Search Results (22)

Forest fires, intensified by climate change, threaten tropical ecosystems by accelerating biodiversity loss, releasing carbon emissions, and altering hydrological cycles. Continuous detection of fire-induced forest loss is therefore critical. However, commonly used optical-based methods often face limitations, particularly due to cloud cover and coarse spatial resolution. This study explores the use of C-band Sentinel-1 Synthetic Aperture Radar (SAR) time series, combined with Bayesian Online Changepoint Detection (BOCD), for detecting and continuously monitoring fire-induced vegetation loss in forested areas. Three BOCD variants are evaluated: two single-polarization approaches individually using VV and VH reflectivities, and a dual-polarization approach ($pol$-BOCD) integrating both channels. The analysis focuses on a fire-affected area in Baixo Uraim (Paragominas, Brazil), supported by field-validated reference data. BOCD performance is compared against widely used optical products, including MODIS and VIIRS active fire and burned area data, as well as Sentinel-2-based difference Normalized Burn Ratio (dNBR) assessments. Results indicate that $pol$-BOCD achieves spatial accuracy comparable to dNBR (88.2% agreement), while enabling detections within a delay of three Sentinel-1 acquisitions. These findings highlight the potential of SAR-based BOCD for rapid, cloud-independent monitoring. While SAR enables continuous detection regardless of atmospheric conditions, optical imagery remains essential for characterizing the type and severity of change. Full article

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article

Climate change and landscape fragmentation have made fires the primary drivers of forest degradation in Southern Amazonia. Understanding their impacts is crucial for informing public conservation policies. In this study, we assessed the effects of repeated fires on trees with a diameter ≥10 cm across three distinct vegetation types in this threatened region: Amazonian successional forest (SF), transitional forest (TF), and ombrophilous forest (OF). Two anthropogenic fires affected all three vegetation types in consecutive years. We hypothesized that SF would be the least impacted due to its more open structure and the presence of fire-adapted savanna (Cerrado) species. As expected, SF experienced the lowest tree mortality rate (9.1%). However, both TF and OF were heavily affected, with mortality rates of 28.0% and 29.7%, respectively. Despite SF’s apparent fire resilience, all vegetation types experienced a significant net loss of species and individuals. These results indicate a fire-induced degradation stage in both TF and OF, characterized by reduced species diversity and structural integrity. Our findings suggest that recurrent fires may trigger irreversible vegetation shifts and broader ecosystem tipping points across the Amazonian frontier. Full article

Forest fires alter multiple soil properties, from those related to the carbon cycle to mineralogy; however, the responses of various soils to thermal impact remain unclear. This study examined the impact of fire-induced heating (300, 600, and 900 °C) on the properties of two contrasted soils (Andisol and Inceptisol) with regard to soil organic carbon (SOC), total organic carbon (TOC), dissolved organic carbon (DOC), recalcitrant organic carbon (ROC), soil pH, electrical conductivity (EC), soil water repellency (SWR), soil aggregate stability (SAS), and mineralogy using X-ray diffraction (XRD). SOC and TOC decreased as temperatures increased, with a more pronounced decrease in Andisol (90% loss) than in Inceptisol (80% loss). DOC and SWR peaked at 300 °C but disappeared above 600 °C. Further, ROC increased at 300 °C in both soils, but behaved differently at higher temperatures, remaining stable in Inceptisol and being eliminated in Andisol. Soil pH increased at 600 and 900 °C; meanwhile, EC increased progressively in Andisol but peaked at 300 °C in Inceptisol. SAS remained high in both soils (between 85 and 95%) despite heating. The mineralogical analysis demonstrated how heating induced transformations in iron minerals into more oxidized forms (as hematite and maghemite) in the Andisol, while clay minerals and gibbsite decreased feldspar and quartz accumulation promotion in the Inceptisol. In summary, the initial properties of each soil influenced their respective responses to fire. Full article

