Chesapeake Bay Program Office Fiscal Year 2023 Request for Applications for: Assessment and Methodology Development for Advanced Automated Techniques and Protocols to Map Chesapeake Bay Submerged Aquatic Vegetation from

The summary for the Chesapeake Bay Program Office Fiscal Year 2023 Request for Applications for: Assessment and Methodology Development for Advanced Automated Techniques and Protocols to Map Chesapeake Bay Submerged Aquatic Vegetation from grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Environmental Protection Agency, which is the U.S. government agency offering this grant.

Chesapeake Bay Program Office Fiscal Year 2023 Request for Applications for: Assessment and Methodology Development for Advanced Automated Techniques and Protocols to Map Chesapeake Bay Submerged Aquatic Vegetation from: There are two overarching objectives of this RFA, which EPA is seeking project proposals to address. The first objective is to map the density and distribution of submerged aquatic vegetation (SAV) accurately, efficiently, and effectively throughout the mesohaline habitat in Chesapeake Bay and its tidal tributaries from high-resolution (approximately 4m per pixel resolution or less) satellite imagery using automated detection and classification methods, and, in doing so, develop and document a protocol that would inform Bay-wide SAV assessment efforts. The second objective is to develop and document a robust, repeatable method of generating vector-based polygons that: 1. represent SAV beds from the pixel-based SAV classifications developed under Objective 1 that are calibrated to results generated from hand-delineated SAV mapping of beds as estimated from the present SAV survey protocol; and 2. applies the polygon bed delineation method to SAV classification results from high resolution satellite imagery to produce SAV cover maps of Zannichellia palustris that are suitable for summarizing acreage and density throughout the mesohaline portion of Chesapeake Bay.