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Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China
Xia, Haoming1,2; Zhao, Wei1; Li, Ainong1; Bian, Jinhu1,3; Zhang, Zhengjian1,3
2017
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume9Issue:1Pages:doi:10.3390/rs9010031
SubtypeArticle
Abstract

Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data. A reference subpixel inundation percentage (SIP) map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m). The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU), Artificial neural networks (ANN), and Regression tree (RT)). Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R-2 = 0.933, RMSE = 8.73%) compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R-2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems.

KeywordWetland Subpixel Inundation Percentage (Sip) Landsat-8 Unmanned Aerial Vehicle (Uav) Linear Spectral Unmixing (Lsu) Regression Tree (Rt) Artificial Neural Networks (Ann) Zoige Plateau
WOS HeadingsScience & Technology ; Technology
DOI10.3390/rs9010031
WOS Subject ExtendedRemote Sensing
Indexed BySCI
WOS KeywordDifference Water Index ; Neural-network ; Time-series ; Impervious Surfaces ; Image-analysis ; Vegetation ; Dynamics ; Features ; Models ; Cover
Language英语
Quartile2区
TOP
WOS SubjectRemote Sensing
WOS IDWOS:000395492600031
Funding OrganizationNational Natural Science Foundation of China(41631180 ; Hundred Young Talents Program of the Institute of Mountain Hazards and Environment(SDSQB-2015-02) ; External Cooperation Program of BIC of the Chinese Academy of Sciences(GJHZ201320) ; Youth Innovation Promotion Association CAS(2016333) ; Program for Innovative Research Team (in Science and Technology) in University of Henan Province(16IRTSTHN012) ; 41571373 ; 41271433)
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/18530
Collection数字山地与遥感应用中心
Corresponding AuthorLi, Ainong
Affiliation1.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Peoples R China
2.Henan Univ, Coll Environm & Planning, Key Lab Geospatial Technol Middle & Lower Yellow, Kaifeng 475004, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Corresponding Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Xia, Haoming,Zhao, Wei,Li, Ainong,et al. Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China[J]. REMOTE SENSING,2017,9(1):doi:10.3390/rs9010031.
APA Xia, Haoming,Zhao, Wei,Li, Ainong,Bian, Jinhu,&Zhang, Zhengjian.(2017).Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China.REMOTE SENSING,9(1),doi:10.3390/rs9010031.
MLA Xia, Haoming,et al."Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China".REMOTE SENSING 9.1(2017):doi:10.3390/rs9010031.
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