Prediction of Land Use/Land Cover Change in Forest Area Using a Probability Density Function

This study aimed to predict changes in forest area using a probability density function, in order to promote effective forest management in the area north of the civilian control line (known as the Minbuk area) in Korea. Time series analysis (2010 and 2016) of forest area using land cover maps and accessibility expressed by distance covariates (distance from buildings, roads, and civilian control line) was applied to a probability density function. In order to estimate the probability density function, mean and variance were calculated using three methods: area weight (AW), area rate weight (ARW), and sample area change rate weight (SRW). Forest area increases in regions with lower accessibility (i.e., greater distance) from buildings and roads, but no relationship with accessibility from the civilian control line was found. Estimation of forest area change using different distance covariates shows that SRW using distance from buildings provides the most accurate estimation, with around 0.98-fold difference from actual forest area change, and performs well in a Chi-Square test. Furthermore, estimation of forest area until 2028 using SRW and distance from buildings most closely replicates patterns of actual forest area changes, suggesting that estimation of future change could be possible using this method. The method allows investigation of the current status of land cover in the Minbuk area, as well as predictions of future changes in forest area that could be utilized in forest management planning and policymaking in the northern area.