Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data
文献类型：期刊论文
| 作者 | Xia, Qing3,4; Qin, Cheng-Zhi1,4,5; Li, He4; Huang, Chong4; Su, Fen-Zhen1,4; Jia, Ming-Ming2 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2020-06-01 |
| 卷号 | 113页码:14 |
| ISSN号 | 1470-160X |
| 关键词 | Mangrove forests Vegetation indices Submerged mangrove recognition index (SMRI) High-resolution images Medium-resolution images |
| DOI | 10.1016/j.ecolind.2020.106196 |
| 通讯作者 | Qin, Cheng-Zhi(qincz@lreis.ac.cn) |
| 英文摘要 | For effective mangrove forest mapping, it is valuable to develop vegetation indices from remote-sensing imagery that can characterize the unique characteristics of mangrove forests and differentiate them from other land cover types (especially other vegetation types). In addition to diverse range of vegetation indices derived from single-phase, remote-sensing imagery that has been applied to mangrove forest classifications, recently a submerged mangrove recognition index (SMRI for short) that considers multi-tidal, high-resolution, remote-sensing imagery, and which is based on the differential spectral signature of mangrove forests under high and low tides, was proposed for use in mangrove forest classifications (Xia et al., 2018). However, to date SMRI has not been compared with existing vegetation indices that are often applied in mangrove forest classifications based on remote-sensing imagery in detail. In this study, the SMRI values obtained from medium- and high-resolution images (i.e., Landsat 8 OIL/TIRS and GF-1 respectively) are compared with four vegetation indices widely used in mangrove forest classifications (i.e., the normalized difference vegetation index, ratio vegetation index, enhanced vegetation index, and soil adjusted vegetation index). One more vegetation index, which was only available for remote-sensing imagery with visible bands, a short-wave infrared band, and a mid-wave infrared band, i.e., Landsat 8 OIL/TIRS images, was also compared with the SMRI obtained from the medium-resolution images. The results from experiments with medium- and high-resolution images of Yulin City, Guangxi Zhuang Autonomous Region of China show that the SMRI can distinguish submerged mangrove forests more effectively than the compared vegetation indices, especially in areas between high- and low-tide levels. Furthermore, the SMRI results obtained from high-resolution images perform better than those obtained from medium-resolution images. |
| WOS关键词 | LAND-COVER ; FORESTS ; VEGETATION ; CLASSIFICATION ; GULF ; ECOSYSTEMS ; MANAGEMENT ; DYNAMICS ; IMAGERY ; EXTENT |
| 资助项目 | Science and Technology Basic Resources Investigation Program of China[2017FY100706] ; Open Fund of Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province (Changsha University of Science Technology)[kfj190601] |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000523335900044 |
| 资助机构 | Science and Technology Basic Resources Investigation Program of China ; Open Fund of Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province (Changsha University of Science Technology) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/133433]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Qin, Cheng-Zhi |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun 130102, Peoples R China 3.Changsha Univ Sci & Technol, Engn Lab Spatial Informat Technol Highway Geol Di, Changsha 410114, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xia, Qing,Qin, Cheng-Zhi,Li, He,et al. Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data[J]. ECOLOGICAL INDICATORS,2020,113:14. |
| APA | Xia, Qing,Qin, Cheng-Zhi,Li, He,Huang, Chong,Su, Fen-Zhen,&Jia, Ming-Ming.(2020).Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data.ECOLOGICAL INDICATORS,113,14. |
| MLA | Xia, Qing,et al."Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data".ECOLOGICAL INDICATORS 113(2020):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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