This grant, offered by the Earth Resources Observation and Science (EROS) Center, is specifically designed to advance critical land cover research. Its core purpose involves a comprehensive investigation into optimal sampling and statistical strategies, which are fundamental for achieving accurate assessment and precise area estimation of land cover changes. Moreover, the funding will support in-depth research exploring the significant impacts of reference data uncertainties. Understanding these uncertainties is absolutely vital for effectively characterizing and ensuring the overall validity and reliability of key USGS land cover change products. These include widely utilized and essential resources such as the National Land Cover Database (NLCD) and the Land Change Monitoring, Assessment, and Projection (LCMAP) land change products. This initiative seeks to enhance the robustness and utility of crucial environmental monitoring datasets.
Opportunity ID: 293701
General Information
| Document Type: | Grants Notice |
| Funding Opportunity Number: | G17AS00065 |
| Funding Opportunity Title: | Cooperative Ecosystem Studies Unit, Great Lakes Northern Forests |
| Opportunity Category: | Discretionary |
| Opportunity Category Explanation: | – |
| Funding Instrument Type: | Cooperative Agreement |
| Category of Funding Activity: | Science and Technology and other Research and Development |
| Category Explanation: | – |
| Expected Number of Awards: | 1 |
| Assistance Listings: | 15.808 — U.S. Geological Survey_ Research and Data Collection |
| Cost Sharing or Matching Requirement: | No |
| Version: | Synopsis 1 |
| Posted Date: | May 08, 2017 |
| Last Updated Date: | – |
| Original Closing Date for Applications: | May 22, 2017 |
| Current Closing Date for Applications: | May 22, 2017 |
| Archive Date: | Aug 08, 2017 |
| Estimated Total Program Funding: | $40,000 |
| Award Ceiling: | $40,000 |
| Award Floor: | $0 |
Eligibility
| Eligible Applicants: | Others (see text field entitled “Additional Information on Eligibility” for clarification) |
| Additional Information on Eligibility: | This financial assistance opportunity is being issued under a Cooperative Ecosystem Studies Unit (CESU) Program. CESUâ¿¿s are partnerships that provide research, technical assistance, and education. Eligible recipients must be a participating partner of the Great Lakes Northern Forests Cooperative Ecosystem Studies Unit (CESU) Program. |
Additional Information
| Agency Name: | Geological Survey |
| Description: | The Earth Resources Observation and Science (EROS) Center is offering a funding opportunity that supports land cover research by focusing on investigating sampling and statistical strategies for accuracy assessment and area estimation, and the impacts of reference data uncertainties for characterizing the validity of USGS land cover change products such as National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP) land change products. |
| Link to Additional Information: | https://www.grants.gov/ |
| Grantor Contact Information: | If you have difficulty accessing the full announcement electronically, please contact:
Faith Graves 703-648-7356
fgraves@usgs.gov Email:fgraves@usgs.gov |
Version History
| Version | Modification Description | Updated Date |
|---|---|---|
Related Documents
Folder 293701 Full Announcement-FUNDING OPPORTUNITY -> FUNDING OPPORTUNITY.pdf
Packages
| Agency Contact Information: | Faith Graves 703-648-7356 fgraves@usgs.gov Email: fgraves@usgs.gov |
| Who Can Apply: | Organization Applicants |
| Assistance Listing Number | Competition ID | Competition Title | Opportunity Package ID | Opening Date | Closing Date | Actions |
|---|---|---|---|---|---|---|
| 15.808 | G17AS00065 | Cooperative Ecosystem Studies Unit, Great Lakes Northern Forests | PKG00232430 | May 08, 2017 | May 22, 2017 | View |
Package 1
Mandatory forms
293701 SF424_2_1-2.1.pdf
293701 ProjectNarrativeAttachments_1_2-1.2.pdf
293701 SF424A-1.0.pdf
293701 SF424B-1.1.pdf
Share This Post, Choose Your Platform!
About the Author: osnqt
