DOI · DOI-0085 · record R2667

Detecting and tracking reed canarygrass (Phalaris arundinacea) invasion in the Upper Mississippi River floodplain using remote sensing and artifial intelegence.

Department of the Interior · USGS

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: Image and video analysis Derived by keyword rules; see methodology

Problem the AI is intended to solve

We have a limited understanding of how the distribution of an invasive grass has changed over time. We seek to use satellite imagery to identify and track annual changes in the distribution of the invasive grass reed canarygrass.

Expected benefits

This will lead to a better understanding of the dynamics (e.g., changes in inundation) that drive changes in the distribution of the invasive grass reed canarygrass to help achieve management objectives.

System outputs

Predictions of reed canarygrass invasion across the Upper Mississippi River system at an annual or subannual time step.

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: We will publish the code in ScienceBase upon completion of the project.

Sourcing

How it was built: Not reported
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: Not reported
Federal Data Catalog: Not reported

Governance

5 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 placeSource: a) Yes – by another appropriate agency office or reviewer not directly involved in the AI’s development

Ongoing monitoringReported in placeSource: a) Yes, sufficient monitoring protocols have been established

Operator trainingReported in placeSource: a) Yes, sufficient and periodic training has been established

Fail-safeNot applicableSource: b) Not applicable

Appeal processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported