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A new time series vegetation-water index of phenological-hydrological trait across species and functional types for Poyang Lake wetland ecosystem

文献类型： 外文期刊

作者： Wang, Lin 1 ; Dronova, Iryna 5 ; Gong, Peng 1 ; Yang, Wenbo 3 ; Li, Yingren 3 ; Liu, Qing 3 ;

作者机构： 1.Chinese Acad Sci, Inst Remote Sensing Applicat, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

2.Beijing Normal Univ, Beijing 100101, Peoples R China

3.Chinese Acad Fishery Sci, Resources & Ecoenvironm Res Ctr, Beijing 100141, Peoples R China

4.Poyang Lake Ecol Res Stn Environm & Hlth, Duchang 332600, Peoples R China

5.Univ Calif Berkeley, Div Ecosyst Sci, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA

6.Tsinghua Univ, Ctr Earth Syst Sci, Minist

关键词： Beijing-1 microsatellite;Classification;Multitemporal;Phenology;Plant Functional Types (PFTs);Poyang Lake;Remote sensing;Submersion time;Support vector machines (SVM);Trait;Wetland

期刊名称：REMOTE SENSING OF ENVIRONMENT （ 影响因子：10.164； 五年影响因子：11.057 ）

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收录情况： SCI

摘要： Despite the long history of vegetation mapping with remote sensing, challenges remain in effectively linking remote sensing observations with plant traits and environmental disturbance across species and functional types. This study proposes a classification of aquatic and wetland vegetation plant functional types (PFTs) using time series remotely sensed data applicable to wetland ecosystems with large annual water level changes like Poyang Lake, the largest freshwater lake-wetland of China. We first developed the aquatic and wetland PFT classification scheme which included perennial C_4 grasses, perennial C_3 reed, C_3 (sedges and taller forbs), short C_3 forbs, floating aquatic macrophytes, and submerged aquatic macrophytes. Using this scheme, time series normalized difference vegetation index (time series NDVI) images and time series vegetation-water index (time series VWI) images extracted from 32-m spatial resolution Beijing-1 microsatellite data were applied to perform two PFT classifications with a support vector machine (SVM) algorithm. We found that the time series NDVI-based SVM classification method mapped aquatic and wetland PFT distribution with an overall accuracy of 81.3%. However, when the information on water level fluctuation was combined with time series NDVI into a new time series index (referred to as time series VWI), the overall accuracy increased to 90.4%. Results suggest that time series VWI has a stronger capability in distinguishing wetland PFTs with different submersion times and flood tolerances than time series NDVI, and thus may be considered as a key input dataset for future PFT classifications in seasonally dynamic wetlands.

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