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Affected by global warming and increased extreme weather, Hunan Province saw a phased and concentrated outbreak of forest fires in 2022, causing significant damage and impact. Predicting the occurrence of forest fires can enhance the ability to make early predictions and strengthen early warning and responses. Currently, fire prevention and extinguishing in China’s forests and grasslands face severe challenges due to the overlapping of natural and social factors. Existing forest fire occurrence prediction models mostly take into account vegetation, topographic, meteorological and human activity factors; however, the occurrence of forest fires is closely related to the forest fuel moisture content. In this study, the traditional driving factors of forest fire such as satellite hotspots, vegetation, meteorology, topography and human activities from 2004 to 2021 were introduced along with forest fuel factors (vegetation canopy water content and evapotranspiration from the top of the vegetation canopy), and a database of factors for predicting forest fire occurrence was constructed. And a forest fire occurrence prediction model was built using machine learning methods such as the Random Forest model (RF), the Gradient Boosting Decision Tree model (GBDT) and the Adaptive Augmentation Model (AdaBoost). The accuracy of the models was verified using Area Under Curve (AUC) and four other metrics. The RF model with an AUC value of 0.981 was more accurate than all other models in predicting the probability of forest fire occurrence, followed by the GBDT (AUC = 0.978) and AdaBoost (AUC = 0.891) models. The RF model, which has the best accuracy, was selected to predict the monthly forest fire probability in Changsha in 2022 and combined with the Inverse Distance Weight Interpolation method to plot the monthly forest fire probability in Changsha. We found that the monthly spatial and temporal distribution of forest fire probability in Changsha varied significantly, with March, April, May, September, October, November and December being the months with higher forest fire probability. The highest probability of forest fires occurred in the central and northern regions. In this study, the core drivers affecting the occurrence of forest fires in Changsha City were found to be vegetation canopy evapotranspiration and vegetation canopy water content. The RF model was identified as a more suitable forest fire occurrence probability prediction model for Changsha City. Meanwhile, this study found that vegetation characteristics and combustible factors have more influence on forest fire occurrence in Changsha City than meteorological factors, and surface temperature has less influence on forest fire occurrence in Changsha City.

In any region affected, fires exhibit a strong seasonal cycle driven by the dynamic of fuel moisture and ignition sources throughout the year. In this paper we investigate the global patterns of fire seasonality, which we relate to climatic, anthropogenic, land-cover and land-use variables. Global, with detailed analyses from single 1°x 1° grid cells. We use a fire risk index, the Chandler burning index (CBI), as an indicator of the 'natural', eco-climatic fire seasonality, across all types of ecosystems. A simple metric, the middle of the fire season, is computed from both gridded CBI data and satellite-derived fire detections. We then interpret the difference between the eco-climatic and observed metrics as an indicator of the human footprint on fire seasonality. Deforestation, shifting cultivation, cropland production or tropical savanna fires are associated with specific timings due to land-use practices, sometimes largely decoupled from the CBI dynamics. Detailed time series from relevant locations provide comprehensive information about these practices and how they are adapted to eco-climatic conditions. We find a great influence of anthropogenic activities on global patterns of fire seasonality. The specificity of the main fire practices and their easy identification from global observation is a potential tool to support land-use monitoring efforts. Our results should also prove valuable in the development of a methodological approach for improving the representation of anthropogenic fire practices in dynamic global vegetation models.

Background: Wildfires are a growing threat to many ecosystems, bringing devastation to human safety and health, infrastructure, the environment and wildlife.Aims: A thorough understanding of the characteristics determining the susceptibility of an area to wildfires is crucial to prevention and management activities. The work focused on a case study of 13 countries in the eastern Mediterranean and southern Black Sea basins.Methods: A data-driven approach was implemented where a decade of past wildfires was linked to geoclimatic and anthropic descriptors via a machine learning classification technique (Random Forest). Empirical classification of fuel allowed linking of fire intensity and hazard to environmental drivers.Key results: Wildfire susceptibility, intensity and hazard were obtained for the study area. For the first time, the methodology is applied at a supranational scale characterised by a diverse climate and vegetation landscape, relying on open data.Conclusions: This approach successfully allowed identification of the main wildfire drivers and led to identification of areas that are more susceptible to impactful wildfire events.Implications: This work demonstrated the feasibility of the proposed framework and settled the basis for its scalability at a supranational level.

