Shorelines from High-resolution WorldView Satellite Imagery, Real-time Kinematic Global Positioning Data, and Aerial Imagery for 2013 to 2020 for Study Sites Within Grand Bay National Estuarine Research Reserve, Mississippi

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021)

Smith, K.E.L., Terrano, J.F., Pitchford, J.L., and Archer, M.J., 2021, Coastal wetland shoreline change monitoring-A comparison of shorelines from high-resolution WorldView satellite imagery, aerial imagery, and field surveys: Remote Sensing, v. 13, no. 15 (special issue), art. 3030, https://doi.org/10.3390/rs13153030.