Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove
文献类型：期刊论文
| 作者 | Yang, Gang1,4; Huang, Ke4; Sun, Weiwei4; Meng, Xiangchao2; Mao, Dehua3; Ge, Yong1 |
| 刊名 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 189页码:236-254 |
| ISSN号 | 0924-2716 |
| 关键词 | Mangrove Vegetation index Mangrove Vegetation Index (MVI) Hyperspectral imagery ZY1-02D |
| DOI | 10.1016/j.isprsjprs.2022.05.003 |
| 通讯作者 | Sun, Weiwei(sunweiwei@nbu.edu.cn) ; Ge, Yong(gey@lreis.ac.cn) |
| 英文摘要 | As a specific forest community in tropical and subtropical coastal zones, mangrove has unique ecological functions and great social and economic value. Accurate mangrove mapping is important to the protection and restoration of mangrove ecosystem. Traditional classification methods rely on a large number of samples and complex classifiers, which are unsuitable for the large-scale extraction of mangroves because of low computational efficiency and poor generalization ability. This study proposes an Enhanced Mangrove Vegetation Index (EMVI) based on hyperspectral images. This index enhances the difference in greenness and canopy moisture content between mangroves and other vegetation using a green band and two shortwave-infrared bands in the form (Green-SWIR2)/(SWIR1-Green). Six typical mangrove areas (i.e., Qinglan Harbor in Hainan, Zhenzhu Harbor-Fangcheng Harbor in Guangxi, Lianzhou Bay in Guangxi, Zhangjiang Estuary in Fujian, Quanzhou Bay in Fujian, and Oujiang Estuary in Zhejiang) were selected as the study areas, and sample datasets were produced by field surveys and Google Earth high-resolution images. Compared with other VIs, such as the Normalized Difference Vegetation Index, Enhanced Vegetation Index, Moisture Stress Index, Mangrove Vegetation Index, and Combined Mangrove Recognition Index, EMVI exhibited better ability to distinguish mangroves and other vegetation. EMVI was applied to mangrove extraction in the six study areas based on ZY1-02D images, and the extraction results were compared with existing mangrove maps (GMW_2016 and CAS_Mangrove 2015) and the results of SVM. Results showed that EMVI featured the better overall accuracy and the Kappa coefficient than existing mangrove maps and the performance was similar to SVM. Further tests showed that EMVI was also suitable to other hyperspectral remote sensing images (i.e., GF-5, Hyperion, and PRISMA), but not to Sentinel-2 images. These results indicate that EMVI can be applied to different hyperspectral remote sensing images and different types of mangrove extraction. This index also has excellent application potential in mangrove mapping. |
| WOS关键词 | FOREST ; CHINA ; WATER ; DISCRIMINATION ; CLASSIFICATION ; REFLECTANCE |
| 资助项目 | National Natural Science Foundation of China[42122009] ; National Natural Science Foundation of China[41971296] ; Ningbo Science and Technology Innovation 2025 Major Special Project[2021Z107] ; Public Projects of Ningbo City[2021S089] ; China Postdoctoral Science Foundation[2020M670440] ; Fundamental Research Funds for the Provincial Universities of Zhejiang[SJLZ2022002] ; Zhejiang Provincial Natural Science Foundation of China[LR19D010001] |
| WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000808371000001 |
| 资助机构 | National Natural Science Foundation of China ; Ningbo Science and Technology Innovation 2025 Major Special Project ; Public Projects of Ningbo City ; China Postdoctoral Science Foundation ; Fundamental Research Funds for the Provincial Universities of Zhejiang ; Zhejiang Provincial Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/179232]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Sun, Weiwei; Ge, Yong |
| 作者单位 | 1.Inst Geog Sci & Nat Resources Res, Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Peoples R China 3.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun 130102, Peoples R China 4.Ningbo Univ, Dept Geog & Spatial Informat Tech, Ningbo 315211, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Gang,Huang, Ke,Sun, Weiwei,et al. Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2022,189:236-254. |
| APA | Yang, Gang,Huang, Ke,Sun, Weiwei,Meng, Xiangchao,Mao, Dehua,&Ge, Yong.(2022).Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,189,236-254. |
| MLA | Yang, Gang,et al."Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 189(2022):236-254. |
入库方式： OAI收割
来源：地理科学与资源研究所
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