Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China

Abstract

Fires are a recurrent environmental and economic emergency throughout the world. Fire risk analysis and forest fire risk zoning are important aspects of forest fire management. MODIS remote sensing datasets for Shanxi Province from 2002 to 2012 were used to build a spatial logistic forest fire risk model, based on the spatial distribution of forest fires and forest fire-influencing factors, using geographic information system technology. A forest fire risk zoning study was conducted at a large temporal scale and a provincial spatial scale. The resulting logistic model of forest fire risk, built with spatial sampling, showed a good fit (p < 0.05) between the distribution of forest fires and forest fire impact factors. The relative operating characteristic value was 0.757, and a probability distribution map for forest fire was developed, using layer computing. The forest fire area of Shanxi Province was divided into zones of zero, low, moderate, high and extremely high fire risk. The influences of altitude (GC), land-use type (LT), land surface temperature (LST), normalized difference vegetation index (NDVI) and global vegetation moisture index (GVMI) on fire events presented significant spatial variability, whereas the influences of slope and distance to the nearest path exhibited insignificant spatial variability in Shanxi Province. The influences of NDVI and LST on fire events were significant throughout Shanxi Province, whereas the influences of GC, LT and GVMI were only significant locally. Seven fire-prevention regions were delineated, based on the fire-influencing factors. Different fire-prevention policies and emphases should be taken into consideration for each of the seven fire-prone regions.