Grants / Department of Defense / Dept. of the Army -- Corps of Engineers

W81EWF-23-SOI-0004: Machine Learning (ML) of Forest Stand Metrics to Quantify Carbon Storage

Closed Closed May 22, 2023 Opportunity W81EWF-23-SOI-0004 ☆ Save

This grant is closed. A newer notice from the same program is open now: ERDC Broad Agency Announcement (W912HZ26S0001). Status: Closes January 1, 2027 (55 days left). It is in our archive of Dept. of the Army -- Corps of Engineers notices from 2023.

• Award ceiling$150,000
• Total funding$480,000
• Expected awards1
• Cost sharingNot required
• Funding typeCooperative Agreement

Key dates

Who can apply

This opportunity was open to the applicants described in the notice.

• Others (see text field entitled "Additional Information on Eligibility" for clarification)

This opportunity is restricted to non-federal partners of the Great Lakes – Northern Forest Cooperative Ecosystems Studies Unit (CESU).

Is this a realistic fit?

• Past Basic, Applied, and Advanced Research in Science and Engineering awards averaged $1.1 million; this notice allows up to $150,000 per award.
• Most of the program's money (69% over five years) went to colleges and universities.
• No cost sharing or match is required.

What the grant funds

This research project focuses on quantifying basic forest stand metrics through the application of ML to remotely sensed data. The project will leverage global data to develop understanding of forest growth and successional conditions at a local level. Numerous environmental variables and forest inventory data must be incorporated to train ML algorithms on high performance computing systems (HPCs) to achieve resolutions that lead to understanding of carbon stores at a local level (e.g., a single DOD installation). Knowing that understanding dominant forest habitat type and forest volume (as calculated from tree height, diameter, and density) will yield significant understanding to forest carbon storage, the purpose of this work is to demonstrate that basic forest inventory metrics (e.g., tree diameter and density) may be effectively quantified from ML. The Government is not expecting the periods of performances to overlap. Objectives: The objectives of the project for the initial year are as follows: 1. Develop technical team and identify initial study area(s) of interest. 2. Develop and test a proof of concept outlining novel methods to quantify basic forest stand metrics. 3. Compile a repository of forest inventory data from national and international partners. 4. Validate accuracy of resulting, prototype forest stand metrics. The objectives of the project for Optional Year 1 are as follows: 1. Expand the study area(s) and refine the prototype novel methods (developed during initial year) to quantify basic forest stand metrics. 2. If required, expand the repository of forest inventory data from national and international partners to cover the second year’s study area. 3. Validate accuracy of resulting, large area forest stand metrics by prioritized areas of interest. 4. Generate peer-reviewed journal article with ERDC researchers to describe the application of novel methodologies to quantify basic forest stand metrics developed during initial year of the project. The objectives of the project for Optional Year 2 are as follows: 1. Conduct a final accuracy assessment and if required, refine the established methods to increase basic forest stand metric accuracy. 2. Generate a peer-reviewed journal article(s) in conjunction with ERDC researchers integrating all study conclusions. 3. Develop and present public seminars based on study findings.

Category: Science and Technology and other Research and Development.

Applying now

This notice no longer takes applications. Apply under the newer notice, W912HZ26S0001, which is open now.

The notice on Grants.gov keeps its full text and attachments, which is useful for planning a resubmission.