DOI · DOI-0068 · record R2685

Invasive Grass Mapping

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: 2025-04-04 (day precision)Source: 4/4/2025
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

USGS mapping of invasive exotic annual grasses is critical because these species, such as cheatgrass, alter ecosystems by increasing the frequency and intensity of wildfires and outcompeting native vegetation. Accurate, large-scale maps help land managers anticipate fire risk, prioritize restoration efforts, and guide grazing or herbicide treatments. Beyond fire management, these maps support wildlife habitat conservation, water resource protection, and long-term monitoring of ecosystem change, providing essential information for both local decision-making and national land management strategies.

Expected benefits

DNNs have greatly improved our ability to accurately map invasive annual exotic grasses. We now produce weekly estimates of invasive grasses for the western US from early April until July, providing fire and land managers with critical data.

System outputs

Weekly maps depicting invasive exotic annual grass extent and coverage for the western United States, with weekly products from early April to early July.

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: Harmonized Landsat and Sentinel (HLS) data form the core dataset for mapping weekly exotic annual grasses (EAG). Bureau of Land Management’s Assessment, Inventory, and Monitoring (AIM) plots are used to train deep learning models on HLS data to produce EAG estimates.
Federal Data Catalog: www.sciencebase.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