U.S. Geological Survey
Jon Dewitz
20210604
National Land Cover Database (NLCD) 2019 Impervious Surface descriptor Conterminous United States
remote-sensing image
None
None
Sioux Falls, SD
U.S. Geological Survey
https://doi.org/10.5066/P9KZCM54
https://www.mrlc.gov/data
https://www.mrlc.gov/data-services-page
Yang, L., et al.
201812
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.
publication
https://doi.org/10.1016/j.isprsjprs.2018.09.006
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.
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.
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)
2001
2019
ground condition
Every 2-3 years
-130.2328
-63.6722
52.8510
21.7423
ISO 19115 Topic Category
imageryBaseMapsEarthCover
biota
NGDA Portfolio Themes
NGDA
National Geospatial Data Asset
Land Use Land Cover Theme
USGS Thesaurus
Land cover
Image processing
GIS
U.S. Geological Survey (USGS)
digital spatial data
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
Common Geographic Areas
United States
None. Please see 'Distribution Info' for details.
None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations.
U.S. Geological Survey
Customer Service Representative
mailing and physical
USGS EROS
Sioux Falls
SD
57198-0001
USA
605/594-6151
custserv@usgs.gov
U.S. Geological Survey
None
Unclassified
N/A
Microsoft Windows 10; ESRI ArcCatalog 10.5.1, ERDAS Imagine (alternative)
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.
Unknown
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.
See https://www.mrlc.gov/data for the full list of products available.
This NLCD product is the version dated June 4, 2021.
N/A
N/A
U.S. Geological Survey
20200408
Landsat—Earth Observation Satellites
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
Digital and/or Hardcopy
1984
2013
ground condition
Landsat MSS
Landsat Multispectral Scanner (MSS)
U.S. Geological Survey
20200408
Landsat—Earth Observation Satellites
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
Digital and/or Hardcopy
1984
2013
ground condition
Landsat TM
Landsat Thematic Mapper (TM)
U.S. Geological Survey
Jon Dewitz
201901
NLCD 2016 Impervious Surface Conterminous United States
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
Digital and/or Hardcopy
2001
2016
ground condition
DEM
Digital Elevation Module (DEM)
Julia A. Barsi
Brian L. Markham
Jeffrey S. Czapla-Myers
Dennis L. Helder
Simon J. Hook
John R. Schott
Md. Obaidul Haque
20160919
Landsat-7 ETM+ radiometric calibration status
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
Digital and/or Hardcopy
1999
2020
ground condition
Landsat ETM+
Landsat Enhanced Thematic Mapper Plus (ETM+)
U.S. Geological Survey
Jon Dewitz
2017
Statistical relative gain calculation for Landsat 8
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
Digital and/or Hardcopy
2001
2016
ground condition
Landsat OLI
Landsat Operational Land Imager (OLI)
Julia A. Barsi
Brian L. Markham
Matthew Montanaro
Aaron Gerace
Simon Hook
John R. Schott
Nina G. Raqueno
Ron Morfitt
2017
Landsat-8 TIRS thermal radiometric calibration status
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
Digital and/or Hardcopy
2013
2020
ground condition
Landsat TIRS
Landsat Thermal Infrared Sensor (TIRS)
U.S. Geological Survey
Jon Dewitz
201901
NLCD 2016 Land Cover Conterminous United States
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
Digital and/or Hardcopy
2001
2016
ground condition
USGS National Land Cover Database
United States Geological Survey (USGS) National Land Cover Database (NLCD)
John L. Dwyer
David P. Roy
Brian Sauer
Calli B. Jenkerson
Hankaui K. Zhang
Leo Lymburner
20180828
Analysis Ready Data: Enabling Analysis of the Landsat Archive
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
Digital and/or Hardcopy
2018
ground condition
Landsat ARD
Landsat Analysis Ready Data (ARD)
National Geophysical Data Center (NGDC), now part of NOAA National Centers for Environmental Information (NCEI)
2011
Defense Meteorological Satellite Program (DMSP) Nighttime Lights
raster digital data
https://sos.noaa.gov/datasets/nighttime-lights/
Digital and/or Hardcopy
1994
1995
ground condition
Defense Meteorological Satellite Program (DMSP)
The Nighttime Lights of the World data set was complied from Defense Meteorological Satellite Program (DMSP) data spanning October 1994 - March 1995.
NASA/NOAA
2016
Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night band Nighttime Lights
raster digital data
https://earthdata.nasa.gov/worldview/worldview-image-archive/the-day-night-band-enhanced-near-constant-contrast-of-viirs
Digital and/or Hardcopy
2016
observed
Visible Infrared Imaging Radiometer Suite (VIIRS)
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.
