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.

Urban afforestation is increasingly regarded as a key strategy for fostering biodiversity to restore and enhance the ecosystem services needed to counteract the effects of climate change in built-up areas. In Italy, several experimental afforestation projects have been launched as part of the National Recovery and Resilience Plan (NRRP), focusing on cities or metropolitan areas such as Milan, Rome, Pistoia and Campobasso. These projects follow a multidisciplinary approach, integrating botanists, foresters, urban planners, landscape architects and remote sensing specialists. The goal is to address the challenging complexity of urban forest restoration through reforestation and afforestation actions. Key innovations include the integration of transdisciplinary methodologies (landscape analysis, landscape design, forest and plant ecology) with the application of advanced remote sensing technologies and participatory community engagement frameworks to address ecological and social challenges. Experimental plots have been set up across various urban areas, testing a range of planting schemes to maximise climate change resilience and ensure long-term ecological sustainability. Emphasis has been placed on selecting drought-tolerant and thermophilic species that are better adapted to widespread warming and local urban heat islands. ‘Biodiversity strips’ with perennial flowers for insects, shrubs with berries for birds and nests for wild bees and vertebrates have been set up to enhance biodiversity in new afforestation areas. Advanced monitoring tools, such as Light Detection and Ranging (LiDAR) and multi-sensor drones, have been employed alongside field observations to assess forest growth, species survival, structural complexity and biodiversity enhancement over time. Historical analyses of landscape patterns and ecological connectivity over the past 200 years, along with evaluations of afforestation projects from the last 70 years, have provided critical insights into the successes and challenges of previous interventions, serving as a guide for future efforts. By focusing on ecological connectivity, the integration of afforested areas into the urban matrix, and citizen engagement, the current project aims to align urban forestry efforts with sustainable development goals. This comprehensive project framework addresses environmental restoration and the social and aesthetic impacts on local communities, contributing to the overall resilience and well-being of urban and peri-urban ecosystems.

Woodlands cover 41% of the surface of the European Union and contribute to human well-being through the ecosystem services they provide. However, their extension and condition are under threat due to the impacts of climate change. Forest traits are commonly used in ecological and climate studies for the assessment of plants health status. In this contest, the use of unmanned aircraft vehicles (UAVs) is rapidly developing for forest monitoring and inventory. UAVs allow to acquire high spatial resolution data using different sensors, as LiDAR or optical sensor, with low operational costs. The main focus of this contribution is integrating LiDAR and multispectral data to detect and classify single trees and retrieve forest traits at tree scale employing machine learning approaches. The study area is a natural reserve located in the Ticino Valley Regional Park, in eastern Lombardy along the Po river (Italy). An intensive field campaign was conducted in the summer of 2022 to collect forest traits (leaf chlorophyll concentration - LCC and leaf area index - LAI) and UAV data. The UAV mounted a DJI L1 LiDAR sensor and a MAIA S2 multispectral camera. First, the individual trees were identified using the “lidR”, “rLidar” and “ForestTools” R packages. Each tree was then classified using a Random Forest classifier with an accuracy of 84% (Kappa coefficient =0.74). For the retrieval of the forest traits of interest, different machine learning regression algorithms (MLRAs) were tested. LAI was best estimated by the Gaussian Processes Regression (GPR), (R2=0.903, nRMSE=8.66%) and the Canopy Chlorophyll Content (CCC = LAI x LCC) by the Support Vector Regression (SVR) (R2=0.8327, nRMSE=9.1684%). MLR algorithms showed satisfactory performances in plant trait retrieval in forest ecosystem from UAV, opening interesting perspectives for forest monitoring, both at leaf and canopy level except for the LCC.