A single-tree identification method has been applied to light detection and ranging (LiDAR) data acquired over a protected coastal area in Tuscany (San Rossore Regional Park, Central Italy). The method, which is based on the computation of the convergence index from the LiDAR tree-height image, is capable of identifying individual pine trees in densely populated stands. The main features of each pine tree (height and crown size) are also estimated, which allows the final prediction of stem volume. The accuracy of the stem volume estimates is first assessed through a comparison with the ground measurements of a recent forest inventory of the park [San Rossore Forest Inventory (SRFI)]. This test indicates that stem volume is predicted with moderate accuracy at stand level ($r$ around 0.65). The stem volume estimates are then used to drive a modeling strategy which, on the basis of remotely sensed and ancillary data, is capable of predicting stem volume current annual increment (CAI). A final accuracy assessment indicates that the use of LiDAR stem volumes in place of the SRFI measurements only slightly deteriorates the quality of the obtained stand CAI estimates.

Ficus carica L., present in Italy since ancient times, is represented by numerous cultivars grown in both southern and central regions. In recent years, local accessions, as a source of genetic biodiversity, have gained increasing interest for both genetic conservation and their agronomic characteristics, which are well suited for ‘sustainable agriculture’. Based on these considerations, we conducted a parallel study combining the genetic analysis (SSRs marker) and chemical profiling (polyphenols and antioxidant capacity) of fig leaves to characterize 15 cultivars of the Tuscany region. A genetic analysis performed using seven SSR oligonucleotide primers successfully allowed the discrimination of the cultivars studied, with primers MFC2, MFC3, and LMFC30 exhibiting the highest polymorphism. The phytochemical profiling of the leaves, conducted by HPLC-DAD-TOF-MS, revealed 17 phenolic compounds, among which caffeic acid derivatives were the most abundant. The psolaren compound was absent only in Gigante di Carmignano cv. The antiradical capacity varied among cultivars, with Perticone, Brogiotto Nero, and Paradiso exhibiting the highest antiradical capacity. Additionally, Brogiotto Bianco, Corbo, Dottato, Paradiso, Pecciolo Nero, and Verdino contained the highest concentrations of total sugars. Statistically significant differences were observed in total polyphenol content (values ranging from 14.1 to 36.6 mgGAE/gDW) and in flavonoid content (ranging from 25.7 to 52.3 mgQE/gDW). In terms of sugars, the sum of fructose, glucose, and sucrose ranged from 20.60 (Bianco di Carmignano) to 49.24 mg g⁻¹ (Verdino), with fructose present in the highest amounts. In conclusion, the 15 cultivars were completely characterized genetically and chemically, offering valuable insights for both conservation strategies and agricultural applications.

Ink disease, primarily caused by the pathogen Phytophthora xcambivora, significantly threatens the health and productivity of sweet chestnut (Castanea sativa Mill.) orchards, highlighting the need for accurate detection methods. This study investigates the efficacy of machine learning (ML) classifiers combined with high-resolution multispectral imagery acquired via unmanned aerial vehicles (UAVs) to assess chestnut tree health at a site in Tuscany, Italy. Three machine learning algorithms—support vector machines (SVMs), Gaussian Naive Bayes (GNB), and logistic regression (Log)—were evaluated against eight vegetation indices (VIs), including NDVI, GnDVI, and RdNDVI, to classify chestnut tree crowns as either symptomatic or asymptomatic. High-resolution multispectral images were processed to derive vegetation indices that effectively captured subtle spectral variations indicative of disease presence. Ground-truthing involved visual tree health assessments performed by expert forest pathologists, subsequently validated through leaf area index (LAI) measurements. Correlation analysis confirmed significant associations between LAI and most VIs, supporting LAI as a robust physiological metric for validating visual health assessments. GnDVI and RdNDVI combined with SVM and GNB classifiers achieved the highest classification accuracy (95.2%), demonstrating their superior sensitivity in discriminating symptomatic from asymptomatic trees. Indices such as MCARI and SAVI showed limited discriminative power, underscoring the importance of selecting appropriate VIs that are tailored to specific disease symptoms. This study highlights the potential of integrating UAV-derived multispectral imagery and machine learning techniques, validated by LAI, as an effective approach for the detection of ink disease, enabling precision forestry practices and informed orchard management strategies.

The dynamics of Land Use/Land Cover changes are crucial to environmental sustainability, socio-economic development, and spatial planning. These changes stem from complex interactions between human activities, natural processes, and policies. In recent decades, LULC transformations have been linked to global challenges such as biodiversity loss, climate change, and resource degradation. Key drivers include urban sprawl, agricultural expansion and abandonment, and deforestation, emphasizing the need for effective frameworks to monitor and assess their impacts. This study investigates Land Use/Land Cover (LULC) changes in Tuscany (Italy) over the period from 2007 to 2019. To achieve this, statistical analyses were conducted to quantify variations in LULC across different classes and administrative territories represented by provincial local authorities. Specifically, data spanning five temporal intervals (2007, 2010, 2013, 2016, and 2019) enabled a comprehensive comparative analysis of spatial persistence in LULC patterns. Changes were assessed using a statistical approach based on Odds Ratios (OR). Additionally, Generalized Linear Models (GLMs) at the provincial level were employed to facilitate one-to-many provincial comparisons and to evaluate the statistical significance of observed LULC changes. The analysis revealed that certain classes exhibit a greater susceptibility to changes compared to others. Specifically, the classes categorized under ’Artificial Surfaces’ (LC_100) were, on average, 6.7 times more likely to undergo changes than those classified as ’Agricultural Areas’ (LC_200) and 11 times more likely than those under ’Forest and Semi-natural Areas’ (LC_300). Over time, the areas classified as artificial territories have exhibited a progressively decreasing probability of change. Notably, during the first update period (2007–2010), these areas were 3.5 times more susceptible to change compared to the most recent update period (2016–2019). An additional significant finding emerged from the statistical comparison of LULC changes across administrative regions governed by different authorities (Provinces). These findings underscore the potential of using administrative indicators and morphological parameters to analyze LULC change trends. The proposed approach provides a robust framework for interpreting territorial resilience and informing spatial planning strategies effectively.

This paper analyses chestnut distribution in Calabria based on various informative layers such as CORINE Land Cover 3, 1978-1982 b/w aerial photos, 1998 digital ortophoto, management plans cartography. On the basis of this information the map of chestnut distribution in Calabria in scale 1:250.000 has been drawn. Further more, the work defines the field of ecological suitability of the species, in Calabria, through the parameters (e.g. climatical and geopedological) that characterize the vegetation area.