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Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia

文献类型：期刊论文

作者Xiao, Han1,2,3; Su, Fenzhen1,2,3,4; Fu, Dongjie1,2,3; Lyne, Vincent5; Liu, Gaohuan1; Pan, Tingting1,2,3; Teng, Jiakun1,2
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2021-07-01
卷号99页码:13
关键词Remote sensing band selection Large-scale mapping Mangrove mapping Multi-source data
ISSN号1569-8432
DOI10.1016/j.jag.2021.102320
通讯作者Su, Fenzhen(sufz@lreis.ac.cn) ; Fu, Dongjie(fudj@lreis.ac.cn)
英文摘要A band selection model was described for efficient and accurate remotely-sensed vegetation mapping in cloudy mixed-vegetation areas, demonstrated with an application on mapping mangroves in Southeast Asia (SE Asia). We show how to use multi-source satellite imagery and Cloud Computing Platforms to improve mapping and computational efficiency in complex environments. A key element of the method relies upon field surveys to establish a detailed sample database that includes easily-confused land cover. The Maximal Separability and Information (MSI) model was developed to select key bands for target land cover classification from multiple satellite imagery based on two principles: 1. maximize separability of the target cover from other land cover; and 2. maximize and prioritize information from band combinations. Application of the MSI model to map mangroves in SE Asia using three optical and SAR data systems (Landsat OLI, Sentinel-2 and Sentinel-1) showed: 1. Sentinel-2 is better at classifying mangrove than Landsat and Sentinel-1; and 2. SWIR, NIR and Red bands (with SWIR in particular) are effective in separating mangrove from other vegetation. The MSI-mapped mangroves showed lower computation cost compared to using all bands from individual satellites, and higher accuracy (above 90%) when applied to SE Asia. It was robust in tolerating smaller sample sizes, thereby demonstrating computational feasibility and substantial improvements with the MSI model for large-scale land cover mapping in complex environments.
WOS关键词SPECTRAL BAND SELECTION ; LANDSAT 8 ; FOREST ; COVER ; SENTINEL-2 ; CLASSIFICATION ; REFLECTANCE ; INDEX ; MAPS
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDA19060304] ; Science and Technology Basic Resources Investigation Program of China[2017FY201401] ; President's International Fellowship Initiative of Chinese Academy of Sciences[2020VEA0009]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000642475100002
出版者ELSEVIER
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; Science and Technology Basic Resources Investigation Program of China ; President's International Fellowship Initiative of Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/161590]  
专题中国科学院地理科学与资源研究所
通讯作者Su, Fenzhen; Fu, Dongjie
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Nanjing Univ, Collaborat Innovat Ctr South China Sea Studies, Nanjing 210093, Peoples R China
4.Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China
5.Univ Tasmania, IMAS Hobart, Hobart, Tas 7004, Australia
推荐引用方式
GB/T 7714		 Xiao, Han,Su, Fenzhen,Fu, Dongjie,et al. Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2021,99:13.
APA Xiao, Han.,Su, Fenzhen.,Fu, Dongjie.,Lyne, Vincent.,Liu, Gaohuan.,...&Teng, Jiakun.(2021).Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,99,13.
MLA Xiao, Han,et al."Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 99(2021):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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