Abstract
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 (R2) of 0.98 and an Root Mean Square Error (RMSE) of 0.61 m, case study 2 (Iwate, Japan) has shown an R2 of 0.94 and an RMSE of 1.50 m, and case study 3 (Miyagi, Japan) has shown an R2 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.
Highlights
Near-shore bathymetry is one of the most important parameters for investigations of coastal processes and hydrodynamic models in coastal areas
The results indicate that the module can be used to estimate Satellite-Derived Bathymetry (SDB) from various conditions of coastal water, density of calibration depth points, and spatial and radiometric resolutions of the satellite data
Our study aims to generate an Integrated Coastal Relief Model (ICRM) over parts of the Miyagi Prefecture by combining derived SDB with various resolutions of topographic and bathymetry data
Summary
Near-shore bathymetry is one of the most important parameters for investigations of coastal processes and hydrodynamic models in coastal areas. The ability to derive near-shore bathymetry using remote sensing techniques is a topic of increasing interest in coastal monitoring and research. Remote sensing is considered an alternative for near-shore bathymetry estimation since a large number of multi-constellation, multi-spectral, and multi-spatial satellite data is available as Open Data. Researchers have investigated SDB algorithms over the last 30 years and proposed estimation methods falling into categories such as spectral rationing [1,6] and radiative transfer models [7,8,9]. Reliable SDB is possible when the water is clear and when water quality and bottom types are homogeneous When such conditions are satisfied, single band water depth models can provide a reasonable estimate of depth. The ICRM is used to evaluate a practical application scenario of the SDB in tsunami simulation
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