Wildfire effects and postfire forest dynamics
Description
Predicting what a forest will be like after it burns is difficult since forests respond to disturbance in a variety of complex ways and forest conditions change over the first few decades after a fire.
Detailed example
Outputs identified predictors of different post-fire forest outcomes.
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
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Improving our understanding of how forests in the Pacific Northwest respond in the years after a wildfire to provide information for management of fire-affected areas.
Controls / human review
ATO: No; PIA: Not published
Data needed
Publicly available forest resource data collected by the Forest Inventory and Analysis program of the USDA Forest Service.