Modeling Anthropogenic Fire Occurrence in the Boreal Forest of China Using Logistic Regression and Random Forests

Forests 2016, 7(11), 250; doi:10.3390/f7110250

Author

Futao Guo ^1,* , Lianjun Zhang ²

, Sen Jin ³

, Mulualem Tigabu ⁴

, Zhangwen Su ¹

 and Wenhui Wang ¹

¹

College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China

²

Department of Forest and Natural Resources Management, College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA

³

Faculty of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China

⁴

Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, Alnarp SE-230 52, Sweden

^*

Author to whom correspondence should be addressed. 

Academic Editors: Michael C. Stambaugh and Timothy A. Martin

Received: 15 June 2016 / Revised: 13 October 2016 / Accepted: 18 October 2016 / Published: 25 October 2016

View Full-Text   |     Download PDF [3705 KB, uploaded 25 October 2016]   |    

 Browse Figures

 

Abstract 

Frequent and intense anthropogenic fires present meaningful challenges to forest management in the boreal forest of China. Understanding the underlying drivers of human-caused fire occurrence is crucial for making effective and scientifically-based forest fire management plans. In this study, we applied logistic regression (LR) and Random Forests (RF) to identify important biophysical and anthropogenic factors that help to explain the likelihood of anthropogenic fires in the Chinese boreal forest. Results showed that the anthropogenic fires were more likely to occur at areas close to railways and were significantly influenced by forest types. In addition, distance to settlement and distance to road were identified as important predictors for anthropogenic fire occurrence. The model comparison indicated that RF had greater ability than LR to predict forest fires caused by human activity in the Chinese boreal forest. High fire risk zones in the study area were identified based on RF, where we recommend increasing allocation of fire management resources. View Full-Text

Keywords: human-caused fire;  driving factors;  forest fire;  Daxing’an Mountains;  ROC curve

▼ Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

For further details log on website :
http://www.mdpi.com/1999-4907/7/11/250