Hui Liu

Lightning-ignited wildfires are an increasing hazard in boreal forests, with their frequency amplified by global warming and more frequent thunderstorms. However, the mechanisms governing lightning-induced ignition and the subsequent smoldering–flaming transition remain poorly understood. This study aims to understand the ignition mechanisms of lightning-induced forest fires by combining a physics-based heat-balance model and controlled laboratory simulations. Experiments were conducted using twelve representative surface fuel types collected from six typical forest types in the Daxing’anling region, a lightning fire-prone area in northern China. Three fundamental stages of fire behavior development were systematically investigated, including the lightning-induced ignition, smoldering propagation, and the smoldering-to-flaming transition. Fuel moisture content was varied from 5% to 45%, and wind speed was adjusted between 0 and 5 m/s. The results demonstrated that discharge energy and wind speed significantly increased ignition probability, while fuel moisture content was negatively correlated with smoldering spread rate. Wind speed showed the greatest influence on the smoldering-to-flaming transition. The findings provide new mechanistic insights into the thermal and physical processes driving lightning-induced fires, supporting predictive modeling of ignition thresholds and fire behavior under changing meteorological and fuel conditions.

Lightning-induced forest fires represent a dominant natural ignition source in boreal and temperate ecosystems, yet their climatic and biophysical controls remain poorly understood. This study investigates the spatiotemporal patterns and environmental drivers of 646 lightning-induced forest fires across the Da Hinggan Ling region, Northeast China, during 2001–2024. Multi-source datasets from ERA5-Land, MODIS, and ETCCDI were integrated to quantify short-term meteorological variability, vegetation water status, and long-term climatic extremes. Using Random Forest and XGBoost models combined with SHAP interpretability analysis, we identified key predictors and nonlinear responses of burned area to environmental forcing. Results reveal pronounced interannual fluctuations in fire activity, with 2010, 2016, and 2022 emerging as compound extreme years characterized by co-occurring drought and heatwaves. Vegetation moisture index (NDMI), diurnal temperature range (DTR), and heatwave duration (HWDI) were the most influential variables controlling burned area variability. The total burned area and fire duration showed significant declining trends, while high burned-area fires exhibited spatial clustering in dry, low-LAI regions. These findings demonstrate that compound dry–hot conditions coupled with vegetation desiccation are the primary drivers of large lightning fires. The study provides a process-based understanding of climate–fuel–fire linkages and supports improved fire risk forecasting under a warming climate.

In recent years, the increasingly significant impacts of climate change and human activities on the environment have led to more frequent occurrences of extreme events such as forest fires. The recurrent wildfires pose severe threats to ecological environments and human life safety. Consequently, forest fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing ecological and economic losses, improving forest fire management efficiency, and ensuring personnel safety and property security. To enhance comprehensive understanding of wildfire prediction research, this paper systematically reviews studies since 2015, focusing on two key aspects: datasets with related tools and prediction algorithms. We categorized the literature into three categories: statistical analysis and physical models, traditional machine learning methods, and deep learning approaches. Additionally, this review summarizes the data types and open-source datasets used in the selected literature. The paper further outlines current challenges and future directions, including exploring wildfire risk data management and multimodal deep learning, investigating self-supervised learning models, improving model interpretability and developing explainable models, integrating physics-informed models with machine learning, and constructing digital twin technology for real-time wildfire simulation and fire scenario analysis. This study aims to provide valuable support for forest natural resource management and enhanced environmental protection through the application of remote sensing technologies and artificial intelligence algorithms.