In 2019, a GIS analysis was performed to produce a status and trends report to measure habitat loss or gain from previous studies. In 2016, the MBNEP produced habitat maps of Mobile and Baldwin counties at one-meter resolution. Due to the higher resolution of the 2016 imagery and a new methodology for classifying habitats, the habitat map product produced several inconsistencies, and as a result, these maps were not useful for conduct of a full analysis of habitat change. Because of the different applications of Cowardin and Anderson habitat classification codes (Anderson et al., 1976; Cowardin et al., 1979) by different contractors classifying habitats in 2001/2002 and 2015/2016, initial analyses revealed some large and unexpected changes across the 10 common habitats used in this schema. The differing methodologies used for classifying habitat data made it very difficult to analyze any changes in habitat types over the time period from 2001 to 2016. Due to the inconsistency between mapping products, the MBNEP attempted to translate the 2016 mapping products to match the 2010 identified priority habitat types: pine savanna, longleaf pine, freshwater wetlands, maritime forest, intertidal marshes and flats, and beaches and dunes. This attempt produced overlapping Cowardin/Anderson systems, sub-systems, classes, sub-classes, and modifiers across several habitat types, resulting in a duplication of acreage for several habitat types. The MBNEP successfully resolved the errors by using a recently completed soil survey for Mobile County, an existing soil survey for Baldwin County, and additional data and guidance from Natural Resources Conservation Service (NRCS) soil experts. To address the “double counting” of acreage, selected criteria were employed to identify locations of certain habitat types. The selected criteria are NRCS soil survey data and Alabama Department of Environmental Management’s (ADEM) continuous 10-foot contour line.

INTENDED AUDIENCE: Current, accurate land cover and change information is a common foundational data set that can be used to address a wide range of management issues, from flooding risk and natural infrastructure to policy evaluation and land use planning. Detailed information related to impervious areas, wetland features, and other cover types can inform endless applications. MAIN USE: For almost two decades, NOAA’s Office for Coastal Management (OCM) has been producing standardized, 30-meter, land cover and change information for the coastal United States through its Coastal Change Analysis Program (C-CAP). More recently, NOAA has been working to establish an operational high resolution (1-meter) land cover product line, bringing the national C-CAP framework to the local level and allowing for more site-specific, local applications. This presentation will discuss the status of current pilot projects within the Gulf of Mexico, the advanced AI Machine and Deep Learning methods used in their creation, highlight the advantages of this more detailed data compared to previous/existing land cover data, and discuss future vision for creating these products in additional geographies. GEOGRAPHY & SCALE: NOAA’s vision is to produce 1-meter land cover products for all the Coastal areas of the U.S., and update these products every 4 to 6 years. Currently, several pilot projects are taking place, including the 6 coastal counties in MS. In addition, 10-meter products have been recently released for all the coastal areas within the Gulf of Mexico (and much of the rest of the country). ACCESSIBILITY: The C-CAP Land Cover data is available within NOAA’s Digital Coast website, which is managed by OCM and can be accessed via https://coast.noaa.gov/digitalcoast/data/ccaphighres.html. A brief overview video can be found at this web address along with background information and technical assistance resources.

There is an overwhelming consensus among conservation experts that an efficient, data-driven, science-based geospatial conservation prioritization tool can help guide or optimize the dollars spent on land conservation. In this work, we developed and implemented a conservation prioritization framework that integrates open-source data and an optimization method based on stochastic multicriteria acceptability analysis (SMAA). SMAA is capable of handling problems with multiple constraints, unknown user preferences, and insufficient or missing data. The data measures were developed from openly available peer-reviewed data from federal and state agencies. These data were used to evaluate projects based on their ecological merit. Since the data that indicate ecological factors tend to be on different scales (geospatial, non-geospatial) and type (raster/vector/table), each of these data measures were converted into a one square km hexagonal grid format over the entire study region (Gulf of Mexico RESTORE Region) for further analysis. SMAA algorithms incorporated over 100,000 user preferences to evaluate the merit of conservation projects by assigning random weights. The increasing use of web applications to provide visualizations of geospatial data has improved access to scientific information. Yet many applications lack capabilities in “on-the-fly” processing and analytics. The framework developed as part of this work was implemented as a web-based, user-friendly, geospatial tool that can prioritize conservation lands “on-the-fly” based on geospatial footprints. From early validations with the stakeholders, the developed framework produces valuable recommendations for conservation agencies based on an easy to use geospatial web interface. This tool will be a part of the Strategic Conservation Assessment project toolkit.

In the past decade, the boom of large scale spatial products enrich conservation experts with more resources and data to make smarter and more comprehensive land conservation decisions on large study areas. Web application, as one of the most accessible methods to share data across a large audience, has been increasingly used to provide intuitive visualization to support conservation efforts. However, as the data increase in size, visualizing large volumes of spatial datasets dynamically through web application has become increasingly challenging. In this research, we developed the Conservation Visualization Tool (CVT), which provides a region-wide visualization based on the existing data measures available in the Strategic Conservation Assessment (SCA) database. The tool was developed and tested with five conservation goals proposed by the Gulf of Mexico (GoM) Restore Council and was used as a framework for grouping the identified conservation plans and projects. The goals are 1. Restore and Conserve Habitat, 2. Restore Water Quality, 3. Replenish and Protect Living and Marine Resources, 4. Enhance Community Resilience, and 5. Restore and Revitalize the Gulf Economy. The SCA database contains over 20 ecological and socioeconomic indicators across the five Restore goals. Each of these data measures was converted into a one square km hexagonal grid format over the entire GoM Region. Based on users’ organizational priorities and conservation emphasis, the system would perform a calculation that combines the existing data values within the database with the users’ conservation priorities and generate a region-wide spatial visualization ‘on-the-fly’. The CVT would be used to identify potential conservation hotspots based on customized priorities schema as well as explore the spatial distribution of conservation potential across the region.