USFS Tree Canopy Cover v2023-5 (CONUS and OCONUS)

Page Summary

• This dataset is part of the Tree Canopy Cover (TCC) data suite and includes modeled TCC, standard error, and NLCD TCC data annually from 1985 to 2023.

• The TCC data is produced by the USDA Forest Service and is part of the National Land Cover Database project managed by the USGS.

• The dataset covers the Conterminous United States, with data for southeast Alaska, Hawaii, and Puerto Rico-US Virgin Islands expected to be released in late summer 2025.

• Each image in the dataset includes a data mask band indicating areas of no data, mapped tree canopy cover, and non-processing areas.

• The TCC model utilizes outputs from LandTrendr, terrain information, and reference data from USFS Forest Inventory and Analysis.

Dataset Availability

1985-06-01T00:00:00Z–2023-09-30T00:00:00Z

Dataset Provider

USDA Forest Service (USFS) Field Services and Innovation Center Geospatial Office (FSIC-GO)

Earth Engine Snippet

ee.ImageCollection("USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5")

Tags

forest gtac landuse-landcover redcastle-resources usda usfs usgs

Description

Overview

The Tree Canopy Cover (TCC) data suite, produced by the United States Department of Agriculture, Forest Service (USFS), are annual remote sensing-based map outputs spanning from 1985-2023. These data support the National Land Cover Database (NLCD) project, which is managed by the US Geological Survey (USGS) as part of the Multi-Resolution Land Characteristics (MRLC) consortium. The project aims to use the latest technology to create a consistent, "best available" map of tree canopy cover. The geographic scope includes the Conterminous United States (CONUS) and OCONUS regions (Southeast Alaska (SEAK), Hawaii, Puerto Rico, and the US Virgin Islands (PRUSVI)).

Products

The TCC data suite includes three products:

• Science TCC: The raw, direct outputs from the model.

• Science standard error (SE): The model standard deviation of the predicted values from all regression trees.

• NLCD TCC: A refined product derived from the annual Science TCC images. It undergoes post-processing to reduce interannual noise, highlight long-term trends, and mask specific features (such as water and non-tree agriculture).

Each image includes a data mask band that has three values representing areas of no data (0), mapped tree canopy cover(1), and non-processing area (2). The non-processing areas are pixels in the study area with no cloud or cloud shadow-free data. No data and non-processing area pixels are masked in TCC and SE images.

Data and Methods

We developed training data and random forest models for CONUS, SEAK, PRUSVI and HAWAII using the USFS Forest Inventory and Analysis (FIA) photo-interpreted TCC as reference data. We leveraged Google Earth Engine (GEE) (Gorelick et al., 2017) to process fitted LandTrendr and terrain predictors. Terrain data from the 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019) include elevation, slope, sine of aspect, and cosine of aspect. For CONUS, we also included the Crop Data Layer (CDL) as a predictor (Lin et al., 2022).

We utilized USGS Collection 2 Landsat Tier 1 and Sentinel 2A/2B Level-1C top of atmosphere reflectance imagery to produce annual medoid composites. To ensure data quality, we applied various algorithms to mask clouds and shadows, including cFmask (Foga et al., 2017; Zhu and Woodcock, 2012), cloudScore (Chastain et al., 2019), s2cloudless (Sentinel-Hub, 2021), Cloud Score+ (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019). Once masked, we computed annual medoids to create a single cloud-free composite for each year. Finally, the composite time series was temporally segmented using LandTrendr (Kennedy et al., 2010, 2018; Cohen et al., 2018).

For CONUS, we used 70% of the reference data for calibration and 30% for independent error assessment. Given the ecological diversity of CONUS, we divided the modeling area into 54 tiles (480 km × 480 km). On local computers we built a unique random forest model for each tile (Breiman, 2001), training it on reference data intersecting a 5×5 window around the center tile. The models were then deployed in GEE to predict wall-to-wall TCC. For OCONUS regions, we used an 80/20 split and developed a single random forest model for each region.

Additional Resources

• Please see the TCC Methods Brief or Science of Remote Sensing journal article for detailed information regarding methods and accuracy assessment.

• Please see the TCC Geodata Clearinghouse for data downloads, metadata, and support documents.

• The string HAWAII will be updated to HI in the upcoming v2025.6 data release.

Contact [[e-mail]] with any questions or specific data requests.

Bands

Pixel Size
30 meters

Bands

Name	Units	Pixel Size	Description
Science_Percent_Tree_Canopy_Cover	%	meters	The raw direct model outputs. Each pixel has a mean predicted tree canopy cover value for each year.
Science_Percent_Tree_Canopy_Cover_Standard_Error	%	meters	The standard deviation of the predicted values from all regression trees we refer to as standard error. Each pixel has a standard error for each year.
NLCD_Percent_Tree_Canopy_Cover	%	meters	To produce NLCD tree canopy cover, a post-processing workflow is applied to the direct model output that identifies and sets non-treed pixel values to zero percent tree canopy cover.
data_mask		meters	Three values representing areas of no data, mapped tree canopy cover, and non-processing area. The non-processing area is where pixels within the study area have no cloud or cloud shadow-free data available to produce an output.
Bitmask for data_mask Bits 0-1: Three values representing areas of no data, mapped tree canopy cover, and non-processing area. 0: No data 1: Mapped tree canopy cover 2: Non-processing area

Image Properties

Image Properties

Name	Type	Description
study_area	STRING	TCC currently covers CONUS, Southeastern Alaska, Puerto Rico-US Virgin Islands and Hawaii. This version contains data for CONUS, AK, PRUSVI, and HAWAII. Possible values: 'CONUS, AK, PRUSVI, HAWAII'
version	STRING	This is the fifth version of the TCC product released in the MRLC consortium that is part of the National Land Cover Database (NLCD)'
startYear	INT	'Start year of the product'
endYear	INT	'End year of the product'
year	INT	'Year of the product'

Terms of Use

Terms of Use

The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.

