Metadata for 2019 National Land Cover Data (NLCD) Conterminous United States Metadata for 2019 National Land Cover Data (NLCD) Conterminous United States
Identification Information:
Citation:
Originator: U.S. Geological Survey
Publication date: 06/04/2021
Title:
2019 National Land Cover Data (NLCD) Conterminous United States
Edition: 2019
Series name: None
Issue identification: None
Publication place: Sioux Falls, SD
Publisher: U.S. Geological Survey
Online linkage: https://doi.org/10.5066/P9KZCM54
Online linkage: https://www.mrlc.gov/data
Online linkage: https://www.mrlc.gov/data-services-page
Larger Work Citation:
Title: 2016 National Land Cover Data
Online linkage: https://mslservices.mt.gov/Geographic_Information/Data/DataList/datalist_Details.aspx?did={7670ad63-9233-4754-87d6-da0138c7f8e6}

Abstract:
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.
Purpose:
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.
Supplemental information:
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)

Time period of content:
Beginning date: 2001
Ending date: 2019
Currentness reference: ground condition
Status:
Progress: In work
Maintenance and update frequency: Every 2-3 years
Access constraints: None
Use constraints: None
Point of contact:
Customer Service Representative
U.S. Geological Survey
USGS EROS
47914 252nd Street
Sioux Falls, SD 57198-0001


Telephone: (605) 594-6151
E-Mail: custserv@usgs.gov


Data set credit: U.S. Geological Survey
Security information:
Security classification system: None
Security classification: Unclassified
Security handling description: N/A
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Data Quality Information:
Attribute accuracy report:
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.
Quantitative attribute accuracy assessment:
Attribute accuracy value: Unknown
Attribute accuracy explanation:
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.

Logical consistency report:
See https://www.mrlc.gov/data for the full list of products available.
Completeness report: This NLCD product is the version dated June 4, 2021.


Horizontal Positional Accuracy Report: N/A

Vertical positional accuracy report: N/A


Lineage:

Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, LAND RESOURCES
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
The two classifications were processed with ancillary data and the segmentation polygons to produce eight initial land cover maps.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


Process step:
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.
Process date: 2019
Process contact:
Jon Dewitz
GEOGRAPHER
U.S. Geological Survey, CORE SCIENCE SYSTEMS
47914 252Nd Street
Sioux Falls, SD 57198


Telephone: 605-594-2715
E-Mail: dewitz@usgs.gov


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Spatial Data Organization Information:
Raster object information:
Raster object type: Pixel
Row count: 104424
Column count: 161190
Vertical count: 1
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Spatial Reference Information:
Horizontal coordinate system definition:
Map projection:
Map projection name: Albers Conical Equal Area
Albers conical equal area:
Albers Conical Equal Area
Standard parallel: 29.5
Standard parallel: 45.5
Longitude of central meridian: -96.0
Latitude of projection origin: 23.0
False easting: 0.0
False northing: 0.0
Planar distance units: meters
Geodetic model:
Horizontal datum name: WGS_1984
Ellipsoid name: WGS 84
Semi-major axis: 6378137.0
Denominator of flattening ratio: 298.257223563
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Entity and Attribute Information:
Entity type label: NLCD Land Cover Layer Attribute Table
Entity type definition:
Land Cover class counts and descriptions for the NLCD Land Cover Database

Attribute label: OID
Attribute definition: Internal feature number.
Attribute domain:
Sequential unique whole numbers that are automatically generated.

Attribute label: Count
Attribute definition:
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.
Attribute domain: Integer

Attribute label: NLCD Land Cover Class
Attribute definition: Land Cover Class Code Value.


Attribute
Value 		Definition of
Attribute Value
0 Unclassified


Attribute
Value 		Definition of
Attribute Value
11 Open Water - All areas of open water, generally with less than 25% cover or vegetation or soil


Attribute
Value 		Definition of
Attribute Value
12 Perennial Ice/Snow - All areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover.


Attribute
Value 		Definition of
Attribute Value
21 Developed, Open Space - Includes areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20 percent of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes.


Attribute
Value 		Definition of
Attribute Value
22 Developed, Low Intensity -Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20-49 percent of total cover. These areas most commonly include single-family housing units.


Attribute
Value 		Definition of
Attribute Value
23 Developed, Medium Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50-79 percent of the total cover. These areas most commonly include single-family housing units.


