Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

Futao Guo,Zhangwen Su,Fangfang Lin,Huiling Liang,Mulualem Tigabu,Guangyu Wang,Xiajie Yang

doi:10.3390/f8060180

Abstract

Fires in urban-forest ecosystems (UFEs) are frequent with complex causes, posing a serious hazard to human lives and infrastructure. Thus, quantifying wildfire risks in UFEs and their spatial pattern is quintessential to develop appropriate fire management strategies. The aim of this study was to explore spatial (geographically weighted logistic regression, GWLR) versus non-spatial (logistic regression, LR) modelling approaches to determine the relationship between forest fire occurrence and driving factors in Yichun, a typical urban-forest ecosystem in China. As drivers of fire, 13 factors related to topographic, vegetation, infrastructure, meteorological and socio-economy were considered and regressed against fire occurrence data from 1980 to 2010. Results demonstrate the superiority of GWLR models over LR in terms of prediction accuracy, goodness of fit and model residuals. The GWLR model further captured the spatial variability of driving factors over a broad study area, and the fire likelihood maps identified areas with different zones of fire risk in the study area. In conclusion, the study demonstrates quantitatively and spatially the importance of accounting for local variation in drivers of fires, thereby improving fire management and prevention strategies. The findings also contribute to the emerged field of fire management and fire risk assessment in UFEs.

Highlights

Uncontrolled forest fire results in loss of forest resources and land degradation, while affecting air quality and posing a threat to human life and property [1,2,3]
Negative correlations were found between fire occurrence and distance to railway, distance to settlement, slope, elevation, monthly average relative humidity, and per capita GDP; while a positive correlation was observed between fire occurrence and distance to the road and average monthly temperature
This study provides an improved understanding of the spatial variability of fire occurrence in Yichun, China, a typical Urban-forest ecosystems (UFEs), as well as the relative importance of various underlying factors

Summary

Introduction

Uncontrolled forest fire results in loss of forest resources and land degradation, while affecting air quality and posing a threat to human life and property [1,2,3]. Compared to natural forests or remote forested regions, more forest fires occur in UFEs due to the high frequency of human activities and density of infrastructure [4]. These fires are generally small due to early detection, intense suppression efforts and better firefighter accessibility [5], every ignition source has the potential to grow into a large fire. The large and increasing number of lives and infrastructures being exposed to wildfire hazard highlights the need to quantify wildfire risks and understand the fire drivers in UFEs. the causes of UFE fires are usually more complex than those in pure forested areas due mainly to various human activities, a fire prediction tool, which can fully account for the complexity between fire occurrence and its driving factors, is urgently needed

Objectives

Methods

Results

Discussion

Conclusion