Puerto Rico and the US Virgin Islands NLCD2011 USFS Percent Tree Canopy Cover (Analytical 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: 20160131

Title: Puerto Rico and the US Virgin Islands NLCD2011 USFS Percent Tree Canopy Cover (Analytical Version)

Edition: 1.0

Geospatial_Data_Presentation_Form: raster digital data

Series_Information:

Series_Name: none

Issue_Identification: none

Publication_Information:

Publication_Place: USGS/EROS, Sioux Falls, SD, 57198-0001, US, 47914 252nd Street

Publisher: U.S. Geological Survey

Other_Citation_Details:

References:

Baig, M.H.A., L. Zhang, T. Shuai, and Q. Tong, 2014. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance, Remote Sensing Letters, 5(5):423-431.

Brand, G.J., M.D. Nelson, D.G. Wendt, and K.K. Nimerfro, 2000. The hexagon/panel system for selecting FIA plots under an annual inventory. In: McRoberts, R. E., G.A. Reams, and P.C. Van Deusen, 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 45:15-32.

Coulston, J.W., D.M. Jacobs, C.R. King, and I.C. Elmore, 2013. The influence of multi-season imagery on models of canopy cover: a case study. Photogrammetric Engineering & Remote Sensing 79(5):469-477.

Coulston, J.W., G.G. Moisen, B.T. Wilson, M.V. Finco, W.B. Cohen, C.K. Brewer, 2012. Modeling percent tree canopy cover: a pilot study. Photogrammetric Engineering & Remote Sensing 78(7):715-727.

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

Huang, C., L. Yang, B. Wylie, and C. Homer, 2001. A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas. In: Third International Conference on Geospatial Information in Agriculture and Forestry; November 5-7, 2001; Denver, Colorado. CD-ROM, 1 disk.

Liaw, A. and M. Wiener, 2002. Classification and regression by randomForest. R News. 2(3): 18-22.

McRoberts, R.E., M.H. Hansen, 1999. Annual forest inventories for the north central region of the United States. Journal of Agricultural, Biological, and Environmental Statistics. 4(4): 361-371.

Moisen, G.G., J.W. Coulston, B.T. Wilson, W.B. Cohen, and M.V. Finco, 2012. Choosing appropriate subpopulations for modeling tree canopy cover nationwide. In: McWilliams, Will; Roesch, Francis A., eds. 2012. Monitoring Across Borders: 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern Mensurationists. e-Gen. Tech. Rep. SRS-157. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 195-200.

Nowacki, G, P. Spencer, M. Fleming, T. Brock, and T. Jorgenson, 2001. Ecoregions of Alaska: 2001. U.S. Geological Survey. Open-File Report 02-297 (map).

R Core Team, 2013. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org. (Accessed: 24 Feb 2015.)

Ruefenacht, B., 2016. Using Landsat-5 TM composites to model percent tree canopy cover. Photogrammetric Engineering & Remote Sensing (In Press).

Tipton, J., G.Moisen, P. Patterson, T.A. Jackson, and J. Coulston, 2012. Sampling intensity and normalizations: Exploring cost-driving factors in nationwide mapping of tree canopy cover. In: McWilliams, Will; Roesch, Francis A., eds. 2012. Monitoring Across Borders: 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern Mensurationists. e-Gen. Tech. Rep. SRS-157. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 201-208.

Zhu, Z. and C.E. Woodcock, 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment. 118(2012): 83-94.

Description:

Abstract:

The National Land Cover Database 2011 (NLCD2011) percent tree canopy cover layer (TCC 2011) was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium (www.mrlc.gov). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management, NASA, and the U.S. Army Corps of Engineers. One of the primary goals of the project was to generate a current, consistent, and seamless national land cover, percent tree canopy cover, and percent impervious cover at medium spatial resolution. Puerto Rico and the US Virgin Islands TCC 2011 was produced by the USDA Forest Service Remote Sensing Applications Center (RSAC). The Puerto Rico and the US Virgin Islands TCC 2011 dataset has two layers: percent tree canopy cover (PTCC) and standard error. For the PTCC layer, the pixel values range from 0 to 100 percent. For the standard error layer, the pixel values range from 0 to 44 percent. For both layers, 255 represents the background value. The standard error represents the model uncertainty associated with the corresponding pixel in the PTCC layer. The PTCC layer was produced using random forest and the standard error layer was calculated from the variance of the canopy cover estimates from the random forest regression trees. The PTCC has data gaps due to persistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the number 127.

