This study explored the effects of forest fires on soil microbial activity in forest soils classified by rock origin (igneous, metamorphic, and sedimentary) and stratified by subsoil depth (topsoil, subsoil). Microbial activity, indicated by average well color development (AWCD) and Shannon diversity indices, was higher in undamaged topsoils compared to fire-damaged ones. In contrast, fire-damaged subsoils, particularly in metamorphic and sedimentary soils, exhibited increased microbial activity over time due to organic matter decomposition. A significant increase in substrate utilization was observed in undamaged soils across all rock types (*p < 0.05, **p < 0.01) in topsoil, with sedimentary rock exhibiting the highest microbial diversity based on Shannon indices. The dehydrogenase activity followed a similar pattern, with reduced activity in fire-damaged topsoil but higher activity in damaged metamorphic and sedimentary subsoils. Principal component analysis (PCA) linked microbial indicators (AWCD, Shannon index) to mineral compositions like orthoclase and hornblende, highlighting the role of soil chemistry in shaping microbial responses to fire. These insights advance the understanding of fire-induced changes in soil microbial functions across diverse geological contexts.

Extreme weather events are increasing the frequency and intensity of forest fires, generating serious environmental and socio-economic impacts. These fires cause soil loss through erosion, organic matter depletion, increased surface runoff and the release of greenhouse gases, intensifying climate change. They also affect biodiversity, terrestrial and aquatic ecosystems, and soil quality. The assessment of forest fires by remote sensing, such as the use of the Normalised Difference Vegetation Index (NDVI), allows rapid analysis of damaged areas, monitoring of vegetation changes and the design of restoration strategies. On the other hand, models such as RUSLE are key tools for calculating soil erosion and planning conservation measures. A study of the impacts on soils and vegetation in the south of Salamanca, where one of the worst fires in the province took place in 2022, has been carried out using RUSLE and NDVI models, respectively. The study confirms that fires significantly affect soil properties, increase erosion and hinder vegetation recovery, highlighting the need for effective restoration strategies. It was observed that erosion intensifies after fires (the maximum rate of soil loss before is 1551.85 t/ha/year, while after it is 4899.42 t/ha/year) especially in areas with steeper slopes, which increases soil vulnerability, according to the RUSLE model. The NDVI showed a decrease in vegetation recovery in the most affected areas (with a maximum value of 0.3085 after the event and 0.4677 before), indicating a slow regeneration process. The generation of detailed cartographies is essential to identify critical areas and prioritise conservation actions. Furthermore, the study highlights the importance of implementing restoration measures, designing sustainable agricultural strategies and developing environmental policies focused on the mitigation of land degradation and the recovery of fire-affected ecosystems.

AimHigh temperatures during forest fires can cause significant damage to tropical dry forest areas and alter their ecological stability, particularly by affecting seed viability and seedling emergence. This study evaluates the seedling emergence response of 18 dry forest species to fire-simulated temperatures, aiming to assess their potential for restoration in fire-prone Colombian ecosystems.LocationThe seeds used in this study were obtained from three tropical dry forests in Colombia.MethodsA total of 9832 seeds from 18 dry forest species were collected directly from the soil seed bank in three tropical dry forests in Colombia. These seeds were then exposed to simulated forest fire temperatures (100 degrees C, 150 degrees C, and 200 degrees C) for 10 min. Seed viability was analyzed using the 2,3,5-triphenyl tetrazolium chloride reagent (tetrazolium test) and assessed using a generalized linear model. Seedling emergence and mean emergence time were evaluated using one-way analysis of variance (ANOVA) with temperature treatments as factors.ResultsThe study revealed that seedling emergence significantly decreased with higher heat shock temperatures. Notably, Hura crepitans and Parkinsonia aculeata tolerated temperatures up to 100 degrees C, while Caesalpinia pulcherrima and Enterolobium cyclocarpum showed increased emergence at that temperature. Based on their emergence responses, species were classified as stimulated, tolerant, sensitive, or vulnerable. Seed viability declined with rising temperatures, and the mean emergence time increased in species like Cordia alba, Crescentia cujete, and Lonchocarpus violaceus.ConclusionsThis study shows that heat shocks at 150 degrees C and 200 degrees C significantly reduced seed bank viability for most Colombian dry forest species. However, Caesalpinia pulcherrima and Enterolobium cyclocarpum were stimulated by 100 degrees C heat shocks, while Hura crepitans and Parkinsonia aculeata showed no adverse effects. Vulnerable species like Coccoloba acuminata and Pithecellobium dulce exhibited no viable seeds at higher temperatures, suggesting potential local extinctions. These results emphasize the need to focus on heat-tolerant species for restoration efforts in fire-prone ecosystems.

