Detection and Assessment of Forest Cover Change in Gangwon Province, Inter-Korean, Based on Gaussian Probability Density Function

Abstract

The 2018 United Nations Development Programme (UNDP) report announced that deforestation in North Korea is the most extreme situation and in terms of climate change, this deforestation is a global scale issue. To respond deforestation, various study and projects are conducted based on remote sensing, but access to public data in North Korea is limited, and objectivity is difficult to be guaranteed. In this study, the forest detection based on density estimation in statistic using Landsat imagery was conducted in Gangwon province which is the only administrative district divided into South and North. The forest spatial data of South Korea was used as data for the labeling of forest and Non-forest in the Normalized Difference Vegetation Index (NDVI), and a threshold (0.6658) for forest detection was set by Gaussian Probability Density Function (PDF) estimation by category. The results show that the forest area decreased until the 2000s in both Korea, but the area increased in 2010s. It is also confirmed that the reduction of forest area on the local scale is the same as the policy direction of urbanization and industrialization at that time. The Kappa value for validation was strong agreement (0.8) and moderate agreement (0.6), respectively. The detection based on the Gaussian PDF estimation is considered a method for complementing the statistical limitations of the existing detection method using satellite imagery. This study can be used as basic data for deforestation in North Korea and Based on the detection results, it is necessary to protect and restore forest resources.