Establishing seafloor mapping priorities for coastal states. (1st February 2022) - Hapke, CherylJ
Abstract: The Florida Coastal Mapping Program (FCMaP) is a consortium of State, Federal and academic partners that is undertaking the coordination of the collection and dissemination of consistent, high-resolution seafloor data for Florida's coastal zone. The coastal zone in the context of FCMaP refers to the area extending from the shoreline to the 200-m isobath. The high-resolution data is critical for a myriad of ocean and coastal resource management applications. An existing data gap analysis revealed that less than 20% of Florida's coastal waters have been mapped using modern bathymetric methods (multibeam sonar or airborne lidar), and in some areas, less than 5% of the seafloor has modern data; where data do exist, they often date to the 1800s. Addressing the need for a more comprehensive modern map of the seafloor will take an enormous amount of effort and funding, coordination and prioritization will be critical to success. FCMaP also undertook a formal statewide seafloor mapping prioritization to solicit input from a variety of stakeholders. The results provide the first statewide perspective of user and stakeholder mapping prioritization needs for the State of Florida. The prioritization dataset identifies specific locations that would benefit the most users or stakeholders, which can help to refine targeted mapping strategies. We found that new, consistent data would greatly support and improve multiple management activities. The approach used for this effort demonstrates an effective and replicable approach to addressing the need for seafloor mapping. Highlights: Statewide coastal seafloor mapping prioritization was accomplished for Florida. 80% of Florida's coastal seafloor has not been mapped with modern, high resolution technologies. A novel online participatory GIS tool was developed to accomplished the prioritization process. In addition to elevation information (bathymetry) the most desired data need is bottom type (hardness/smoothness). A geospatial cluster analysis pinpoints specific areas where the highest numbers of respondents would benefit from data collection.