Abstract
Accurately mapping the boundary between land and water (the ‘waterline’) is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu’s method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50–3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.
Highlights
Mapping the boundary between land and water is critical for tracking coastal change and managing water resources in an era characterised by the increasing environmental impacts of development and anthropogenic climate change
We calculated two summary statistics to evaluate the extracted waterlines: root mean square error (RMSE) assessed the absolute accuracy of the derived waterlines compared to the reference waterline, while standard deviation allowed us to compare overall precision, or how closely the extracted waterlines replicated the overall shape of the reference shoreline even if the lines were consistently offset in a water or land-ward direction
We evaluated waterline extraction performance based on our synthetic landscape approach by comparing the distribution of distances in metres from the modelled to the reference shoreline, using root mean squared error (RMSE) to evaluate whether errors were tightly distributed around 0 (Figure 4)
Summary
Mapping the boundary between land and water (the ‘waterline’) is critical for tracking coastal change and managing water resources in an era characterised by the increasing environmental impacts of development and anthropogenic climate change. While early applications of the technique applied waterline extraction to soft-classified layers where the exact proportion of water and land within each pixel was known (e.g., [35,36,37,38]), recent applications have instead used remote sensing water indices such as NDWI which can be calculated directly from open source remote sensing imagery [28,38,39] These approaches have proven able to extract waterline positions with high levels of accuracy in sandy beach environments (e.g., up to 5.7 m against a 30 year validation dataset at Narrabeen Beach in eastern Australia, [38]). We test the method using high resolution Worldview-2 imagery to verify our experimental findings in a complex, real-world coastal case study, and use our results to make best-practice recommendations for the future application of sRuembo-tpe iSxeenls.a2p01p9r, o11a,c2h98e4s for mapping waterlines consistently across time and space
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