# Machine Learning (ML) of Forest Stand Metrics to Quantify Carbon Storage

Source: https://welltoldgrants.com/grants/w81ewf-23-soi-0004-machine-learning-ml-of-forest-stand-metrics-to/  
Official notice: https://www.grants.gov/search-results-detail/347097

> Dept. of the Army -- Corps of Engineers offered grants of up to $150,000 to the applicants described in the notice. It expected to make one award worth $480,000 in total. Applications closed on May 22, 2023.

| Field | Value |
|---|---|
| Opportunity number | W81EWF-23-SOI-0004 |
| Agency | Dept. of the Army -- Corps of Engineers |
| Status | Forecast |
| Deadline | May 22, 2023 |
| Award ceiling | $150,000 |
| Award floor | not stated |
| Expected awards | 1 |
| Total funding | $480,000 |
| Cost sharing | not required |
| Assistance Listings | 12.630 |

## Who can apply

- 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).

## 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.

## Questions people ask

### Is W81EWF-23-SOI-0004 still open?

No. It closed on May 22, 2023. A newer notice is open: W912HZ26S0001, closes January 1, 2027 (55 days left).

### Who can apply for Machine Learning (ML) of Forest Stand Metrics to Quantify Carbon Storage?

Dept. of the Army -- Corps of Engineers accepted applications from the applicants described in the notice. The notice adds further conditions, so read its eligibility section.

### What is the deadline for Machine Learning (ML) of Forest Stand Metrics to Quantify Carbon Storage?

Closed May 22, 2023. Applications are no longer accepted; the program's newer notices are on this page when there are any.

### How much funding is available?

Awards of up to $150,000 each. About $480,000 in total for one award.

### Who has won Basic, Applied, and Advanced Research in Science and Engineering grants before?

The largest recipients over five fiscal years were Battelle Memorial Institute, Msi Stem Research & Development Consortium, University of California San Diego. The program made 1,101 awards worth $1.3 billion in that time.