IGSNRR OpenIR
Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery
Xia, Qing1; Qin, Cheng-Zhi1,2,3,4; Li, He1; Huang, Chong1; Su, Fen-Zhen1
2018-09-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:9Pages:20
Corresponding AuthorQin, Cheng-Zhi(qincz@lreis.ac.cn)
AbstractMangrove 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.
Keywordmangrove forest mapping high-resolution satellite imagery tide SVM classifier spectral signature
DOI10.3390/rs10081343
WOS KeywordOBJECT-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
Indexed BySCI
Language英语
Funding ProjectScience and Technology Basic Resources Investigation Program of China[2017FY100706]
Funding OrganizationScience and Technology Basic Resources Investigation Program of China
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000449993800023
PublisherMDPI
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52505
Collection中国科学院地理科学与资源研究所
Corresponding AuthorQin, Cheng-Zhi
Affiliation1.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
Recommended Citation
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.
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