints from all 50 U.S. states available as open data. The building footprints were extracted from Bing imagery using a combination of deep learning to identify building polygons and then a polygonization algorithm to clean up the edges of the buildings. Building Footprints in the US US Building Footprint 2019-01-01 Microsoft application/service https://www.gislounge.com/almost-125-million-building-footprints-us-now-available-open-data/#:~:text=Microsoft%20has%20made%20124%2C885%2C597%20footprints%20from%20all%2050,extract%20and%20refine%20building%20footprints%20from%20aerial%20imagery. observed 2019-01-01 This feature class/shapefile represents Oil and Natural Gas Wells. An Oil and Natural Gas Well is a hole drilled in the earth for the purpose of finding or producing crude oil or natural gas; or producing services related to the production of crude or natural gas. Oil and Natural Gas Wells Oil and Natural Gas Wells 2019-06-21 Oak Ridge National Laboratory Argonne National Laboratory vector digital data https://gii.dhs.gov/HIFLD https://hifld-geoplatform.opendata.arcgis.com observed 2019-01-01 The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. The United States Wind Turbine Database (USWTDB) US Wind Turbine 2020-01-01 Wind Energy Technologies Office (WETO) via the Lawrence Berkeley National Laboratory (LBNL) Electricity Markets and Policy Group U.S. Department of Energy (DOE) U.S. Geological Survey (USGS) Energy Resources Program American Clean Power Association (ACP) application/service Hoen, B.D., Diffendorfer, J.E., Rand, J.T., Kramer, L.A., Garrity, C.P., and Hunt, H.E., 2018, United States Wind Turbine Database (V4.0, (April 9, 2021): U.S. Geological Survey, American Clean Power Association, and Lawrence Berkeley National Laboratory data release, https://doi.org/10.5066/F7TX3DN0. https://eerscmap.usgs.gov/uswtdb/ observed 2019-01-01 Landsat Enhanced Thematic Mapper Plus (ETM+) Landsat-7 ETM+ radiometric calibration status Landsat ETM+ 2016-09-19 John R. Schott Dennis L. Helder Md. Obaidul Haque Jeffrey S. Czapla-Myers Brian L. Markham Simon J. Hook Julia A. Barsi publication https://www.usgs.gov/core-science-systems/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con https://doi.org/10.1117/12.2238625 ground condition 1999-01-01 2020-01-01 Landsat Thermal Infrared Sensor (TIRS) Landsat-8 TIRS thermal radiometric calibration status Landsat TIRS 2017-01-01 Brian L. Markham Matthew Montanaro Simon Hook John R. Schott Aaron Gerace Julia A. Barsi Nina G. Raqueno Ron Morfitt publication https://www.usgs.gov/core-science-systems/nli/landsat/landsat-8?qt-science_support_page_related_con=0#qt-science_support_page_related_con https://doi.org/10.1117/12.2276045 ground condition 2013-01-01 2020-01-01 Landsat Operational Land Imager (OLI) Statistical relative gain calculation for Landsat 8 Landsat OLI 2017-01-01 U.S. Geological Survey Jon Dewitz raster digital data Yang, L., et al. (2018). "A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies." ISPRS Journal of Photogrammetry and Remote Sensing 146: 108-123. https://doi.org/10.5066/P96HHBIE ground condition 2001-01-01 2016-01-01 LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture Forest Service and U.S. Department of the Interior, providing landscape scale geo-spatial products to support cross-boundary planning, management, and operations. LANDFIRE (LF), Landscape Fire and Resource Management Landfire 2019-01-01 USDA Forest Service Department of Interior (DOI) raster digital data https://landfire.gov/getdata.php observed 2019-01-01 The NAVSTREETS digital street network database is built using the industry’s most extensive development process to compile, test and retest data on the road. (formerly known as Navteq) Now known as HERE Map Data, it includes streets of all classes, parks, water features, points-of-interest, political boundaries and much more. NAVSTREETS™ (formerly known as Navteq), now known as HERE Map Data NAVSTREETS™ 2019-01-01 Korem tabular digital data https://www.navmart.com/products/here-navstreets/ observed 2019-01-01 The VIIRS Nighttime Imagery (Day/Night Band, Enhanced Near Constant Contrast) layer shows the Earth’s surface and atmosphere using a sensor designed to capture low-light emission sources, under varying illumination conditions. Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night band Nighttime Lights Visible Infrared Imaging Radiometer Suite (VIIRS) 2016-01-01 NASA/NOAA raster digital data https://earthdata.nasa.gov/worldview/worldview-image-archive/the-day-night-band-enhanced-near-constant-contrast-of-viirs observed 2016-01-01 The Nighttime Lights of the World data set was complied from Defense Meteorological Satellite Program (DMSP) data spanning October 1994 - March 1995. Defense Meteorological Satellite Program (DMSP) Nighttime Lights Defense Meteorological Satellite Program (DMSP) 2011-01-01 National Geophysical Data Center (NGDC), now part of NOAA National Centers for Environmental Information (NCEI) raster digital data https://sos.noaa.gov/datasets/nighttime-lights/ ground condition 1994-01-01 1995-01-01 Landsat Analysis Ready Data (ARD) Analysis Ready Data: Enabling Analysis of the Landsat Archive Landsat ARD 2018-08-28 Brian Sauer Calli B. Jenkerson John L. Dwyer Hankaui K. Zhang David P. Roy Leo Lymburner publication https://www.usgs.gov/core-science-systems/nli/landsat/us-landsat-analysis-ready-data?qt-science_support_page_related_con=0#qt-science_support_page_related_con