Forest fire occurrence prediction remains challenging despite advances in operational fire danger rating systems. In South Korea, the Korea Forest Fire Danger Rating Index (KFDRI) incorporates meteorological conditions, terrain (elevation, aspect), and forest type to assess regional fire danger. While KFDRI successfully assesses environmental fire danger at the pixel level, it does not explicitly account for human activity patterns that create substantial occurrence variability among locations with similar environmental conditions. This limitation is critical in human-dominated landscapes where where the main source of fire occurrence is anthropogenic. This study developed a Random Forest (RF) model to predict forest fire occurrence probability and propose management priorities during the forest fire prevention season (November–May) along the eastern coast of Korea, explicitly integrating human proximity variables (distance to agricultural areas and roads) with topographical (elevation, slope, aspect), surface fuel load, and meteorological variables (SMAP soil moisture, cumulative precipitation). Using forest fire occurrence records (1112 fire occurrence records) and background samples from 2015 to 2024, the model was trained with monthly stratified sampling and 10-fold cross-validation. The model achieved stable classification performance, with an overall F1-score of 0.515 and accuracy of 0.733. According to the SHAP (SHapley Additive exPlanations) analysis, distance to agricultural areas, elevation, slope, aspect, 5-day cumulative precipitation, and forest type were the most influential predictors. In particular, occurrence probability tended to increase in areas close to agricultural land (<180 m), at low elevations (≤200 m), on moderately steep slopes (≥8°), on south- and west-facing aspects, and under dried conditions. These results emphasize that fire occurrence risk is primarily structured by human proximity within areas of similar environmental danger. We propose an operational integration in which the RF model provides a 30 m “where-to-focus” occurrence layer that is used alongside KFDRI’s daily danger rating to prioritize prevention and patrol efforts.

Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy.

Fire recurrence plays a key role in shaping landscapes in Mediterranean ecosystems. Short-term recurrent fires, in particular, are increasingly affecting highly urbanised landscapes. Studies worldwide have addressed fire recurrence by analysing environmental, climatic and human-driven factors. Current models use fire recurrence polygons as the dependent variable; yet no published study has focused its analysis on fire recurrence considering recurrent ignition points as the response variable. To fill this gap, remote sensing and available local data were combined to analyse the influence of human and biophysical variables in predicting both the likelihood and frequency of recurrent fire ignition points over a 9-year period (2004–12) in southern Italy. For this purpose, we used the Negative Binomial Hurdle model owing to the stochastic nature of the phenomenon of fire recurrence and the (large) number of non-occurrences. Results on the likelihood and frequency of recurrent fire ignition points (dependent variables) suggested that road distance was the strongest predictor, followed by the presence of shrublands and grasslands. The response variable showed a negative relationship with population density and road distance and a positive relationship with land-cover variables. Vegetation indices were also good predictors of fire recurrence. More broadly, this study is intended to be a further experimental step in fire-management analysis characterised by the continuous interaction between human and natural systems. Constant changes between these systems due to causes such as urban sprawl and climate change can create the conditions for short-term-interval recurrent fires.

Background Extreme fire seasons in the Mediterranean basin have received international attention due to the damage caused to people, livelihoods, and vulnerable ecosystems. There is a body of literature linking increasingly intense, large fires to a build-up of fuel from rural land abandonment exacerbated by climate change. However, a better understanding of the complex factors driving fires in fire-prone landscapes is needed. We use a global database based on the MODIS Fire CCI51 product, and the Greater Côa Valley, a 340,000-ha area in Portugal, as a case study, to investigate the environmental drivers of fire and potential tools for managing fires in a landscape that has undergone changing agricultural and grazing management. Results Between 2001 and 2020, fires burned 32% (1881.45 km 2 ) of the study area. Scrublands proportionally burnt the most, but agricultural land and forests were also greatly impacted. The risk of large fires (> 1 km 2 ) was highest in these land cover types under dry conditions in late summer. Areas with higher sheep densities were more likely to burn, while cattle density had no apparent relationship with fire occurrence. There was also a 15% lower probability of a fire occurring in protected areas. Conclusion Future climatic changes that increase drought conditions will likely elevate the risk of large fires in the Mediterranean basin, and abandoned farmland undergoing natural succession towards scrubland will be at particularly high risk. Our results indicate that livestock grazing does not provide a simple solution to reducing fire risk, but that a more holistic management approach addressing social causes and nature-based solutions could be effective in reducing fire occurrence.

