A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery

Accurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatia...

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Main Authors: Xue Ji, Yi Ma, Jingyu Zhang, Wenxue Xu, Yanhong Wang
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/15/14/3570
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author Xue Ji
Yi Ma
Jingyu Zhang
Wenxue Xu
Yanhong Wang
author_facet Xue Ji
Yi Ma
Jingyu Zhang
Wenxue Xu
Yanhong Wang
author_sort Xue Ji
collection DOAJ
description Accurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with extensive repetitive coverage. Many previous empirical SDB approaches are unsuitable for precision bathymetry mapping in various scenarios, due to the assumption of homogeneous bottom over the whole region, as well as the neglect of various interfering factors (e.g., turbidity) causing radiation attenuation. Therefore, this study proposes a bottom-type adaption-based SDB approach (BA-SDB). Under the consideration of multiple factors including suspended particulates and phytoplankton, it uses a particle swarm optimization improved LightGBM algorithm (PSO-LightGBM) to derive depth of each pre-segmented bottom type. Based on multispectral images of high spatial resolution and in situ observations of airborne laser bathymetry and multi-beam echo sounder, the proposed approach is applied in shallow water around Yuanzhi Island, and achieves the highest accuracy with an RMSE value of 0.85 m compared to log-ratio, multi-band, and classical machine learning methods. The results of this study show that the introduction of water-environment parameters improves the performance of the machine learning model for bathymetric mapping.
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spelling doaj.art-3189873c1c0442c091bb55eac6922bc92023-11-18T21:12:40ZengMDPI AGRemote Sensing2072-42922023-07-011514357010.3390/rs15143570A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite ImageryXue Ji0Yi Ma1Jingyu Zhang2Wenxue Xu3Yanhong Wang4Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, ChinaTechnology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, ChinaTechnology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, ChinaFirst Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, ChinaFirst Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, ChinaAccurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with extensive repetitive coverage. Many previous empirical SDB approaches are unsuitable for precision bathymetry mapping in various scenarios, due to the assumption of homogeneous bottom over the whole region, as well as the neglect of various interfering factors (e.g., turbidity) causing radiation attenuation. Therefore, this study proposes a bottom-type adaption-based SDB approach (BA-SDB). Under the consideration of multiple factors including suspended particulates and phytoplankton, it uses a particle swarm optimization improved LightGBM algorithm (PSO-LightGBM) to derive depth of each pre-segmented bottom type. Based on multispectral images of high spatial resolution and in situ observations of airborne laser bathymetry and multi-beam echo sounder, the proposed approach is applied in shallow water around Yuanzhi Island, and achieves the highest accuracy with an RMSE value of 0.85 m compared to log-ratio, multi-band, and classical machine learning methods. The results of this study show that the introduction of water-environment parameters improves the performance of the machine learning model for bathymetric mapping.https://www.mdpi.com/2072-4292/15/14/3570satellite-derived bathymetryairborne laser bathymetryseafloor substratescoastal bathymetry mapping
spellingShingle Xue Ji
Yi Ma
Jingyu Zhang
Wenxue Xu
Yanhong Wang
A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery
Remote Sensing
satellite-derived bathymetry
airborne laser bathymetry
seafloor substrates
coastal bathymetry mapping
title A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery
title_full A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery
title_fullStr A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery
title_full_unstemmed A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery
title_short A Sub-Bottom Type Adaption-Based Empirical Approach for Coastal Bathymetry Mapping Using Multispectral Satellite Imagery
title_sort sub bottom type adaption based empirical approach for coastal bathymetry mapping using multispectral satellite imagery
topic satellite-derived bathymetry
airborne laser bathymetry
seafloor substrates
coastal bathymetry mapping
url https://www.mdpi.com/2072-4292/15/14/3570
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