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首页> 外文期刊>ISPRS International Journal of Geo-Information >Implementation of Algorithm for Satellite-Derived Bathymetry using Open Source GIS and Evaluation for Tsunami Simulation
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Implementation of Algorithm for Satellite-Derived Bathymetry using Open Source GIS and Evaluation for Tsunami Simulation

机译：基于开源GIS的卫星衍生测深算法的实现和海啸仿真评估

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Accurate and high resolution bathymetric data is a necessity for a wide range of coastal oceanographic research topics. Active sensing methods, such as ship-based soundings and Light Detection and Ranging (LiDAR), are expensive and time consuming solutions. Therefore, the significance of Satellite-Derived Bathymetry (SDB) has increased in the last ten years due to the availability of multi-constellation, multi-temporal, and multi-resolution remote sensing data as Open Data. Effective SDB algorithms have been proposed by many authors, but there is no ready-to-use software module available in the Geographical Information System (GIS) environment as yet. Hence, this study implements a Geographically Weighted Regression (GWR) based SDB workflow as a Geographic Resources Analysis Support System (GRASS) GIS module ( i.image.bathymetry ). Several case studies were carried out to examine the performance of the module in multi-constellation and multi-resolution satellite imageries for different study areas. The results indicate a strong correlation between SDB and reference depth. For instance, case study 1 (Puerto Rico, Northeastern Caribbean Sea) has shown an coefficient of determination (R 2) of 0.98 and an Root Mean Square Error (RMSE) of 0.61 m, case study 2 (Iwate, Japan) has shown an R 2 of 0.94 and an RMSE of 1.50 m, and case study 3 (Miyagi, Japan) has shown an R 2 of 0.93 and an RMSE of 1.65 m. The reference depths were acquired by using LiDAR for case study 1 and an echo-sounder for case studies 2 and 3. Further, the estimated SDB has been used as one of the inputs for the Australian National University and Geoscience Australia (ANUGA) tsunami simulation model. The tsunami simulation results also show close agreement with post-tsunami survey data. The i.mage.bathymetry module developed as a part of this study is made available as an extension for the Open Source GRASS GIS to facilitate wide use and future improvements.
机译：准确而高分辨率的测深数据是广泛的沿海海洋研究主题所必需的。主动感测方法，例如基于舰船的探测和光探测与测距（LiDAR），是昂贵且耗时的解决方案。因此，由于多星座，多时间和多分辨率遥感数据作为开放数据的可用性，在最近十年中，卫星衍生的测深法（SDB）的重要性有所提高。许多作者已经提出了有效的SDB算法，但是地理信息系统（GIS）环境中尚没有可用的即用型软件模块。因此，本研究将基于地理加权回归（GWR）的SDB工作流实现为地理资源分析支持系统（GRASS）GIS模块（i.image.bathymetry）。进行了一些案例研究，以检查模块在不同研究区域的多星座和多分辨率卫星图像中的性能。结果表明，SDB与参考深度之间具有很强的相关性。例如，案例研究2（日本岩手县）的案例研究1（东北加勒比海波多黎各）的确定系数（R 2）为0.98，均方根误差（RMSE）为0.61 m。 R 2为0.94，RMSE为1.50 m，案例研究3（日本宫城）显示R 2为0.93，RMSE为1.65 m。参考深度是通过案例研究1的LiDAR以及案例研究2和3的回声测深仪获得的。此外，估算的SDB已用作澳大利亚国立大学和澳大利亚地球科学（ANUGA）海啸模拟的输入之一。模型。海啸模拟结果也表明与海啸后的调查数据非常吻合。作为本研究的一部分开发的i.mage.bathymetry模块可作为开源GRASS GIS的扩展使用，以促进广泛使用和未来的改进。

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