• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.259-270
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• 2020

Extreme weather events, such as heat and drought, have occurred frequently over the past two decades. This has led to continuous reports of cases of forest damage due to physiological stress, not pest damage. In 2014, pine trees were collectively damaged in the forest genetic resources reserve of Sogwang-ri, Uljin, South Korea. An investigation was launched to determine the causes of the dieback, so that a forest management plan could be prepared to deal with the current dieback, and to prevent future damage. This study aimedto 1) understand the topographic and structural characteristics of the area which experienced pine tree dieback, 2) identify the main causes of the dieback, and 3) predict future risk areas through the use of machine-learning techniques. A model for identifying risk areas was developed using 14 explanatory variables, including location, elevation, slope, and age class. When three machine-learning techniques-Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to the model, RF and SVM showed higher predictability scores, with accuracies over 93%. Our analysis of the variable set showed that the topographical areas most vulnerable to pine dieback were those with high altitudes, high daily solar radiation, and limited water availability. We also found that, when it came to forest stand characteristics, pine trees with high vertical stand densities (5-15 m high) and higher age classes experienced a higher risk of dieback. The RF and SVM models predicted that 9.5% or 115 ha of the Geumgang Pine Forest are at high risk for pine dieback. Our study suggests the need for further investigation into the vulnerable areas of the Geumgang Pine Forest, and also for climate change adaptive forest management steps to protect those areas which remain undamaged.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.430-438
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• 2009

An attempt was made to establish the suitability classes of lands for the cultivation of ginseng(Panax ginseng C. A. Meyer). For this study, the relationships between various soil characteristics and ginseng yields were investigated on altogether 450 ginseng fields (150 sites in paddy and 300 sites in upland), across Kangwon, Kyunggi, Chungbug, Chungnam, Jonbug and Kyungbug Provinces, where ginseng is widely cultivated. In the paddy fields, most influential properties of soil on the ginseng yields was found to be the drainage class. Texture of surface soil and available soil depths affected the ginseng yields to some extents. However, the topography, slope, and the gravel content were found not to affect the ginseng yields. In the uplands, the texture of surface soil was most influential and the topography, slope, and occurrence depth of hard-pan were least influential on the performance of the crop. Making use of multiple regression, by SAS, the contribution of soil morphological and physical properties such as, topography, surface soil texture, drainage class, slope, available soil depth, gravel content, and appearance depth of hard-pan, for the suitability of land for ginseng cultivation was analyzed. Based on the results of above analysis, adding up all of the suitability indices, land suitability classes for ginseng cultivation were proposed. On top of this, taking the weather conditions into consideration, suitability of land for ginseng cultivation was established in paddy field and in uplands. As an example, maps showing the distribution of suitable land for ginseng cultivation were drawn, adopting the land suitability classes obtained through current study, soil map, climate map, and GIS information, for Eumsung County, Chungbug Province. Making use of the information on the land suitability for ginseng cultivation obtained from current study, the suitability of lands currently under cultivation of ginseng was investigated. The results indicate that 74.0% of them in paddy field and 88.3% in upland are "highly suitable" and "suitable".