NLCD 2011 to 2016 Percent Tree Canopy Change (Puerto Rico and the US Virgin Islands)

Metadata also available as

Metadata:


Identification_Information:
Citation:
Citation_Information:
Publication_Date: 20191018
Title: NLCD 2011 to 2016 Percent Tree Canopy Change (Puerto Rico and the US Virgin Islands)
Geospatial_Data_Presentation_Form: raster digital data
Series_Information:
Series_Name: none
Issue_Identification: none
Publication_Information:
Publication_Place: Sioux Falls, SD
Publisher: U.S. Geological Survey
Description:
Abstract:
The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include:
  • The initial model outputs referred to as the Analytical data;
  • A masked version of the initial output referred to as Cartographic data;
  • And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.

The Analytical data are the initial model outputs generated in the production workflow. 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 nominal years of 2011 and 2016 are available.

The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available.

The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of “2011 TCC + change in TCC = 2016 TCC”. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel’s values meet the criterion of “2011 TCC + change in TCC = 2016 TCC”. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.

These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:

The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. Percent canopy gains are represented as values 0 to 100. Percent canopy losses are represented as values 101 to 200 such that a percent canopy loss of 25 would be represented by the value 125. The background is represented by the value 227 and data gaps are represented by the value 210 since this is an unsigned 8-bit image.

Purpose:
The goal of this project is to provide the Nation with complete, current and consistent public domain information on its tree canopy cover.
Supplemental_Information:
Corner Coordinates (center of pixel, meters): upper left: 2890755 (X), 189555 (Y); lower right: 3591885 (X), -219375 (Y).
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20131111
Ending_Date: 20170917
Currentness_Reference: Ground condition
Status:
Maintenance_and_Update_Frequency: As needed
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -67.950181
East_Bounding_Coordinate: -64.398022
North_Bounding_Coordinate: 19.319894
South_Bounding_Coordinate: 17.013923
Keywords:
Theme:
Theme_Keyword_Thesaurus: NGDA Portfolio Themes
Theme_Keyword: Land Use Land Cover Theme
Theme_Keyword: National Geospatial Data Asset
Theme_Keyword: NGDA
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Category
Theme_Keyword: BaseMaps
Theme_Keyword: Environment
Theme_Keyword: EarthCover
Theme_Keyword: Imagery
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: Tree Canopy Cover
Theme_Keyword: Tree Density
Theme_Keyword: Percent Tree Canopy
Theme_Keyword: Continuous
Theme_Keyword: Remote Sensing
Theme_Keyword: Digital Spatial Data
Theme_Keyword: GIS
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Categories
Theme_Keyword: imageryBaseMapsEarthCover
Theme_Keyword: environment
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: USVI
Place_Keyword: USA
Place_Keyword: United States of America
Place_Keyword: PR
Place_Keyword: US Virgin Islands
Place_Keyword: U.S. Virgin Islands
Place_Keyword: US
Place_Keyword: Puerto Rico
Place_Keyword: United States
Place_Keyword: U.S.A.
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. 2019. NLCD 2011 to 2016 Percent Tree Canopy Change (Puerto Rico and the US Virgin Islands) 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: U.S. Geological Survey
Contact_Position: Customer Services Representative
Contact_Address:
Address_Type: mailing and physical
Address: 47914 252nd Street
Address: USGS/EROS
City: Sioux Falls
State_or_Province: SD
Postal_Code: 57198-0001
Country: US
Contact_Voice_Telephone: 605-594-6151
Contact_TDD/TTY_Telephone: 605/594-6933
Contact_Facsimile_Telephone: 605-594-6589
Contact_Electronic_Mail_Address: custserv@usgs.gov
Hours_of_Service: 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT)
Contact_Instructions:
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, email: mrlc@usgs.gov
Data_Set_Credit:
Funding for this project was provided by the U.S. Forest Service (USFS). RedCastle Resources, Inc. 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: Version 6.2 (Build 9200) ; Esri ArcGIS 10.5.1.7333
Cross_Reference:
Citation_Information:
Originator: USDA Forest Service
Publication_Date: 20191018
Title: NLCD 2011 to 2016 Percent Tree Canopy Change (Puerto Rico and the US Virgin Islands)
Geospatial_Data_Presentation_Form: raster digital data
Series_Information:
Series_Name: none
Issue_Identification: none
Publication_Information:
Publication_Place: Salt Lake City, UT
Publisher: USDA Forest Service
Online_Linkage: https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/

Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
No formal, independent accuracy assessment of this product has been made at the time of publication. However, an assessment is planned. Users should check at https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ or send an inquiry to the metadata contact to inquire if new accuracy information is available.

