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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MDPI AG
2023-07-01
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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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institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-11T00:40:48Z |
publishDate | 2023-07-01 |
publisher | MDPI AG |
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series | Remote Sensing |
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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