Publications

Zhang, H; Liang, YS; Ren, HY; Ban, QY (2025). Comparing Grassland Fire Drivers and Models in Inner Mongolia Using Field and Remote Sensing Data. FIRE-SWITZERLAND, 8(3), 93.

Abstract
Frequent and intense grassland fires represent a significant threat to the stability and sustainability of grassland ecosystems. Therefore, understanding the driving factors of grassland fire and the occurrence of fire is key to formulating effective fire management policies and management plans. Based on the fire dataset (manually recorded data, satellite remote sensing data) from 2001 to 2022, this study uses six models to analyze the differences in grassland fire driving factors in different regions and fire prevention periods in the study area, determine the relative importance of fire driving factors, and draw a probability map of grassland fire. The results show that both types of data selected the Boosted Regression Trees (BRT) model as the optimal model for predicting grassland fires in the Inner Mongolia Autonomous Region. Meteorological factors are the main driving factors of grassland fire in the Inner Mongolia Autonomous Region, and topographic factors and socio-economic factors are important factors. The number and probability of fires gradually decreased from east to west, and fires were mainly concentrated in the northeast and middle of the study area. Therefore, our study functioned to explore the spatio-temporal pattern of grassland fire, accurately predict the probability of grassland fire at different scales, and provide a scientific basis for the rational allocation of grassland fire prevention resources in the study area.

DOI:
10.3390/fire8030093

ISSN: