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nlcd_2011_land_cover_l48_20210604
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20210930
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20210930
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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
20210930
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
File Geodatabase Raster Dataset
nlcd_2011_land_cover_l48_20210604
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
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.
U.S. Geological Survey
U.S. Geological Survey
Customer Service Representative
(605) 594-6151
47914 252nd Street
Sioux Falls
SD
57198-0001
US
custserv@usgs.gov
United States
Common Geographic Areas
imageryBaseMapsEarthCover
biota
ISO 19115 Topic Category
Land Use Land Cover Theme
NGDA
National Geospatial Data Asset
NGDA Portfolio Themes
GIS
Image processing
Land cover
digital spatial data
U.S. Geological Survey (USGS)
USGS Thesaurus
United States
U.S.
US
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
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
None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations.
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 10.7.1.11595
-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
-130.232828
-63.672192
52.877264
21.742308
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
With each composite we generated a date image based on the ARD observation used for that date. In addition, we generated a clear image from the observations that were flagged as either water or clear by FMask or pixel quality information. To reduce latency, we generated the composites using the USGS High Performance Computing (HPC) Denali system.
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
Landsat ARD
USGS High Performance Computing (HPC) Denali system
USGS National Land Cover Database
The classifier was run twice, once with all land cover classes processed and the 1986 to 2019 disturbance year data included, and again with two classes - Urban and Water - omitted from the classification and the disturbance year data not included in processing as these classes have separate process steps. Urban is directly derived from percent impervious, and water is directly derived from the first classification and derived water indices from Landsat data to remove areas of spectral confusion such as shadows and deep forest.
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
Landsat TIRS
Landsat ETM+
Landsat OLI
Landsat TM
Landsat ARD
DEM
Landsat MSS
USGS National Land Cover Database
The two classifications were processed with ancillary data and the segmentation polygons to produce eight initial land cover 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
USGS National Land Cover Database
The National Land Cover Database (NLCD) is fundamentally based on the analysis of Landsat data. In previous NLCD product generation, we used individual Landsat scenes for our imagery. For NLCD 2019, we used composite images rather than individual scenes. Compositing made imagery generation more automated, reduced latency, and increased the mapping extent. For the mapping extent for NLCD 2019, we divided CONUS into 50 blocks, each containing approximately 9 path/rows.
2019-01-01
Jon Dewitz
U.S. Geological Survey, LAND RESOURCES
GEOGRAPHER
605-594-2715
47914 252Nd Street
Sioux Falls
SD
57198
US
dewitz@usgs.gov
Landsat OLI
Landsat TM
Landsat MSS
Landsat ETM+
DEM
Landsat ARD
Landsat TIRS
USGS National Land Cover Database
The final integration step resolved class label issues pertinent to local environments (such as coastal areas), and, for land cover classes other than Water (which is directly derived from a combination of Landsat indices and initial classifications) and Developed (which is directly derived from percent developed impervious surface), ensured that all pixels in a segmentation object were in the same class. Pixel-based and object-based land cover labels were checked for differences, which were reconciled by a rule-based model. Water and Developed classes kept pixel values intact even in areas that were smaller than segmentation objects. Change trajectories for each class were checked for consistency through all years.
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
Landsat ETM+
Landsat OLI
Landsat ARD
Landsat MSS
Landsat TM
Landsat TIRS
DEM
USGS National Land Cover Database
Because 2019 imagery is based upon composites, and 2001 to 2016 were previously based upon single date path rows, a bridge between these two types of imagery was needed. All preprocessing, change trajectory, and spectral indices follow the same logic as the 2001 to 2016 process. However, since the 2001 to 2016 process used static dates that could be a year prior or post the of the target year (for example, both 2015 and 2017 images were used over about 1/5 of the United States for the 2016 target year), overlap between this type of imagery was as needed. Composites were made for leaf on and leaf off in 2014, 2016, and 2019. The 2014 and 2016 images dovetail with the path row imagery previously used. This allows alignment of change dates where needed. It also provides similar imagery where comparisons between pre-and post dates for change (2014 to 2016, or 2016 to 2019) are essential. The use of the same style change pairs ensures proper phenological matches and similar spectral properties.
