Next Article in Journal
Protective Effect of Nitric Oxide (NO) against Oxidative Damage in Larix gmelinii Seedlings under Ultraviolet-B Irradiation
Previous Article in Journal
Water, Rather than Temperature, Dominantly Impacts How Soil Fauna Affect Dissolved Carbon and Nitrogen Release from Fresh Litter during Early Litter Decomposition
Article Menu
Issue 11 (November) cover image

Export Article

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

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

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]   |  

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 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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Guo, F.; Zhang, L.; Jin, S.; Tigabu, M.; Su, Z.; Wang, W. Modeling Anthropogenic Fire Occurrence in the Boreal Forest of China Using Logistic Regression and Random Forests. Forests 2016, 7, 250.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top