Integrating Multi-Source Data for Forest Fire Risk Assessment: A Case Study of Liangshan, China

Forest fires pose significant threats to ecological security, human settlements, and sustainable regional development, particularly in mountainous regions with complex environmental and anthropogenic conditions. Previous studies have yet to construct a forest fire risk assessment framework that integrates multi-source data, which limits the comprehensiveness and accuracy of existing assessments. To address this gap, taking Liangshan Prefecture in China as a case study, this research selected eight risk factors to characterize vegetation conditions, topographic features, climatic conditions, and human activities. A combined weighting approach integrating the mandatory determination method and the coefficient of variation method was employed to determine the weights of different indicators. Forest fire risk probability was calculated using a weighted comprehensive evaluation model, and spatial autocorrelation analysis based on global Moran’s I and local indicators of spatial association (LISA) was further conducted to investigate spatial clustering characteristics. The results indicate that high-risk and very high-risk areas are mainly concentrated in southeastern Liangshan, particularly in Xichang, Jinyang, Ningnan, Huili, and Huidong, where warmer climatic conditions, dense vegetation coverage, mountainous terrain, and intensive human activities jointly contribute to elevated forest fire risk. The global Moran’s I value of 0.219175 indicates significant positive spatial autocorrelation in forest fire risk distribution. Validation using historical fire-scar data from 2010 to 2020 showed that 83.66% of the fire scars were distributed within medium-, high-, and very high-risk areas, suggesting that the proposed assessment framework provides a reasonable representation of forest fire risk patterns in Liangshan. The findings of this study can support regional forest fire prevention planning, targeted resource allocation, and risk management in mountainous areas. © 2026 by the authors.