This study explores the spatiotemporal patterns and drivers of deforestation and forest degradation along the politically sensitive Iraq–Turkey border within the Duhok Governorate between 2015 and 2024. Utilizing paired remote sensing (RS) and high-end machine learning (ML) methods, forest dynamics were simulated from Sentinel-2 imagery, climate datasets, and topographic variables. Seven ML models were evaluated, and XGBoost consistently outperformed the others, yielding predictive accuracies (R²) of 0.903 (2015), 0.910 (2019), and 0.950 (2024), and a low RMSE (≤0.035). Model interpretability was further improved through the application of SHapley Additive exPlanations (SHAP) to estimate variable contributions and a Generalized Additive Model (GAM) to elucidate complex nonlinear interactions. The results showed distinct temporal shifts; climatic factors (rainfall and temperature) primarily influenced vegetation cover in 2015, whereas anthropogenic drivers such as forest fires (NBR), road construction (RI), and soil exposure (BSI) intensified by 2024, accounting for up to 12% of the observed forest loss. Forest canopy cover decreased significantly, from approximately 630 km² in 2015 to 577 km² in 2024, mainly due to illegal deforestation, road network expansion, and conflict-induced fires. This study highlights the effectiveness of an ML-driven RS analysis for geoinformation needs in geopolitically complex and data-scarce regions. These findings underscore the urgent need for robust, evidence-based conservation policies and demonstrate the utility of interpretable ML techniques for forest management policy optimization, providing a reproducible methodological blueprint for future ecological assessment. Full article

Larch forests in the permafrost zone of Eastern Eurasia are exposed to frequent wildfires, which are expected to increase with climate warming. However, little is known about how fire-derived charcoal is linked to the decomposition process in these forests. Fire-derived charcoal can affect the faunal communities in the forest litter. In a two-year field litterbag experiment, we investigated the effect of fire-derived charcoal on the colonisation by microarthropods (Collembola and Acari) of three decomposing litter species dominant in boreal larch forests. Charcoal addition led to an average 15% decrease in body size of collembola but significantly increased their abundance by 5 times throughout the experiment and acari by 1.5 times in the second year of decomposition, and this effect was consistent across all litter species. The increased microarthropod community may have hampered microbial activity and mass loss rate in the presence of charcoal. Charcoal altered the microarthropod community composition, increasing the proportion of collembola up to 20% compared to acari. The difference in abiotic conditions (increased litter water content during dry periods) induced by fire-derived charcoal was a more substantial factor determining the microarthropod community than litter species in the boreal larch forest. Our results indicate that fire-derived charcoal influences the biological drivers of decomposition in boreal larch forests, stimulating the growth of microarthropod community in decomposing litter. Full article

In terms of researching fire-related structure loss, various factors can affect structure survival during a wildfire. This paper aims to assess which factors were determinants in house resistance in the specific context of a case study of an extreme wildfire in the Central Region of Portugal and therefore which factors should be taken into account in the definition of a municipal mitigation strategy to defend buildings against wildfires. In this context, it is possible to conclude that various factors presented a predominant influence, some in building destruction and others in building survival. The existence of overhanging vegetation and lack of defensible space constitute major factors for structure destruction. the inherent wildfire severity, the location in the forest area, and the structure’s isolation from major roads were equally important factors that induced house destruction. Building survival was determined by its increasing distance from the forest and by its location in a dense urban agglomeration. Thus, a strategy to enhance resilience should include the prohibition of roof overhanging vegetation and the restriction of building permits in forest areas, in isolated locations, and/or very far from major roads. These orientations can be extrapolated to municipalities with similar susceptibility and vulnerability to wildfires. Full article

Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning (ML) and deep learning (DL) have helped researchers to predict forest fires. However, ML and DL strategies pose some challenges such as large multidimensional data, communication lags, transmission latency, lack of processing power, and privacy concerns. Federated Learning (FL) is a recent development in ML that enables the collection and process of multidimensional, large volumes of data efficiently, which has the potential to solve the aforementioned challenges. FL can also help in identifying the trends based on the geographical locations that can help the authorities to respond faster to forest fires. However, FL algorithms send and receive large amounts of weights of the client-side trained models, and also it induces significant communication overhead. To overcome this issue, in this paper, we propose a unified framework based on FL with a particle swarm-optimization algorithm (PSO) that enables the authorities to respond faster to forest fires. The proposed PSO-enabled FL framework is evaluated by using multidimensional forest fire image data from Kaggle. In comparison to the state-of-the-art federated average model, the proposed model performed better in situations of data imbalance, incurred lower communication costs, and thus proved to be more network efficient. The results of the proposed framework have been validated and 94.47% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of early warning systems for forest fires. Full article