These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

USDA Forest Service. 2025. USFS Tree Canopy Cover v2023.5 (Conterminous United States and Outer Conterminous United States). Salt Lake City, Utah.

Citations

Citations:

• USDA Forest Service. 2025. USFS Tree Canopy Cover v2023.5 (Conterminous United States and Outer Conterminous United States). Salt Lake City, Utah.

• Breiman, L., 2001. Random Forests. In Machine Learning. Springer, 45: 5-32 doi:10.1023/A:1010933404324

• Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K., 2019. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment. Science Direct, 221: 274-285 doi:10.1016/j.rse.2018.11.012

• Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N., 2018. A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment. Science Direct, 205: 131-140 doi:10.1016/j.rse.2017.11.015

• Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B., 2017. Cloud detection algorithm comparison and validation for operational Landsat data products. In Remote Sensing of Environment. Science Direct, 194: 379-390 doi:10.1016/j.rse.2017.03.026

• Kennedy, R. E., Yang, Z., and Cohen, W. B., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment. Science Direct, 114(12): 2897-2910 doi:10.1016/j.rse.2010.07.008

• Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S., 2018. Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing. MDPI, 10(5): 691 doi:10.3390/rs10050691

• Lin, L.; Di, L.; Zhang, C.; Guo, L.; Di, Y.; Li, H.; Yang, A. 2022. Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm. Scientific Data. 9(1): 63. doi:10.3390/rs10050691

• Pasquarella, V. J., Brown, C. F., Czerwinski, W., and Rucklidge, W. J., 2023. Comprehensive Quality Assessment of Optical Satellite Imagery Using Weakly Supervised Video Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2124-2134. doi:10.1109/cvprw59228.2023.00206

• Sentinel-Hub, 2021. Sentinel 2 Cloud Detector. [Online]. Available at: https://github.com/sentinel-hub/sentinel2-cloud-detector

• U.S. Geological Survey, 2019. USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m

• Zhu, Z., and Woodcock, C. E., 2012. Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment. Science Direct, 118: 83-94 doi:10.1016/j.rse.2011.10.028

DOIs

• https://doi.org/10.1016/j.rse.2010.07.008
• https://doi.org/10.1016/j.rse.2011.10.028
• https://doi.org/10.1016/j.rse.2017.03.026
• https://doi.org/10.1016/j.rse.2017.11.015
• https://doi.org/10.1016/j.rse.2018.11.012
• https://doi.org/10.1023/A:1010933404324
• https://doi.org/10.1038/s41597-022-01169-w
• https://doi.org/10.3390/rs10050691

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Code Editor (JavaScript)

// Import the tree canopy cover collection
var dataset = ee.ImageCollection('USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5');

//Filter collection to 2021 and CONUS study area 
var tcc = dataset.filter(ee.Filter.calendarRange(2023, 2023,'year'))  // range: [1985, 2023]
               .filter('study_area == "CONUS"') // CONUS, AK, HAWAII, PRUSVI 
               .first();

// TCC palette
var tcc_palette = [
    'CDA066',
    'D7C29E',
    'C2D096',
    'B7D692',
    'ADDD8E',
    '78C679',
    '5CB86B',
    '41AB5D',
    '39A156',
    '329750',
    '238443',
    '11763D',
    '006837',
    '004529'
  ]

// SE palette 
var se_palette = [
    '000000',
    'FFFFFF',
    ]

              
// Display images on map 
Map.addLayer(tcc.select('data_mask'), {min:0,max:2}, 'Data Mask',false);
Map.addLayer(tcc.select('Science_Percent_Tree_Canopy_Cover'), {min:0,max:60,palette:tcc_palette}, 'Science Percent Tree Canopy Cover');
Map.addLayer(tcc.select('Science_Percent_Tree_Canopy_Cover_Standard_Error'), {min:0,max:4000,palette:se_palette}, 'Science Percent Tree Canopy Cover Standard Error');
Map.addLayer(tcc.select('NLCD_Percent_Tree_Canopy_Cover'), {min:0,max:60,palette:tcc_palette}, 'NLCD Percent Tree Canopy Cover');

Map.setCenter(-98.58, 38.14, 4);

Open in Code Editor

USFS Tree Canopy Cover v2023-5 (CONUS and OCONUS)

Overview The Tree Canopy Cover (TCC) data suite, produced by the United States Department of Agriculture, Forest Service (USFS), are annual remote sensing-based map outputs spanning from 1985-2023. These data support the National Land Cover Database (NLCD) project, which is managed by the US Geological Survey (USGS) as part of …

USGS/NLCD_RELEASES/2023_REL/TCC/v2023-5, forest,gtac,landuse-landcover,redcastle-resources,usda,usfs,usgs

1985-06-01T00:00:00Z/2023-09-30T00:00:00Z

20.38379 -135.286387 52.459364 -56.446306

Google Earth Engine

https://developers.google.com/earth-engine/datasets

• https://doi.org/10.3390/rs10050691
• https://doi.org/10.3390/rs10050691