The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region.

Background: The Panxi region in China is among the areas that are most severely impacted by forest fires. Despite this, there is currently a lack of comprehensive and systematic research on the spatial and temporal distribution patterns, as well as the drivers, of forest fires in the region. To reveal bio-geo-climatic and anthropogenic influences, this study investigated the temporal and spatial characteristics of forest fires and migration patterns of the center of gravity of forest fires in Panxi. A parametric optimal geographical detection model was utilized to quantify the influence of various individual factors and their combinations on the spatial patterns of forest fire occurrence in the whole Panxi region and sub-region, by analyzing the forest fire dataset from 2004 to 2020. Results: From 2004 to 2020, the Panxi region experienced an upward trend in the number of forest fires and the area burned. However, the trends were not consistent over the entire period. Between 2004 and 2014, both the number of fires and the area burned showed fluctuations and an overall increase. In contrast, between 2015 and 2020, there was a significant decrease in the number of fires, while the area burned showed a continued upward trend. The study identified abrupt changes in the frequency of forest fires and burned areas, primarily in 2007 and 2016. Spatially, forest fires in Panxi exhibited a positive correlation and local clustering. The river valley basin and hilly regions displayed a higher incidence of forest fires, which were concentrated mainly along the hill edges. In the whole area of Panxi, climatic factors have a predominant influence on forest fire occurrences. Specifically, evaporation, maximum temperature, average temperature, number of days without rain, and minimum temperature demonstrated the strongest explanatory power. Furthermore, this relationship was found to be reinforced when combined with topographical, human activities, and vegetation factors. The spatial variation of drought within each sub-district has a stronger explanatory power for the distribution characteristics of forest fires in the region than at the Panxi-wide scale. The factor with the maximum interaction in most regions was the dual factor of rainfall and drought. Conclusions: The study’s findings validate the applicability of geographic probes for identifying the drivers of fire occurrence and enhance our understanding of the drivers and their combined effects on the spatial context of the fire-incident study area.

Maintaining and improving the soil conservation function of an ecosystem is of positive significance to the sustainable and stable development of that ecosystem. We used the RUSLE model to evaluate the soil conservation function of the Qinling-Daba Mountains from 1982, 1995, 2005, and 2015 in order to analyze the spatio-temporal evolution characteristics of soil conservation. Our conclusions are as follows: (1) During the study period, the amount of average actual soil erosion in the Qinling-Daba Mountains was 955.39 × 10⁸ t, the amount of actual soil erosion fluctuated greatly from year after year, there were obvious spatial aggregation and temporal and spatial transfer phenomena, and there was serious soil nutrient loss in the east. (2) From 1982 to 2015, soil conservation in the Qinling-Daba Mountains increased by 27.75 × 10⁸ t during fluctuations. The soil conservation was negatively correlated with elevation and slope, and was positively correlated with vegetation coverage. (3) The average soil conservation of forest ecosystems and farmland ecosystems accounts for 78.11% of the total soil conservation, but there are differences in the ways in which to achieve soil conservation function. The order for soil conservation function of different vegetation types is crops > shrub > broad-leaved forest > coniferous forest > grass > meadow > grassland > coniferous and broad-leaved mixed forest > alpine plant > swamp. (4) The average retention of N, P and K elements in soil were 75.57 × 10⁴ t, 25.35 × 10⁴ t and 737.28 × 10⁴ t, respectively. The soil elements had the consistency of spatial difference in spatial distribution and were time scaled. The soil nutrient loss in the eastern region is serious. Shrubs, broadleaf forests and crops have the greatest effect on soil nutrient retention. Alpine plants retain the greatest amount of soil nutrients per unit area. Therefore, the establishment of reasonable soil conservation strategies and scientific vegetation interplanting measures will help to enhance the soil conservation function of the Qinling-Daba Mountains ecosystem and improve the ecosystem production capacity.

Background The Panxi region in China is among the areas that are most severely impacted by forest fires. Despite this, there is currently a lack of comprehensive and systematic research on the spatial and temporal distribution patterns, as well as the drivers, of forest fires in the region. To reveal bio-geo-climatic and anthropogenic influences, this study investigated the temporal and spatial characteristics of forest fires and migration patterns of the center of gravity of forest fires in Panxi. A parametric optimal geographical detection model was utilized to quantify the influence of various individual factors and their combinations on the spatial patterns of forest fire occurrence in the whole Panxi region and sub-region, by analyzing the forest fire data-set from 2004-2020. Results From 2004 to 2020, the Panxi region experienced an upward trend in the number of forest fires and the area burned. However, the trends were not consistent over the entire period. Between 2004 and 2014, both the number of fires and the area burned showed fluctuations and an overall increase. In contrast, between 2015 and 2020, there was a significant decrease in the number of fires, while the area burned showed a continued upward trend. The study identified abrupt changes in the frequency of forest fires and burned areas, primarily in 2007 and 2016. Spatially, forest fires in Panxi exhibited a positive correlation and local clustering. The river valley basin and hilly regions displayed a higher incidence of forest fires, which were concentrated mainly along the hill edges. In the whole area of Panxi, climatic factors have a predominant influence on forest fire occurrences. Specifically, evaporation, maximum temperature, average temperature, number of days without rain, and minimum temperature demonstrated the strongest explanatory power. Furthermore, this relationship was found to be reinforced when combined with topographical, human activities, and vegetation factors. The spatial variation of drought within each sub-district has a stronger explanatory power for the distribution characteristics of forest fires in the region than at the Panxi-wide scale. The factor with the maximum interaction in most regions was the dual factor of rainfall and drought. Conclusion The study’s findings validate the applicability of geographic probes for identifying the drivers of fire occurrence and enhance our understanding of the drivers and their combined effects on the spatial context of the fire-incident study area.

Climate change amplifies urban sustainability challenges, with intensifying sand and dust storm (SDS) hazards highlighting the important role of Ecosystem wind erosion prevention (EWEP) as an ecosystem service (ES). In northern China, a region prone to wind erosion, EWEP mitigates aeolian processes at sand sources and reduces downwind dust transport to urban centers. This study employs the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate diffusion dynamics of EWEP and to assess its hazard mitigation effects for cities in northern China. The findings are as follows: (1) EWEP capacity increased consistently from 2000 to 2024; (2) Aggregated preventive benefits rose, which aligns with the interpretation that systemic ecological restoration reduces dust dispersion; (3) Preventive benefits exhibit stratification across different urban agglomerations. These findings can inform SDS risk management and climate adaptation strategies to support urban sustainability.