USFS CONUS 2016 Percent Tree Canopy (Standard Error of Science Version)

Metadata:

Identification_Information
Data_Quality_Information
Spatial_Data_Organization_Information
Spatial_Reference_Information
Entity_and_Attribute_Information
Distribution_Information
Metadata_Reference_Information

Identification_Information:

Citation:

Citation_Information:

Publication_Date: 20230401

Title: USFS CONUS 2016 Percent Tree Canopy (Standard Error of Science Version)

Edition: v2021-4

Geospatial_Data_Presentation_Form: raster digital data

Series_Information:

Series_Name: USFS Percent Tree Canopy Cover (Standard Error of Science Version)
Issue_Identification: v2021-4

Publication_Information:

Publication_Place: Salt Lake City, Utah
Publisher: USDA Forest Service

Description:

Abstract:

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover (TCC) data to serve needs of multiple user communities. These datasets encompass the conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include:

- The raw model outputs referred to as the annual Science data; and

- A modified version built for the National Land Cover Database referred to as NLCD data. They are available at the following locations:

Science:: https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/; https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife

NLCD:: https://www.mrlc.gov/data; https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife

The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here.

Purpose:

The goal of this project is to provide CONUS and OCONUS with complete, current and consistent public domain tree canopy cover information.

Supplemental_Information:

Corner Coordinates (center of pixel, meters): upper left: -2493045.0 (X), 3310005.0 (Y); lower right: 2342655.0 (X), 177285.0 (Y).

Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Currentness_Reference: Ground condition

Status:

Progress: Complete
Maintenance_and_Update_Frequency: As needed

Spatial_Domain:

West_Bounding_Coordinate: -130.23282801589895
East_Bounding_Coordinate: -73.59459648889016
North_Bounding_Coordinate: 48.70739591304975
South_Bounding_Coordinate: 22.07673063066848

Keywords:

Theme:

Theme_Keyword_Thesaurus: None
Theme_Keyword: Tree Density
Theme_Keyword: Digital Spatial Data
Theme_Keyword: Tree Canopy Cover
Theme_Keyword: Continuous
Theme_Keyword: Percent Tree Canopy
Theme_Keyword: Remote Sensing
Theme_Keyword: GIS
Theme_Keyword: Change
Theme_Keyword: Landsat
Theme_Keyword: Sentinel-2
Theme_Keyword: LandTrendr

Theme:

Theme_Keyword_Thesaurus: NGDA Portfolio Themes
Theme_Keyword: NGDA
Theme_Keyword: National Geospatial Data Asset

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Category
Theme_Keyword: BaseMaps
Theme_Keyword: EarthCover
Theme_Keyword: Imagery
Theme_Keyword: Environment

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Categories
Theme_Keyword: environment
Theme_Keyword: imageryBaseMapsEarthCover

Place:

Place_Keyword_Thesaurus: 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
Place_Keyword: U.S.
Place_Keyword: USA
Place_Keyword: United States of America
Place_Keyword: Lower 48
Place_Keyword: Conterminous United States
Place_Keyword: CONUS
Place_Keyword: United States of America

Access_Constraints: None

Use_Constraints:

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. 2023. USFS Percent Tree Canopy (Standard Error of Science Version) CONUS v2021-4. Salt Lake City, UT.

Appropriate use includes regional to national assessments of tree cover, total extent of tree cover, and aggregated summaries of tree cover

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Contact_Person: Kevin Megown
Contact_Position: Program Lead: Resource, Mapping, Inventory and Monitoring

Contact_Address:

Address_Type: mailing and physical
Address: 125 S. State Street, Suite 7105
City: Salt Lake City
State_or_Province: UT
Postal_Code: 84138
Country: US

Contact_Voice_Telephone: 801-975-3826

Contact_Facsimile_Telephone: 801-975-3478

Contact_Electronic_Mail_Address: sm.fs.tcc@usda.gov

Hours_of_Service: 0800 - 1600 MT, M - F

Data_Set_Credit:

Funding for this project was provided by the U.S. Forest Service (USFS). RedCastle Resources produced the dataset under contract to the USFS Geospatial Technology and Applications Center.

