Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images

Shallow water bathymetry is important for nautical navigation to avoid stranding, as well as for the scientific simulation of high tide and high waves in coastal areas. Although many studies have been conducted on satellite derived bathymetry (SDB), previously used methods basically require supervis...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-05, Vol.11 (10), p.1155
Main Authors: Sagawa, Tatsuyuki, Yamashita, Yuta, Okumura, Toshio, Yamanokuchi, Tsutomu
Format: Article
Language:English
Subjects:
Artificial intelligence
Bathymeters
Bathymetry
Coastal zone
Coasts
Computer simulation
Forest management
Google Earth Engine
Handbooks
High tide
Landsat
Landsat satellites
Landsat-8
Learning algorithms
Machine learning
Mapping
Methods
multi-temporal
random forest
Remote sensing
satellite derived bathymetry
Satellite imagery
Satellites
Sensors
Shallow water
Stranding
Studies
Water depth
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Summary:Shallow water bathymetry is important for nautical navigation to avoid stranding, as well as for the scientific simulation of high tide and high waves in coastal areas. Although many studies have been conducted on satellite derived bathymetry (SDB), previously used methods basically require supervised data for analysis, and cannot be used to analyze areas that are unreachable by boat or airplane. In this study, a mapping method for shallow water bathymetry was developed, using random forest machine learning and multi-temporal satellite images to create a generalized depth estimation model. A total of 135 Landsat-8 images, and a large amount of training bathymetry data for five areas were analyzed with the Google Earth Engine. The accuracy of SDB was evaluated by comparison with reference bathymetry data. The root mean square error in the final estimated water depth in the five test areas was 1.41 m for depths of 0 to 20 m. The SDB creation system developed in this study is expected to be applicable in various shallow water regions under highly transparent conditions.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11101155