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: 20150225

Currentness_Reference: Ground condition at time of imagery capture

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: ISO 19115 Category

Theme_Keyword: Earth Covers

Theme_Keyword: Imagery

Theme_Keyword: Base Map

Theme:

Theme_Keyword_Thesaurus: None

Theme_Keyword: GIS

Theme_Keyword: Percent Tree Canopy

Theme_Keyword: USFS

Theme_Keyword: Tree Canopy Cover

Theme_Keyword: Remote Sensing

Theme_Keyword: Continuous

Theme_Keyword: Digital Spatial Data

Theme_Keyword: U.S. Forest Service

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:

Any hardcopy or electronic products utilizing these datasets will clearly indicate their source. If the user has modified the data in any way, they are obligated to describe the types of modifications they have performed. User specifically agrees not to misrepresent these data sets, nor to imply that the MRLC approved the changes. Any data downloaded must be properly cited.

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: USGS/EROS

Address: 47914 252nd Street

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, refer to: <http://www.mrlc.gov/mrlc2k.asp> or email: mrlc@usgs.gov

Data_Set_Credit: USDA Forest Service Remote Sensing Applications Center

Security_Information:

Security_Classification: Unclassified

Native_Data_Set_Environment:

Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; ESRI ArcGIS 10.0.5.4400

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

No formal independent accuracy assessment of the Puerto Rico and the US Virgin Islands TCC 2011 has been made. The random forest regression algorithm (Breiman 2001; Liaw and Wiener 2002; Cutler et al. 2007) employed in the Puerto Rico and the US Virgin Islands TCC 2011 mapping calculates the mean of squared residuals along with percent variability explained by the model for assessing prediction reliability. The random forest 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. The standard error is the square root of the variance of the estimates given by all trees. A summary of the random forest model is available in the supplemental metadata.

Completeness_Report:

This Puerto Rico and the US Virgin Islands TCC 2011 product is version 1, dated 2016. The Puerto Rico and the US Virgin Islands TCC 2011 dataset consists of two main data products: (1) per-pixel tree canopy cover and (2) per- pixel standard error for the predicted tree canopy cover. A summary of all the Landsat images and the significant predictor variables used for modeling tree canopy cover with the random forest regression algorithm is available in the supplemental metadata. For detailed information, please refer to <http://www.mrlc.gov>.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Title: Landsat 8 Operational Land Imager (OLI) Imagery

Geospatial_Data_Presentation_Form: raster digital data

Publication_Information:

Publication_Place: Sioux Falls, SD

Publisher: U.S. Geological Survey

Type_of_Source_Media: None

Source_Citation_Abbreviation: L8

Source_Contribution: spectral information

Source_Information:

Source_Citation:

Citation_Information:

Originator: USDA Forest Service Remote Sensing Applications Center

Title: Photointerpreted Canopy Cover

Geospatial_Data_Presentation_Form: vector digital data

Type_of_Source_Media: None

Source_Citation_Abbreviation: PI_CC

Source_Contribution: canopy cover estimate (training/validation)

Source_Information:

Source_Citation:

Citation_Information:

Originator: USDA Forest Service Remote Sensing Applications Center

Title: X-Coordinate

Geospatial_Data_Presentation_Form: raster digital data

Type_of_Source_Media: None

Source_Citation_Abbreviation: XCoord

Source_Contribution: east-west location (relative to 96.0 W longitude)

Source_Information:

Source_Citation:

Citation_Information:

Originator: USDA Forest Service Remote Sensing Applications Center

Title: Y-Coordinate

Geospatial_Data_Presentation_Form: raster digital data

Type_of_Source_Media: None

Source_Citation_Abbreviation: YCoord

Source_Contribution: north-south location (relative to 37.5 N latitude)

Process_Step:

Process_Description:

Creation of Landsat composite. Multiple Landsat 8 scenes were selected and processed for each WRS-2 path/row. Selected scenes were acquired between 2013 and 2015. The selection process favored scenes with minimal cloud cover. An automated cloud masking algorithm, Fmask, (Zhu and Woodcock 2012) was used to remove clouds from each scene. Eight spectral bands (OLI bands 1-7 and 9) within each scene were transformed to surface reflectance (<http://landsat.usgs.gov/Landsat8_Using_Product.php>). A median value was calculated using all available pixel values for each geolocation (Ruefenacht 2016). These median values were combined to create a composite image for each path/row.

Source_Used_Citation_Abbreviation: L8

Process_Date: 20150617

Source_Produced_Citation_Abbreviation: L8Comp

Process_Step:

Process_Description:

Creation of Landsat derivatives. Spectral derivative images were calculated from the Landsat composite image for each WRS-2 path/row. NDMI (normalized difference moisture index), NDVI (normalized difference vegetation index), and tasseled cap transformation (Baig et al. 2014) were calculated following industry standards.

