Yiyu Wang

The increasing frequency and severity of wildfires pose growing challenges to ecological security in remote forest regions. In road-sparse primeval forests, wildfire prevention and ground emergency response are constrained not only by fire-prone environmental conditions, but also by limited tactical access routes. Existing wildfire susceptibility studies can identify areas with higher fire occurrence potential, whereas route planning studies often optimize access without explicitly considering where fires are more likely to occur. This study developed a GIS-based decision-support framework linking wildfire susceptibility modelling with firefighting access route planning in the northern primeval forest region of the Greater Khingan Mountains, China, to improve the efficiency of wildfire prevention and response in areas with sparse road networks. Using 887 historical fire points and nine environmental and anthropogenic predictors, Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models were compared to identify relatively wildfire-prone areas. High-susceptibility locations were grouped into operational management zones using K-means clustering. A generalized forest traversal cost surface was constructed by integrating terrain, vegetation, land cover, water constraints, and existing-road accessibility, and a hybrid simulated annealing and 2-opt algorithm was used to design candidate access corridors. Results show that the RF model achieved the best internal-validation performance (AUC = 0.948; overall accuracy = 0.873), and feature-importance comparison showed that land surface temperature, proximity to roads, and NDVI were the most influential predictors. In total, 386 target points extracted from the high- and extreme-susceptibility classes were grouped into 12 spatial clusters. The optimized network identified 1008.46 km of candidate corridors and reduced the mean nearest-access distance for 13 historical wildfire events by 53.7% after the planned network was incorporated. After incorporating the planned corridors into the existing road system, the road-network density increased from 0.96 to 2.015 m/hm². These findings demonstrate that susceptibility-driven route planning can translate predicted fire-prone areas into prioritized management units and candidate access corridors, thereby reducing spatial accessibility gaps and supporting phased patrol deployment and emergency-resource allocation in road-sparse primeval forests.

The lack of scientific methods for selecting monitoring indicators and equipment undermines the efficiency of forest fire emergency response. To address this gap, we developed a novel evaluation model for emergency monitoring indicators based on the spatiotemporal evolution of forest fires. The model, comprising four primary and eight secondary factors, leverages a hybrid TriFAHP and DBN approach to objectively determine factor weights based on survey data from 20 domain experts. The results indicate that the primary factor weights rank as follows: Monitorability (0.3807) > Timeliness (0.3353) > Sensitivity (0.1874) > Feasibility (0.0966). Four indicators (wind speed, temperature, flame, and gas) were identified as the most suitable for core monitoring. Furthermore, stage-specific monitoring strategies were proposed, prioritizing different core indicators across the ignition, spread, and fully developed fire stages. An indicator and equipment association was established, recommending optimal configurations such as UAV-mounted thermal imagers and lidar anemometers. The practical applicability of the proposed framework was successfully validated through real-world case studies, including the 2019 to 2020 Australia bushfires. This study provides a standardized framework aligning indicators, equipment, and scenarios, offering theoretical and practical guidance for optimizing emergency monitoring systems.