This work investigates the discriminative power of wavelet decomposition based texture features in forest cover classification. Our texture features are used as inputs in a random forests classifier. The performances of this tree-based ensemble classifier are assessed by classification accuracy as well as classification confidence provided by an unsupervised version of ensemble margin. The effectiveness of the proposed texture based multiple classifier system is demonstrated in performing mapping of very high resolution forest imagery. Traditional grey level co-occurrence matrix derived texture features are also evaluated through our ensemble classification framework for comparison.