Process_Date: 20150630

Source_Produced_Citation_Abbreviation: NDMI, NDVI, TasCap

Process_Step:

Process_Description:

Photointerpretation of Sample Plots. Response data used to train the computer models is made by photographic interpretation of sample plots. Each plot representing approximately 950 hectares is part of a national hexagonal grid which covers all land types and is considered an equal probability sample for the total surface area (McRoberts et al., 1999). There are 990 plots encompassed by Puerto Rico and the US Virgin Islands. A circle with a radius of 43.9 m (144 ft.) was placed over each plot center. Each circle contained a 109-dot grid oriented 15 degrees east of true north with each dot separated by 8 m. Photo-interpreters evaluated each dot as being either tree or not tree. For each plot, fractional tree canopy cover was calculated as the number of dots attributed tree divided by 109. The imagery upon which the photo interpreters based their observations was an orthographic aerial image mosaic acquired by the U.S. Army Corps of Engineers. The aerial imagery was provided to the TCC Project by the USDA Natural Resources Conservation Service (NRCS) National Geospatial Center of Excellence (NGCE). Image tiles within the mosaic were circa 2009 through 2012.

Process_Date: 20150911

Source_Produced_Citation_Abbreviation: PI

Process_Step:

Process_Description:

Creation of percent tree canopy cover dataset (main process). The NLCD 2011 percent tree canopy cover (TCC 2011) for Puerto Rico and the US Virgin Islands was created as a single model using six Landsat WRS-2 path/rows. Five major steps were employed to map tree canopy cover: 1) collection of reference data, 2) acquisition and/or creation of predictor layers, 3) calibration of the random forest regression models using response data and predictor layers, 4) application of those models to predict per-pixel tree canopy cover across Puerto Rico and the US Virgin Islands, and 5) creation of the Puerto Rico and the US Virgin Islands mosaic. The methodology is described further below and in Coulston et al. (2012) and Ruefenacht (2016). Response data, consisting of estimated tree canopy cover at each of 990 plot locations, were generated via photographic interpretation (PI) of high spatial resolution images, as described in the Photointerpretation of Sample Plots process step. Predictor layers included Landsat 8 OLI composite imagery, Landsat spectral derivatives (NDMI, NDVI, and tasseled cap), NLCD 2001 land cover, and explicit location data (x,y). Modeling was carried out using the random forest algorithm as implemented in R (Liaw and Wiener, 2002; R Core Team, 2013) as outlined in the Attribute Accuracy Report above. The random forest model trained from the response data was applied to the predictor data layers for each Landsat WRS-2 path/row encompassing Puerto Rico and the US Virgin Islands producing a 2-layered image. The first layer was the random forest estimate of percent tree canopy cover and the second layer was the standard error, which is the per-pixel square root of the variance of the random forest estimates from the individual trees.

Source_Used_Citation_Abbreviation: PI_CC, L8Comp, NDMI, NDVI, TasCap, XCoord, YCoord

Process_Date: 20151112

Spatial_Data_Organization_Information:

Direct_Spatial_Reference_Method: Raster

Raster_Object_Information:

Raster_Object_Type: Grid Cell

Row_Count: 4656

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: nlcd2011_usfs_prusvi_canopy_analytical.img.vat

Attribute:

Attribute_Label: OID

Attribute_Definition: Internal feature number.

Attribute_Definition_Source: ESRI

Attribute_Domain_Values:

Unrepresentable_Domain:

Sequential unique whole numbers that are automatically generated.

Attribute:

Attribute_Label: Value

Attribute_Definition: Percent Canopy Cover

Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 0

Range_Domain_Maximum: 100

Attribute_Units_of_Measure: Percent

Unrepresentable_Domain: 127 = No data available 255 = Background

Attribute:

Attribute_Label: Count

Attribute:

Attribute_Label: Red

Attribute:

Attribute_Label: Green

Attribute:

Attribute_Label: Blue

Attribute:

Attribute_Label: Opacity

Distribution_Information:

Distributor:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: U.S. Geological Survey

Contact_Position: Customer Services Representative

Contact_Address:

Address_Type: mailing and physical

Address: USGS/EROS

Address: 47914 252nd Street

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, refer to: <http://www.mrlc.gov/mrlc2k.asp> or email: mrlc@usgs.gov

Distribution_Liability: See access and use constraints information.

Metadata_Reference_Information:

Metadata_Date: 20160131

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, refer to: <http://www.mrlc.gov/mrlc2k.asp> or 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