DISTRIB-II: Habitat Suitability of Eastern United States Tree
Description
The goal of this use case is to provide information about current and future habitat suitability of east United States tree species. Machine learning is used to develop models that can predict relative abundance of individual tree species in the future.
Detailed example
Outputs from this use case include predictive maps of individual tree species abundance at resolutions of 10 and 20-km grids across the eastern United States under current (1981 to 2010) and potential future climate projections (2070 to 2099).
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
These models provide forest land managers with information about how habitats could change under various future scenarios which can assist in resource planning. The information provided by the models can also result in cost saving and resource effectiveness by reducing costs to manage unsuitable tree species and instead suggesting species that could be more suitable.
Controls / human review
ATO: No; PIA: Not published
Data needed
Data used to train the machine learning models included USDA Forest Service Forest Inventory and Analysis data, elevation, 30-year mean interpolated climate (1981-2010), USDA Natural Resources Conservation Service gridded soil survey database.