Korem
2019
NAVSTREETS™ (formerly known as Navteq), now known as HERE Map Data
tabular digital data
https://www.navmart.com/products/here-navstreets/
Digital and/or Hardcopy
2019
observed
NAVSTREETS™
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.
USDA Forest Service
Department of Interior (DOI)
2019
LANDFIRE (LF), Landscape Fire and Resource Management
raster digital data
https://landfire.gov/getdata.php
Digital and/or Hardcopy
2019
observed
Landfire
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.
U.S. Department of Energy (DOE)
Wind Energy Technologies Office (WETO) via the Lawrence Berkeley National Laboratory (LBNL) Electricity Markets and Policy Group
U.S. Geological Survey (USGS) Energy Resources Program
American Clean Power Association (ACP)
2020
The United States Wind Turbine Database (USWTDB)
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/
Digital and/or Hardcopy
2019
observed
US Wind Turbine
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.
Oak Ridge National Laboratory
Argonne National Laboratory
20190621
Oil and Natural Gas Wells
vector digital data
https://gii.dhs.gov/HIFLD
https://hifld-geoplatform.opendata.arcgis.com
Digital and/or Hardcopy
2019
observed
Oil and Natural Gas Wells
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.
Microsoft
2019
Building Footprints in the US
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.
Digital and/or Hardcopy
2019
observed
US Building Footprint
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.
U.S. Geological Survey (USGS)
2020
Land Change Monitoring, Assessment, and Projection
tabular digital data
https://www.usgs.gov/core-science-systems/eros/lcmap/data-tools
Digital and/or Hardcopy
1985
2019
observed
LCMAP
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.
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
These two sets of regression tree models were the basis of two 2011 initial impervious surface maps.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
Landsat MSS
Landsat TM
DEM
Landsat ETM+
Landsat OLI
Landsat TIRS
Landsat ARD
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
NAVSTREETS™
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
Landfire
US Wind Turbine
Oil and Natural Gas Wells
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
US Wind Turbine
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
Oil and Natural Gas Wells
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
US Building Footprint
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
Impervious pixels from LCMAP were used to fill in gaps left when roads were updated from previous versions of NLCD.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
LCMAP
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
Each of the input layers were added to the Impervious Descriptor in the following order: roads, energy production, nonroad impervious.
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
2019
USGS National Land Cover Database
Jon Dewitz
U.S. Geological Survey, CORE SCIENCE SYSTEMS
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
Raster
Grid Cell
104424
161190
1
Albers Conical Equal Area
29.5
45.5
-96.0
23.0
0.0
0.0
row and column
30.0
30.0
meters
WGS_1984
WGS 84
6378137.0
298.257223563
NLCD Impervious Descriptor Surface Attribute Table
Product showing the attributes for the impervious descriptor cover throughout CONUS
National Land Cover Database
OID
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
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
Count
A nominal integer value that designates the number of pixels that have each value in the file; histogram column in ERDAS Imagine raster attributes table.
NLCD 2019
Integer
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
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
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
Opacity
A measure of how opaque, or solid, a color is displayed in a layer.
NLCD 2019
0
1
0.01
Class_NamesImpervious Descriptor Layer definitions.NLCD 2019
127
Background value
Producer defined
0
Unclassified
Producer defined
20 - Primary road
Interstates and other major roads. Pixels were derived from the 2018 NavStreets Street Data
Producer-defined
21 - Secondary road
Non-interstate highways. Pixels were derived from the 2018 NavStreets Street Data
Producer defined
22 - Tertiary road
Any two-lane road. Pixels were derived from the 2018 NavStreets Street Data
Producer defined
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
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
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
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
27 - Wind turbines
Pixels derived from the US Wind Turbine Dataset, accessed on 1/9/2020
Producer defined
28 - Well pads
Pixels derived from the 2019 Oil and Natural Gas Wells dataset from the Oak Ridge National Laboratory
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
Impervious Surface Attributes
Attributes defined by USGS and ESRI.
U.S. Geological Survey
GS ScienceBase
mailing address
Denver Federal Center, Building 810, Mail Stop 302
Denver
CO
80225
United States
1-888-275-8747
sciencebase@usgs.gov
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.
ERDAS
Imagine 2018
.img
1012.0
https://doi.org/10.5066/P9KZCM54
None
ESRI ArcMap Suite and/or Arc/Info software, and supporting operating systems.
20210610
U.S. Geological Survey
Customer Services Representative
mailing and physical
47914 252nd Street
Sioux Falls
SD
57198-0001
USA
605/594-6151
605/594-6589
custserv@usgs.gov
FGDC Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998
local time