Abstract:

Due to considerable deforestation in North Korea, there is a need to plan forest restoration programs based on scientific forest management. In this study, a methodology was developed for estimating the site index values of six major tree species and the forest productivity potential. The site index values of these tree species were derived in South Korea using the Chapman-Richards equation. These values were used with data from the 6th National Forest Inventory, which included 20 types of edaphic, topographic, and climatic factors, and random forest analysis—a widely used machine learning technique for spatial prediction—to develop a new model for estimating the site index values of these species across South Korea. The prediction accuracy of this model was evaluated using the root mean square error. The results show that the prediction accuracy was high, with a root mean square error of ~ 1 m. Moreover, the importance of the variables related to climate and geography was generally high. The proposed site index estimation model for six tree species was applied across North Korea, and its effectiveness tested by comparing the estimated values with those reported in literature from North Korea. The differences between the model outputs and recorded data in the northern alpine regions were presumably due to the lack of data for high-altitude regions in South Korea. This model is based on the determination of the suitability of tree species in restoration efforts. Therefore, it can contribute to the evaluation of forest productivity in North Korea and may help plan efficient forest restoration programs.

Key words: Environmental factors, Forest productivity, Machine learning, North Korea, Random forest, Site index