20240905
11013500
1.0
FALSE
Ky_NLCD_2021_30M
3798907.390396
6018391.140396
3348784.212636
4307246.862636
1
file://\\kyimagestorage.ky.gov\ImageServerData2\GDB\Ky_Landcover_NLCD_2021.gdb
Local Area Network
002
Projected
GCS_North_American_1983
Linear Unit: Foot_US (0.304801)
NAD_1983_StatePlane_Kentucky_FIPS_1600_Feet
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.6'><WKT>PROJCS["NAD_1983_StatePlane_Kentucky_FIPS_1600_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",4921250.0],PARAMETER["False_Northing",3280833.333333333],PARAMETER["Central_Meridian",-85.75],PARAMETER["Standard_Parallel_1",37.08333333333334],PARAMETER["Standard_Parallel_2",38.66666666666666],PARAMETER["Latitude_Of_Origin",36.33333333333334],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",3089]]</WKT><XOrigin>-117434800</XOrigin><YOrigin>-91579000</YOrigin><XYScale>36807330.96050825</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102763</WKID><LatestWKID>3089</LatestWKID></ProjectedCoordinateSystem>
CopyRaster F:\ImageServerData2\Imagery\NLCD_Landcover_2021\nlcd_2021_land_cover_l48_20230630.img F:\ImageServerData2\GDB\Ky_Landcover_NLCD_2021gdb.gdb\nlcd_2021_land_cover_l48_20230630 # # # NONE NONE # NONE NONE "Esri Grid format" NONE CURRENT_SLICE NO_TRANSPOSE
Clip \\kyimagestorage.ky.gov\ImageServerData2\GDB\Ky_Landcover_NLCD_2021.gdb\nlcd_2021_land_cover_l48_20230630 "3798937.75612089 3348809.25030097 6018368.99375872 4307216.69010957" \\kyimagestorage.ky.gov\ImageServerData2\GDB\Ky_Landcover_NLCD_2021.gdb\Ky_NLCD_2021_30M \\kyimagestorage.ky.gov\ImageServerData2\GDB\Ky_Landcover_NLCD_2021.gdb\ky_bnd 255 ClippingGeometry NO_MAINTAIN_EXTENT
20240905
13342500
20240905
13342500
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
20240905
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 ArcGIS Pro software and supporting operating systems.
1012
https://doi.org/10.5066/P9JZ7AO3
https://www.mrlc.gov/data-services-page
https://www.mrlc.gov/data
https://doi.org/10.5066/P9JZ7AO3
File Geodatabase Raster Dataset
Ky_NLCD_2021_30M
2023-06-30
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 now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. NLCD 2021 adds an additional year to the map products produced for NLCD 2019, with a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. The overall accuracy of the 2019 Level I land cover was 91%. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2021 operational mapping (see https://doi.org/10.1080/15481603.2023.2181143 for the latest accuracy assessment publication). Questions about the NLCD 2021 land cover product can be directed to the NLCD 2021 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 U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. (2021)
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
Land Use Land Cover Theme
NGDA
National Geospatial Data Asset
NGDA Portfolio Themes
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 cover
Land Use Land Cover Theme
imageryBaseMapsEarthCover
United States
U.S. Geological Survey (USGS)
NGDA
US
GIS
U.S.
National Geospatial Data Asset
Image processing
digital spatial data
biota
Land cover
Land Use Land Cover Theme
United States
imageryBaseMapsEarthCover
United States
U.S. Geological Survey (USGS)
United States
U.S.
NGDA
US
GIS
US
U.S.
Land Use Land Cover Theme
National Geospatial Data Asset
NGDA
Image processing
digital spatial data
National Geospatial Data Asset
biota
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.
National Land Cover Database 2019: A New Strategy for Creating Clean Leaf-On and Leaf-Off Landsat Composite Images
2023-02-21
Brian Granneman
Zhe Zhu
American Association for the Advancement of Science (AAAS)
n/a
Kelcy Smith
Jon Dewitz
Patrick Danielson
Catherine Costello
Suming Jin
publication
Journal of Remote Sensing
vol. 3
https://doi.org/10.34133/remotesensing.0022
Overall Methodology Design for the United States National Land Cover Database 2016 Products
2019-12-11
MDPI AG
n/a
Suming Jin
Collin Homer
Zhe Zhu
Patrick Danielson
George Xian
Congcong Li
Limin Yang
Danny Howard
Jon Dewitz
publication
Remote Sensing
vol. 11, issue 24
ppg. 2971
https://doi.org/10.3390/rs11242971
National Land Cover Database 2019: A Comprehensive Strategy for Creating the 1986–2019 Forest Disturbance Product
2023-02-16
Congcong Li
Brian Granneman
Catherine Costello
Daniel Sorenson
Leila Gass
Suming Jin
Patrick Danielson
American Association for the Advancement of Science (AAAS)
n/a
Zhe Zhu
Adam Case
Md Rakibul Islam Shogib
Jon Dewitz
publication
Journal of Remote Sensing
vol. 3
https://doi.org/10.34133/remotesensing.0021
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 10.6.1.9270
ground condition
2001-01-01
2021-01-01
-130.2328
-63.6722
21.7423
52.851
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
-89.704542
-81.884152
39.152004
36.456904
See https://www.mrlc.gov/data for the full list of products available.
