Classification of urban tree species based on multi-source remote sensing data

Precise classification of tree species has significant value for urban forest structure and function research. However, current urban tree species classification is limited by complex urban backgrounds and the suboptimal performance of single classifiers. This study selected a forest park in Zhengzhou City, Henan Province, China, as the research area. The study integrated 10 m spatial resolution hyperspectral satellite imagery, 1 m spatial resolution GF-2 panchromatic satellite imagery and Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) point cloud data. Different feature combination were constructed by extracting features, and a multi-classifier ensemble algorithm was employed to classify tree species. The results showed that: (1) Compared to single-source data, multi-source data fusion achieves higher accuracy. Especially, spectral and texture features combined with canopy height features yielded the highest classification accuracy. The overall accuracy (OA) was 94.59% and the Kappa coefficient was 0.9336. (3) Compared to a single machine learning algorithm, the weighted voting ensemble learning algorithm had superior classification result in the study area. The OA improved by 3.22% and the Kappa coefficient increased by 0.0441. This study demonstrates that multi-source remote sensing data can achieve feasible accuracy for tree species classification in complex urban surface environments.