Classification of urban tree species using LiDAR data and WorldView-2 satellite imagery in a heterogeneous environment

Feature complexity and heterogeneity of urban areas pose a challenge for tree species classification. This study examined the effectiveness of the integrated Worldview-2 (WV-2) bands, vegetation indices and normalized Digital Surface Model (nDSM) dataset in mapping common urban tree species and other land use and land cover (LULC) types using Random Forest (RF) and Support Vector Machine (SVM) algorithms. The study also ranked the importance of nDSM, WV-2 bands and vegetation indices. The results indicate that the integrated dataset was effective as shown by high classification accuracies of 97% for the RF and 94% for SVM classifiers. The nDSM was the top-ranked variable with high Mean Decrease in Accuracy (MDA) and Mean Decrease in Gini (MDG) scores of 0.98 and 0.61, respectively. This research provides information to municipalities on the methods and data that can be used for the sustainable management of urban tree species.