Fused airborne LiDAR and hyperspectral data for tree species identification in a natural temperate forest

Abstract

This paper presents a method for the fusion of the airborne Light Detection and Ranging (LiDAR) Canopy Height Model (CHM) and the hyperspectral Compact Airborne Spectrographic Imager (CASI) data (CASI+CHM) to take advantage of vertical structural and spectral information as well as to evaluate the classification capacity of fusion data. Tree species in a natural temperate forest were successfully identified and compared with CASI data. Based on the vertical information obtained by using LiDAR, forest gap pixels were masked, whereas canopy pixels were acquired. In addition to the mean heights of tree species, training samples were extracted using the first derivative of a spectral curve with curve matching technology. Classification accuracies were compared between the fused data and data without CHM. The results show that the total accuracy and kappa coefficient (83.88%, 0.80) of CASI+CHM is better than those of CASI data alone (76.71%, 0.71), with the mapping accuracy and user accuracy of dominant species reaching a range of 78.43%89.22% and 75.15%95.65%, respectively. These results are also better than those obtained through CASI data alone (68.51%84.69% and 63.34%95.45%). The proposed method for tree species identification in a natural temperate forest is feasible with fused LiDAR and hyperspectral data.