Combining lidar-derived metrics with rgb-nir images to improve tree species classification in a tropical urban area

Accurate information on urban tree species distribution can reveal insights into how street trees provide ecosystem services like mitigating air pollution and cooling surfaces. Here, we used LiDAR-derived structural properties of individual tree crowns (ITCs) and digital aerial images to classify urban tree species. We fused LiDAR features with RGB-NIR digital aerial images using a fully convolutional neural network. The fusion strategy consisted in stacking one LiDAR feature at a time with RGB-NIR bands. The results show that surface normals of tree leaves improve the F1-score of all species, with the highest increase reaching 13.7 percentage points.