Modeling the spatial patterns of human wildfire ignition in Yunnan province, China

Abstract

A B S T R A C TDespite wildfire being an important regulator of dryland ecosystems, uncontrolled wildfire can be harmful to both forest ecosystems and human society, and wildfire prevention and control continue to raise worldwide concern. Wildfire management depends on knowledge of wildfire ignitions, both for cause and location. The regimes and factors influencing wildfire ignition have been studied at length. Humans have a profound effect on fire regimes and human activity is responsible for igniting the largest number of fires in our study area. Understanding the spatial patterns of ignitions is foremost to achieving efficiency in wildfire prevention. Previous studies mainly concentrate on overall wildfire risk integrating numerous factors simultaneously, yet the importance of human factors on ignition has not received much attention. In this study, we mapped human accessibility to explore the influence of human activity on wildfire ignition in a simple and straightforward way. A Bayesian weights-of-evidence (WofE) method was developed based on fire hotspots in China's Yunnan province extracted from satellite images and verified as known wildfires for the period 2007-2013. We considered a set of factors that impact fire ignition as associated with human accessibility: the locations of settlements, roads, water and farmland susceptible to human wildfire ignition. Known points of likely wildfire ignition were selected as training samples and all suspected thematic maps of the factors were taken as explanatory layers. Next, the weights of each layer in terms of its explanatory power were computed and used to generate evidence based on a threshold to pass a statistical test. The conditional independence (CI) of each layer was checked with the Agterberg-Cheng test. Finally, the posterior probability was calculated and its precision validated using samples of both presence and absence by withheld validation data. A comparison of WofE models was made to test the predictability. Results show proximity to villages, roads and farmland are strongly associated with human wildfire ignition and that wildfire more often occurs at an intermediate distance from high-density human activity. The WofE method proved more powerful than logistic regression, improving predictive accuracy by 10% and was more straightforward in presenting the association of dependence and independence. In addition, WofE with 1000 m buffer bands is more robust in predicting human wildfire ignition risk than binary or 100 m buffers for the ecoregion studied. Our results are significant for advising practical wildfire management and resource allocation, evaluation of human ignition control and also provides a foundation for future efforts toward integrated wildfire prediction.

Assessing the Influence of Roads on Fire Ignition: Does Land Cover Matter?

“…There has been a long research tradition focusing on the socio-economic aspects of wildfire [17,30,33,[37][38][39][40]. In this study, we analyzed existing interactions between roads and land cover in driving fire ignition.…”

“…The intrinsic fire selectivity of each LULC is further modulated by the presence of infrastructure such as housing or roads, which are also well known to influence fire ignition [21][22][23][24][25][26][27][28][29]. However, while LULC data and distance from roads have been already used for modeling fire ignition risk e.g., [30][31][32][33], to the best of our knowledge, a detailed analysis of the combined effect of road network and land cover is provided here for the first time.…”

Improvement of human-induced wildfire occurrence modeling from a spatial variation of anthropogenic ignition factor in the CLM5

“…Regions with frequently-happened wildfires are typically associated with an extensive forest cover and a warm climate (Fan et al 2011). Such a pattern bears a resemblance to the scattered fire occurrences in the same region as in Ye et al (2017) and the prescribed wildfire risk . Note that for this area, wildfires are typically characterized by a large number of ignitions while small burned areas after each ignition (Chen et al 2017).…”

Spatial pattern prediction of forest wildfire susceptibility in Central Yunnan Province, China based on multivariate data

“…Johnston et al (2020) established a forest re risk assessment method suitable for Canada based on the possibility of re occurrence, exposure, and vulnerability and their potential impacts (Woo et al 2017;You et al 2017;Molaudzi et al 2018). Ye et al(2017) Used weights-of-evidence to explore the spatial distribution of forest wild re points in Yunnan Province from 2007 to 2013. These previous studies focused more on the direct effects of anthropogenic impacts on re risk with less focus on the indirect impacts of the ecology.…”

“…The assessment of forest re risk at any spatial scale can be based on short-or long-term indicators or integrated systems. The assessment of forest re risk has made a great contribution to the prevention of forest res (Ye et al 2017). These studies only consider climate factors, the research period is short (about 10 years), and applied a simple spatial weighted overlay method to classify forest re risks resulting in low prediction accuracies.…”