Coastal water bathymetry for critical zone management using regression tree models from Gaofen-6 imagery. (15th April 2021)

• Record Type: Journal Article
• Authors: Sun, Minxuan; Yu, Linjun; Zhang, Ping; Sun, Qiangqiang; Jiao, Xin; Sun, Danfeng; Lun, Fei
• Is Part Of: Ocean & coastal management. Volume 204(2021)
• Publication Date: 2021-04-15
• Subjects: Bathymetry -- Violet-related spectral ratio -- Gaofen-6 satellite -- Regression trees -- SVR -- Critical coastal zone management; Marine resources -- Management -- Periodicals; Coastal zone management -- Periodicals; Coastal ecology -- Periodicals; Ressources marines -- Périodiques; Littoral -- Aménagement -- Périodiques; Écologie littorale -- Périodiques; Coastal ecology; Coastal zone management; Marine resources -- Management; Periodicals; Electronic journals; 551.46
• DOI: 10.1016/j.ocecoaman.2021.105522
• Languages: English
• ISSNs: 0964-5691

Abstract

Abstract: Coastal water depth information is fundamental to coast management and coastal critical zone development. The traditional bathymetric sounding method depends on radar or surveying vessels, which are expensive and time-consuming. Thus, it is of high importance to develop a rapidly updating water depth detection method. In order to bridge this gap, we explored the Chinese Gaofen-6 wide field of view (GF-6 WFV) visible-near infrared satellite imagery for large-scale accurate coastal bathymetric mapping and to understand the vertical water column environment with regression tree models. The predictors, including the Blue band/Violet band (BV), Green band/Violet band (GV), Yellow band/Violet band (YV), Green band (G), Yellow band (Y), and red-edge1 band (Re1), were derived via statistical analysis, as well as spectroscopy knowledge and experience. Compared with the conventional bathymetric method, such as single band algorithm (SBA), band ratio algorithm and support vector regression (SVR), the two regression trees used in this study yielded better accuracy. The R 2, MAE and RMSE of the classification and regression tree (CART), the Cubist tree model were 0.74, 3.78 m and 5.35 m vs 0.78, 3.56 m and 4.88 m. Besides the improved accuracies, these tree-based models can effectively reveal water depth-associated environment types with the hierarchical relationships present in the visible spectral characteristics, which supports spatial zoning strategies for coastal critical zone management based on the Gaofen-6 imagery. Graphical abstract: Image 1 Highlights: The potential of China Gaofen-6 imagery is explored for coastal bathymetric mapping. Violet band and yellow band are essential for water depth and environment modeling. Tree-based models effectively distinguish water depth associated environment types. The developed approach supports spatial zoning strategy for coastal management.