The random forests regression algorithm (R Core Team 2017; Cutler et al. 2007; Breiman 2001) employed in creating this product calculates the mean of squared residuals along with percent variability explained by the model for assessing prediction reliability. The random forests models consisted of 500 decision trees, which were used to determine the final response value. The response of each tree depended on a randomly chosen subset of predictor variables chosen independently (with replacement) for evaluation by that tree. The responses of the trees were averaged to obtain an estimate of the dependent variable. Because the random forests bias correction option was used, it was possible to obtain estimates less than 0 or greater than 100. These estimates were reset to either 0 or 100. The estimates were also rounded to the nearest integer. The standard error is the square root of the variance of the estimates given by all trees.

References

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

Cutler, R.D.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. 2007. Random forests for classification in ecology. Ecology 88 (11):2783-2792.

R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL www.R-project.org.

Completeness_Report:
Data is for Puerto Rico and the US Virgin Islands only.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: USFS Geospatial Technology and Applications Center (GTAC)
Publication_Date: 20190305
Title: 2011 and 2016 USFS Puerto Rico and the US Virgin Islands analytical products
Geospatial_Data_Presentation_Form: raster digital data
Publication_Place: Salt Lake City, UT
Publisher: USDA Forest Service
Type_of_Source_Media: None
Source_Citation_Abbreviation: USFSAnalytical
Source_Contribution: modeled tree canopy cover and associated error estimates
Source_Information:
Source_Citation:
Citation_Information:
Originator: USFS Geospatial Technology and Applications Center (GTAC)
Publication_Date: 20190726
Title: 2011 and 2016 USFS Puerto Rico and the US Virgin Islands cartographic products
Geospatial_Data_Presentation_Form: raster digital data
Publication_Place: Salt Lake City, UT
Publisher: USDA Forest Service
Type_of_Source_Media: None
Source_Citation_Abbreviation: USFSCartographic
Source_Contribution: masked tree canopy cover
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service Forest Inventory and Analysis Program
Publication_Date: 20190826
Title: EVALIDator Web Tool
Geospatial_Data_Presentation_Form: tabular digital data
Type_of_Source_Media: None
Source_Citation_Abbreviation: FIAEV
Source_Contribution: estimates of landscape change
Process_Step:
Process_Description:
Creation of percent tree canopy cover change dataset for the 2016 NLCD (main process). The percent tree canopy cover change Puerto Rico and the US Virgin Islands dataset was built as the last component in the TCC production workflow utilized by the USFS for the 2016 NLCD. The dataset represents an estimate of tree canopy cover change between the nominal years of 2011 and 2016. The percent tree canopy cover change dataset for the 2016 NLCD was built with components from upstream steps in the 2016 TCC production workflow, specifically, the standard error layer from the “FS-Analytical” TCC product and TCC values from the “FS-Cartographic” TCC product. (See metadata for the “FS-Analytical” and “FS-Cartographic” products for descriptions of how those products were constructed.) For this percent tree canopy cover change dataset for the 2016 NLCD, eight steps were employed:

Step 1: Disturbance data from the USFS Forest Inventory and Analysis (FIA) program were analyzed to estimate the “percent of area disturbed” between the nominal years of 2011 and 2016, for Puerto Rico and the US Virgin Islands.

Step 2: Pixels were sampled from the 2011 and 2016 “FS-Analytical” TCC products for Puerto Rico and the US Virgin Islands, at 1,100 locations. TCC and standard error values were collected for each sampled pixel.

Step 3: The compiled TCC and standard error values from each sampled pixel were input to a numerical optimization tool. The target of the optimization was the “percent of area disturbed”, as derived from the FIA data described in Step 1 above. The optimization tool was used to determine the condition such that (A) the percentage of the Puerto Rico and the US Virgin Islands sampled pixels identified as loss between the nominal years of 2011 and 2016 matched (B) the “percent of area disturbed” between the nominal years of 2011 and 2016, as derived from FIA data. The conditions estimated using the optimization tool, sampled data from the 2011 and 2016 datasets, and the target were two multiplier values that were subsequently used to determine whether canopy cover had changed or not, between the nominal years of 2011 and 2016 for individual pixels.

