U.S. Geological Survey
Jon Dewitz
20210604
National Land Cover Database (NLCD) Land Cover Change Index 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
47914 252nd Street
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 TM
Landsat Thematic Mapper (TM)
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
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+)
Cody Anderson
Dennis Helder
Drake Jeno
2017
Statistical relative gain calculation for Landsat 8
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
Digital and/or Hardcopy
2013
2020
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
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
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)
U.S. Geological Survey
2019
USGS High Performance Computing (HPC) Denali system
application/service
https://www.usgs.gov/center-news/denali-tallgrass-eros-launch-new-era-high-performance-computing-capabilities
Digital and/or Hardcopy
2019
observed
USGS High Performance Computing (HPC) Denali system
Two new high-performance computing (HPC) options—Denali and Tallgrass.
Google
2019
Google Earth Engine
raster digital data
https://earthengine.google.com/
Digital and/or Hardcopy
2019
observed
Google Earth Engine (GEE)
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.
U.S. Geological Survey (USGS) National Geospatial Program
2020
The 3D Elevation Program
raster digital data
https://viewer.nationalmap.gov/basic/
https://www.usgs.gov/core-science-systems/ngp/3dep
Digital and/or Hardcopy
2019
2019
observed
3D Elevation Program (3DEP) digital elevation data
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.
U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS)
2017
Cropland Data Layer
raster digital data
https://nassgeodata.gmu.edu/CropScape/
https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php
Digital and/or Hardcopy
2008
2017
observed
Cropland Data Layer (CDL)
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).
U.S. Fish and Wildlife Service
2021
National Wetlands Inventory
vector digital data
https://www.fws.gov/wetlands/Data/Web-Map-Services.html
https://www.fws.gov/wetlands/Data/Data-Download.html
Digital and/or Hardcopy
1977
2021
observed
National Wetlands Inventory (NWI)
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 Cooperative Soil Survey
2019
Soil Survey Geographic (SSURGO) Database
vector digital data
https://gdg.sc.egov.usda.gov/
Digital and/or Hardcopy
2019
observed
Soil Survey Geographic (SSURGO) Database
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.
USDA Natural Resources Conservation Service (NRCS)
2019
State Soil Geographic (STATSGO2) Database
vector digital data
https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm
Digital and/or Hardcopy
2019
observed
State Soil Geographic (STATSGO2) Database
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.
U.S. Geological Survey
2019
Multi-Index Integrated Change Analysis (MIICA)
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.
Digital and/or Hardcopy
2001
2019
observed
Multi-Index Integrated Change Analysis (MIICA)
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.
USDA Forest Service
2019
Vegetation Change Tracker (VCT) software
application/service
https://doi.org/10.1016/j.rse.2018.11.029
Digital and/or Hardcopy
1986
2008
observed
Vegetation Change Tracker (VCT)
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.
NOAA Office for Coastal Management
2019
Coastal Change Analysis Program (C-CAP)
application/service
https://coast.noaa.gov/digitalcoast/tools/lca.html
Digital and/or Hardcopy
2019
observed
C-CAP land cover
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).
United States Department of Agriculture (USDA)
National Agricultural Statistics Service (NASS)
2019
Cropland Data Layer
raster digital data
https://www.nass.usda.gov/Research_and_Science/Cropland/Release/
Digital and/or Hardcopy
2008
2019
observed
cultivated cropland 2008 to 2019 dataset
The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer.
USDA Natural Resources Conservation Service
2019
Hydric Soils database
vector digital data
https://data.nal.usda.gov/dataset/soil-use-hydric-soils-database
Digital and/or Hardcopy
2019
observed
hydric soils dataset
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.
RuleQuest
2019
See5 decision tree classification software
application/service
https://www.rulequest.com/see5-info.html
Digital and/or Hardcopy
1986
2019
observed
See5
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.
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.
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, LAND RESOURCES
GEOGRAPHER
mailing address
47914 252Nd Street
Sioux Falls
SD
57198
US
605-594-2715
dewitz@usgs.gov
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.
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
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.
Landsat ARD
USGS High Performance Computing (HPC) Denali system
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
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.
Landsat ARD
Google Earth Engine (GEE)
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
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.
3D Elevation Program (3DEP) digital elevation data
Cropland Data Layer (CDL)
National Wetlands Inventory (NWI)
Soil Survey Geographic (SSURGO) Database
State Soil Geographic (STATSGO2) Database
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
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.
Multi-Index Integrated Change Analysis (MIICA)
Vegetation Change Tracker (VCT)
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
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.
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 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.
C-CAP land cover
Cropland Data Layer (CDL)
National Wetlands Inventory (NWI)
cultivated cropland 2008 to 2019 dataset
hydric soils dataset
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
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.
See5
Vegetation Change Tracker (VCT)
Landsat ARD
DEM
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 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.
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 classifications were processed with ancillary data and the segmentation polygons to produce eight initial land cover maps.
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
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.
Cropland Data Layer (CDL)
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 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.
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
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 Land Cover Change Layer Attribute Table
Land cover class counts and descriptions for the NLCD Land Cover Change Database
National Land Cover Database
OID
Internal feature number.
ESRI
Sequential unique whole numbers that are automatically generated.
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
Class_NamesLand Cover Change Class Code Value.NLCD Legend Land Cover Change Class Descriptions0UnclassifiedProducer defined
1
No Change
NLCD Legend Land Cover Change Class Descriptions
2
Change from or to Water
NLCD Legend Land Cover Change Class Descriptions
3
Change from or to any of the four Urban classes (open space; low, medium, and high intensity)
NLCD Legend Land Cover Change Class Descriptions
4
Change from Herbaceous Wetland to Woody Wetland, or vice versa
NLCD Legend Land Cover Change Class Descriptions
5
Change from or to Herbaceous Wetland
NLCD Legend Land Cover Change Class Descriptions
6
Change from Cultivated Crops to Hay / Pasture, or vice versa
NLCD Legend Land Cover Change Class Descriptions
7
Change from or to Cultivated Crops
NLCD Legend Land Cover Change Class Descriptions
8
Change from or to Hay / Pasture
NLCD Legend Land Cover Change Class Descriptions
9
Persistent Grassland and Shrubland change. This change index attempts to identify changes to persistent Grassland and Shrubland areas verses transitional shrubland areas such as regenerating forests.
NLCD Legend Land Cover Change Class Descriptions
10
Change from or to Barren
NLCD Legend Land Cover Change Class Descriptions
11
Change from or to any of the three Forest classes (Evergreen, Deciduous, and Mixed)
NLCD Legend Land Cover Change Class Descriptions
12
Change from or to Woody Wetland
NLCD Legend Land Cover Change Class Descriptions
13
Change from or to Snow
Producer defined
Red
Red color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
NLCD 2019
0
255
Green
Green color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
NLCD 2019
0
255
Blue
Blue color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
NLCD 2019
0
255
Opacity
A measure of how opaque, or solid, a color is displayed in a layer.
NLCD 2019
0
0.1
0.01
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
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
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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.
20210611
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FGDC Content Standard for Digital Geospatial Metadata
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local time