Optimized feature fusion of LiDAR and hyperspectral data for tree species mapping in closed forest canopies
This study deals with data fusion of hyperspectral and LiDAR sensors for tree species mapping in complex, closed forest canopies in Belgium. In particular, seven tree species were mapped: Beech, Ash, Larch, Poplar, Copper beech, Chestnut and Oak. The added value of LiDAR height profile data on tree species mapping was assessed. Sensor data were fused in the PCA domain, while optimal feature combination was derived from the best classification performance (in terms of Kappa and producer's accuracy) based on 5-fold cross-validation. Besides, varying training set sizes were tested (resp. 10%, 30% and 50% number of samples per tree species class). Feature fusion of PCA-transformed HS and LiDAR data was most effective for small sample set sizes reaching a Kappa accuracy improvement of 10.51%.