Realizing the potential usage, the country has identified National Bamboo mission for addressing the issues relating to the development of bamboo in the country. Therefore knowledge of spatial distribution of bamboo patches becomes necessary for the evaluation and monitoring of this resource. Medium resolution satellite imagery is ineffective for accurate classification when bamboo occurs in mosaic of small and mixed patches. The present study attempts to address this problem using Very High Resolution (VHR) multispectral (MS) WorldView 2 (WV 2) imagery in South 24 Parganas, West Bengal. West Bengal, a part of Bengal Bay ecological region is one of the areas where bamboo grows naturally. The classification was carried out using additional features namely second order texture components of the first 3 principal components (PCs) of pan-sharpened 8 MS bands. Supervised kernel based (Support Vector Machine, SVM) and ensemble based (Random Forest, RF) machine learning algorithms were applied on the dataset. Moreover, RF based variable importance was analyzed to find the most informative input predictors for classification of bamboo. Altogether seven land use land cover classes were mapped which include agricultural land, built-up, bamboo patches, two canopy classes, fallow land and water body. Variable importance analysis indicated that mean texture measure of PC1 and PC3, and spectral information of MS band 8 (NIR 2) were the most important predictor layers for bamboo mapping. Unlike, RF (accuracy 72%) higher overall accuracy of 80% was achieved using SVM classifier for bamboo mapping. Intermixing of bamboo was seen with other canopies.