The dry lowland and mangrove forests of Kutai National Park (KNP) in Indonesia provide invaluable ecosystem services to local human populations (>200,000 in number), serve as immense carbon sinks to recapture anthropogenic emissions, and safeguard habitats for thousands of wildlife species including the critically endangered Northeast Bornean orangutan (Pongo pygmaeus morio). With recent reports of ongoing illegal logging and large-scale wildfires within this National Park, we sought to leverage the extensive catalogue and processing power of Google Earth Engine to track the rates and influences of forest loss within KNP over various time periods since 1997. We present estimates of forest loss from the Hansen Global Forest Change v1.9 dataset (2000–2021) which detected a loss of 15% (272 km²) of forest cover within KNP since 2000, half of which (137 km²) coincided with the El Niño-induced wildfires of 2015–2016. Using the MCD64A1 C6.1 MODIS dataset, we found significant spatial overlap between burned area and forest loss detections during the 2015–2016 period but identified considerable omissions in the burned area dataset over smallholder farms within KNP. We discuss the implications of deforestation in areas of primary orangutan habitat and how patterns of forest loss have influenced drought and fire dynamics within KNP. Finally, we compare time-series estimates of precipitation, the ENSO index, burned area, and forest loss to demonstrate that fire risk within KNP depends largely—but not exclusively—on drought severity, and that rates of non-fire (gradual) and fire-related (extreme) forest loss threaten the remaining forests of this National Park. Full article

Wildfire risks are increasing due to the atmospheric and vegetation aridity under global warming. Plant hydraulic stress (PHS) functions regulate water transport along the soil–plant–atmosphere continuum under water stress conditions, which probably results in shifts in ecosystem wildfire regimes. Currently, how the PHS functions affect wildfire occurrence and subsequently the ecosystem carbon cycle via carbon loss at a global scale remains unclear. Here, we conducted global simulations during 1850–2010 using Community Land Model version 5 with and without the PHS configuration and quantified the PHS-induced changes. From the global perspective, the PHS functions increased plant transpiration, induced hydraulic redistribution (HR) of soil water by root, and decreased soil moisture; then, the functions increased fire occurrence (count), fire induced carbon loss, and ecosystem net primary productivity by 72%, 49%, and 15%, respectively. Spatially, the PHS functions greatly promoted fire occurrence and the consequent carbon loss in circumboreal forests and tropical savannas; whereas, the fire occurrence was limitedly affected or even decreased in equatorial rainforests. The strong downward HR process in the humid rainforests transported rainwater into deep soil layers, and strict stomatal regulation of the tropical trees restricted transpiration increment under atmospheric aridity, both of which helped to buffer the rainforests against drought and thus decreased fire risk. In contrast, dry savannas showed substantial upward HR, which increased water loss via soil evaporation and transpiration of the grasses with shallow roots. The tree–grass competition for limited soil moisture in the savannas benefited soil evaporation, which could aggravate plant hydraulic failure and increase wildfire risk. Full article

The occurrence of short-interval, severe wildfires are increasing drastically at a global scale, and appear as a novel phenomenon in areas where fire historically returns in large time lapses. In forest ecosystems, these events induce drastic changes in population dynamics, which could dramatically impact species diversity. Here, we studied the effect on diversity of recent short-interval, severe wildfires (SISF), which occurred in rapid succession in the summers of 2002 and 2015 in Chilean Northern Patagonian Araucaria–Nothofagus forests. We analyzed the diversity of deadwood-dependent (i.e., saproxylic) and fire-sensitive beetles as biological indicators across four conditions: 2002-burned areas, 2015-burned areas, SISF areas (i.e., burned in 2002 and again in 2015), and unburned areas. Saproxylic beetles were collected using window traps in 2017 to 2019 summer seasons. To investigate the mechanisms underpinning the fire-related disturbance of the assemblage, we evaluated the effects of post-fire habitat quality (e.g., dead wood decomposition) and quantity (e.g., burned dead wood volume and tree density) on the abundances and species richness of the entire assemblage and also multiple trophic groups. Compared with the unburned condition, SISF drastically reduced species richness, evenness, and Shannon’s diversity and altered the composition of the saproxylic beetle assemblages. The between-condition variation in composition was accounted for by a species replacement (turnover) between SISF and 2015-burned areas, but both species replacement and extinction (nestedness) between SISF and unburned areas. Dead wood decomposition and tree density were the variables with the strongest effects on the abundance and species richness of the entire saproxylic beetle assemblage and most trophic groups. These results suggest that SISF, through degraded habitat quality (dead wood decomposition) and quantity (arboreal density), have detrimental impacts on diversity and population dynamics of saproxylic beetle assemblages. Therefore, habitat loss is a central mechanism underpinning fire-related biodiversity loss in these forest ecosystems. Full article

To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model. The YOLOv3 showed a detection speed up to 27 FPS, indicating it is a real-time smoke detector. By comparing these algorithms with the current existing forest fire smoke detection algorithms, it can be found that the deep convolutional neural network algorithms result in better smoke detection accuracy. In particular, the EfficientDet algorithm achieves an average detection accuracy of 95.7%, which is the best real-time forest fire smoke detection among the evaluated algorithms. Full article

