Long-term monitoring of northern spotted owl habitat and populations
Department of Agriculture · Natural Resources and Environment
Overview
- Development stage
- PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
- High-impact designation
- Not high-impactSource: c) Not high-impact
- Impact justification
- Not reported
- Start date
- 2011-10-01 (day precision)Source: 10/1/2011
- Withheld from public reporting
- Not reported
Mission
- Topic area
- Science
- Operational functions
- Summarization, Forecasting and prediction Derived by keyword rules; see methodology
Problem the AI is intended to solve
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.
Expected benefits
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.
System outputs
Annual time series of maps and periodic monitoring reports that summarize the data to inform adaptive management, such as Forest Plan revisions and amendments.
Technology
- AI classification
- Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
- System name(s)
- Not reported
- Custom-developed code
- No
- Public source code
- biodiversityinformatics.amnh.org ↗
Sourcing
- How it was built
- Developed in-houseSource: b) Developed in-house
- Vendor (as reported)
- Not reported
- Vendors (standardized)
- None on the standard list
A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.
Data and privacy
- Involves PII
- No
- Privacy Impact Assessment
- Not reported
- Authorization to Operate
- No
- Demographic features
- k) None of the above
- Training and evaluation data
- Species (owl) locations and environment predictor variables (e.g., forest structure and species composition covariates, topography, etc.).
- Federal Data Catalog
- Not reported
Governance
0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.
Potential impacts and how they were identified
Not reported