https://doi.org/10.3390/rs10091363 ground condition 2018-01-01 United States Geological Survey (USGS) National Land Cover Database (NLCD) NLCD 2016 Land Cover Conterminous United States USGS National Land Cover Database 2019-01-01 U.S. Geological Survey Jon Dewitz raster digital data Yang, L., et al. (2018). "A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies." ISPRS Journal of Photogrammetry and Remote Sensing 146: 108-123. https://doi.org/10.5066/P96HHBIE ground condition 2001-01-01 2016-01-01 Landsat Multispectral Scanner (MSS) Landsat—Earth Observation Satellites Landsat MSS 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 2013-01-01 Digital Elevation Module (DEM) NLCD 2016 Impervious Surface Conterminous United States DEM 2019-01-01 U.S. Geological Survey Jon Dewitz raster digital data Yang, L., et al. (2018). "A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies." ISPRS Journal of Photogrammetry and Remote Sensing 146: 108-123. https://doi.org/10.5066/P96HHBIE ground condition 2001-01-01 2016-01-01 Landsat Thematic Mapper (TM) Landsat—Earth Observation Satellites Landsat TM 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 2013-01-01 These two sets of regression tree models were the basis of two 2011 initial impervious surface maps. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) USGS National Land Cover Database Also in 2019, we obtained well pad locations from the Oil and Natural Gas Wells dataset. Again, point locations were converted to pixels, sorted by year with our current change detection methods, and added to the Impervious Descriptor layer. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) Oil and Natural Gas Wells USGS National Land Cover Database The nonroad impervious surface class was improved by the addition of Microsoft US Building Footprint dataset and the LCMAP impervious pixels. For low-intensity developed locations where buildings were not previously captured, the center points of the Microsoft Buildings vector polygons were converted to pixels, which were then sorted by year and added to the Impervious Descriptor Layer. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov US Building Footprint Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) USGS National Land Cover Database Defense Meteorological Satellite Program (DMSP) Nighttime Lights and Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night band Nighttime Lights have been applied by many researchers as a way to study such topics as population density and economic activity. These two datasets are used in the generation of training data for the new NLCD 2019 Impervious Surface for 2011 and 2016, respectively. DMSP Nighttime Lights (for 2011) and VIIRS Day/Night band Nighttime Lights (for 2016) were superimposed on NLCD 2011 Impervious Surface data to exclude low density impervious areas outside urban and suburban centers, and ensure that only core urban areas were included in training data development. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) USGS National Land Cover Database Each of the input layers were added to the Impervious Descriptor in the following order: roads, energy production, nonroad impervious. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) USGS National Land Cover Database The impervious descriptor layer categorizes developed pixels according to source and type. This allows users extra flexibility for manipulating and categorizing impervious surface and gives them the ability to more accurately identify new impervious growth for each year. The impervious descriptor information is generated from roads, urban areas, and energy production sites. Roads for all years were derived from NAVSTREETS™ and hand edits for the entire United States. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) NAVSTREETS™ USGS National Land Cover Database The last step is a clean-up to correct mapping errors with automated processes and some hand editing. For example, false impervious estimates in mines and barren land were removed, and developed areas with low imperviousness, such as city parks and golf courses, were added. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) USGS National Land Cover Database Two training datasets, one of larger extent, one smaller to have different proportions of higher and lower relative impervious according to the extent of brighter and dimmer nighttime lighting, were assembled by imposing two separate thresholds of nighttime lights imagery onto the NLCD 2016 Impervious Surface data layer. Each of the two training datasets were used separately to build regression tree models for predicting percent impervious surface from zero to 100% using Landsat imagery from each respective year as predictive variables. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) USGS National Land Cover Database The two pairs of initial impervious surface maps - 2 each for an earlier and later image (for example, 2004 was compared to 2001, 2006 to 2004, etc. up to 2019.), - were compared to remove false estimates caused by high reflectance in non-urban areas, as well as to retain impervious values unchanged from the earlier year. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) USGS National Land Cover Database The same two training datasets were used with 2016 Landsat imagery to create two sets of regression tree models and two 2016 initial impervious surface maps. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Defense Meteorological Satellite Program (DMSP) Landsat TIRS Landsat OLI DEM Landsat TM Landsat ARD Landsat ETM+ Visible Infrared Imaging Radiometer Suite (VIIRS) Landsat MSS USGS National Land Cover Database In 2019 NLCD obtained wind turbine locations from the US Wind Turbine dataset. Point locations were converted to pixels, sorted by year according to our change detection methods, and added to the Impervious Descriptor layer. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Defense Meteorological Satellite Program (DMSP) US Wind Turbine Visible Infrared Imaging Radiometer Suite (VIIRS) USGS National Land Cover Database Three data sources were used to capture energy production areas. For the 2016 NLCD energy production sites were generated by the Landfire project (a partner in the MRLC consortium). This included wind turbines and well pads. Some wells were manually digitized and added to the layer if they were not captured with spectral methods. After the energy production areas were identified, the NLCD team used shape extraction features to isolate each site into a single feature, and linked these single features to the land cover disturbance map used to create the land cover change products. This linkage enabled the individual site features to be labeled with a date of change and subsequently represented with the correct year of change in the Impervious Surface product. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Landfire US Wind Turbine Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) Oil and Natural Gas Wells USGS National Land Cover Database Impervious pixels from LCMAP were used to fill in gaps left when roads were updated from previous versions of NLCD. 2019-01-01 Jon Dewitz U.S. Geological Survey, CORE SCIENCE SYSTEMS GEOGRAPHER 605-594-2715 47914 252Nd Street Sioux Falls SD 57198 US dewitz@usgs.gov Visible Infrared Imaging Radiometer Suite (VIIRS) Defense Meteorological Satellite Program (DMSP) LCMAP USGS National Land Cover Database 2 104424 161190 1 SDE_VAT_135 Product showing the attributes for the impervious descriptor cover throughout CONUS National Land Cover Database Table 12 OBJECTID OBJECTID OID 4 10 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Blue Blue color code for RGB slice by value for imperviousness image display purposes. The value is arbitrarily assigned by the display software package, unless defined by user. Standard user defined ramp for NLCD project is start color light gray, end color red. NLCD 2019 0 255 Blue Integer 4 10 0 Count_ Count_ Double 8 38 8 Green Green color code for RGB slice by value for imperviousness image display purposes. The value is arbitrarily assigned by the display software package, unless defined by user. Standard user defined ramp for NLCD project is start color light gray, end color red. NLCD 2019 0 255 Green Integer 4 10 0 Opacity A measure of how opaque, or solid, a color is displayed in a layer. NLCD 2019 0 1 0.01 Opacity Integer 4 10 0 Red Red color code for RGB slice by value for imperviousness image display purposes. The value is arbitrarily assigned by the display software package, unless defined by user. Standard user defined ramp for NLCD project is start color light gray, end color red. NLCD 2019 0 255 Red Integer 4 10 0 Value *while the file structure shows values in range from 0-255, the values of 0-100 are the only real populated values, in addition to a background value of 127. NLCD 2019 0 100 percentage 0.1 Value Integer 4 10 0 Class_Names Impervious Descriptor Layer definitions. NLCD 2019 23 - Thinned road Small tertiary roads that generally are not paved and have been removed from the landcover but remain as part of the impervious surface product. Pixels were derived from the 2018 NavStreets Street Data Producer defined 28 - Well pads Pixels derived from the 2019 Oil and Natural Gas Wells dataset from the Oak Ridge National Laboratory Producer defined 0 Unclassified Producer defined 25 - Microsoft buildings Buildings not captured in the NLCD impervious process, and not included in the nonroad impervious surface class. Pixels derived from the Microsoft US Building Footprints dataset Producer defined 29 - Other energy production Areas previously identified as well pads and wind turbines and classified in coordination with the Landfire project Producer defined 127 Background value Producer defined 21 - Secondary road Non-interstate highways. Pixels were derived from the 2018 NavStreets Street Data Producer defined 24 - Non-road non-energy impervious Developed areas that are generally not roads or energy production; includes residential/commercial/industrial areas, parks, and golf courses Producer defined 26 - LCMAP impervious Impervious pixels from LCMAP that were used to fill in gaps left when roads were updated from previous versions of NLCD Producer defined 22 - Tertiary road Any two-lane road. Pixels were derived from the 2018 NavStreets Street Data Producer defined 27 - Wind turbines Pixels derived from the US Wind Turbine Dataset, accessed on 1/9/2020 Producer defined 20 - Primary road Interstates and other major roads. Pixels were derived from the 2018 NavStreets Street Data Producer-defined Class_Names String 255 0 0 Impervious Surface Attributes Attributes defined by USGS and ESRI. dataset Pixel 40235 41251 30.000000 30.000000 8 1 Upper Left TRUE LZ77 1 pixel codes SDR TRUE row and column 30.000000 30.000000 20220421