Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring

Forests are critical for providing ecosystem services and contributing to human well-being, but their health and extent are threatened by climate change, requiring effective monitoring systems. Traditional field-based methods are often labour-intensive, costly, and logistically challenging, limiting their use for large-scale applications. Drones offer advantages such as low operating costs, versatility, and rapid data collection. However, challenges remain in optimising data processing and methods to effectively integrate the acquired data for forest monitoring. This study addresses this challenge by integrating drone-based LiDAR and multispectral data for forest species classification and health monitoring. We developed the methodology in Ticino Park (Italy), where intensive field campaigns were conducted in 2022 to collect tree species compositions, the leaf area index (LAI), canopy chlorophyll content (CCC), and drone data. Individual trees were first extracted from LiDAR data and classified using spectral and textural features derived from the multispectral data, achieving an accuracy of 84%. Key forest traits were then retrieved from the multispectral data using machine learning regression algorithms, which showed satisfactory performance in estimating the LAI (R2 = 0.83, RMSE = 0.44 m2 m−2) and CCC (R2 = 0.80, RMSE = 0.33 g m−2). The retrieved traits were used to track species-specific changes related to drought. The results obtained highlight the potential of integrating drone-based LiDAR and multispectral data for cost-effective and accurate forest health monitoring and change detection.