USDA · USDA-051 · record R1352

DISTRIB-II: Habitat Suitability of Eastern United States Tree

Department of Agriculture · Natural Resources and Environment

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2019-09-12 (day precision)Source: 9/12/2019
Withheld from public reporting: Not reported

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The goal of this use case is to provide information about current and future habitat suitability of east United States tree species. Machine learning is used to develop models that can predict relative abundance of individual tree species in the future.

Expected benefits

These models provide forest land managers with information about how habitats could change under various future scenarios which can assist in resource planning. The information provided by the models can also result in cost saving and resource effectiveness by reducing costs to manage unsuitable tree species and instead suggesting species that could be more suitable.

System outputs

Outputs from this use case include predictive maps of individual tree species abundance at resolutions of 10 and 20-km grids across the eastern United States under current (1981 to 2010) and potential future climate projections (2070 to 2099).

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: Not reported

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: Data used to train the machine learning models included USDA Forest Service Forest Inventory and Analysis data, elevation, 30-year mean interpolated climate (1981-2010), USDA Natural Resources Conservation Service gridded soil survey database.
Federal Data Catalog: www.fs.usda.gov ↗

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported