DOI · DOI-0171 · record R2592

Rangeland Condition Monitoring Assessment and Projection (RCMAP) [2024 INV#WO0000000107126]

Department of the Interior · USGS

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: 2022-12-31 (day precision)Source: 12/31/2022
Withheld from public reporting: NoSource: a) No

Mission

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

Problem the AI is intended to solve

To address the need for long-term tracking of vegetation change, scientists from the USGS and Bureau of Land Management (BLM) developed the Rangeland Condition Monitoring Assessment and Projection (RCMAP) project.

Expected benefits

RCMAP quantifies the percent cover of ten rangeland components (annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree cover and shrub height) at yearly time-steps across the western U.S.

System outputs

RCMAP provides maps of vegetation cover at yearly time-steps, a critical refence to advancing science in the BLM and assessing Landscape Health standards.

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: field training data, Landsat imagery
Federal Data Catalog: Not reported

Governance

6 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 reviewReported in progressSource: d) In-progress

Ongoing monitoringReported in progressSource: b) Development of monitoring protocols is in-progress

Operator trainingReported in progressSource: b) Establishment of sufficient and periodic training is in-progress

Fail-safeNot applicableSource: b) Not applicable

Appeal processNot applicableSource: b) Not applicable

User and public consultationReported in progressSource: e) In-progress

Potential impacts and how they were identified

Improved disaster planning process, improved infrastructure planning and development.