Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm

Wenyuan Ma,Shilin Chen,Zhuxin Cheng,Fengge Wang,Zhongke Feng

doi:10.3390/f11050507

Abstract

Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.

Highlights

Forests are ecosystems with rich biodiversity [1,2,3], and they play an important role in soil and water conservation, climate regulation, the carbon cycle and other aspects [4,5]
The driving factors of forest fires and their effect were analysed in 6 geographical regions: Northeast region (NE), North China region (N), East China region (E), Northwest region (NW), Southwest region (SW) and Mid-south region (MS)
The AUC values of each final model and intermediate model are greater than 0.85, and the prediction accuracy is between 70.0% and 91.4% (Table 5), which indicates that the model predictive prediction accuracy is between 70.0% and 91.4% (Table 5), which indicates that the model capability is good

Summary

Introduction

Forests are ecosystems with rich biodiversity [1,2,3], and they play an important role in soil and water conservation, climate regulation, the carbon cycle and other aspects [4,5]. Fire, which affects the biodiversity, species composition and ecosystem structure of forest ecosystems, is the dominant disturbance factor in many forest ecosystems [6,7,8,9]. Forest fires threaten the sustainable development of modern forestry and human security [13]. As an important component of global environmental change, forest fires have become the focus of forestry and ecological research [14,15]. Forest fires are affected complexly by many driving factors, so it is very important to select appropriate forest fire driving factors and prediction models

Methods

Findings

Discussion

Conclusion