Factor contribution to fire occurrence, size, and burn probability in a subtropical coniferous forest in East China

Ye, Tao; Wang, Yao; Guo, Zhixing; Li, Yijia

doi:10.1371/journal.pone.0172110

Cited by 38 publications

(30 citation statements)

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“…From Table 4, not all of our previously suspected human factors were good predictors of wildfire ignition, our study revealed that proximity to roads, villages and farmland are strong predictors of wildfire ignition, while urban settlements and access to water show poor predictability, which is consistent with other findings (Catry et al, 2010;Dlamini, 2010;Fusco et al, 2016; J. Ye et al Applied Geography 89 (2017) 150-162 ; s(C) is stand deviaton of C; C S is the studentized C. ; s(C) is stand deviaton of C; C S is the studentized C. Martell, & Martín, 2010;Ye et al, 2017;Zhang, Zhang, & Zhou, 2010).…”

Section: Determinants Of Human Wildfire Ignitionsupporting

confidence: 88%

“…Consequently, an improved understanding of wildfire risk should address the patterns of human activity and its relation to fire ignition (Dickson et al, 2006;Narayanaraj & Wimberly, 2012;Prestemon, Pye, Butry, Holmes, & Mercer, 2002). Significant research effort has been undertaken to explore the relationship between wildfire and its causative factors with the goal of building predictive models (Cardille et al, 2001; Chas-Amil, Prestemon, McClean, & Touza, 2015;Maingi & Henry, 2007;Narayanaraj & Wimberly, 2012;Romero-Calcerrada, BarrioParra, Millington, & Novillo, 2010;Román-Cuesta et al, 2003;Salis et al, 2013;Syphard et al, 2007;Watts & Hall, 2016;Ye, Wang, Guo, & Li, 2017), and has concluded that wildfire tends to occur in areas near human infrastructure on the human-wildland interface (Zhang, Lim, & Sharples, 2016), and frequently exhibits nonlinear relationships (Hawbaker et al, 2013). However, 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.…”

Section: Introductionmentioning

confidence: 99%

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

Deng

et al. 2017

Applied Geography

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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.

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Section: Determinants Of Human Wildfire Ignitionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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

Deng

et al. 2017

Applied Geography

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“…However, if we consider the analysis in the long term, air temperature presents a positive relation with fire sources. Results with a negative relation between fire and air temperature were also found by Chang et al (2013); Guo et al (2015); Ye et al (2017), again indicating the complexity of these analyses.…”

Section: Determinant Factors In Fire Occurrencementioning

confidence: 61%

Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique

Nhongo

Fontana

Guasselli

et al. 2019

Geomatics, Natural Hazards and Risk

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Fires are one of the main factors for disturbances in Niassa Reserve-Mozambique, with economic and environmental impacts. There are cyclical records of fire occurrences across the reserve. However, studies on the main causative factors and identification of more susceptible locations are very limited. In this perspective, this study had as objectives: (1) determine the main significant factors for wildfire occurrences; (2) Map the probability of wildfire occurrences, using logistic regression. Independent variables included vegetation index (NDVI), climatic, topographic and socioeconomic data. The analysis period was from 2001 to 2015 and comprised the months with more occurrences of wildfire (May to December). According to the results, the main factors that determine the occurrence of fires were: NDVI, temperature, elevation, followed by precipitation, slope, relative humidity and human settlements. The spatial distribution of probability of fire occurrence reveals that zones with high and very high risk are located at the west and central west zones (areas with higher accumulation of dry biomass); medium risk zones are located in the centre of the reserve, while in central east and east zones the probability of fire occurrence is low and very low risk. Results showed that the expectation of wildfire ignition using logistic regression presented good precision (area under the curve 74%).

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“…The map of wildfire ignition susceptibility can be further used to generate an ignition probability map by using the Monte Carlo approach (Metropolis 1987;Ye et al 2017). To illustrate this, we used the annual ignition data from 1990 to 2015 of Yunnan Province to fit a distribution and randomly generated the number of annual ignitions for 1000 years.…”

Section: Spatial Patternmentioning

confidence: 99%

Wildfire Susceptibility Assessment in Southern China: A Comparison of Multiple Methods

Cao

Wang

2017

Int J Disaster Risk Sci

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Wildfire is a primary forest disturbance. A better understanding of wildfire susceptibility and its dominant influencing factors is crucial for regional wildfire risk management. This study performed a wildfire susceptibility assessment using multiple methods, including logistic regression, probit regression, an artificial neural network, and a random forest (RF) algorithm. Yunnan Province, China was used as a case study area. We investigated the sample ratio of ignition and nonignition data to avoid misleading results due to the overwhelming number of nonignition samples in the models. To compare model performance and the importance of variables among the models, the area under the curve of the receiver operating characteristic plot was used as an indicator. The results show that a cost-sensitive RF had the highest accuracy (88.47%) for all samples, and 94.23% accuracy for ignition prediction. The identified main factors that influence Yunnan wildfire occurrence were forest coverage ratio, month, season, surface roughness, 10 days minimum of the 6 h maximum humidity, and 10 days maxima of the 6 h average and maximum temperatures. These seven variables made the greatest contributions to regional wildfire susceptibility. Susceptibility maps developed from the models provide information regarding the spatial variation of ignition susceptibility, which can be used in regional wildfire risk management.

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Factor contribution to fire occurrence, size, and burn probability in a subtropical coniferous forest in East China

Cited by 38 publications

References 50 publications

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

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

Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique

Wildfire Susceptibility Assessment in Southern China: A Comparison of Multiple Methods

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