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Annual NLCD Land Cover Dataset

The USGS Land Cover program integrates methodologies from the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment, and Projection (LCMAP), along with advanced deep learning, to create Annual NLCD a dataset suite that includes six products, each representing various U.S. land cover and change characteristics. The U.S. Geological Survey’s (USGS) Annual NLCD Collection 1.0 leverages innovations from the National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP) projects, incorporating modern deep learning techniques to deliver accurate, annual land cover and surface change data across the U.S.

Since 1985, Annual NLCD provides six products covering land cover, change, confidence, impervious surfaces, and spectral changes based on Landsat data, facilitating resource management and decision-making.These products leverage Landsat satellite data and are intended for applications in science, resource management, and decision-making, spanning from 1985 to 2023. This dataset supports various environmental analyses, such as urban growth studies, wetland monitoring, agricultural management, and climate impact assessments. Its annual updates and classification confidence features provide essential insights for long-term land use planning and change detection. You can acces User Guide here

Dataset Products and Descriptions

  • Land Cover: A sixteen-class system based on the modified Anderson Level II classification, categorizing dominant surface types like water, forests, and urban areas per pixel. RGB values visually differentiate these categories, ensuring compatibility across federal systems.

  • Land Cover Change: Tracks annual land cover shifts by comparing consecutive years, using concatenated codes (e.g., 9590 for wetland transitions) to identify changes. Areas without change retain their classification.

  • Land Cover Confidence: Provides confidence scores based on deep learning probabilities, indicating the model’s certainty in class assignments. Scores are uncalibrated but gauge classification reliability.

  • Fractional Impervious Surface: Measures the percentage of impermeable surfaces (0-100%) within a 30-meter pixel, informing developed area classifications like urban or suburban based on defined thresholds.

  • Impervious Descriptor: Differentiates urban, non-urban, and road surfaces within developed areas, offering a clear map of roads distinct from other urban features for detailed analysis.

  • Spectral Change Day of Year: Identifies the day significant spectral changes occur (values 1-366), pinpointing disturbances (e.g., fires) beyond seasonal variations, enabling temporal change tracking.

Expand to show Land Cover Classes

Class Value Class Name RGB Color
11 Open Water #466b9f
12 Perennial Ice/Snow #d1def8
21 Developed, Open Space #dec5c5
22 Developed, Low Intensity #d99282
23 Developed, Medium Intensity #eb0000
24 Developed, High Intensity #ab0000
31 Barren Land #b3ac9f
41 Deciduous Forest #68ab5f
42 Evergreen Forest #1c5f2c
43 Mixed Forest #b5c58f
52 Shrub/Scrub #ccb879
71 Grassland/Herbaceous #dfdfc2
81 Pasture/Hay #dcd939
82 Cultivated Crops #ab6c28
90 Woody Wetlands #b8d9eb
95 Emergent Herbaceous Wetlands #6c9fb8

Key Information

  • Data Type: UINT8, UINT16 (product-dependent)
  • Spatial Resolution: Based on the Landsat 30m grid
  • Temporal Coverage: 1985–2023 (updated annually)
  • Access: Products are accessible via multiple platforms, including MRLC Viewer and AWS S3.

Layer Name Class Values (Range) Min Max NoData Value
Land Cover Various land cover types (11, 12, ..., 95) N/A N/A 250
Land Cover Change Change class categories AABB AABB 9999
Land Cover Confidence Confidence levels 1 100 250
Fractional Impervious Surface Imperviousness percentage 0 100 250
Impervious Descriptor Impervious surface types (0: Non-Urban, 1: Roads, 2: Urban) N/A N/A 250
Spectral Change Day of Year Julian days of change 1 366 9999

Citation

U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products: U.S. Geological Survey data release,
https://doi.org/10.5066/P94UXNTS.

annual_nlcd

Earth Engine Snippet

var nlcd_landcover = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/NLCD-ANNUAL-LANDCOVER

annual_nlcd_layers-optimized

var nlcd_landcover_confidence = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER_CONFIDENCE");
var nlcd_landcover_change = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/LANDCOVER_CHANGE");
var nlcd_fractional_impervious_surface = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/FRACTIONAL_IMPERVIOUS_SURFACE");
var nlcd_impervious_descriptor = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/IMPERVIOUS_DESCRIPTOR");
var nlcd_spectral_change_doy = ee.ImageCollection("projects/sat-io/open-datasets/USGS/ANNUAL_NLCD/SPECTRAL_CHANGE_DOY");

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:regional-landuse-landcover/NLCD-ANNUAL-LANDCOVER-LAYERS

License

NLCD datasets are provided under a Creative Commons Zero v1.0 Universal license.

Provided by: USGS

Curated in GEE by: Samapriya Roy

Keywords: Land Cover, Land Change, Landsat, Deep Learning, Annual NLCD, USGS, Environmental Monitoring

Last updated in GEE: 2024-10-25

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