Zoning forest fire risk at a county-level based on geographically weighted logistic regression: a case study of Wuyishan, China

Identification of the fire risk area at the county level is the key spatial unit for local forest resource protection and fire prevention. However, current methods and standards focus on the rating of risk level at a national scale which are not necessarily applicable at a local level. Wuyishan is a county-level city in southeastern China, which is rich in forest resources and is important for biodiversity conservation. We used a binary logistic regression (BLR) model and a geographically weighted logistic regression (GWLR) model to examine the indicators of forest fire occurrence and map the forest risk zones in the study area based on historical fire survey data from 1999 to 2013. The results showed that the BLR model simulation found that four indicators (daily average relative humidity, daily sunshine hours, elevation, and distance to the closest railway) had a significant impact on the risk of forest fires in Wuyishan City. Daily sunshine hours had a positive correlation with forest fire risk, and the other three factors were negatively correlated. The GWLR model incorporated the spatial heterogeneity of indicators into the simulation and further demonstrated that only daily average relative humidity was correlated over the entire study area. In contrast, daily sunshine hours, elevation, and distance to the closest railway were effective indicators of fire risk at a local level. The prediction accuracy of the GWLR model (85.3%) was slightly higher than that of the BLR model (84.4%). Around 19.9% of the study area was in a high fire risk zone, 34.0% was in a medium-risk zone, and 46.1% was in a low-risk zone. The high-risk zones were mainly concentrated in the central and southern areas. Our results indicate that, during the fire prevention period, the forest fire management department needs to increase the frequency of daily inspections of the forest edge areas in the high- and medium-risk areas based on the fire risk zoning map. Our approach may improve the identification of forest fire risk and fire prevention and suppression management at a county-level in mountainous and hilly areas.