Security_Information:

Security_Classification_System: none
Security_Classification: Unclassified
Security_Handling_Description: n/a

Native_Data_Set_Environment: Google Earth Engine v0.1.321; GDAL 3.4.3

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

Model performance metrics including mean of squared residuals and percent variability explained were obtained from the random forest regression model (Breiman, 2001; R Core Team 2020) used to derive tree canopy cover estimates. The maximum mean of squared residuals was 91.3 and the minimum was 206.5. The maximum percent variability explained was 60.1 and the minimum was 89.7. Additionally, 500 random forest trees were used to derive a tree canopy cover prediction for each pixel. Standard errors were calculated for each pixel using the estimates from the 500 trees. The standard errors provide information on the certainty of TCC predictions. We conducted an independent error assessment over the 2011 NLCD TCC v2021.4 map output. Thirty percent of our response data was withheld from model calibration to be used to assess error. In order to account for inconsistencies between the FIA plot location density between different states, along with plots that we did not use due to quality assurance measures, we estimated the area weight of each plot by computing Thiessen polygons for each plot centroid. In order to avoid extremely large weights for edge plots, any plot on the edge assumed the average of the weight of neighboring non-edge plots polygon weights. We then computed the weighted root mean squared error (RMSE) and mean absolute error (MAE) using the reference TCC value as the truth, and the final NLCD TCC 2011 map value as the predicted value.

References:: Breiman, L. 2001. Random forests. Machine Learning 45:15-32.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Attribute_Accuracy_Results:

TCC_Accuracy: For CONUS the weighted map RMSE is 12.8% TCC.

Completeness_Report: Data extend across the lower 48 conterminous United States

Lineage:

Reference data include the USFS Forest Inventory and Analysis (FIA) program photo-interpreted percent tree canopy cover (TCC). Predictor data sources include Landsat 4, 5, 7, 8, and 9 Collection 2 Tier 1 Level 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data, USGS 3D Elevation Program (3DEP) elevation data, and for the CONUS only the annual Crop Data Layer (CDL). These serve as the foundational model predictor data sources. The predictor data originate from the US Geological Survey Earth Resource Observation and Science (EROS) Center (Landsat and 3DEP data), the European Space Agency (ESA; Sentinel 2 data), and the USDA National Agricultural Statistics Service (CDL data). All predictor data access and processing is performed using the Google Earth Engine API (Gorelick, 2017).

