| dc.description.abstract | Coastal zones serving as economic and cultural hubs, prominently feature activities such as fisheries, transportation, and tourism, underscoring their significance. Furthermore, ecologically, coastal intertidal zones play crucial roles in climate regulation, water filtration, and flood prevention. However, the coastal areas are easily affected by erosion and sediment deposition, coupled with the rapid morphological changes in intertidal zones, emphasizing the need to develop long-term, rapid, and large-scale monitoring methods. Such methods could serve as essential data sources for future planning and development of these regions.
The study area of this study is on the largest tidal flat along the coast of Taiwan, the Waisanding Tidal Flat. Traditionally, the topography of this shallow shoal has been surveyed using methods such as Single Beam Echo Sounder (SBES), airborne Light Detection and Ranging (LiDAR), or stereoscopic imagery captured by unmanned aerial vehicles. However, these methods are often time-consuming and resource-intensive.
Utilizing satellite imagery offers a solution to meet the requirements of rapid and large-scale terrain reconstruction in intertidal zones. Past studies have employed radar and optical imagery or both to enhance temporal resolution. This study adopts a methodology for automatic tidal flat reconstruction. It utilizes optical satellite imagery from Sentinel-2, Landsat 7/8, from 2014 to 2017. The (Modified) Normalized Difference Water Index ((M)NDWI) is calculated for each image, employing thresholding based on variations in pixel intensity to delineate water and land. Subsequently, multiple images within a time interval are used to construct flood probability maps. The study then focuses on addressing errors caused by uneven sampling of tide heights corresponding to the images by proposing the Sampling Errors Reduction (SER) method. This method incorporates tide heights simulated by the DTU16 global ocean tide model for each image, analyzing their distribution, and iteratively improves flood probability maps at a pixel level. Finally, elevation values in the intertidal zone are assigned proportionally based on the highest and lowest tide heights corresponding to the images, resulting in an intertidal Digital Elevation Model (DEM) situated between Mean Higher High Water (MHHW) and Mean Lower Low Water (MLLW).
Comparing the results with DEM collected from the SBES, the DEM of the intertidal zone constructed using the proposed method achieves a Root Mean Square Difference (RMSD) of 28.8 cm, with a 6.2% improvement through the SER method. Furthermore, higher accuracy and improvement rates are observed with more images, although the improvement rate is influenced by the distribution of tide heights corresponding to the images. | en_US |