20210915
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FALSE
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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
20221104
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 ArcMap Suite and/or Arc/Info software, and supporting operating systems.
1012
https://doi.org/10.5066/P9KZCM54
https://www.mrlc.gov/data
https://www.mrlc.gov/data-services-page
https://doi.org/10.5066/P9KZCM54
Raster Dataset
nlcd_2019_impervious_reclass10_Va_partialNC_Albers
2021-06-04
U.S. Geological Survey
Sioux Falls, SD
U.S. Geological Survey
Jon Dewitz
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><P><SPAN>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.</SPAN></P></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) 2019 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
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><P><SPAN>None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations.</SPAN></P></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
Version 6.2 (Build 9200) ; Esri ArcGIS 10.8.0.12790
-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)
1
-83.916938
-74.357598
40.098316
34.303964
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
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
Hankaui K. Zhang
John L. Dwyer
Calli B. Jenkerson
Leo Lymburner
David P. Roy
Brian Sauer
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
Landsat Operational Land Imager (OLI)
Statistical relative gain calculation for Landsat 8
Landsat OLI
2017-01-01
Jon Dewitz
U.S. Geological Survey
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 Enhanced Thematic Mapper Plus (ETM+)
Landsat-7 ETM+ radiometric calibration status
Landsat ETM+
2016-09-19
Dennis L. Helder
Julia A. Barsi
Md. Obaidul Haque
Brian L. Markham
Simon J. Hook
Jeffrey S. Czapla-Myers
John R. Schott
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 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
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
Landsat Thermal Infrared Sensor (TIRS)
Landsat-8 TIRS thermal radiometric calibration status
Landsat TIRS
2017-01-01
Ron Morfitt
Nina G. Raqueno
Brian L. Markham
Matthew Montanaro
Julia A. Barsi
Simon Hook
John R. Schott
Aaron Gerace
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
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
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
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
Jon Dewitz
U.S. Geological Survey
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
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
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
Defense Meteorological Satellite Program (DMSP)
Visible Infrared Imaging Radiometer Suite (VIIRS)
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
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
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
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
Visible Infrared Imaging Radiometer Suite (VIIRS)
Landsat OLI
Landsat MSS
Landsat TIRS
Landsat TM
Landsat ARD
Defense Meteorological Satellite Program (DMSP)
Landsat ETM+
DEM
USGS National Land Cover Database
2
17480
30.000000
25330
30.000000
1
1
0
1084275.000000 1447125.000000
1084275.000000 1971525.000000
1844175.000000 1971525.000000
1844175.000000 1447125.000000
1464225.000000 1709325.000000
nlcd_2019_impervious_reclass_20210604_Va_partialNC_Albers.img.vat
Product showing the attributes for the impervious cover throughout CONUS
National Land Cover Database
Table
91
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
Count
Double
19
0
0
OID
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
OID
OID
4
0
0
Value
Percent Imperviousness. *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
127
Background value
Producer defined
0
100
percentage
0.1
Value
Integer
10
10
0
Class_Name
Class_Name
String
254
0
0
Impervious Surface Attributes
Attributes defined by USGS and ESRI.
dataset
Layer_1
90.000000
0.000000
8