Seasonal differences in the spatial patterns of wildfire drivers and susceptibility in the southwest mountains of China

Spatiotemporal analysis of wildfires in Alberta, Canada over the past sixty years: Increased wildfire frequency by human activities

Wildfires are a major socio-ecological issue in Alberta. The region's extensive forests and grasslands provide abundant natural fuel. Since the onset of the 21st century, climate change and human activities have led to an increase in wildfire frequency. While provious studies have focused on specific events and short-term periods, they often lack a comprehensive analysis of long-term trends and fail to integrate environmental and human factors. This study addresses these gaps by providing a detailed analysis of wildfire dynamics in Alberta from 1961 to 2020, utilizing historical datasets to examine trends in frequency and burned areas. Unlike prior research, this study employs advanced spatial analysis methods, including spatial autocorrelation and hotspot analysis, and incorporates 12 variables related to climate, human activities, and topography into a geographically weighted logistic regression (GWLR) model. The results reveal a significant increase in wildfire frequency since the 21st century, with human-caused fires clustering in specific areas, while lightning-caused fires are more dispersed. The GWLR model highlights the spatial variability of influencing factors, with population density emerging as the most significant driver of burned areas, underscoring the critical role of human activities. This research innovates by integrating multiple variables, extending the time frame, and emphasizing the interplay of human, climatic, and topographic factors. The findings provide valuable insights for optimizing wildfire prevention strategies and informing spatial planning policies to mitigate future risks.

Integrating flora, fauna, and indigenous practices into spatial optimization for prescribed burning

Climate change has intensified wildfire activity, necessitating a shift towards sustainable fire management strategies that embrace the concept of fire coexistence. Fire coexistence recognizes the role of fire as a natural ecological process and integrates the adaptations of flora (e.g., fire-resistant bark, regenerative capacity), fauna (e.g., fuel reduction through grazing, creation of natural firebreaks), and traditional land management practices (e.g., controlled burns, agricultural firebreaks) that enable ecosystems to persist with fire. These "coexistence factors" are crucial for effective prescribed burning, ensuring minimal disruption to fire-adapted species and maximizing long-term ecosystem resilience. While prescribed burning is a recognized management tool, a comprehensive framework for spatially integrating these coexistence factors into regional-scale planning is lacking. This study addresses this gap by developing a novel approach that spatially optimizes prescribed burning by integrating fire risk and coexistence capacity. Applying this approach to the Jialing River watershed (China), a fire-prone mountainous region, we use machine learning and deep learning to predict fire risk and identify areas with high coexistence potential. Zonation 5 is then employed for spatial prioritization. Results reveal a significant spatial correlation between fire risk and coexistence capacity, with high-value clusters concentrated in the central and southern parts of the study area, particularly around the Jialing River and forested regions. Specifically, 4% of the study area in the central and southern regions (value > 0.679) was classified as very high fire risk, while the top 10% of the area exhibited high coexistence capacity (value > 0.9). Based on Zonation 5 optimization, 5% of fire-prone forests with high coexistence capacity were identified as priority areas for prescribed burning, concentrated primarily in eastern Beibei. This integrated approach offers valuable guidance for policymakers, land planners, and stakeholders in sustainably managing fire hazards in similar mountainous regions globally.

Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China

Ambient PM2.5 pollution poses a major risk to public health in China, contributing to significant mortality and morbidity. While overall PM2.5 concentrations have declined in recent years, the changes in PM2.5 chemical constituents remain inadequately understood due to limited ground monitoring networks. We developed a Super Learner model that integrates MISR satellite data, chemistry transport model simulations, and land use information to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. The model achieved high predictive accuracy with a cross-validation R2 of 0.84 and an RMSE of 4.9 μg/m3. Our findings show elevated OC levels in Northern China, driven by industrial activities with concentrations exceeding 30 μg/m3 during the heating season. In contrast, forest fires were the primary contributors in Yunnan, raising OC concentrations to 20-30 μg/m3 during fire seasons. Over the 17-year period, the national OC trend declined by 1.3 % annually. Regionally, the Beijing-Tianjin-Hebei region and the Fenwei Plain experienced faster reductions at annual rates of 1.5 % and 2.0 %, respectively, while Yunnan exhibited no significant trends. To better understand pollution source contributions, we analyzed the OC/EC ratio, which indicated higher ratios in less populated rural areas, suggesting agricultural and biogenic emissions, while lower ratios in urban clusters pointed to primary sources such as traffic and industrial activities. Notably, since 2013, significant decreases in the OC/EC ratio have been observed in the North China Plain, likely reflecting the impact of stringent air pollution control policies on biomass burning. This study provides valuable exposure estimates for epidemiological research on the long-term health effects of OC in China, offering insights for evaluating air quality policies and guiding future management strategies.

