Over the past few decades, wildfire activity has increased globally. In the Central Zagros Mountains of Iran, widespread oak decline has significantly altered fuel structures and raised ecological concerns. However, the specific contribution of long-term vegetation degradation to wildfire burn severity remains under-quantified. This study addresses this gap by investigating whether decadal declines in the Normalized Difference Vegetation Index (NDVI) are associated with higher burn severity, while assessing their relative importance against short-term pre-fire environmental conditions, fuel types, topography, and human access gradients. Focusing on a major wildfire in June 2024 in the Kashkan Watershed, we mapped burn severity using the differenced Normalized Burn Ratio (dNBR) derived from Sentinel-2 imagery. A comprehensive suite of predictors was evaluated, including the long-term NDVI trend (May- September 2014-2023), pre-fire Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), fuel categories based on ESA WorldCover, slope, aspect, spatial texture contrast, and distance to roads and settlements. A stratified sampling design supported statistical inference using ordinary least squares regression and predictive modeling via Random Forest and XGBoost algorithms with five-fold cross-validation. XGBoost demonstrated the highest predictive performance (cross-validated R2 = 0.70; RMSE = 0.11), outperforming Random Forest (R2 = 0.64) and linear regression (R2 = 0.34). Fuel moisture (NDMI) and thermal stress (LST) emerged as the primary drivers of severity, followed by land cover, topography, and spatial texture. While the long-term NDVI decline showed a small but consistent positive association with dNBR at the landscape scale, the results highlight a distinct multi-scale mechanism. In this dynamic, pre-fire moisture and temperature establish the broad watershed-scale environmental susceptibility, whereas specific fuel types and their spatial configurations drive the local-level amplification or damping of burn severity. These findings provide a framework for proactive vulnerability mapping in Zagros oak woodlands, suggesting that integrating dynamic fire-weather data and field-based fuel measurements could further refine future predictability.
Accurate volume measurement of stacked timber logs is essential for the timber industry. Photo-optical Automated Volume Measuring Systems (AVMS) offer efficient alternatives to manual methods but require verification to ensure reliability. This paper presents a simple, reliable, and easily implementable verification method for AVMS, based on measuring the wood stack front surface area. The proposed method utilizes reference models, calibrated using traceable working standards, to simulate timber log stacks with defined contours. This involves comparing the AVM’s measured area to the reference model’s area, enabling the determination of area measurement errors. The study investigates the selection of reference instruments, proposes a comprehensive AVMS verification procedure, and evaluates the method’s metrological characteristics. Laboratory tests involving 110 AVMS units revealed a repeatability of 0.46% and a total uncertainty of approximately 1.1%. The method successfully identified shortcomings in commercially available AVMS, including non-compliance with manufacturer-declared errors and design imperfections related to camera limitations. The developed method can be used in industry, metrological supervision, and scientific research to ensure accurate and reliable measurements of wood stacks and the long-term reliability of AVMS. This study also highlights the need to further improve photo-optical measurement systems to increase their reliability and accuracy.
Growing concerns about climate change and biodiversity loss have increased the importance of research on vegetation ecology and ecosystem functioning for understanding forests and savannas. Functional diversity is a field of study that has expanded in recent years, aiming to explain ecosystem functions through organisms' functional characteristics. The Cerrado, the second-largest biome in South America, is regarded as the world's most biodiverse savanna and plays a crucial role in biomass accumulation. In this context, the present study aimed to investigate the role of functional traits in the accumulation of woody aboveground biomass (AGB) in the Cerrado by analyzing the relationship between Community Weighted Means (CWMs) of functional traits and functional diversity metrics across three distinct physiognomies (Cerrado T & iacute;pico, Cerrado Denso, and Cerrad & atilde;o). We hypothesized that the relationships between biomass and functional diversity metrics would differ among physiognomies. To assess these relationships, we tested correlations between biomass and both CWMs and functional diversity metrics. We measured five traits for dominant species selected according to cumulative abundance criteria in each physiognomy: specific leaf area (SLA), wood density (WD), crown area (CA), maximum diameter (Dmax), and maximum height (Htmax). These traits were used to calculate the respective CWMs, along with five functional diversity metrics: Functional Richness (Fric), Functional Evenness (FEve), Functional Divergence (FDiv), Functional Dispersion (FDis), and Rao's Quadratic Entropy (RaoQ). We used forest inventory data from the study areas to fit linear models for each physiognomy and mixed-effects models for the complete dataset, with physiognomic type as a random effect. The physiognomies differed in structural and taxonomic characteristics, displaying growth patterns proportional to vegetation stature. Functional differences were also observed: biomass in Cerrad & atilde;o was more strongly associated with functional traits, whereas biomass in Cerrado Denso and Cerrado T & iacute;pico was more strongly associated with functional diversity metrics. The CWMs Dmax, WD, and CA, along with the diversity metrics Fric and FEve, were the predictors most frequently retained in the best-performing models. General models that included both CWMs and functional diversity metrics, or only CWM Dmax, outperformed models that included only Fric or excluded random effects.
Regeneration dynamics in forest ecosystems depend on seedlings' ability to persist in shaded understories and, following canopy opening, to rapidly adjust physiologically to new, contrasting light and soil moisture conditions. In this study, we examined the real-time efficiency of the photosynthetic apparatus and the short- to intermediate-term acclimatory response of photosynthetic pigments on Nothofagus pumilio seedlings exposed to different light and soil moisture levels. We evaluated phenology, chlorophyll fluorescence and pigment content in leaves, on 2-3 years-old seedlings in a controlled greenhouse experiment with three levels of light intensity (low = 4%, medium = 26%, high = 64% of the natural incident irradiance) and two levels of soil moisture (optimal = 40-60%, excess = 80-100% of soil field capacity). We measured six samples per treatment (light intensity & times; soil moisture levels) monthly during one growing season (n = 216). Data were analyzed by multiple ANOVAs. Although maximum quantum yield remained stable (> 0.80) across treatments, seedlings exhibited differential eco-physiological responses primarily driven by light availability. Structural investment was synergistic and negatively affected by the light & times; soil moisture interaction, resulting in reduced leaf production, chlorophyll, and carotenoids during peak season under the high-light and excess-soil-moisture treatment. Seedlings under medium light showed optimal performance, with maximum pigment accumulation and high PSII efficiency without signs of chronic photoinhibition. N. pumilio seedlings decouple short-term functional photochemical efficiency from their structural investment strategy (pigment content), a mechanism that ensures survival under variable canopy cover but results in reduced growth under sub-optimal conditions.
Digitalization is increasingly recognized as a major driver of innovation in the forestry sector. However, the adoption of digital technologies remains uneven, particularly in structurally fragmented forestry systems such as those found in Southern Europe. This study aims to analyze and discuss stakeholders’ expectations, perceived barriers, and opportunities related to the adoption of digital technologies in Italian operational forestry, based on a participatory process that included a webinar, an online questionnaire, and a focus group, engaging a broad range of forestry stakeholders. Because the survey relied on a voluntary, non-probability sample, the findings should be interpreted as reflecting the perceptions and priorities of the surveyed stakeholders rather than as statistically representative estimates of the entire Italian forestry sector. The results reveal a growing interest in digital technologies across all operational domains, particularly in GIS-based decision support systems, remote sensing, product traceability, and precision harvesting. Nevertheless, stakeholders identified several persistent barriers, including high investment costs, limited interoperability among systems, insufficient digital skills, and weak connectivity in rural and mountainous areas. Training emerged as the most critical enabling factor, often considered even more important than direct investment in technologies. Overall, the evidence suggests that digitalization cannot advance without systematic investment in human capital, interoperability, and shared information infrastructures. Tools such as knowledge hubs, communities of practice, and targeted training programs may play a key role in bridging the gap between technological availability and operational implementation. Although rooted in the Italian context, the insights provided by this study offer relevant indications for digitalization policies and practices across Southern European countries and in forestry systems characterized by fragmented ownership structures and heterogeneous operational conditions.
