• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.1
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• pp.1-19
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• 2022

The Geumgang Pine (Pinus densiflora) Forest in Sogwang-ri, Uljin has traditionally been a pine tree protection area (prohibited forest) for timber production purposes, and is now designated and managed as a protected area for forest genetic resource conservation by the Korea Forest Service. This study, we analyzed topographical characteristics, existing vegetation, tree age, and plant community structure, and proposed a sustainable management method for the Geumgang Pine (Pinus densiflora) Forest in Sogwang-ri, Uljin for timber havesting purposes. The topographical characteristics of the target area were 36.7% ridges and 38.7% valleys; the ratio of ridges to valleys was similar, and the slopes formed 24.7% of the total area. The types of pine forest communities are divided into six types based on the progress of pine forest renewal, the competition with other species such as deciduous broadleaf trees, and the formation of layered structures. It has been confirmed that the age of the large-diameter pine trees (40~60cm in diameter) is approximately 60~70 years, which is relatively low. As a result of the analysis of the relative importance percentage and layered structure, differences depended on the progress of the pine forest renewal project, and not only the maintenance of the pine forest, but also the creation of a secondary growth forest, the density adjustment of pine trees, and the active management of competitive trees. The average basal area by the community was 12,642.1~25,424.4cm² for the tree layer and 1.8~1,956.5cm² for the low tree layer based on a quadrat of 400m². The difference in the basal area appeared to depend on the size and number of trees forming the tree layer and the degree of pine forest renewal (the degree of time elapsed after thinning pine trees). The average number of species that appeared in each community was 8.7-20.3; there were many species located in valleys, and the type competes with deciduous broadleaf trees due to the lack of management. The diversity of species ranged from 0.6915-1.0942, and was evaluated as low compared to pine communities in central temperate zones. In this paper, we determined the management goals of Geumgang Pine (Pinus densiflora) Forest in Sogwang-ri, Uljin to produce timber with high economic value, and suggested efficient vegetation management for continuous afforestation, the establishment of a timber production system, and improvement of wood production as a management direction.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.