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Estimating the spatial pattern of human-caused forest fires using a generalized linear mixed model with spatial autocorrelation in South Korea

Estimating the spatial pattern of human-caused forest fires using a generalized linear mixed model with spatial autocorrelation in South Korea

Hanbin Kwaka , Woo-Kyun Leea*, Joachim Saborowskib , Si-Young Leec , Myoung-Soo
Wond , Kyo-Sang Kood , Myung-Bo Leed and Su-Na Kima
aDepartment of Environmental Science and Ecological Engineering, Korea University, Seoul,
Republic of Korea; bChair of Ecoinformatics, Biometrics and Forest Growth and Chair of
Ecosystem Modelling, Georg-August-University Göttingen, Göttingen, Germany; cDepartment of
Disaster Prevention and Safety Engineering, Kangwon National University, Samcheck-si,
Gangwon-do, Republic of Korea; dDivision of Forest Disaster Management, Korea Forest Research
Institute, Seoul, Republic of Korea

Most forest fires in Korea are spatially concentrated in certain areas and are highly
related to human activities. These site-specific characteristics of forest fires are analyzed
by spatial regression analysis using the R-module generalized linear mixed model
(GLMM), which can consider spatial autocorrelation. We examined the quantitative
effect of topology, human accessibility, and forest cover without and with spatial autocorrelation.
Under the assumption that slope, elevation, aspect, population density,
distance from road, and forest cover are related to forest fire occurrence, the explanatory
variables of each of these factors were prepared using a Geographic Information
System-based process. First, we tried to test the influence of fixed effects on the occurrence
of forest fires using a generalized linear model (GLM) with Poisson distribution.
In addition, the overdispersion of the response data was also detected, and variogram
analysis was performed using the standardized residuals of GLM. Second, GLMM was
applied to consider the obvious residual autocorrelation structure. The fitted models
were validated and compared using the multiple correlation and root mean square error
(RMSE). Results showed that slope, elevation, aspect index, population density, and
distance from road were significant factors capable of explaining the forest fire occurrence.
Positive spatial autocorrelation was estimated up to a distance of 32 km. The
kriging predictions based on GLMM were smoother than those of the GLM. Finally,
a forest fire occurrence map was prepared using the results from both models. The
fire risk decreases with increasing distance to areas with high population densities, and
increasing elevation showed a suppressing effect on fire occurrence. Both variables are
in accordance with the significance tests.