文章基本信息

标题：The Extraction of Near-Shore Bathymetry using Sentinel-2A Satellite Imagery: Algorithms and Their Modifications
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作者：Abdi Sukmono ; Sentanu Aji ; Fauzi Janu Amarrohman 等
期刊名称：TEM Journal
印刷版ISSN：2217-8309
电子版ISSN：2217-8333
出版年度：2022
卷号：11
期号：1
页码：150-158
DOI：10.18421/TEM111-17
语种：English
出版社：UIKTEN
摘要：Satellite-Derived Bathymetry (SDB) is one of the solution technologies for medium-scale bathymetry mapping in large areas. Various basic algorithms for bathymetry extraction with SDB have been developed. However, they require study and modification for different satellite imageries and different regional characteristics. In this study, the researchers explore three basic SDB algorithms which are often used, namely the Lyzenga algorithm, the Stumpf algorithm, and the Van Hengel & Spitzer (VHS) algorithm. These three algorithms are modified using the multilinear regression method with the ‘average if’ function to find out the in-situ depth using Sentinel-2A satellite imagery. These three algorithms can estimate the depth of shallow water bathymetry effectively up to a depth of 20 m. The accuracy test on the extraction results of the modification of the three basic algorithms proves to be able to increase the accuracy of the SDB depth estimation in the depth range of 0 – 20 m to an accuracy of 1.888 m for the Lyzenga algorithm, 2.093 m for the Stumpf algorithm, and 2.868 m for the VHS algorithm.