Forest Mapping using G-LiHT airborne data in interior Alaska
Description
This project supports the first comprehensive forest inventory of interior Alaska in the modern era. Machine learning techniques will be used to predict various forest attributes (volume, biomass, composition).
Detailed example
Maps and tabular estimates of inventory attributes over all forested regions of interior Alaska.
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
Use of multi-sensor airborne data will improve the accuracy and precision of forest inventory estimates over interior Alaska, improving the quality and reliability of products provided to the public.
Controls / human review
ATO: No; PIA: Not published
Data needed
Airborne remote sensing data and Forest Inventory and Analysis field inventory data.