Fire regimes are often considered to be either driven by climate, fuel load or human activities. A significant proportion of fires across various ecosystems occur via large fire events. Recently, suggestions have been made that fires are becoming more severe and frequent as a consequence of current climate change. Although there are many factors influencing fire events, scientists have not found a suitable framework that can provide for understanding fires at the macroscale level. This review article proposes a new conceptual framework to better understand fire regimes. The proposed framework relies on a biogeographical perspective of fire regimes that include characteristics that have been underestimated in previous frameworks and to mitigate time as well as spatial scale issues at the macrolevel.

Severe forest fires have erupted in numerous tropical regions globally, threatening carbon storage in tropical ecosystems, the survival of plant species, and human health. Consequently, developing more precise prediction models for tropical forest fire hazards is essential for establishing effective fire prevention and management strategies. Although traditional logistic regression is widely employed for mapping forest fire probabilities, machine learning methods such as random forest have become more prevalent over the past decade. The applicability of random forest and logistic regression in predicting tropical forest fire probabilities has not been explored, leading to insufficient understanding of the driving factors of tropical forest fires on this tropical continental island with diverse forest types. This study integrated ground-based fire statistics from the Hainan Forestry Department and moderate resolution imaging spectroradiometer (MODIS) fire point data to create a highly accurate forest fire dataset for Hainan Island, spanning 20 years (2000–2020). Both logistic regression and random forest were used to develop tropical forest fire hazard models and explore the driving mechanisms of fires on Hainan Island. The results show that: (1) climatic factors contribute most significantly to the tropical forest fire probability, followed by human activities and topography, while vegetation factors (i.e., normalized difference vegetation index) made no significant contribution; (2) temperature and rainfall are the dominant factors influencing fire probability, with rising temperatures and decreasing rainfall substantially increasing the forest fire hazard; and (3) both logistic regression and random forest are reliable for predicting tropical forest fire hazards, but random forest demonstrates greater adaptability. In conclusion, our evidence suggests that the probability of tropical forest fires will increase under global warming and drought. The logistic regression and random forest models developed in this study provide valuable insights for identifying high-hazard forest fire areas in tropical regions. These findings have important implications for global tropical forest management and fire prevention, aiding in the formulation of targeted control strategies.

The tropics is an area with high incidence of wildfire all over the world in recent years, and the forest ecosystem in the tropics is extremely fragile. Thus, it is very important to identify drivers of wildfire in the tropics for developing effective fire management strategy. In this paper, global logistic regression (GLR) and geographically weighted regression (GWLR) models were employed to analyze the spatial distribution and drivers of tropical wildfires in Xishuangbanna and Leizhou Peninsula in tropical China from 2001 to 2018. The results show that the overall distribution of wildfire in Xishuangbanna and Leizhou Peninsula from 2001 to 2018 was spatially aggregated. In these tropical seasonal forest ecosystems, wildfire was mainly driven by meteorological factors, particularly by daily temperature range and precipitation. In Xishuangbanna (inland) peninsula, the impact of driving factors tended to be global, and the GLR model predicted the probability of wildfire occurrence better than the GWLR model. Drivers of wildfire in Leizhou Peninsula (coastal area) had clear spatial variation, and the GWLR model better explained the relationship. Furthermore, wildfire in Leizhou was more driven by human activities, especially management of agricultural lands. Our results demonstrate that effective forest management practice needs to adopt fire management practices with regional characteristics. The forest management strategy and traditional agriculture production system should pay more attention to changes in these driving factors and their relationship with wildfire.