Validating tree inventory: Analysing tree structural properties from high-density airborne LiDAR point clouds and UAV imagery

Forests are critical natural resources for human life and wildlife, as they sustain and protect biodiversity, and supply multiple ecosystem services. However, these ecosystems are vulnerable to human-driven climate change, necessitating automated systems to monitor structural changes at the individual tree level and assess forest responses to climate anomalies. Forest inventories, that contain accurate and detailed measurements of forest structure, are essential to improving our knowledge of ecosystem services and functions. UAV and LiDAR-based tree canopy detection is valuable for estimating essential ecosystem variables (EEVs). In this research, we have validated tree structural properties primarily diameter at breast height (DBH) and tree height obtained from UAV imagery and airborne LiDAR point cloud data using field measured data. We developed Drone4Tree, a user-friendly platform built on Streamlit and Flask that provides an end-to-end solution for processing UAV imagery. The platform processes UAV-acquired data to generate orthomosaics using OpenDroneMap, delineate tree crowns using U-Net based segmentation, and derive tree attributes such as tree height, canopy area etc.

The LiDAR data was processed using forest structural complexity tool (FSCT). This tool applies sensor agnostic semantic segmentation on the point cloud to obtain individual trees, stems and their structural properties. The LiDAR and UAV derived properties were joined with the field obtained parameters. Comparative analysis shows strong agreement between field DBH and LiDAR-derived DBH (R² = 0.97), indicating reliable DBH estimation from LiDAR data. For tree height, the LiDAR-based measurements correlated well with field measured tree heights (R² = 0.73), though comparisons with the UAV-based tree height (R² = 0.97) obtained from canopy height models (CHM) revealed a lower correlation (R² = 0.66). UAV-based tree height measurements show statistically significant relation with field measured height (R² = 0.57). These results indicate that LiDAR and UAV data complement each other, with UAVs offering efficient monitoring capabilities while LiDAR providing additional precision.

These findings underscore the potential of integrating UAV and LiDAR technologies for accurate and efficient forest monitoring, enabling improved assessment of ecosystem functions and responses to climate change. By combining these complementary methods, platforms like Drone4Tree can support sustainable forest management and contribute to addressing the monitoring of global environmental changes.