MFI: A mudflat index based on hyperspectral satellite images for mapping coastal mudflats
文献类型：期刊论文
| 作者 | Yang, Gang3,4,5; Shao, Chunchen; Zuo, Yangyan; Sun, Weiwei4; Huang, Ke2; Wang, Lihua; Chen, Binjie; Meng, Xiangchao2; Ge, Yong5 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 133页码:104140 |
| 关键词 | Coastal mudflat Spectral index Mudflat Index (MFI) Hyperspectral data |
| DOI | 10.1016/j.jag.2024.104140 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | China's coastal mudflats, threatened by artificial reclamation and climate change, are undergoing drastic changes and their accurate mapping is important for their conservation and restoration. Traditional classification methods, which require large samples and complex classifiers, tend to have low computational efficiency and poor generalization ability; thus, they are unsuitable for the rapid and accurate extraction of coastal mudflats. This study proposes a Mudflat Index (MFI) based on hyperspectral images. MFI amplifies the difference in spectral characteristics between mudflats and other land cover types in intertidal environments, effectively improving the discrimination between coastal mudflats, salt marshes, mangroves, and muddy waters. Four typical coastal mudflat areas (i.e., the Yellow River Delta in Shandong, the Radial Sand Ridges of the South Yellow Sea in Jiangsu, Hangzhou Bay in Zhejiang, and the Qinzhou Bay-Nanliu River Estuary in Guangxi) based on ZY1-02D were selected as the study areas. The extraction accuracies in the four study areas are 97.60%, 96.88%, 97.16% and 96.97%, respectively. The further extraction experiments were calculated based on hyperspectral data from GF-5, PRISMA, and Hyperion. Sample datasets were produced using field surveys and Google Earth high-resolution imagery. Compared to the Hyperspectral Bare Soil Index (HBSI), Normalized Difference Bare Soil Index (NDBSI) and Microphytobenthos Index (MPBI), MFI demonstrates superior performance with average SDI value improvements of 0.82, 0.71 and 1.17, respectively, in distinguishing mudflats from other typical land cover types in the intertidal zone. The extraction results were also compared with those derived from Support Vector Machine (SVM) and Random Forest (RF) classifications, showing that MFI outperformed SVM and RF by an average of 1.52% and 0.58%. The results show that MFI can be applied to different hyperspectral remote sensing images and different areas of mudflat extraction. The MFI-based method is simple, fast and accurate at extracting the mudflat in the intertidal environment. |
| WOS关键词 | INTERTIDAL ZONE ; TIME-SERIES ; TIDAL FLATS ; SOIL INDEX ; SEDIMENT ; WETLANDS ; SEA ; MANAGEMENT |
| WOS研究方向 | Remote Sensing |
| WOS记录号 | WOS:001309506700001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207936]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Peoples R China 2.Ningbo Univ, Ningbo Univ Collaborat Innovat Ctr Land & Marine S, Ningbo 315211, Peoples R China 3.Ningbo Univ, Inst East China Sea, Ningbo 3152112, Peoples R China 4.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 5.Ningbo Univ, Dept Geog & Spatial Informat Tech, Ningbo 315211, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Gang,Shao, Chunchen,Zuo, Yangyan,et al. MFI: A mudflat index based on hyperspectral satellite images for mapping coastal mudflats[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,133:104140. |
| APA | Yang, Gang.,Shao, Chunchen.,Zuo, Yangyan.,Sun, Weiwei.,Huang, Ke.,...&Ge, Yong.(2024).MFI: A mudflat index based on hyperspectral satellite images for mapping coastal mudflats.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,133,104140. |
| MLA | Yang, Gang,et al."MFI: A mudflat index based on hyperspectral satellite images for mapping coastal mudflats".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 133(2024):104140. |
入库方式： OAI收割
来源：地理科学与资源研究所
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