Remote Sensing Assessment of Combustible Load in Mountainous Power Transmission Corridors: A Beijing Case Study

This study proposes a quantitative estimation and risk assessment method for combustible material loads in Beijing’s power transmission corridors using remote sensing data and machine learning techniques. Ground plot surveys provided biomass data, including tree species, diameter at breast height, tree height, understory biomass, and total plot biomass. These data were combined with remote sensing, meteorological data, and digital elevation models (DEM) to train and validate a random forest model for estimating the spatial distribution of biomass and generating combustible material load maps across Beijing. Reclassification and kernel density analysis were then used to systematically assess fire risk. Results. The results show that there are significant spatial distribution differences in the combustible material load in Changping District and Mentougou District of Beijing, showing a gradient change trend from low to high. High-load areas are concentrated in the mountainous and hilly areas of the north and west, while low-load areas are mainly distributed in the central and plain areas. This spatial analysis and risk assessment offer valuable tools for power grid operators to manage vegetation growth and combustible material accumulation, enhancing grid safety and reliability.