Abstract
This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (DDWI). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a >99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.
Highlights
Coastal areas are some of the most densely populated areas in the world, with average population densities three times higher than the average world density [1]
We presented a LiDAR-equipped unmanned aerial vehicle (UAV) that was utilized for high-resolution shoreline and beach topography quantification for Lake Michigan [8,9]
The first test was a direct comparison between the LiDAR-derived shoreline positions and the shoreline detected from the satellite imagery acquired within several days of the LiDAR surveys
Summary
Coastal areas are some of the most densely populated areas in the world, with average population densities three times higher than the average world density [1]. Coastal areas are increasingly exposed to many hazards that are enhanced by sea level rises, such as flooding, shoreline erosion, and coastal storms [3], all of which can cause dramatic changes to the shoreline position, in turn threatening infrastructure and communities. The ability to detect and quantify these changes is essential for the development and refinement of models that can predict such shoreline changes, in turn guiding the design and management of coastal areas that are more resilient to ever-increasing coastal hazards. Remote-sensing approaches have long been utilized to determine shoreline positions, allowing for the accurate mapping of shorelines over large geographical areas. In addition to aerial imagery, other remote-sensing modalities, e.g., airborne LiDAR, can be used for shoreline detection. We presented a LiDAR-equipped unmanned aerial vehicle (UAV) that was utilized for high-resolution shoreline and beach topography quantification for Lake Michigan [8,9]
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