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
Landsat TIRS
Landsat OLI
Landsat ETM+
Landsat ARD
DEM
Landsat TM
Landsat MSS
USGS National Land Cover Database
NLCD 2019 was produced by modeling land cover change over eight intervals between 2001 and 2019, with consistent change trajectories built into the process. The first set of models in this process are for multi-spectral change detection. The Multi-Index Integrated Change Analysis (MIICA) model outputs a change map between two dates of imagery. Five spectral indices are also calculated, and a disturbance map is produced by the Vegetation Change Tracker (VCT) software. The MIICA outputs, the five spectral indices, and the 1986 to 2019 disturbance map are the inputs to the training dataset assembly stage.
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
Vegetation Change Tracker (VCT)
Multi-Index Integrated Change Analysis (MIICA)
USGS National Land Cover Database
For compositing, we generated 2014, 2016, and 2019 leaf-on, leaf-off, and reference composite using Analysis Ready Data (ARD) Surface Reflectance data. The leaf-on composite used data from May 1 to September 30. The leaf-off composite used data from November 1 through April 1. Finally, for reference we generated a 16-month composite image. Each composite that was generated used the Euclidean norm, which is the sum of the squares for each observation. We took the Euclidean norm across the individual band differences from their respective medians; the observation with the closest per-band median values for all six bands in the ARD composite is the actual surface reflectance value.
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
Landsat ARD
USGS National Land Cover Database
At this point, mappers evaluated the final composites, and if they found any additional areas that needed to be masked out, they updated the masks and created new final composites. Other datasets used as direct input into classifier along with the Landsat composites are: all NLCD land cover products produced for the 2019 edition; 3D Elevation Program (3DEP) digital elevation data; Cropland Data Layer (CDL); National Wetlands Inventory (NWI); Soil Survey Geographic (SSURGO) Database; and State Soil Geographic (STATSGO2) Database. SSURGO (with STATSGO2 to fill in gaps) was the basis for a hydric soils data layer used in training data assembly.
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
3D Elevation Program (3DEP) digital elevation data
State Soil Geographic (STATSGO2) Database
Soil Survey Geographic (SSURGO) Database
National Wetlands Inventory (NWI)
Cropland Data Layer (CDL)
USGS National Land Cover Database
Once generated, each leaf-on and leaf-off composite was then screened and masked for additional clouds, shadows, and poorly filled areas that were missed by FMask or pixel quality information For each block, we also evaluated the ARD reference composite—if that composite had any zeros in the bands, we filled in those areas with a 16-month reference surface reflectance composite, which was generated from Google Earth Engine (GEE), and produced a final reference composite. This composite is based on the image cloud cover percentage that is less than 30 percent. For each block we created a final leaf-on/leaf-off composite. If an ARD composite had no mask, the ARD composite was the final composite. If the ARD composite had areas that were masked, the leaf-on/leaf-off composite used the final reference composite to fill in those areas to create the final composite.
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
Landsat ARD
Google Earth Engine (GEE)
USGS National Land Cover Database
For each of the eight target years of Landsat data, two percent of all available training data per path/row was drawn from the data as training samples, and one percent was drawn as validation samples. The See5 decision tree classification software was run on the training samples to generate a set of rules, and the decision rules were applied to generate a land cover classification for each of the eight target years. The See5 software was run with four sets of independent variables: the 1986 to 2019 disturbance year data derived from VCT; the set of Landsat images; compactness indices from image segmentation; and a DEM and its derivatives.
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
See5
Landsat ARD
Vegetation Change Tracker (VCT)
DEM
USGS National Land Cover Database
The set of models previously developed to assemble a training dataset for each land cover class for the 2001 to 2016 process was repeated for 2014 to 2016, and 2016 to 2019. The training dataset models were built with Landsat images and derived indices, spectral change products, trajectory analysis, and ancillary data: previous years’ NLCD land cover; C-CAP land cover; CDL; NWI; a cultivated cropland 2008 to 2019 dataset; and a hydric soils dataset . Image segmentation, using Ecognition, was performed on the Landsat scenes and composites, and the resulting image objects were used to mitigate noise in the training data. The final output of this stage is training data for each of the target years, used as input into the initial land cover classification stage.
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
Cropland Data Layer (CDL)
National Wetlands Inventory (NWI)
cultivated cropland 2008 to 2019 dataset
hydric soils dataset
C-CAP land cover
USGS National Land Cover Database
A post-classification refinement process was developed to correct classification errors in each target year, check for consistency of land cover labels over time, and improve spatial coherence of land cover distribution. Refinement was conducted class-by-class in hierarchical order: (1) Water, (2) Wetlands, (3) Forest and forest transition, (4) Permanent snow, (5) Agricultural lands, and (6) Persistent shrubland and herbaceous. Models were developed for refinement of each class and each type of confusion. For example, confusion between coniferous forest and water, both spectrally "dark" could be corrected by reclassifying water to coniferous forest where slope was greater than 2 percent. Confusion between forest and cropland could be mitigated with CDL data, and so forth.
