Tigabu, Mulualem
- Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences
Research article2016Peer reviewedOpen access
Guo, Futao; Zhang, Lianjun; Jin, Sen; Tigabu, Mulualem; Su, Zhangwen; Wang, Wenhui
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.
human-caused fire; driving factors; forest fire; Daxing'an Mountains; ROC curve
Forests
2016, Volume: 7, number: 11, article number: 250
Publisher: MDPI AG
Forest
SDG15 Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
SDG13 Take urgent action to combat climate change and its impacts
SDG11 Make cities and human settlements inclusive, safe, resilient and sustainable
Forest Science
DOI: https://doi.org/10.3390/f7110250
https://res.slu.se/id/publ/81148