Si Young Lee

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

Evaluation for damaged degree of vegetation by forest fire using lidar and a digital aerial photograph.

The amount of vegetation physically damaged by forest fire can be evaluated using lidar (Light Detection And Ranging) data because the loss of canopy height and width by forest fire can be relevant to the number of points transmitted to the ground through the canopy of the damaged forest. On the other hand, the biological damage of vegetation caused by forest fire can be obtained from the Normalized Difference Vegetation Index (NDVI), which determines the vegetation vitality. In this study, the degree of physical damage from the lidar data was classified into serious physical damage (SPD) and light physical damage (LPD). The degree of biological damage using NDVI was likewise classified into serious biological damage (SBD) and light biological damage (LBD). Finally, the damaged area was graded into four categories: (a) SPD and SBD, (b) LPD and SBD, (c) SPD and LBD, and (d) LPD and LBD. The accuracy assessment for the area classified into four grades showed an overall accuracy of 0.74, and a kappa value of 0.61 which provides improvement over previous works.