Citation:: Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031

Process_Step: Process_Description: The USFS Forest Inventory and Analysis (FIA) program photo-interpreted percent tree canopy cover (TCC) response data. Photointerpretation (PI) measured TCC using a custom ArcGIS plug-in tool (Goeking et al., 2012) from 105-point grids placed in 90x90 squares centered on USFS FIA plot design. A total of 55,242 PI plots were used in TCC modeling. Citation: Goeking, S.A., Liknes, G.C., Lindblom, E., Chase, J., Jacobs, D.M., and Benton, R. (2012). A GIS-based tool for estimating tree canopy cover on fixed-radius plots using high-resolution aerial imagery. In: R. Morin, S. Randall, G.C. Liknes (Comps.), Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012 December 4-6, Baltimore MD (pp. 237-241). (General Technical Report NRS-P-105). U.S. Department of Agriculture, Forest Service, Northern Research Station. Newtown Square, PA. https://www.fs.fed.us/nrs/pubs/gtr/gtr_nrs-p-105.pdf Originator: USDA Forest Service Forest Inventory and Analysis Program Title: Photo-interpreted Canopy Cover (FIA) Geospatial_Data_Presentation_Form: vector digital data Process_Date: 20120101 Source_Citation_Abbreviation: FIACC Source_Contribution: canopy cover estimate (response/validation) Process_Step: Process_Description: Creation of Digital Elevation Model (DEM) derivatives. A CONUS-wide terrain dataset used a predictor layer was provided by the USGS 3D Elevation Program (U.S. Geological Survey, 2019). Slope, aspect, and the sine and cosine of aspect were calculated for each pixel following industry standards. Citation: 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 Originator: U.S. Geological Survey Title: Digital Elevation Model (DEM) Geospatial_Data_Presentation_Form: raster digital data Process_Date:20220901 Source_Citation_Abbreviation: DEM Source_Contribution: elevation data Process_Step: Process_Description: Creation of cropland data layer (CDL) binary mask. The annual binary agriculture data were produced by classifying all non-tree CDL crops as agriculture and everything else as non-agriculture. Citation: USDA National Agricultural Statistics Service Cropland Data Layer. (2007-2022). Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape USDA-NASS, Washington, DC. Originator: USDA Title: USDA National Agricultural Statistics Service Cropland Data Layer Geospatial_Data_Presentation_Form: raster digital data Process_Date:20130101 Source_Citation_Abbreviation: CDL Source_Contribution: crop data Process_Step: Process_Description: Two sets of annual medoid composites were created. Set 1 does not include any Landsat 7 data occurring after 2002. Set 2 includes all available Landsat 7 data through 2015. To generate annual composites Landsat and Sentinel 2 imagery were collected from 1984-2022 from Julian day 153-273 for 1984-2015, and Julian day 182-244 for 2016-2022. Landsat 7 imagery were used from 1999-2002, and not used after 2002 due to scan line correction failure in 2003. For Landsat image collections, the CFmask cloud masking algorithm, an implementation of Fmask 2.0 was applied (Zhu and Woodcock 2012; Foga et al., 2017), and the cloudScore algorithm (Chastain et al., 2019). For Sentinel-2 data, the s2Cloudless algorithm was used to mask clouds (Zupanc, 2017). We used the Temporal Dark Outlier Mask (TDOM) method to mask cloud shadows in both Landsat and Sentinel-2 (Chastain et al., 2019). For each year, the annual geometric medoid was computed to summarize the data into a single annual composite for each of the 54 tiles that span CONUS. Citation: 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 (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012 Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., and Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. In Remote Sensing of Environment (Vol. 194, pp. 379-390). http://doi.org/10.1016/j.rse.2017.03.026. Zhu, Z., and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment, 118, pp. 83-94. Zupanc, A. (2017) Improving Cloud Detection With Machine Learning. Online: https://medium.com/sentinel-hub/improvingcloud-detection-with-machine-learningc09dc5d7cf13. Accessed 20 November 2022. Originator: U.S. Geological Survey and European Space Agency Title: Annual Landsat-Sentinel2 image composites Geospatial_Data_Presentation_Form: raster digital data Process_Date:20221001 Source_Citation_Abbreviation: L4TM, L5TM, L7ETM+, L8OLI, L9OLI, S2 Source_Contribution: image composite data Process_Step: Process_Description: The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) temporal segmentation algorithm was applied to the two sets of composite time series in Google Earth Engine (GEE) (Kennedy et al., 2018; Cohen et al., 2018). The resulting two sets of LandTrendr time-series fitted values were used as independent predictor variables in random forest models (Breiman 2001). Stripping artifacts were observed in preliminary modeling of TCC when LandTrendr set 2 visible bands - derived from composite set 2 data that includes all Landsat 7 data through 2015 - were included as predictor layers. To avoid stripping artifacts the visible bands from LandTrendr set 2 fitted values were not used in modeling. Citation: Breiman, L. (2001). Random forests. Machine learning (Vol. 45, pp. 15-32). https://doi.org/10.1023/A:1010933404324 Cohen, W.B., Yang, Z., Healey, S.P., Kennedy, R.E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection, Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015 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 (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691 Originator: GTAC Title: Annual LandTrendr Fitted Images Geospatial_Data_Presentation_Form: raster digital data Process_Date:20221001 Source_Citation_Abbreviation: LT Source_Contribution: landtrendr fitted data Process_Step: Process_Description: Creation of the percent tree canopy cover (TCC) Science dataset (main process). The CONUS FS Science TCC dataset was created for the 2008 through 2021. For CONUS, 54 tiles were used in a 5x5 moving window where model calibration data was gathered from the moving windows and random forest models were created. The random forest models were applied applied to the center tiles. The final dataset is a mosaic of TCC values. Five major steps were employed to map TCC and produce the Science product: 1) collection of reference data, 2) acquisition and/or creation of predictor layers, 3) calibration of random forests regression models for each mapping area using response data and predictor layers, 4) application of those models to predict per-pixel TCC across the entire mapping area, and 5) exporting Science images from Google Earth Engine (GEE) to local computers for further post-processing that includes the creation of the CONUS-wide mosaic. The methodology is described further below, in the technical methods document (Housman et al., 2023), and in an upcoming manuscript in preparation (Heyer et al., 2023). Step 1: Reference data, consisting of estimated TCC at each of the 63,010 FIA plot locations, were generated via aerial image interpretation of high spatial resolution images collected and supplied by the U.S. Forest Service Forest Inventory and Analysis (FIA) program. The spatial distribution of the sample points follows the FIA systematic grid (Brand et al. 2000). Low quality FIA PI observations were removed for a total of 55,242 FIA plots used in modeling Step 2: Predictor layers include two sets of LandTrendr fitted images spectral derivatives. Set 1 (no Landsat 7 data after 2002) includes all optical bands and indices. Set 2 (includes all Landsat 7 data through 2015) excludes Landsat 7 visible bands to avoid stripping artifacts. Other predictor layers include binary agriculture layer (1=agriculture, 0 = non-agriculture), elevation data, and terrain derivatives (slope, aspect, sine of aspect, cosine of aspect). The processes for creating the derived layers are described separately (see related Process Steps). Step 3: For each 480 km x 480 km moving window tile, a random forest model was built from 2011 response and predictor data that fell over a 5x5 tile neighborhood for that tile. For each model, the variable selection R package VSURF (Genuer et al., 2015) was used to determine the number of variables to randomly sample at tree splits (mtry). Models were generated locally using the random forest regression algorithm "sklearn.ensemble.RandomForestRegressor" from the Scikit-Learn package in python (Pedregosa et al. 2011). Step 4: In Google Earth Engine (GEE), models were applied to each tile for CONUS, producing a 2-layered Science image. The first layer was the random forests mean predicted TCC value and the second layer was the standard error (SE), which is the per-pixel standard error of the random forests regression predictions from the individual regression trees. In addition to the model output, a statistic that normalized the expected error, which we refer to as tau (Coulston et al., 2016), was calculated for each processing area. The tau statistics can be used to mask erroneous pixels as described in Coulston et al. (2016). The tau statistics are provided in the supplemental metadata. Step 5: Following model application, the Science SE images were exported from GEE to local computers for post-processing. During post-processing mosaics were created, cog tifs were generated, statistics were calculated, pyramid layers were built, and color ramps were applied to each Science SE image. During post-processing a colormap was applied to each image. For each Science SE image, the standard error non-area processing value is 65534, and the background value is 65535. Citation: Brand, G.J., Nelson, M.D., Wendt, D.G., and Nimerfro, K.K. (2000). The hexagon/panel system for selecting FIA plots under an annual inventory. In: McRoberts, R.E.; Reams, G.A.; Van Deusen, P.C., eds. Proceedings of the First Annual Forest Inventory and Analysis Symposium; Gen. Tech. Rep. NC-213. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station: 8-13 Breiman, L. (2001). Random forests. Machine Learning (Vol. 45, pp. 5-32) Brooks, E.B., Thomas, V.A., Wynne, R.H., Coulston, J.W. (2012). Fitting the multitemporal curve: a fourier series approach to the missing data problem in remote sensing analysis. IEEE Transactions on Geoscience and Remote Sensing (Vol. 50, Issue 9, pp. 3340-3353) Coulston, J.W., Blinn, C. E., Thomas, V. A., and Wynne, R. H. (2016). Approximating prediction uncertainty for random forest regression models. Photogrammetric Engineering and Remote Sensing, (Vol. 82, Issue 3, pp. 189-197. Genuer, R., Poggi, J. M., and Tuleau-Malot, C. (2015). VSURF: an R package for variable selection using random forests. The R Journal (Vol. 7, Issue 2, pp. 19-33) Heyer, J., Schleeweis, K., Ruefenacht, B., Housman, I., Megown, K., and Bogle, M. 2023. A time invariant modeling approach to produce annual tree-canopy cover for the conterminous United States. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Geospatial Technology and Applications Center. [Manuscript in Preparation] Housman, I., Heyer, J., Ruefenacht, B., Schleeweis, K., Bogle, M., and Megown, K. 2023. National Land Cover Database Tree Canopy Cover Methods v2021-4. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Geospatial Technology and Applications Center. [Manuscript in Preparation] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830). Originator: GTAC Title: USFS Percent Tree Canopy Cover (Standard Error of Science Version) Geospatial_Data_Presentation_Form: raster digital data Process_Date:20230201 Source_Citation_Abbreviation: TCC Source_Contribution: tree canopy cover (standard error science version)