Wildfires are one of the most important natural disturbances in vegetation biomes. In recent decades, both the number and severity of fires have significantly increased in Mediterranean forests, frequently resulting in catastrophic events. In this scenario, we aimed to explore the flow of ecosystem services and their related economic value that was disrupted by human-induced megafires in the Mediterranean forest of Vesuvius National Park in the summer of 2017. We adopted an innovative approach by merging two methodologies: an ecological approach to evaluate the status of the forest ecosystem after the wildfires and an economics methodology to estimate the monetary value of the interruption to ecosystem services. Losses related to the following six services were estimated: woody biomass, soil erosion control, habitat maintenance, pollination, carbon stock, and ecotourism. In 2017, 3350 ha of forest (88% of the total forested area of Vesuvius National Park) burnt over a period of 49 days. The total estimated monetary loss amounted to €14.363 M, 56.9% of which comprised of provisioning ecosystem services, while 34.7% encompassed maintenance and regulation services, and 8.5% were so-called cultural services. Suppression costs accounted for 16% of the total estimated economic loss of ecosystem services. Our results provide useful insights for decision-makers when allocating financial resources, suggesting that they should invest in fire prevention rather than fire suppression and post-fire restoration. This explicit valuation of the footprint of the wildfires, although not exhaustive, can also lead to greater awareness among the public regarding the benefits conferred by Mediterranean forest ecosystems. This is the first study to economically evaluate the interruption of ecosystem services after megafires in the Mediterranean basin. Full article

Forest fire is a common concern in Mediterranean watersheds. Fire-induced canopy mortality may cause the degradation of chemical–physical properties in the soil and influence hydrological processes within and across watersheds. However, the prediction of the pedological and hydrological effect of forest fires with heterogenous severities across entire watersheds remains a difficult task. A large forest fire occurred in 2017 in northern Italy providing the opportunity to test an integrated approach that exploits remote and in-situ data for assessing the impact of forest fires on the hydrological response of semi-natural watersheds. The approach is based on a combination of remotely-sensed information on burned areas and in-situ measurements of soil infiltration in burned areas. Such collected data were used to adapt a rainfall–runoff model over an experimental watershed to produce a comparative evaluation of flood peak and volume of runoff in pre- and post-fire conditions. The model is based on a semi-distributed approach that exploits the Soil Conservation Service Curve Number (SCS-CN) and lag-time methods for the estimation of hydrological losses and runoff propagation, respectively, across the watershed. The effects of fire on hydrological losses were modeled by adjusting the CN values for different fire severities. Direct infiltration measurements were carried out to better understand the effect of fire on soil infiltration capacity. We simulated the hydrological response of the burned watershed following one of the most severe storm events that had hit the area in the last few years. Fire had serious repercussions in regard to the hydrological response, increasing the flood peak and the runoff volume up to 125% and 75%, respectively. Soil infiltration capacity was seriously compromised by fire as well, reducing unsaturated hydraulic conductivity up to 75% compared with pre-fire conditions. These findings can provide insights into the impact of forest fires on the hydrological response of a whole watershed and improve the assessment of surface runoff alterations suffered by a watershed in post-fire conditions. Full article

Russian boreal forests hold a considerable carbon (C) stock and are subjected to frequent surface fires that unbalance C storage and ecosystem function. Although postfire ecological changes aboveground are well understood, biological C flows (e.g., decomposition in the postfire period) remain unclear. We present the results of a long-term field litterbag experiment on needle litter decomposition in typical Larix gmelinii boreal forests in the Russian Far East. For 3 years, we measured mass loss, C and nitrogen (N) concentrations, lignin and manganese dynamics, respiration intensity and enzyme activity in decaying needles, and environmental conditions (temperature and litter moisture). The decomposition rate at 850 days was 0.435 and 0.213 yr⁻¹ in a control forest and in a forest 15 years after a surface fire, respectively. Early stages of needle decay did not differ among sites, whereas decomposition slowed in later stages in burned forest relative to the control (p < 0.01). This was supported by hampered respiration, slow lignin accumulation in decaying needles, and low peroxidase activity in burned forest. We found no direct N release, and decaying litter immobilization was more pronounced in the control forest. In the later stages, we revealed restrained mass loss and associated C release from larch litter in burned forest. Slow and delayed N release may alter organic matter accumulation, the N cycle, and regeneration of the fire-disturbed larch ecosystem. Our investigations highlight hampered C flow from aboveground litter to soil humus even decades after surface fire in a larch ecosystem. Given the climate-induced increase of fire activity, C retained in the litter layer represents a pool that is more vulnerable to the next fire event. Full article