Machine Learning (ML) of Forest Stand Metrics to Quantify Carbon Storage — Dept. of the Army -- Corps of Engineers funding opportunity
Dept. of the Army -- Corps of Engineers · Federal agency

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

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 successio...

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Award $0–$150k Deadline 1185 days ago Location Alabama Type grant Level Federal Closed posted Mar 23, 2023
✦ AI Summary
  • Who can apply: Federal-level applicants (see eligibility for details).
  • Funding amount: up to $150,000 (total pool ~$480,000).
  • Next deadline: May 22, 2023.
  • Issued by: Dept. of the Army -- Corps of Engineers.
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How was this generated?

The “key facts” mode pulls structured fields directly from the official source posting (amount, deadline, eligibility tags). The AI mode adds a short plain-English narrative on top, generated from the same source. Always verify with the agency before applying.

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Who can apply — at a glance

  • Eligible applicants: see the Eligibility tab for the criteria from the official announcement.
  • Where: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming, District of Columbia.
  • Award: $0–$150k. Total program pool: $480,000.
  • Matching funds: not required.
  • Deadline type: Fixed.

Compiled from the official listing's structured fields — always verify with the funder before applying.

About the funder

Dept. of the Army -- Corps of Engineers is a federal-level funder.

Award amount
$0–$150k
Deadline
1185 days ago
May 22, 2023
Total pool
$480k

About this opportunity

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 ( a single DOD installation). Knowing that understanding dominant forest habitat type and forest volume (as calculated from tree density) will yield significant understanding to forest carbon storage, the purpose of this work is to demonstrate that basic forest inventory metrics ( 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. Successful applicants should have expert knowledge of: 1) forestry, natural resources, and carbon storage; 2) field data collection capabilities; 3) compiling national and global forest inventory databases; 4) experience developing novel approaches to machine learning of forest characteristics. Areas of expertise that may be required in combination to perform this study include:1) Capacity to collect and/or compile forest inventory data at up to global scales.2) Advanced computing capabilities for ML applications to characterize forest metrics.3) Development of novel ML approaches to improve forest inventory, forest characterization, and/or forest carbon storage research with local and global applications.

Funding agency

Dept. of the Army -- Corps of Engineers
Federal agency

Tags

Agriculture & Farming Education & Research Climate & Environment
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Who can apply

Eligibility details aren't on file yet — check the agency source link in the Documents tab for the latest rules.

Geographic eligibility

  • Alabama
  • Alaska
  • Arizona
  • Arkansas
  • California
  • Colorado
  • Connecticut
  • Delaware
  • Florida
  • Georgia
  • Hawaii
  • Idaho
  • Illinois
  • Indiana
  • Iowa
  • Kansas
  • Kentucky
  • Louisiana
  • Maine
  • Maryland
  • Massachusetts
  • Michigan
  • Minnesota
  • Mississippi
  • Missouri
  • Montana
  • Nebraska
  • Nevada
  • New Hampshire
  • New Jersey
  • New Mexico
  • New York
  • North Carolina
  • North Dakota
  • Ohio
  • Oklahoma
  • Oregon
  • Pennsylvania
  • Rhode Island
  • South Carolina
  • South Dakota
  • Tennessee
  • Texas
  • Utah
  • Vermont
  • Virginia
  • Washington
  • West Virginia
  • Wisconsin
  • Wyoming
  • District of Columbia

How to apply

We don't have application instructions on file yet — head straight to the official source.

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Source documents

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Canonical NOFO, application packet, and forms
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Citation details

Source systemgrants.gov
Source ID347097
PostedMar 23, 2023

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