Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery
文献类型：期刊论文
| 作者 | Xia, Qing1; Qin, Cheng-Zhi1,2,3,4 ; Li, He1; Huang, Chong1; Su, Fen-Zhen1
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| 刊名 | REMOTE SENSING
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| 出版日期 | 2018-09-01 |
| 卷号 | 10期号:9页码:20 |
| ISSN号 | 2072-4292 |
| 关键词 | mangrove forest mapping high-resolution satellite imagery tide SVM classifier spectral signature |
| DOI | 10.3390/rs10081343 |
| 通讯作者 | Qin, Cheng-Zhi(qincz@lreis.ac.cn) |
| 英文摘要 | Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery. |
| WOS关键词 | OBJECT-ORIENTED CLASSIFICATION ; NEURAL-NETWORK CLASSIFICATION ; LAND-COVER CLASSIFICATION ; VEGETATION WATER-CONTENT ; SUPPORT VECTOR MACHINES ; TEXAS GULF-COAST ; AERIAL PHOTOGRAPHS ; QUICKBIRD IMAGERY ; TEXTURAL FEATURES ; SPECTRAL INDEXES |
| 资助项目 | Science and Technology Basic Resources Investigation Program of China[2017FY100706] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000449993800023 |
| 资助机构 | Science and Technology Basic Resources Investigation Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/52504]  |
| 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.Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210097, Jiangsu, Peoples R China 4.Nanjing Normal Univ, Sch Geog, Nanjing 210097, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xia, Qing,Qin, Cheng-Zhi,Li, He,et al. Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery[J]. REMOTE SENSING,2018,10(9):20. |
| APA | Xia, Qing,Qin, Cheng-Zhi,Li, He,Huang, Chong,&Su, Fen-Zhen.(2018).Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery.REMOTE SENSING,10(9),20. |
| MLA | Xia, Qing,et al."Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery".REMOTE SENSING 10.9(2018):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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