Step 4: The Puerto Rico and the US Virgin Islands multiplier values, output from the optimization tool, were applied with an ERDAS model across Puerto Rico and the US Virgin Islands. Specifically, upper and lower bounds of a range of TCC were computed for each pixel, in each year, by (A) using the pixel’s TCC value as the midpoint of the range, (B) setting the lower bound of the range to be the pixel’s TCC value minus the product of the multiplier value and the pixel’s standard error value, and (C) setting the upper bound of the range to be the TCC value plus the product of the multiplier value and the pixel’s standard error value. If the range computed for a pixel in 2011 did not overlap with the range computed for a pixel in 2016, the pixel was identified as changed. If the ranges overlapped, then the pixel was identified as “No change”.

Step 5: For pixels identified as changed, the pixel value in the percent tree canopy cover change dataset was set to the simple difference between the 2016 and 2011 “FS-Cartographic” TCC values for the pixel.

Step 6: For pixels in which confidence in actual change was low or non-existent (i.e., the ranges computed in Step 4 above overlapped and the pixel was identified as “No change”), the pixel value in the percent tree canopy cover change dataset for the 2016 NLCD was set to zero.

Step 7: Results were reviewed by geospatial analysts, especially in areas of severe change (e.g., severe fires, harvest and regrowth in timber areas, etc.), and compared to high-resolution aerial photography and imagery.

Step 8: Spatial filtering was also applied to clean up noise and speckle. While the integrated data stack was achieved, minor visual artifacts (e.g., very small islands of “No Change” surrounded by pixels identified as change) may still be present within the tree canopy cover products included in the overall 2016 NLCD Product Suite.

Source_Used_Citation_Abbreviation:
USFSAnalytical, USFSCartographic, FIAEV
Process_Date: 20191001

Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Pixel
Row_Count: 4654
Column_Count: 12176

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:
Geodetic_Model:
Horizontal_Datum_Name: D North American 1983
Ellipsoid_Name: GRS 1980
Semi-major_Axis: 6378137.0
Denominator_of_Flattening_Ratio: 298.257222101

Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: nlcd_2011_to_2016_prusvi_treecanopy_change_20191017_u8.img.vat
Attribute:
Attribute_Label: Value
Attribute_Definition: Percent tree canopy cover
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 0
Range_Domain_Maximum: 200
Attribute_Units_of_Measure: Percent
Attribute:
Attribute_Label: Count
Attribute:
Attribute_Label: Red
Attribute:
Attribute_Label: Green
Attribute:
Attribute_Label: Blue
Attribute:
Attribute_Label: OID
Attribute_Definition: Internal feature number.
Attribute_Definition_Source: ESRI
Attribute_Domain_Values:
Unrepresentable_Domain: 210 = No data available 227 = Background
Sequential unique whole numbers that are automatically generated.

Distribution_Information:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological Survey
Contact_Position: Customer Services Representative
Contact_Address:
Address_Type: mailing and physical
Address: 47914 252nd Street
Address: USGS/EROS
City: Sioux Falls
State_or_Province: SD
Postal_Code: 57198-0001
Country: US
Contact_Voice_Telephone: 605-594-6151
Contact_TDD/TTY_Telephone: 605/594-6933
Contact_Facsimile_Telephone: 605-594-6589
Contact_Electronic_Mail_Address: custserv@usgs.gov
Hours_of_Service: 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT)
Contact_Instructions:
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, email: mrlc@usgs.gov
Resource_Description: Downloadable data
Distribution_Liability: See access and use constraints information.

Metadata_Reference_Information:
Metadata_Date: 20191018
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological Survey
Contact_Position: Customer Services Representative
Contact_Address:
Address_Type: mailing and physical
Address: 47914 252nd Street
Address: USGS/EROS
City: Sioux Falls
State_or_Province: SD
Postal_Code: 57198-0001
Country: US
Contact_Voice_Telephone: 605-594-6151
Contact_TDD/TTY_Telephone: 605/594-6933
Contact_Facsimile_Telephone: 605-594-6589
Contact_Electronic_Mail_Address: custserv@usgs.gov
Hours_of_Service: 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT)
Contact_Instructions:
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, email: mrlc@usgs.gov
Metadata_Standard_Name: FGDC Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time
Metadata_Access_Constraints:
There are no restrictions to access for this metadata.

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