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

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

LANDSCAPE SCALE SPECTRAL-TEMPORAL MODELLING OF BAMBOO-DOMINATED FOREST SUCCESSION WITHIN THE ATLANTIC FOREST OF SOUTHERN BRAZIL

Tropical and subtropical ecosystems have become vulnerable to biological invasion (i.e., bamboo) due to human induced forest fragmentation. Bamboo ecological processes have been found to impede forest development, resulting in a state of arrested succession, which has been found to significantly reduce biodiversity, thus contributing to biotic homogenization. In this study we use a semi-empirical approach to develop a community-level spatially explicit ecological process model (hybrid model) using a time-series of Landsat imagery to describe single-landscape scale ecological processes of a pervasive bamboo species (Merostachys skvortzovii) found throughout the Araucaria forest, a critically threatened subtype of Atlantic forest of southern Brazil. The model is subsequently used to map bamboo spatial distribution at a multiple-landscape scale to examine patch pattern throughout a portion of the Araucaria forest. It was determined that the M. skvortzovii lifecycle is a synchronized process occurring at single and multiple-landscapes scale and is comprised of four broad lifecycle phases: pioneer predominance, mature bamboo, dieback and pioneer regeneration. Bamboo patch pattern was found to be associated with human settlement and geographic features, with clusters of patches sharing the same shape and size observed at multiple scales