Forest practices for mountainous areas can enhance the scenery value and function of forests. However, forest scenery management is rarely implemented except for conservation areas and public forests. In this study, we first used the viewshed analysis to extract visible and invisible zones from the surface areas of ordinary mountains in Korea, and then we used spatial aesthetic analysis to interpret the human-recognized characteristics on the visible zones of mountain scenery. Finally, based on the results of both analyses, reasonable guidelines for forest practice planning were proposed to improve the scenery of ordinary mountains. The result shows that the viewshed analysis made it possible to extract visible and invisible areas from the surface areas of ordinary mountains, and to determine the scale of zoning for forest practices to improve mountain scenery. In addition, using spatial aesthetic analysis, it was possible to explain the characteristics of mountain scenery according to distance and elevational differences between viewpoint and target, and to suggest a treatment target and direction for forest practices to improve the mountain scenery. This study is meaningful in that the viewshed and spatial aesthetic analyses were applied to evaluate the current scenery of ordinary mountains and to present guidelines for forest practice planning to promote their own scenery values.
After the division of the Korean peninsula, North Korea overexploited their natural resources especially the forest. It lost about 23% of the total forest from 1990 to 2011, which continues today. However, the country is inaccessible to monitor such changes. Hence, in this study, we aim to use Landsat 8 imagery with the aid of Google Earth to map erosion-prone areas in a subset area of Kangwon Province, North Korea. Pruned Decision Tree (DT) modeling was used in selecting the optimum ratio/index and threshold based on ground truth points extracted for Landsat scenes from May, October, and both months combined. Pruned DT resulted in applying the normalized green, near-infrared (NIR), green ratio vegetation index (GRVI), red-green ratio index (RGRI), infrared percentage vegetation index (IPVI), and slope with the optimum threshold for the segmentation of the study area with reasonable accuracy. The result shows that combining the ground truths from different seasons resulted in rules giving higher overall accuracy (OA) and kappa coefficient than the individual rule results. However, interchanging ground truths of different months is not effective. On average, out of the total land, high and medium erosion-prone areas are 15 and 20%, respectively. The remaining 65% is covered by forest. The result can be useful for estimating loss and restoring resources such as forest and land in the future.
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