title: Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China.
abstract: This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that thek-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weightedk-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer's and user's accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.
authors.affiliation: Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China. tangyw@radi.ac.cn.
authors.forename: Yunwei
authors.initials: Y
authors.lastname: Tang
authors.affiliation: Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China. jinglh@radi.ac.cn.
authors.forename: Linhai
authors.initials: L
authors.lastname: Jing
authors.affiliation: Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China. lihui@radi.ac.cn.
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.affiliation: Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China. liuqj@radi.ac.cn.
authors.forename: Qingjie
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China. yanqi@radi.ac.cn.
authors.forename: Qi
authors.initials: Q
authors.lastname: Yan
authors.affiliation: School of Geography, Beijing Normal University, No. 19 Xinjiekou Wai Street, Beijing 100875, China. lixixi@mail.bnu.edu.cn.
authors.forename: Xiuxia
authors.initials: X
authors.lastname: Li
mesh_terms: D000818:Animals; D002681:China; D003247:Conservation of Natural Resources / Q000379:methods*; D017753:Ecosystem; D001503:Ursidae
keywords: WorldView-2; bamboo mapping; classification; geostatistics; object-based analysis