Forest fire effects remote sensing models
Description
The AI use case is intended to predict the status of forests after wildfires.
Detailed example
The AI system outputs predictions of aboveground biomass, dead aboveground biomass, live trees per hectare, and fire severity.
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
The AI use case supports research into low-cost data sources for mapping forests after a fire.
Controls / human review
ATO: No; PIA: Not published
Data needed
Data from the Forest Inventory and Analysis program was used to train the model. Data from the Sentinel-2 satellite, LiDAR data collected from aircraft, and digital aerial photogrammetric point clouds collected as part of the National Agriculture Imagery Program were used.