This NLCD product is the version dated June 30, 2023.
A formal accuracy assessment has not been conducted for NLCD 2021 Land Cover, NLCD 2021 Land Cover Change, or NLCD 2021 Impervious Surface products. A 2019 accuracy assessment publication can be found here: James Wickham, Stephen V. Stehman, Daniel G. Sorenson, Leila Gass & Jon A. Dewitz (2023) Thematic accuracy assessment of the NLCD 2019 land cover for the conterminous United States, GIScience & Remote Sensing, 60:1, DOI: 10.1080/15481603.2023.2181143.
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
Landsat synthetic imagery - Synthetic images are generated from a model of Landsat surface reflectance (or brightness temperature, etc.), analogous to the approach by Zhu et al. (2015) (https://doi.org/10.1016/j.rse.2015.02.009). Synthetic images in this case are derived from the LCMAP CCDC CONUS 1.3 harmonic models (https://www.usgs.gov/media/files/lcmap-collection-13-ccdc-add). Data is produced for the same six bands considered in composite generation—the thermal band harmonic model is not used. For NLCD 2021 we produced CONUS leaf-on synthetic imagery for 2000-2020 based on the July 1 date. In addition, we generated CONUS leaf-off synthetic imagery for 2018-2020 based on the November 15 date in the northern mapping areas and December 15th in the southern mapping areas.
2021-01-01
Land cover classification (imagery) - In addition to Landsat imagery (leaf-on composite and leaf-off CCDC-synthetic), other datasets used as input into the land cover classification were: segmented-polygon-based mean of Landsat imagery, digital elevation data and derivatives (aspect, slope, and position index); forest disturbance year 1984-2021, 90th percentile and 20th percentile Normalized Difference Vegetation Index (NDVI), and 20th percentile Normalized Difference Water Index (NDWI). Landsat composites, synthetic images, and annual percentile spectral indices were created for leaf on and leaf off in 2019 and 2021 based on NLCD block mapping units (generally 9 Landsat path/rows per block). The use of the same style change pairs ensures proper phenological matches and similar spectral properties. It also reduces the overlap area between this type of imagery compared to individual Landsat path/row images.
2021-01-01
Land cover classification (decision tree) - For each year of Landsat data, two percent of all available training data per NLCD block was drawn from the data as training samples with 5000 minimum samples and 1000000 maximum samples for each land cover class, 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 target year. The See5 classifier was run with five sets of independent variables: the 1986 to 2021 disturbance year map derived from our change detection procedures; the set of Landsat images; polygon-based (from image segmentation) mean of Landsat images; annual percentile spectral indices; and a DEM and derivatives. The See5 classification was run three times on each NLCD block: 1) with all land cover classes plus the 1986 to 2021 disturbance year data; 2) with urban and wetland classes omitted; and 3) with urban and wetland classes omitted, and without the disturbance year data, since these classes have separate process steps. Urban is directly derived from percent impervious, and wetland is derived from the postprocessing of integrating first classification, wetland potential index, change detection, and NLCD wetland base. Wetland potential index is derived from the NWI, hydric soil, and NLCD wetland classes.
2021-01-01
Land cover classification (U-net machine learning) - We also produced a Unet classification for the entire CONUS: inputs included NLCD2019 as training data, leaf-on composite and leaf-off synthetic Landsat imagery, and digital elevation data.
2021-01-01
Land cover classification (modeling change) - NLCD 2021 was produced by modeling land cover change over the time interval between 2019 and 2021. Models used included; the Multi-Index Integrated Change Analysis (MIICA) model (https://doi.org/10.1016/j.rse.2013.01.012), which captures change between two dates of imagery; the Time-Series method Using Normalized Spectral Distance (NSD) index (TSUN), which produces a forest land cover change disturbance year map; a water detection model, which captures water for each date of Landsat imagery; models that detect cultivated crop for each date Landsat imagery; and a time series model to detect cultivated crop change. Landsat imagery, ancillary data (see datasets listed under Source Information), the MIICA outputs, other change detection outputs, and the 1986 to 2021 disturbance year map (derived from our change detection procedures) comprise the training data.
2021-01-01
Land cover classification (training data and image segmentation) - The training dataset models were built with Landsat images and derived indices, spectral change products, trajectory analysis, and ancillary data, including previous years’ NLCD land cover; LANDFIRE-EVH; CDL; NWI; cultivated cropland 2008 to 2021; MTBS fire year, NLCD tree canopy and RCMAP land cover. Image segmentation, using Ecognition, was performed on the synthetic and composite imagery, 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.
2021-01-01
Land cover classification integration - The final integration step resolved class label issues pertinent to local environments and boundary pixels, and ensured that all pixels in a segmentation object were in the same class. Developed classes were derived from percent developed impervious surface. Pixel-based and object-based land cover labels were checked for differences, and were reconciled by a rule-based model. Water and developed classes were left intact in areas that were smaller than segmentation objects. Change trajectories for each class were checked for consistency through all years.