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
Cropland Data Layer (CDL)
USGS National Land Cover Database
Digital Elevation Module (DEM)
NLCD 2016 Land Cover 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
See5 (Windows 8/10) and its Linux counterpart C5.0 are sophisticated data mining tools for discovering patterns that delineate categories, assembling them into classifiers, and using them to make predictions. The See5 decision tree classification software was run on the training samples to generate a set of rules, and the decision rules were applied to generate a land cover classification for each of the eight target years. The See5® software was run with four sets of independent variables: the 1986 to 2019 disturbance year data derived from VCT; the set of Landsat images; compactness indices from image segmentation; and a DEM and its derivatives.
See5 decision tree classification software
See5
2019-01-01
RuleQuest
application/service
https://www.rulequest.com/see5-info.html
observed
1986-01-01
2019-01-01
Hydric soils are defined as those soils that are sufficiently wet in the upper part to develop anaerobic conditions during the growing season. The Hydric Soils section presents the most current information about hydric soils. The lists of hydric soils were created by using National Soil Information System (NASIS) database selection criteria that were developed by the National Technical Committee for Hydric Soils.
Hydric Soils database
hydric soils dataset
2019-01-01
USDA Natural Resources Conservation Service
vector digital data
https://data.nal.usda.gov/dataset/soil-use-hydric-soils-database
observed
2019-01-01
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer.
Cropland Data Layer
cultivated cropland 2008 to 2019 dataset
2019-01-01
National Agricultural Statistics Service (NASS)
United States Department of Agriculture (USDA)
raster digital data
https://www.nass.usda.gov/Research_and_Science/Cropland/Release/
observed
2008-01-01
2019-01-01
This online data viewer provides user-friendly access to coastal land cover and land cover change information developed through NOAA’s Coastal Change Analysis Program (C-CAP).
Coastal Change Analysis Program (C-CAP)
C-CAP land cover
2019-01-01
NOAA Office for Coastal Management
application/service
https://coast.noaa.gov/digitalcoast/tools/lca.html
observed
2019-01-01
Two new high-performance computing (HPC) options—Denali and Tallgrass.
USGS High Performance Computing (HPC) Denali system
USGS High Performance Computing (HPC) Denali system
2019-01-01
U.S. Geological Survey
application/service
https://www.usgs.gov/center-news/denali-tallgrass-eros-launch-new-era-high-performance-computing-capabilities
observed
2019-01-01
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
The 3D Elevation Program is managed by the U.S. Geological Survey (USGS) National Geospatial Program to respond to growing needs for high-quality topographic data and for a wide range of other three-dimensional (3D) representations of the Nation's natural and constructed features.
The 3D Elevation Program
3D Elevation Program (3DEP) digital elevation data
2020-01-01
U.S. Geological Survey (USGS) National Geospatial Program
raster digital data
https://viewer.nationalmap.gov/basic/
https://www.usgs.gov/core-science-systems/ngp/3dep
observed
2019-01-01
2019-01-01
Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.
Google Earth Engine
Google Earth Engine (GEE)
2019-01-01
Google
raster digital data
https://earthengine.google.com/
observed
2019-01-01
Disturbance and regrowth are vital processes in determining the roles of forest ecosystem in the carbon and biogeochemical cycles. Using time series observations, the vegetation change tracker (VCT) algorithm was designed to map the location, timing, and spectral magnitudes of forest disturbance events.
Vegetation Change Tracker (VCT) software
Vegetation Change Tracker (VCT)
2019-01-01
USDA Forest Service
application/service
https://doi.org/10.1016/j.rse.2018.11.029
observed
1986-01-01
2008-01-01
To improve the NLCD 2006 operational process, we developed a Multi-Index Integrated Change Analysis (MIICA) method at the laterstage of the NLCD 2006 project to alleviate commission and omission errors by using four spectral indices that complement each other. In addition to change location, the MIICA also generates change direction information.
Multi-Index Integrated Change Analysis (MIICA)
Multi-Index Integrated Change Analysis (MIICA)
2019-01-01
U.S. Geological Survey
application/service
Jin, Suming & Yang, Limin & Xian, G. & Danielson, P. & Homer, Collin. (2010). A Multi-Index Integrated Change Detection Method for Updating the National Land Cover Database. AGU Fall Meeting Abstracts.
observed
2001-01-01
2019-01-01
The USDA Natural Resources Conservation Service (NRCS) STATSGO2 database is a broad-based inventory of soils and non-soil areas, and is designed for broad planning and management uses covering state, regional, and multi-state areas.
State Soil Geographic (STATSGO2) Database
State Soil Geographic (STATSGO2) Database
2019-01-01
USDA Natural Resources Conservation Service (NRCS)
vector digital data
https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm
observed
2019-01-01
The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey. The information was collected in map units at scales ranging from 1:12,000 to 1:63,360. SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created.
Soil Survey Geographic (SSURGO) Database
Soil Survey Geographic (SSURGO) Database
2019-01-01
National Cooperative Soil Survey
vector digital data
https://gdg.sc.egov.usda.gov/
observed
2019-01-01
The U.S. Fish and Wildlife Service's National Wetlands Inventory (NWI) provides detailed information on the abundance, characteristics, and distribution of wetlands in the United States.
National Wetlands Inventory
National Wetlands Inventory (NWI)
2021-01-01
U.S. Fish and Wildlife Service
vector digital data
https://www.fws.gov/wetlands/Data/Web-Map-Services.html
https://www.fws.gov/wetlands/Data/Data-Download.html
observed
1977-01-01
2021-01-01
Landsat Thermal Infrared Sensor (TIRS)
Landsat-8 TIRS thermal radiometric calibration status
Landsat TIRS
2017-01-01
John R. Schott
Simon Hook
Julia A. Barsi
Ron Morfitt
Matthew Montanaro
Nina G. Raqueno
Brian L. Markham
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
Landsat Operational Land Imager (OLI)
Statistical relative gain calculation for Landsat 8
Landsat OLI
2017-01-01
Dennis Helder
Cody Anderson
Drake Jeno
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
ground condition
2013-01-01
2020-01-01
Data on cultivated crops and confidence indices, available annually for 2008 to 2017 from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS).
Cropland Data Layer
Cropland Data Layer (CDL)
2017-01-01
U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)
raster digital data
https://nassgeodata.gmu.edu/CropScape/
https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php
observed
2008-01-01
2017-01-01
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 Enhanced Thematic Mapper Plus (ETM+)
Landsat-7 ETM+ radiometric calibration status
Landsat ETM+
2016-09-19
Brian L. Markham
Jeffrey S. Czapla-Myers
John R. Schott
Dennis L. Helder
Julia A. Barsi
Md. Obaidul Haque
Simon J. Hook
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 Analysis Ready Data (ARD)
Analysis Ready Data: Enabling Analysis of the Landsat Archive
Landsat ARD
2018-08-28
Leo Lymburner
Hankaui K. Zhang
Calli B. Jenkerson
John L. Dwyer
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
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
2
104424
161190
1
VAT_nlcd_2011_land_cover_l48_20210604
Land Cover class counts and descriptions for the NLCD Land Cover Database
National Land Cover Database
Table
256
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Blue
Blue color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
NLCD 2019
0
255
Blue
Integer
4
0
0
Green
Green color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
NLCD 2019
0
255
Green
Integer
4
0
0
Opacity
A measure of how opaque, or solid, a color is displayed in a layer.
NLCD 2019
0
0.1
0.01
Opacity
Integer
4
0
0
Red
Red color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
NLCD 2019
0
255
Red
Integer
4
0
0
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.
ESRI
Integer
Count
Double
8
0
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
127
Background value
Producer defined
0
100
percentage
0.1
Value
Integer
4
0
0
NLCD_Land_Cover_Class
NLCD_Land_Cover_Class
String
255
0
0
Land Cover Class RGB Color Value Table. The specific RGB values for the NLCD Land Cover Class's that were used for NLCD 2019.
Attributes defined by USGS and ESRI. Value Red Green Blue 0 0 0 0 11 70 107 159 12 209 222 248 21 222 197 197 22 217 146 130 23 235 0 0 24 171 0 0 31 179 172 159 41 104 171 95 42 28 95 44 43 181 197 143 52 204 184 121 71 223 223 194 81 220 217 57 82 171 108 40 90 184 217 235 95 108 159 184
dataset
Pixel
104424
161190
30.000000
30.000000
8
1
Upper Left
TRUE
LZ77
1
pixel codes
FGDBR
TRUE
row and column
30.000000
30.000000