IGSNRR OpenIR
Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data
Xia, Qing1,2; Qin, Cheng-Zhi2,3,4; Li, He2; Huang, Chong2; Su, Fen-Zhen2,3; Jia, Ming-Ming5
2020-06-01
Source PublicationECOLOGICAL INDICATORS
ISSN1470-160X
Volume113Pages:14
Corresponding AuthorQin, Cheng-Zhi(qincz@lreis.ac.cn)
AbstractFor 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.
KeywordMangrove forests Vegetation indices Submerged mangrove recognition index (SMRI) High-resolution images Medium-resolution images
DOI10.1016/j.ecolind.2020.106196
WOS KeywordLAND-COVER ; FORESTS ; VEGETATION ; CLASSIFICATION ; GULF ; ECOSYSTEMS ; MANAGEMENT ; DYNAMICS ; IMAGERY ; EXTENT
Indexed BySCI
Language英语
Funding ProjectScience 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]
Funding OrganizationScience 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)
WOS Research AreaBiodiversity & Conservation ; Environmental Sciences & Ecology
WOS SubjectBiodiversity Conservation ; Environmental Sciences
WOS IDWOS:000523335900044
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/133434
Collection中国科学院地理科学与资源研究所
Corresponding AuthorQin, Cheng-Zhi
Affiliation1.Changsha Univ Sci & Technol, Engn Lab Spatial Informat Technol Highway Geol Di, Changsha 410114, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
5.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun 130102, Peoples R China
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xia, Qing]'s Articles
[Qin, Cheng-Zhi]'s Articles
[Li, He]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xia, Qing]'s Articles
[Qin, Cheng-Zhi]'s Articles
[Li, He]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xia, Qing]'s Articles
[Qin, Cheng-Zhi]'s Articles
[Li, He]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.