Spatial_Data_Organization_Information:

Direct_Spatial_Reference_Method: Raster

Raster_Object_Information:

Raster_Object_Type: Grid Cell
Number_of_Dimensions: 2
Cell_Geometry: Area
Attribute_Description: Percent tree canopy cover standard error
Content_Type: Image
Band_min_value: 0.0
Band_max_value: 4505.0
Band_Units: Percent
Band_bits_per_value: 16

Row (y-axis):

Dimension_Name: Row (y-axis)
Dimension_Size: 161190
Dimension_Resolution: 30 meters

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name: 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_Coordinate_Information:

Planar_Coordinate_Encoding_Method: coordinate pair

Coordinate_Representation:

Abscissa_Resolution: 0.0000000037527980722984474
Ordinate_Resolution: 0.0000000037527980722984474

Planar_Distance_Units: meters

Geodetic_Model:

Horizontal_Datum_Name: WGS 1984
Ellipsoid_Name: GRS 1980
Semi-major_Axis: 6378137.0
Denominator_of_Flattening_Ratio: 298.257222101

Entity_and_Attribute_Information:

Detailed_Description:

Entity_Type: Thematic Classification

Thematic Classification

Entity_Type_Label: science_se_conus_2016_v2021-4.tif

Attribute:

Attribute_Label: OID
Attribute_Definition: ObjectID Field
Attribute_Definition_Source: ESRI
Attribute_Type: Short integer
Attribute_Width: 4 bytes
Attribute_Precision: 5
Attribute_Scale: 0

Attribute:

Attribute_Label: Value

Attribute_Definition: Percent tree canopy cover standard error

Attribute_Definition_Source: ESRI

Attribute_Type: Short integer

Attribute_Width: 4 bytes

Attribute_Precision: 5

Attribute_Scale: 0

Range_Domain:

Range_Domain_Minimum: 0
Range_Domain_Maximum: 4500
Attribute_unit_of_Measure:Percent

Attribute:

Attribute_Label: Blue
Attribute_Definition: Total number of pixels per classification
Attribute_Definition_Source: ESRI
Attribute_Type: Long integer
Attribute_Width: 8 bytes
Attribute_Precision: 10
Attribute_Scale: 0

Attribute:

Attribute_Label: Green
Attribute_Definition: Color ramp red value
Attribute_Definition_Source: ESRI
Attribute_Type: Short integer
Attribute_Width: 1 byte
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Attribute_Scale: 0

Attribute:

Attribute_Label: Red
Attribute_Definition: Color ramp green value
Attribute_Definition_Source: ESRI
Attribute_Type: Short integer
Attribute_Width: 1 byte
Attribute_Precision: 3
Attribute_Scale: 0

Attribute:

Attribute_Label: Count
Attribute_Definition: Color ramp blue value
Attribute_Definition_Source: ESRI
Attribute_Type: Short integer
Attribute_Width: 1 byte
Attribute_Precision: 3
Attribute_Scale: 0

Distribution_Information:

Distributor:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Contact_Person: Kevin Megown
Contact_Position: Program Lead: Resource, Mapping, Inventory and Monitoring

Contact_Address:

Address_Type: mailing and physical
Address: 125 S. State Street, Suite 7105
City: Salt Lake City
State_or_Province: UT
Postal_Code: 84138
Country: US

Contact_Voice_Telephone: 801-975-3826

Contact_Facsimile_Telephone: 801-975-3478

Contact_Electronic_Mail_Address: sm.fs.tcc@usda.gov

Hours_of_Service: 0800 - 1600 MT, M – F

Resource_Description: Downloadable data

Distribution_Liability:

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. Tree Canopy Cover changes 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.

Standard_Order_Process:

Digital_Form:

Digital_Transfer_Option:

Online_Option:

Computer_Contact_Information:

Network_Address:

Network_Resource_Name:: https://data.fs.usda.gov/geodata/rastergateway/LCMS/; https://apps.fs.usda.gov/lcms-viewer

Access_Instructions: Downloadable data

Metadata_Reference_Information:

Metadata_Date: 20230401

Metadata_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Contact_Person: Kevin Megown
Contact_Position: Program Lead: Resource, Mapping, Inventory and Monitoring

Contact_Address:

Address_Type: mailing and physical
Address: 125 S. State Street, Suite 7105
City: Salt Lake City
State_or_Province: UT
Postal_Code: 84138
Country: US

Contact_Voice_Telephone: 801-975-3826

Contact_Facsimile_Telephone: 801-975-3478

Contact_Electronic_Mail_Address: sm.fs.tcc@usda.gov

Hours_of_Service: 0800 - 1600 MT, M – F

Metadata_Standard_Name: ISO 19139 Metadata Implementation Specification

Metadata_Standard_Version: FGDC-STD-001-1998

Metadata_Time_Convention: local time

Metadata_Access_Constraints: