Long-term monitoring of northern spotted owl habitat and populations
Description
Machine learning methods are used to model and map forest structure and species composition as it relates to owl nesting and roosting in forests. Machine learning is also used to model and map annual time series of owl habitat that includes forest type and other variables, such as topography.
Detailed example
Annual time series of maps and periodic monitoring reports that summarize the data to inform adaptive management, such as Forest Plan revisions and amendments.
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 time series maps are used to meet legal owl monitoring requirements and avoid potential litigation if not done. They also build trust with the general public and provide easy access for anyone to explore how these forests and habitats are changing through time in response to forest disturbances and growth.
Controls / human review
ATO: No; PIA: Not published
Data needed
Species (owl) locations and environment predictor variables (e.g., forest structure and species composition covariates, topography, etc.).