Attribute
Value 		Definition of
Attribute Value
24 Developed, High Intensity - Includes highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80 to 100 percent of the total cover.


Attribute
Value 		Definition of
Attribute Value
31 Barren Land (Rock/Sand/Clay) - Barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.


Attribute
Value 		Definition of
Attribute Value
41 Deciduous Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species shed foliage simultaneously in response to seasonal change.


Attribute
Value 		Definition of
Attribute Value
42 Evergreen Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species maintain their leaves all year. Canopy is never without green foliage.


Attribute
Value 		Definition of
Attribute Value
43 Mixed Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75 percent of total tree cover.


Attribute
Value 		Definition of
Attribute Value
51 Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, herbs, and non-vascular vegetation.


Attribute
Value 		Definition of
Attribute Value
52 Shrub/Scrub - Areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions.


Attribute
Value 		Definition of
Attribute Value
71 Grassland/Herbaceous - Areas dominated by grammanoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but can be utilized for grazing.


Attribute
Value 		Definition of
Attribute Value
72 Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of total vegetation. This type can occur with significant other grasses or other grass like plants, and includes sedge tundra, and sedge tussock tundra.


Attribute
Value 		Definition of
Attribute Value
73 Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of total vegetation.


Attribute
Value 		Definition of
Attribute Value
74 Moss - Alaska only areas dominated by mosses, generally greater than 80% of total vegetation.


Attribute
Value 		Definition of
Attribute Value
81 Pasture/Hay - Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20 percent of total vegetation.


Attribute
Value 		Definition of
Attribute Value
82 Cultivated Crops - Areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total vegetation. This class also includes all land being actively tilled.


Attribute
Value 		Definition of
Attribute Value
90 Woody Wetlands - Areas where forest or shrub land vegetation accounts for greater than 20 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water.


Attribute
Value 		Definition of
Attribute Value
95 Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater than 80 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water.

Attribute label: Red
Attribute definition:
Red color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
Range domain minimum: 0
Range domain maximum: 255

Attribute label: Green
Attribute definition:
Green color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
Range domain minimum: 0
Range domain maximum: 255

Attribute label: Blue
Attribute definition:
Blue color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
Range domain minimum: 0
Range domain maximum: 255

Attribute label: Opacity
Attribute definition:
A measure of how opaque, or solid, a color is displayed in a layer.
Range domain minimum: 0
Range domain maximum: 0.1
Attribute measurement resolution: 0.01

Attribute label: Value
Attribute definition:
*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.


Attribute
Value 		Definition of
Attribute Value
127 Background value
Range domain minimum: 0
Range domain maximum: 100
Attribute units of measure: percentage
Attribute measurement resolution: 0.1

Entity and attribute overview:
Land Cover Class RGB Color Value Table. The specific RGB values for the NLCD Land Cover Class's that were used for NLCD 2019.
Entity and attribute detail citation:
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
Entity and attribute overview: N/A
Entity and attribute detail citation:
Attribute accuracy is described, where present, with each attribute defined in the Entity and Attribute Section.
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Distribution Information:
Distributor:
GS ScienceBase
U.S. Geological Survey
Denver Federal Center, Building 810, Mail Stop 302
Denver, CO 80225


Telephone: 1-888-275-8747
E-Mail: sciencebase@usgs.gov


Resource description: Downloadable data


Distribution liability:
Although these data have been processed successfully on a computer system at the USGS, no warranty expressed or implied is made by the USGS regarding the use of the data on any other system, nor does the act of distribution constitute any such warranty. Data may have been compiled from various outside sources. Spatial information may not meet National Map Accuracy Standards. This information may be updated without notification. The USGS shall not be liable for any activity involving these data, installation, fitness of the data for a particular purpose, its use, or analyses results.

Standard order process:
Digital form:
Format name: ERDAS
Transfer size: 1012.0
megabytes
Online option:
https://www.mrlc.gov
Fees: None
Technical prerequisites:
ESRI ArcMap Suite and/or Arc/Info software, and supporting operating systems.
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Metadata Reference Information:
Metadata date: 12/03/2018
Metadata contact:
Customer Services Representative
U.S. Geological Survey
USGS/EROS
47914 252nd Street
Sioux Falls, SD 57198-0001


Telephone: 605/594-6151
TDD/TTY telephone: 605/594-6933
Fax: 605/594-6589
E-Mail: custserv@usgs.gov


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