Modeling the seasonal wildfire cycle and its possible effects on the distribution of focal species in Kermanshah Province, western Iran

Predicting environmental disturbances and evaluating their potential impacts on the habitats of various plant and animal species is a suitable strategy for guiding conservation efforts. Wildfires are a type of disturbance that can affect many aspects of an ecosystem and its species. Therefore, through the integration of spatial models and species distribution models (SDMs), we can make informed predictions of the occurrence of such phenomena and their potential impacts. This study focused on five focal species, namely, the brown bear (Ursus arctos), wild goat (Capra aegagrus), wild sheep (Ovis orientalis), wildcat (Felis silvestris), and striped hyena (Hyaena hyaena). This study used MODIS active fire data and ensemble machine learning methods to model the risk of wildfire occurrence in 2023 for spring, summer, and autumn separately. This study also investigated the suitability of habitats for focal species via SDMs. The predicted probability maps for wildfire risk and habitat suitability were converted to binary values via the true skill statistic (TSS) threshold. The overlap of the habitat suitability map and wildfire occurrence areas was analyzed via GAP analysis. The area prone to fire in spring, summer and winter is equal to 9077.32; 10,199.83 and 13,723.49 KM2 were calculated, which indicates an increase in wildfire risk. Proximity to roads is one of the most important factors affecting the possible effects of wildfires in all seasons. Most fire occurrences are concentrated on agricultural lands, which, when integrated with other land use types, have wildfire potential in all seasons. The use of fire to destroy agricultural residues is a critical factor in the occurrence of wildfires. The distribution range of each focal species is considered the most important component of fire susceptibility. Hence, the suitable habitat for Hyaena hyaena in spring, summer, and autumn, with areas of 5.257, 5.856, and 6.889 km2 respectively, is the most affected by the possibility of fire. In contrast, these areas have the lowest values for Ovis orientalis, with 162, 127, and 396 km2 respectively. Therefore, species that are dependent on human-based ecosystems have the highest vulnerability to wildfire. Conservation efforts should focus on familiarizing farmers with methods of destroying agricultural residues as well as the consequences of intentional fires. The findings of this study can be used to mitigate the negative impacts of wildfire and protect the habitat of focal species.

Spatiotemporal changes and driving factors of alpine land cover in Tianshan world natural heritage sites

Alpine natural heritage sites hold significant value due to their unique global resources. Studying land cover changes in these areas is crucial for maintaining and preserving multiple their values. This study takes Kalajun-Kuerdening, one of the components of Xinjiang Tianshan World Natural Heritage Site, as an example to analyze land cover changes and their driving factors in alpine heritage sites. Highlights include: (1) Between 1994 and 2023, Forest and Grassland increased by 55.96 km2 and 18.16 km2, with notable forest growth from 2007 to 2017. Trends in Forest changes align with forest protection policies, and a substantial amount of Bareland converted to Grassland indicates an increase in vegetation cover. (2) Elevation, precipitation, temperature, and evapotranspiration are key drivers of land cover changes, as validated by Random Forest algorithm and Geodetector model. (3) Favorable conditions for Grassland to Forest transition include annual precipitation between 275 and 375 mm, annual temperature between -2 and 3 °C, annual evapotranspiration between 580 and 750 mm, elevation between 1800 and 2600 m, and aspect between 0 to 110° and 220 to 259.9°. Continuous monitoring of land cover changes and their driving factors in mountain heritage sites contributes to the protection of the ecological environment and provides data and information support for addressing climate change, resource management, and policy making.

High-resolution estimation of near-surface ozone concentration and population exposure risk in China

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China's near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China's eastern region, with population exposure risks mostly ranging from 0.8 to 5.