Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China.

Yunwei Tang,Xiuxia Li,Linhai Jing,Qingjie Liu,Qi Yan,Hui Li

doi:10.3390/s16111957

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 the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-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.

Highlights

As an endangered species, giant pandas (Ailuropoda melanoleuca) are threatened by continuous habitat loss and a low birth rate
We did not two bamboo species are similar in the WV-2 imagery, and it is difficult to identify small patches identify tree species in this study, and the land cover types we focused on were classified as bamboo, of bamboo species using remote sensing techniques without hyperspectral information involved
Bayesian and and support vector machine (SVM) methods, the original andand expanded training datadata werewere used used for classification based were applied appliedin inthis thisstudy

Summary

Introduction

Giant pandas (Ailuropoda melanoleuca) are threatened by continuous habitat loss and a low birth rate. Estimating and mapping suitable habitat are critical to endangered species conservation planning and policy [1]. Knowledge of the spatial distribution of bamboos becomes important for identifying the habitat of giant pandas. There have been ongoing studies for mapping bamboos and other tree species using remote sensing [3,4,5,6,7,8,9] Most of these studies applied classification over large areas using medium or low spatial resolution imagery, such as Landsat TM/ETM+ [10,11,12,13,14]

Objectives

Methods

Results

Conclusion