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

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.

Determining the sensitivity of grassland area burned to climate variation in Xilingol, China, with an autoregressive distributed lag approach

“…ARDL analysis revealed that maximum temperature is the most important variable affecting grassland fires in Xilingol (Table 4), a finding consistent with other studies in northern China and worldwide that show a relationship between higher temperatures and increased fire activity (Kobayashi et al 1994;Su and Liu 2004;Hardy 2005;Kaloudis et al 2005;Zhijun et al 2009;Chen et al 2008). Guo et al (2016) found that as the temperature increased, the amount of boreal forest burned in China increased, even when increased temperatures were accompanied by higher than normal precipitation. Wind and sunlight also explain significant long-term changes in area burned.…”

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

“…Railways reflect a transportation corridor in Yichun, and national fire records reveal that the majority of fire occurrences during this study period were accidental and negligent fires caused by human activities in and around railways, fire accidents by machinery, or lack of controlled burning activities near the tracks and railway infrastructure. Similar negative correlation between distance to railway and fire frequency was reported in the Upper Midwest states and Missouri and boreal forest in China [50][51][52]. On the contrary, Guo et al [53] reported a positive relationship between fire and distance to railway in subtropical forest in China and [3] reported both positive and negative relationships with spatial distribution in the study area.…”

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

“…Nonetheless, various studies [35][36][37][38] have successfully applied the logistic regression approach to evaluate the relationship between factors and fire events. The analysis results in a binary outcome of a fire-event or a non-fire-event.…”