From passive remote sensing to LiDAR technology: investigating Mediterranean forest ecosystems under climate change and management impacts.

The Mediterranean environment is one of the most important global biodiversity hotspot and carbon sinks (FAO and Bleu (UNEP), 2018). It is also a vulnerable biome, threatened by increasing natural and anthropogenic disturbances driven by climate change processes (Ravot et al., 2020), responsible for rapid shifts in dynamics, structural complexity, and species composition. Factors such as human activities, increasing water demand, marginal and rural areas abandonment, and the rising frequency and intensity of extreme climate events shape Mediterranean landscapes and their dynamics (Myers et al., 2000). The complex structure and composition of Mediterranean forests represent both a rich source of ecological value and a significant challenge for their assessment (FAO and Bleu (UNEP), 2018). Although several methods and approaches have been developed for the characterisation of Mediterranean environments, a comprehensive analysis of both dynamic patterns and structural changes over time remains difficult. Remote sensing (RS) techniques are extensively used to monitor, assess, and manage forests, allowing for explicit evaluations across both spatial and temporal scales. Numerous studies have proposed innovative approaches based on remote sensing data, which provide multi-temporal observations of Mediterranean environments (Etteieb et al., 2013; Nicholls and Hoozemans, 1996; Peñuelas et al., 2017; Puletti et al., 2021). In this PhD thesis, both remote and proximal sensing monitoring techniques are employed at various spatial scales, ranging from site-specific to bio-regional, enhancing the study of vegetation pattern dynamics and structural characteristics through the integration of passive satellite sensors and active LiDAR (Light Detection and Ranging) technology. The core aim is to improve monitoring strategies for complex Mediterranean environments affected by natural and anthropogenic disturbances, which represent a challenge using conventional practices. We analysed: I. The evolutionary dynamics and distribution patterns of disturbed Mediterranean riparian ecotones (Sections A-B) through multi-temporal change detection analysis based on remote sensing spectral images at both local and bio-regional scales. The study integrated Landsat-derived vegetation indices, topographic, and hydrographic data, identifying the critical driving factor. Insights were provided into how these ecotones respond to climatic and socioeconomic factors. II. The overview of LiDAR applications in forestry (Section C), with a focus on terrestrial laser scanners (TLS) and airborne laser scanners (ALS). Various approaches used in different ecosystems are compared highlighting their role in precision forestry and discussing the strengths and limitations. III. The inventory parameter assessment capabilities of Terrestrial Laser Scanner in complex Mediterranean stands characterize by strong site-specific adaptation (Section D) comparing data against traditional forest inventories practices. The results demonstrated that TLS provides accurate and detailed assessments of structural attributes such as tree height, crown volume, and stand density. 14 IV. Non-destructive approaches for forest productivity and biomass assessment (Section E) This section focused on non-destructive techniques for estimating forest productivity and biomass using both TLS and automated processing algorithms. These methods were applied to assess above-ground biomass (AGB) and tree volume in Mediterranean oak forests, offering refined insights essential for sustainable forest management and carbon stock estimation. V. Classification algorithm for the analysis of forest gaps to study processes related to photosynthesis, light availability, space distribution. The "crossing3dforest" R package was developed to evaluate empty space structures in forest ecosystems. The package processes TLS point clouds to quantify the size, shape, and connectivity of empty spaces in forest stands. VI. Integration of ALS and Sentinel-2 for large-scale fuel loads modelling (Section G): This study aimed to integrate Airborne Laser Scanning (ALS) with Sentinel-2 multispectral imagery to develop fuel load models over large Mediterranean areas. Additionally, a novel classification algorithm was introduced, integrating Sentinel-2 data with proximal sensing data (ALS) to categorize forest structural types and estimate fuel loads distribution. The study cases addressed diverse yet interconnected topics, demonstrating how the integration of LiDAR and passive remote sensing technologies provides critical insights into the structure, biomass, and fire risk dynamics of Mediterranean forest ecosystems. These methods and results contribute significantly to the development of advanced environmental models and tailored management strategies for Mediterranean landscapes. By integrating various monitoring systems, the research offers high-quality, detailed input data for productivity models, process-based simulations, and forecasting. This facilitates the prediction of management scenarios and the potential impacts of climate change on forest ecosystems. Ultimately, the findings enable the creation of precise forecasting tools and management strategies, which are essential for the sustainable preservation and adaptive management of Mediterranean forests.