AI for regional forest mapping and monitoring
Description
The purpose of this model is to use existing satellite images and forest survey data from the USDA Forest Service to create detailed maps of forest structures. Continuing research to understand and communicate errors and uncertainties in mapping and to improve algorithms over time.
Detailed example
The model outputs Geographic Information System (GIS) data that are predictions of forest attributes, such as tree density and tree species data in a study area.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
In addition to basic and applied research innovations, the application provides maps of forest structure (e.g., tree density and size, timber volume) and composition (tree species) which supports National Environmental Policy Act (NEPA) and other planning operations. These data may provide cost savings by either reducing the need to generate independent data through geographic information systems or field work. This research will help land managers be more effective and efficient with their planning.
Controls / human review
ATO: No; PIA: Not published
Data needed
Forest Inventory and Analysis data collected by the USDA Forest Service (Research & Development). It contains 200,000 individual plot measurements, and hundreds of Geospatial Information System (GIS) features (climate data, satellite images, topography, etc.) stored as GIS data (30-meter raster maps; GeoTiffs).