NDLI: Mapping the distribution of Crofton weed (Eupatorium adenophorum spreng) in southwest of China using time series remote sensing data

Content Provider	IEEE Xplore Digital Library
Author	Ying Qu Zhengqing Xiao Gongqi Zhou Xiaojia Liang Xijia Li
Copyright Year	2011
Description	Author affiliation: School of Geography, Beijing Normal University, China (Ying Qu; Gongqi Zhou; Xiaojia Liang; Xijia Li) \|\| College of Resources Science and Technology, Beijing Normal University, China (Zhengqing Xiao)
Abstract	Crofton weed (Eupatorium adenophorum spreng) is an invasive species widely distributed in southwest of China. In this paper, we proposed a method for mapping the distribution of Crofton weed using time series remote sensing data. Because the reflectance signatures of the white flowers of Crofton weed are quite different from the other green components of plants, dissimilar index and flower index can be applied to map the abundance of the weed. In this study, we proposed a Crofton weed index (CWI) to map the intensity and extent of Crofton weed. The MODIS, AVHRR NDVI data and HJ satellite data are employed as the time series remote sensing data. The distribution map developed by this algorithm shows the extent and density of the invasive species in southwest of China. It was also validated with high resolution remote sensing data and ground survey maps. The comparison results show that this method is accurate and particular useful for mapping invasive species.
Starting Page	660
Ending Page	663
File Size	801285
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049216
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reflectivity Remote sensing Indexes Time series analysis Educational institutions Green products Satellites Crofton weed index Crofton weed time series remote sensing
Content Type	Text
Resource Type	Article

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