2021-01-01
Land cover change refinement - A post-classification refinement process was used to refine the land cover change between NLCD 2019 and NLCD 2021. We checked for consistency of land cover labels between 2019 and 2021; and compared the classification change with imagery-based change detection, ancillary data, and NLCD 2019. Additional refinement was conducted class-by-class in hierarchical order: 1) water; 2) wetlands; 3) forest and forest transition; 4) rangeland shrubland, herbaceous, and barren, and 5) agriculture. Models were developed for refinement of each class and each type of confusion.
2021-01-01
Landsat imagery processing - The National Land Cover Database (NLCD) is fundamentally based on the analysis of Landsat data. Beginning with NLCD 2019, we used composite images rather than individual Landsat scenes. Compositing made imagery generation more automated, reduced latency, and increased the mapping extent. Also, as of NLCD 2019, we divided CONUS into 50 blocks, each containing approximately 9 path/rows. For NLCD 2021 we also used synthetic Landsat imagery.
2021-01-01
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 composite imagery - Composites are generated using the method of Jin et al. (2023) (https://doi.org/10.34133/remotesensing.0022). Per-band median values are determined at each pixel location from valid (no cloud/shadow/snow/fill data in the pixel QA) pixels in an image stack. The observation with the shortest Euclidean distance to this median-value point in six-dimensional (six-band) space provides the values for each band for that pixel. For “leaf-on” (growing season) composite images, the range of dates used to query for eligible Landsat observations is May 1 to September 30, and for “leaf-off”, November 1 through April 1. These composites are generated across all Landsat optical bands (informally, the wavelength bands blue, green, red, NIR, SWIR1, and SWIR2; equivalently, the wavelength ranges for Landsat TM/ETM+ bands 1, 2, 3, 4, 5, and 7). We generated annual leaf-on composites from 2000 to 2021, and annual leaf-off composites from 2018 to 2021.
2021-01-01
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
RCMAP shrub areas
Rangeland Condition Monitoring Assessment and Projection, 1985–2021
RCMAP
2021-01-01
Bureau of Land Management
U.S. Geological Survey
raster digital data
https://www.mrlc.gov/sites/default/files/2023-01/V5%20RCMAP%20Factsheet.pdf
https://www.mrlc.gov
observed
1985-01-01
2021-01-01
LF1999 through LF2020 areas of vegetation, fuels, fire regimes, and disturbances
LANDFIRE (LF)
LANDFIRE
2020-01-01
The Nature Conservancy (TNC) North America Science Team
U.S. Department of Interior (DOI) Office of Wildland Fire
U.S. Department of Agriculture (USDA) Forest Service (USFS) Fire and Aviation Management
USDA Forest Service Forest Inventory and Analysis (FIA)
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center
vector digital data
https://www.landfire.gov/
observed
1999-01-01
2020-01-01
NLCD 2019 Land Cover products
The National Land Cover Database (NLCD)
NLCD 2019
2019-01-01
MRLC consortium
U.S. Geological Survey
raster digital data
https://www.mrlc.gov
observed
2019-01-01
MTBS burn severity
Monitoring Trends in Burn Severity (MTBS)
MTBS
2021-01-01
USDA Forest Service
U.S. Geological Survey
raster digital data
https://www.mtbs.gov/
observed
1984-01-01
2021-01-01
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
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
Data on cultivated crops and confidence indices, available annually for 2008 to 2020 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
2020-01-01
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
Digital Elevation Model (DEM)
LF 2020 Update Elevation products
DEM
2022-01-31
LANDFIRE
USGS 3D Elevation Program (3DEP)
raster digital data
https://landfire.gov/index.php
https://landfire.gov/elevation.php
ground condition
2020-01-01
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
Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), Landsat Operational Land Imager (OLI), Landsat Analysis Ready Data (ARD)
Landsat—Earth Observation Satellites
Landsat Products
2020-04-08
U.S. Geological Survey
raster digital data
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
2021-01-01
USDA Forest Service canopy 2016 data
Tree Canopy Cover (TCC)
Tree Canopy
2016-01-01
USDA Forest Service
MRLC consortium
raster digital data
https://www.mrlc.gov/sites/default/files/TCC_Project_Overview_Brochure-MRLC_2020-06-05.pdf
https://www.mrlc.gov
observed
2016-01-01
2
104424
161190
1
VAT_Ky_NLCD_2021_30M
Land Cover class counts and descriptions for the NLCD Land Cover Database
National Land Cover Database
Table
15
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Opacity
A measure of how opaque, or solid, a color is displayed in a layer.
NLCD 2021
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 2021
0
255
Red
Integer
4
0
0
Blue
Blue color code for RGB. The value is arbitrarily assigned by the display software package, unless defined by user.
NLCD 2021
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 2021
0
255
Green
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 2021
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 2021.
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
EPSG
6.6(9.2.0)
Pixel
9738
22550
98.425000
98.425000
8
1
Upper Left
TRUE
LZ77
1
pixel codes
FGDBR
TRUE
row and column
98.425000
98.425000