Armed conflicts and other types of violence are key drivers of human-induced landscape change. Since March 2011, Syria has been embroiled in a prolonged and devastating armed conflict causing immense human suffering and extensive destruction. As a result, over five million people have been forced to seek refuge outside the country’s borders, while more than six million have been internally displaced. This study focuses on examining the impact of this conflict on forest cover by identifying the drivers of forest change. To assess this change, Landsat and PALSAR imagery were used to differentiate between forested and non-forested areas. Spectral information was synthetized using the Tasseled Cap transformation and the time series data was simplified and despiked using the LandTrendr algorithm. Our results show that between 2010 and 2019 there was a substantial decrease of 19.3% in forest cover, predominantly concentrated in the northwestern region of Syria. This decline was induced by the armed conflict, with several key drivers contributing to the decline, such as illegal logging activities conducted by both locals and refugees living in nearby forest areas. Drivers such as proximity to refugee camps, roads, and settlements played an important role in producing this change by facilitating access to forests. In addition, the occurrence of explosive events such as bombings and shelling near forests also contributed to this decline by causing forest fires. To mitigate further deforestation and reduce dependence on forests for fuel, it is crucial for local governments in the post-conflict period to offer sustainable alternatives for heating and cooking to both the local populations and refugees. Additionally, governments are recommended to enforce strict laws and regulations to protect forests and combat illegal logging activities. These measures are essential for preserving and restoring forests, promoting environmental sustainability, and ensuring the well-being of both displaced populations and local communities.

Forest fires can change forest structure and composition, and low-density Airborne Laser Scanning (ALS) can be a valuable tool for evaluating post-fire vegetation response. The aim of this study is to analyze the structural diversity differences in Mediterranean Pinus halepensis Mill. forests affected by wildfires on different dates from 1986 to 2009. Several types of ALS metrics, such as the Light Detection and Ranging (LiDAR) Height Diversity Index (LHDI), the LiDAR Height Evenness Index (LHEI), and vertical and horizontal continuity of vegetation, as well as topographic metrics, were obtained in raster format from low point density data. In order to map burned and unburned areas, differentiate fire occurrence dates, and distinguish between old and more recent fires, a sample of pixels was previously selected to assess the existence of differences in forest structure using the Kruskal–Wallis test. Then, k-nearest neighbors algorithm (k-NN), support vector machine (SVM) and random forest (RF) classifiers were compared to select the most accurate technique. The results showed that, in more recent fires, around 70% of the laser returns came from grass and shrub layers, yielding low LHDI and LHEI values (0.37–0.65 and 0.28–0.46, respectively). In contrast, the areas burned more than 20 years ago had higher LHDI and LHEI values due to the growth of the shrub and tree strata. The classification of burned and unburned areas yielded an overall accuracy of 89.64% using the RF method. SVM was the best classifier for identifying the structural differences between fires occurring on different dates, with an overall accuracy of 68.79%. Furthermore, SVM yielded an overall accuracy of 75.49% for the classification between old and more recent fires.

Despite the need for preserving the carbon pools in fire-prone southern European landscapes, emission reductions from wildfire risk mitigation are still poorly understood. In this study, we estimated expected carbon emissions and carbon credits from fuel management projects ongoing in Catalonia (Spain). The planning areas encompass about 1000 km² and represent diverse fire regimes and Mediterranean forest ecosystems. We first modeled the burn probability assuming extreme weather conditions and historical fire ignition patterns. Stand-level wildfire exposure was then coupled with fuel consumption estimates to assess expected carbon emissions. Finally, we estimated treatment cost-efficiency and carbon credits for each fuel management plan. Landscape-scale average emissions ranged between 0.003 and 0.070 T CO₂ year⁻¹ ha⁻¹. Fuel treatments in high emission hotspots attained reductions beyond 0.06 T CO₂ year⁻¹ per treated ha. Thus, implementing carbon credits could potentially finance up to 14% of the treatment implementation costs in high emission areas. We discuss how stand conditions, fire regimes, and treatment costs determine the treatment cost-efficiency and long-term carbon-sink capacity. Our work may serve as a preliminary step for developing a carbon-credit market and subsidizing wildfire risk management programs in low-revenue Mediterranean forest systems prone to extreme wildfires.

Mountain forests face important threats from global change and spatio-temporal variation in tree height can help to monitor these effects. In this study, we used the Global Ecosystem Dynamics Investigation space-borne laser sensor to examine the relationship between maximum tree height and elevation, and the role of climate, in the main European mountain ranges. We found a piecewise relationship between elevation and maximum tree height in all mountain ranges, supporting the existence of a common breakpoint that marks the beginning of tree development limitations. Temperature and precipitation were identified as the most important drivers of tree height variation. Additionally, we predicted significant upward displacement of the breakpoint for the period 2080-2100 under climate change scenarios, potentially increasing the area without growth limitations for trees. These findings contribute to understanding the impacts of global warming on mountain forest ecosystems and provide insights for their monitoring and management.