EnviroAtlas - 2016 Percent Tree Canopy Cover by 12-digit HUC for Puerto Rico and the U.S. Virgin Islands

Description

This EnviroAtlas dataset represents the percentage of land area within each 12-digit hydrologic unit code (HUC) in Puerto Rico and the US Virgin Islands that is classified as tree canopy using the National Land Cover Database 2016 (NLCD2016) USFS Tree Canopy Cover (Puerto Rico) dataset. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Resources

Name Format Description Link
API ZIP The endpoint of a web service to access the dataset (REST endpoint, WMS GetCapabilities URL, or a SOAP WSDL endpoint). Open resource
API ZIP The endpoint of a web service to access the dataset (REST endpoint, WMS GetCapabilities URL, or a SOAP WSDL endpoint). Open resource
Download ZIP The URL providing direct access to the downloadable dataset. Open resource
Download ZIP The URL providing direct access to the downloadable dataset. Open resource
Home Page Unknown Alternative landing page used to redirect user to a contextual, Agency-hosted "homepage" for the Dataset or API Open resource
Home Page Unknown Alternative landing page used to redirect user to a contextual, Agency-hosted "homepage" for the Dataset or API Open resource
Documentation Unknown URL for accessing related documents such as data dictionary, technical information about a dataset, developer documentation, etc. Open resource
Documentation Unknown URL for accessing related documents such as data dictionary, technical information about a dataset, developer documentation, etc. Open resource

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