Context Forest fires are key ecological factors affecting pine forests globally. Understanding impacts of varying fire intensities on forest ecosystem components is crucial for predicting recovery and informing management. Objectives This study aimed to assess effects of different surface fire intensities on structural components of pine forests, including tree canopy, herbaceous layer, and surface soil horizons, and identify relationships between fire intensity and ecosystem parameters. Methods The study examined three areas with different fire intensities (severe, moderate, mild) 1 year after a surface fire in Ukraine's Volyn-Polissia region, using vegetation surveys, soil analyses, and statistical methods. Results Fire intensity significantly influenced tree mortality and the vitality structure of Pinus sylvestris stands. Scorch height correlated strongly with stem diameter in mild and moderate intensity zones (P < 0.0001). Herbaceous layer composition showed significant variations in all life-form traits across different fire intensities. Species diversity, dominance, and evenness indices varied with fire intensity, as did species distribution by ecological strategies. Soil physicochemical properties, including surface layer density, ash content, moisture capacity, and pH, also changed. Correlations were found between the condition index of P. sylvestris and soil pH, as well as between herbaceous cover dominance/evenness indices and P2O5 content in surface soil layers. Weaker associations were identified between herbaceous cover diversity and soil density/hygroscopic moisture. The study was conducted over a 1-year period following the fire event, focusing on the short-term responses of vegetation and soil properties. Conclusions Surface fires of varying intensities alter multiple forest ecosystem components. Severely damaged areas may require restoration efforts, including active interventions such as artificial reforestation or other measures to accelerate recovery processes. Moderately and mildly affected zones, on the other hand, show potential for natural self-regulation. These findings have important implications for post-fire forest management strategies.

This study analyzes the forest flammability hazard in the south of Tyumen Oblast (Western Siberia, Russia) and identifies variation patterns in fire areas depending on weather and climate characteristics in 2008-2023. Using correlation analysis, we proved that the area of forest fires is primarily affected by maximum temperature, relative air humidity, and the amount of precipitation, as well as by global climate change associated with an increase in carbon dioxide in the atmosphere and the maximum height of snow cover. As a rule, a year before the period of severe forest fires in the south of Tyumen Oblast, the height of snow cover is insignificant, which leads to insufficient soil moisture in the following spring, less or no time for the vegetation to enter the vegetative phase, and the forest leaf floor remaining dry and easily flammable, which contributes to an increase in the fire area. According to the estimates of the CMIP6 project climate models under the SSP2-4.5 scenario, by the end of the 21st century, a gradual increase in the number of summer temperatures above 35 degrees C is expected, whereas the extreme SSP5-8.5 scenario forecasts the tripling in the number of such hot days. The forecast shows an increase of fire hazardous conditions in the south of Tyumen Oblast by the late 21st century, which should be taken into account in the territory's economic development.

From the beginning of May 2023 to the end of August 2023, the Northern Hemisphere experienced significant wildfire activity with the most widespread fires occurring in Canada. Forest fires in Canada destroyed more than 15.6 million hectares of forests. These wildfires worsened air quality across the region and other parts of the world. The smoke reached southern Europe by the end of June 2023. To better understand the consequences of such forest fires far from the site of origin, aerosol optical, microphysical and radiative properties were analyzed during this event for southern Europe using data from the Visible Infrared Imaging Radiometer Suite (VIIRS), TROPOspheric Monitoring Instrument (TROPOMI), and Aerosol Robotic Network (AERONET). TROPOMI aerosol index (AI) and the carbon monoxide (CO) product confirm that the smoke originated directly from these forest fires. AERONET data from the El Arenosillo site in southern Spain showed maximum aerosol optical depth (AOD) values on June 27 reached 2.36. Data on Angstrom Exponent (AE), aerosol volume size distribution (VSD), single scattering albedo (SSA), fine mode fraction (FMF), volume particle concentration, effective radius (R Eff ), absorption AOD (AAOD), extinction AE (EAE) and absorption AE (AAE) showed that fine-mode particles with carbonaceous aerosols contribution predominated in the atmosphere above the El Arenosillo site. Direct aerosol radiative forcing (DARF) at the top (DARF TOA ) and bottom of atmosphere (DARF BOA ) were-103.1 and-198.93 Wm-2 , respectively. The atmospheric aerosol radiative forcing (DARF ATM ) was found to be 95.83 Wm-2 and with a heating rate 2.69 K day-1 , which indicates the resulting warming of the atmosphere.

