Cooperative Agreement for affiliated Partner with the Rocky Mountain Cooperative Ecosystem Studies Unit | G26AS00122

Grant Opportunity FAQs (USGS Cooperative Agreement - Plant Habitat Detection Using Satellite Data)

What is this funding opportunity about?

This opportunity is a U.S. Geological Survey (USGS) cooperative agreement focused on improving how land managers detect and map plant species habitat across large areas. The project emphasizes using satellite data and modern modeling approaches to produce outputs that directly support practical, on-the-ground management decisions.

What problem is the project trying to solve?

The core challenge is building plant habitat detection tools that land managers trust at local scales while still working reliably across broad geographic regions. This local-to-national reliability gap has been a persistent issue in plant habitat detection and mapping, and the project is designed to improve confidence, usefulness, and transferability of model outputs.

Why does early detection and mapping matter in this program?

Early detection and mapping can help prevent the spread of invasive plants, guide eradication or control efforts, and improve operational efficiency for agencies. The opportunity is framed around making the science operational so land managers can act faster and more effectively.

Who is offering the award?

The award is issued by the U.S. Geological Survey (USGS), with collaboration through the USGS Fort Collins Science Center.

What type of funding instrument is being used?

The funding instrument is a cooperative agreement. This matters because it indicates active collaboration and shared technical engagement between USGS and the university partner, rather than a hands-off arrangement where the recipient works independently.

What program is this opportunity associated with?

This opportunity is offered through the Cooperative Ecosystem Studies Units (CESU) Program, specifically the Rocky Mountain CESU.

Who is eligible to apply?

Eligibility is limited to institutions that are already formal partners in the Rocky Mountain CESU network. Entities that are not partners in that CESU are not eligible under the stated terms.

How many awards are expected?

One award is expected.

What is the maximum award amount (award ceiling)?

The award ceiling is $49,250.

What is the Funding Opportunity Number?

The Funding Opportunity Number is G26AS00122.

What is the CFDA number and program category?

The CFDA number is 15.808. The opportunity is categorized under Science and Technology and other Research and Development.

What is the application deadline?

The posted closing date is June 29, 2026 (original closing date).

What scientific approach is emphasized in the project?

A major emphasis is on detection models that use phenology, meaning the seasonal timing of plant life cycle events. The work aims to use phenology-informed methods to better identify species signals in satellite imagery.

What kinds of research questions or technical themes does the opportunity highlight?

The opportunity highlights testing and refining phenology-informed detection models, evaluating how well models transfer from one region to another, and analyzing where and why model predictions become uncertain across space.

What does "model transferability" mean in this context?

Model transferability refers to how well a detection model developed or calibrated in one geographic region performs when applied in a different region. This opportunity specifically calls for evaluating transferability and understanding performance limits across varying landscapes.

Why is uncertainty in predictions a focus area?

USGS is interested in understanding where model predictions become uncertain across space and why that uncertainty occurs. This is important for decision-making because managers need to know not only what the model predicts, but how reliable those predictions are across different places.

What role do decision support frameworks play in this project?

The project includes improving how model outputs are packaged, interpreted, and delivered through decision support frameworks. The goal is to make products operationally useful for practitioners, not just technically strong in a research setting.

What expertise is USGS looking for in a partner?

USGS is looking to collaborate with a qualified university partner with strong capability in plant ecology, remote sensing, and statistical programming.

What specific contribution areas does USGS call out for the partner?

USGS identifies several areas of interest for the partner to contribute to, including improving existing code and automated data production pipelines, strengthening decision support tools, evaluating model transferability with field data collected across different geographic regions, and synthesizing spatial predictors that help explain where a species is likely to occur.

What are "automated data production pipelines" in the context of this award?

Based on the opportunity description, this refers to code-driven workflows that automate the steps needed to produce consistent data outputs for plant habitat detection and mapping (for example, repeatable processing of satellite inputs and model runs). USGS is specifically interested in improving existing code and these automated workflows.

What are "spatial predictors" and why are they mentioned?

Spatial predictors are variables that help explain where a species is likely to occur across space. The opportunity mentions synthesizing spatial predictors to strengthen species detection and habitat mapping by clarifying the drivers behind predicted distributions.

What types of deliverables are expected?

The expected outcomes are practical research products, especially reusable code and data, that Department of the Interior (DOI) agencies and other land-management partners can use to map current plant distributions and understand the limits of model predictions. The opportunity emphasizes deployable improvements rather than purely academic outputs.

How is this project intended to support land managers?

The project is designed to support manager-requested plant species habitat distribution work across the United States. It aims to deliver more trusted local-scale outputs, reliable broad-scale application, and decision-ready products that improve operational planning and field actions (including early detection, mapping, and management response).

Is the opportunity focused only on invasive plants?

The opportunity strongly emphasizes invasive plants, especially where early detection and mapping can prevent spread and guide control efforts. It also specifically calls out detecting fire-promoting invasive species in support of risk reduction and mitigation activities, indicating that invasive species detection is a central application area.

How does wildfire prevention and response relate to this opportunity?

USGS highlights a strong focus on detecting fire-promoting invasive species to support wildfire risk reduction and mitigation activities. Proposals and work products that connect invasive species detection to wildfire risk management appear to align closely with the stated priorities.

What federal priorities or executive orders are referenced?

The opportunity references Executive Order 14303, "Restoring Gold Standard Science" (May 23, 2025), emphasizing rigorous, credible science that is viewed as accurate and trustworthy by practitioners. It also references Executive Order 14308, "Empowering Commonsense Wildfire Prevention and Response" (June 12, 2025), emphasizing improved wildfire prevention and response and a focus on fire-promoting invasive species detection.

What does "gold standard science" imply for work under this award?

Based on the opportunity framing, it implies an emphasis on rigorous, defensible, and credible methods, with outputs that local practitioners consider accurate and trustworthy. The focus on transparency and defensible methodology is presented as important for practitioner uptake.

Is this a large funding opportunity?

No. It is described as a relatively small, targeted award intended to integrate specialized university expertise into a larger, ongoing USGS effort, with a focus on concrete improvements to methods, pipelines, and decision-ready outputs.

What makes this award "targeted" rather than broad?

The scope is specifically aimed at improving phenology-informed detection models, assessing transferability and uncertainty, and strengthening code pipelines and decision support delivery for plant habitat detection using satellite data. The award is also limited by eligibility (Rocky Mountain CESU partners only) and expects a single awardee.