Mapping tree species of forests in southwest France using Sentinel-2 image time series

In this paper, we study the potential of the new satellite Sentinel-2 (S2) images to identify tree species in temperate forests. Fourteen tree species are classified from eleven S2 images acquired from winter 2015 to autumn 2016 with 2181 reference pixels. Two datasets are compared: (1) the 4-bands dataset including the 10-m VNIR images only and (2) the 10-bands dataset including the red-edge and SWIR bands at 20-m, resampled at 10-m. Three standard supervised algorithms are tested: SVM with three kernel functions, Random Forest, and Gradient Boosted Trees. Depending on the considered dataset and algorithm, we obtain very high performances (Cohen's kappa from 0.92 to 0.97). Black pine and Douglas fir are the most confused species (F1-score of 0.81 and 0.74 respectively). Cultivated tree plantations such as Aspen and Red Oak are the best predicted (F1-score of 0.99 for both). SVM-RBF outperforms systematically the other classifiers. These first results suggest a high potential of the new Sentinel-2 optical images for mapping the distribution of tree species in forest ecosystems.