Forest fires cause serious damage to mountain landforms and trigger frequent post-fire debris flows. Although post-fire debris flow exhibits time evolution, the key factors controlling its evolution remain unclear. A detailed field investigation, rainfall data collection and remote sensing analysis were conducted to study the debris flow events following the 3.08 forest fire in Xiangjiao gully. The destructive effect of forest fires, the control factors and inherent evolution mechanism of post-fire debris flow were explored. The results highlight that the great disturbance of forest fires to the hydrological response and material source supply conditions promote the outbreak of debris flows. In the rapid response stage of fire, the internal driving force of debris flow evolution is the self-healing of hydrological response characteristics of the basin, including material depletion, particle coarsening and vegetation restoration. In the long-term impact stage, the evolution of debris flows is mainly controlled by factors such as a decrease in root-soil strength caused by root rot, multi-stage gully bank landslide activity, and blockage of woody debris. A conceptual model for the evolution of post-fire debris flows is proposed based on the above evolution characteristic analysis. In particularly, this study emphasizes the catastrophic effect of woody debris during the evolution of post-fire debris flows. The research results provide scientific basis for long-term debris flow risk assessment and mitigation design in recently burnt areas.

Current permafrost models in Canadian boreal forests are generally of low spatial resolution as they cover regional or continental scales. This study aims to understand the viability of creating a temperature at the top of permafrost (TTOP) model on a local scale in the boreal wetland environment of What & igrave;, Northwest Territories from short-term field-collected temperature data. The model utilizes independent variables of vegetation, topographic position index, and elevation, with the dependent variables being ground surface temperature collected from 60 ground temperature nodes and 1.5 m air temperature collected from 10 temperature stations. In doing this, the study investigates the relationship vegetation and disturbance have on ground temperature and permafrost distribution. The model predicts that 31% of the ground is underlain by permafrost, based on a mean annual temperature at TTOP of <0 degrees C. This model shows an accuracy of 62.5% when compared to cryotic assessment sites (CAS). Most inaccuracies, showing the limitations of the TTOP model, came from peat plateaus that had been burned in the most recent forest fire in 2014. These resulted in out-of-equilibrium permafrost and climatic conditions that TTOP cannot handle well. Commonly, permafrost mapping places What & igrave; in the extensive discontinuous zone, estimating that between 50% and 90% of the ground is underlain by permafrost. The study shows that a climatically driven TTOP model calibrated with CAS can be used to illustrate ground temperature heterogeneity from short-term data in boreal forest wetland environments. However, this approach likely underestimates permafrost extent and is perhaps not the best-suited modelling choice for nearsurface permafrost, which is currently out of equilibrium with the current climate.

We tested the hypothesis that the number of seedlings from the soil seed bank (SSB) in forests polluted by heavy metals and disturbed by recent fires decreases. It was also assumed that the consequences of pollution and fires for the soil seed bank are additive. We estimated the number of seedlings from the SSB of pine forests located near the Karabash copper smelter (KCS) (contaminated by Cu, Zn, Pb, and Cd) and from uncontaminated forests of the Ilmen State Reserve (ISR). In both areas, samples of the forest litter and humus horizon were taken from forests recently exposed to ground fires and long-term unburned forests. Samples were exhibited from June to September, conducting seven rounds of counting seedlings. Small peculiarities of the emergence of seedlings on the samples of the forest litter and the humus horizon were established. However, the regularities of the reaction of SSB to pollution and fire disturbances did not depend on the soil horizon. The number of seedlings on substrates from contaminated forests was 5-8 times lower than the number of seedlings on substrates from background forests. A decrease in the number of seedlings on polluted substrates was accompanied by an increase in the share of dicots in the total number of seedlings. The relationship between the number of seedlings and the age of fires was not found. The additivity of the consequences of pollution and fires has also not been established. Of the two types of damage, pollution and fires, the pollution factor is of leading importance for SSBs. The results indicate a low recovery capacity of the herb-shrub layer of polluted forests.

Forest fires pose a catastrophic threat to Earth's ecology as well as threaten human beings. Timely and accurate monitoring of forest fires can significantly reduce potential casualties and property damage. Thus, to address the aforementioned problems, this paper proposed an unmanned aerial vehicle (UAV) based on a lightweight forest fire recognition model, Fire-Net, which has a multi-stage structure and incorporates cross-channel attention following the fifth stage. This is to enable the model's ability to perceive features at various scales, particularly small-scale fire sources in wild forest scenes. Through training and testing on a real-world dataset, various lightweight convolutional neural networks were evaluated on embedded devices. The experimental outcomes indicate that Fire-Net attained an accuracy of 98.18%, a precision of 99.14%, and a recall of 98.01%, surpassing the current leading methods. Furthermore, the model showcases an average inference time of 10 milliseconds per image and operates at 86 frames per second (FPS) on embedded devices.