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

• Published in: International journal of geographical information science : IJGIS Vol. 26; no. 9; pp. 1589 - 1602
• Main Authors: Kwak, Hanbin, Lee, Woo-Kyun, Saborowski, Joachim, Lee, Si-Young, Won, Myoung-Soo, Koo, Kyo-Sang, Lee, Myung-Bo, Kim, Su-Na
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01-09-2012; Taylor & Francis LLC
• Subjects: Fires; Forest & brush fires; forest fire; Forest fires; Forests; GLMM; Human factors; Mean square errors; Normal distribution; Plant populations; Population density; Prediction; Regression analysis; Spatial distribution; spatial statistics; Topology; variogram; word; South Korea; INW, Korea, Rep; Korea, Rep
• ISSN: 1365-8816; 1362-3087; 1365-8824
• DOI: 10.1080/13658816.2011.642799

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.