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Mapping of nearshore bathymetry using Gaofen-6 images for the Yellow River Delta-Laizhou Bay, China

文献类型：期刊论文

作者Tan, Kun5,6; Sun, Minxuan4; Sun, Danfeng5; Liu, Xiaojie3; Liu, Xiaohuang2; Wang, Bin1; Dou, Wenjun1; Zhang, Haiyan3; Lun, Fei5,6
刊名ECOLOGICAL INFORMATICS
出版日期2024-05-01
卷号80页码:10
ISSN号1574-9541
关键词Nearshore bathymetry Turbid waters Band ratios CART RFR NDTI GF-6
DOI10.1016/j.ecoinf.2024.102497
通讯作者Lun, Fei(lunfei@cau.edu.cn)
英文摘要Bathymetric mapping is integral to maintaining marine ecosystems, managing coastal zones, and safeguarding the environment. However, achieving accurate large-scale bathymetric maps remains a challenge in China, particularly in nearshore turbid waters. To address this gap, we leveraged seasonal Gaofen-6 (GF-6) data to conduct bathymetry mapping in the Yellow River Delta-Laizhou Bay area. In our study, we found that longer wavelengths, such as those in the red-edge2 and near-infrared (NIR) bands, exhibited superior performance in determining bathymetry. Moreover, specific band ratios derived from GF-6 data-such as Blue/NIR (BN), Violet/ NIR (VN), Blue/Red-edge2 (BE), Violet/Red-edge2 (VE), Green/NIR (GN), and Green/Red-edge2 (GE)-showed promising outcomes, particularly in turbid nearshore waters. When comparing models, the random forest regression (RFR) model outperformed the classification and regression trees (CART) model in turbid nearshore areas, showing higher R2 values and lower RMSE. Notably, both models demonstrated higher accuracy in March compared to May and October. Incorporating the Normalized Difference Turbidity Index (NDTI) notably improved bathymetric results, especially in turbid sea regions. Furthermore, nearshore bathymetry proved highly susceptible to natural processes, seasonal variations, and human activities. The significant discrepancies in bathymetry among coastal areas emphasize the need for tailored management strategies to enhance coastal management and foster sustainable marine economic development.
WOS关键词REMOTE-SENSING TECHNIQUES ; SATELLITE IMAGERY ; WATER DEPTH ; TURBIDITY ; SHALLOW ; LIDAR ; CLASSIFICATION
资助项目Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, MNR, China[220104] ; National Natural Science Foundation of China[41801202]
WOS研究方向Environmental Sciences & Ecology
语种英语
出版者ELSEVIER
WOS记录号WOS:001172225800001
资助机构Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, MNR, China ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/203218]  
专题中国科学院地理科学与资源研究所
通讯作者Lun, Fei
作者单位1.China Geol Survey, Yantai Geol Survey Ctr Coastal Zone, Yantai 264000, Peoples R China
2.CGS, Minist Nat Resources, Command Ctr Nat Resources Comprehens Survey, Key Lab Coupling Proc & Effect Nat Resources Eleme, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Nat Resource Coupling Proc & Effects, Minist Nat Resources, Beijing, Peoples R China
4.Tsinghua Univ, Inst Global Change Studies, Dept Earth Syst Sci, Minist Educ,Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China
5.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China
6.MNR, Key Lab Southeast Coast Marine Informat Intelligen, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Tan, Kun,Sun, Minxuan,Sun, Danfeng,et al. Mapping of nearshore bathymetry using Gaofen-6 images for the Yellow River Delta-Laizhou Bay, China[J]. ECOLOGICAL INFORMATICS,2024,80:10.
APA Tan, Kun.,Sun, Minxuan.,Sun, Danfeng.,Liu, Xiaojie.,Liu, Xiaohuang.,...&Lun, Fei.(2024).Mapping of nearshore bathymetry using Gaofen-6 images for the Yellow River Delta-Laizhou Bay, China.ECOLOGICAL INFORMATICS,80,10.
MLA Tan, Kun,et al."Mapping of nearshore bathymetry using Gaofen-6 images for the Yellow River Delta-Laizhou Bay, China".ECOLOGICAL INFORMATICS 80(2024):10.

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来源：地理科学与资源研究所

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