• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.113-127
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• 2015

Recently, there has been a growing interest in UAS(Unmanned Aerial System), and it is required to develop techniques to effectively detect water body from the recorded images in order to implement flood monitoring using UAS. This study used a UAS with RGB and NIR+RG bands to achieve images, and applied supervised classification method to evaluate the accuracy of water body detection. Firstly, the result for accuracy in water body image classification by RGB images showed high Kappa coefficients of 0.791 and 0.783 for the artificial neural network and minimum distance method respectively, and the maximum likelihood method showed the lowest, 0.561. Moreover, in the evaluation of accuracy in water body image classification by NIR+RG images, the magalanobis and minimum distance method showed high values of 0.869 and 0.830 respectively, and in the artificial neural network method, it was very low as 0.779. Especially, RGB band revealed errors to classify trees or grasslands of Songsan amusement park as water body, but NIR+RG presented noticeable improvement in this matter. Therefore, it was concluded that images with NIR+RG band, compared those with RGB band, are more effective for detection of water body when the mahalanobis and minimum distance method were applied.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.36-49
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• 2019

The objectives of this study were to extract "Field Survey Based Infection Tree of Pine Wilt Disease(FSB_ITPWD)" and "Object Classification Based Infection Tree of Pine Wilt Disease(OCB_ITPWD)" from the Research Forest at Kangwon National University, and evaluate the spatial distribution characteristics and occurrence intensity of wood infested by pine wood nematode. It was found that the OCB optimum weights (OCB) were 11 for Scale, 0.1 for Shape, 0.9 for Color, 0.9 for Compactness, and 0.1 for Smoothness. The overall classification accuracy was approximately 94%, and the Kappa coefficient was 0.85, which was very high. OCB_ITPWD area is approximately 2.4ha, which is approximately 0.05% of the total area. When the stand structure, distribution characteristics, and topographic and geographic factors of OCB_ITPWD and those of FSB_ITPWD were compared, age class IV was the most abundant age class in FSB_ITPWD (approximately 55%) and OCB_ITPWD (approximately 44%) - the latter was 11% lower than the former. The diameter at breast heigh (DBH at 1.2m from the ground) results showed that (below 14cm) and (below 28cm) DBH trees were the majority (approximately 93%) in OCB_ITPWD, while medium and (more then 30cm) DBH trees were the majority (approximately 87%) in FSB_ITPWD, indicating different DBH distribution. On the other hand, the elevation distribution rate of OCB_ITPWD was mostly between 401 and 500m (approximately 30%), while that of FSB_ITPWD was mostly between 301 and 400m (approximately 45%). Additionally, the accessibility from the forest road was the highest at "100m or less" for both OCB_ITPWD (24%) and FSB_ITPWD (31%), indicating that more trees were infected when a stand was closer to a forest road with higher accessibility. OCB_ITPWD hotspots were 31 and 32 compartments, and it was highly distributed in areas with a higher age class and a higher DBH class.

• Journal of Korea Port Economic Association
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• v.28 no.4
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• pp.193-207
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• 2012

The of purpose of this study is to predict export and import container volumes using a Decision Tree analysis. Factors which can influence the volume of container cargo are selected as independent variables; producer price index, consumer price index, index of export volume, index of import volume, index of industrial production, and exchange rate(won/dollar). The period of analysis is from january 2002 to December 2011 and monthly data are used. In this study, CRT(Classification and Regression Trees) algorithm is used. The main findings are summarized as followings. First, when index of export volume is larger than 152.35, monthly export volume is predicted with 858,19TEU. However, when index of export volume is between 115.90 and 152.35, monthly export volume is predicted with 716,582TEU. Second, when index of import volume is larger than 134.60, monthly import volume is predicted with 869,227TEU. However, when index of export volume is between 116.20 and 134.60, monthly import volume is predicted with 738,724TEU.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.643-652
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• 2006

Decision trees are mainly used for the classification and prediction in data mining. The distribution of spatial data and relationships with their neighborhoods are very important when conducting classification for spatial data mining in the real world. Spatial decision trees in previous works have been designed for reflecting spatial data characteristic by rating Euclidean distance. But it only explains the distance of objects in spatial dimension so that it is hard to represent the distribution of spatial data and their relationships. This paper proposes a decision tree based on spatial entropy that represents the distribution of spatial data with the dispersion and dissimilarity. The dispersion presents the distribution of spatial objects within the belonged class. And dissimilarity indicates the distribution and its relationship with other classes. The rate of dispersion by dissimilarity presents that how related spatial distribution and classified data with non-spatial attributes we. Our experiment evaluates accuracy and building time of a decision tree as compared to previous methods. We achieve an improvement in performance by about 18%, 11%, respectively.

• Convergence Security Journal
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• v.23 no.2
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• pp.47-54
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• 2023

The global popularity of K-content(Korean Wave) has led to a continuous increase in copyright infringement cases involving domestic works, not only within the country but also overseas. In response to this trend, there is active research on technologies for detecting illegal distribution sites of domestic copyrighted materials, with recent studies utilizing the characteristics of domestic illegal distribution sites that often include a significant number of advertising banners. However, the application of detection techniques similar to those used domestically is limited for overseas illegal distribution sites. These sites may not include advertising banners or may have significantly fewer ads compared to domestic sites, making the application of detection technologies used domestically challenging. In this study, we propose a detection technique based on the similarity comparison of links and text trees, leveraging the characteristic of including illegal sharing posts and images of copyrighted materials in a similar hierarchical structure. Additionally, to accurately compare the similarity of large-scale trees composed of a massive number of links, we utilize Graph Neural Network (GNN). The experiments conducted in this study demonstrated a high accuracy rate of over 95% in classifying regular sites and sites involved in the illegal distribution of copyrighted materials. Applying this algorithm to automate the detection of illegal distribution sites is expected to enable swift responses to copyright infringements.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.6
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• pp.117-131
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• 2017

This study was conducted to draw vegetation landscape elements in the ancient literature, investigate plant community structure, understand vegetation structure, and suggest reasonal conservation management methods. As a result of analyzing ancient literature, geomorphological landscapes in Guryong valley were canyon landscape and valley surrounded in Mt. Chiak. Plant landscape factors were as followed. Rhododendron spp. spread around valley. Also, Pinus densiflora communities were distributed in Guryong valley around. Especially, the entrance zones of Guryong valley were described as covered with Pinus densiflora and Whangchangkumpyo(黃腸禁標). Therefore, it was estimated that entry of Guryong valley was covered with Pinus densiflora community landscape. As for current vegetation result, the main vegetation was divided into mixed deciduous broad-leaved trees community and Pinus densiflora community. As a result of analysis by TWINSPAN for community classification, five communities(Deciduous broadleaved trees, Pinus densiflora, Pinus densiflora-Abies holophylla, Abies holophylla, and Pinus koraiensis community) were classified. To maintain historic plant landscape and conserve crucial resources, Pinus densiflora community was classified as concentrated conservation management area($105,472m^2$). To maintain Pinus densiflora landscape which has high historical and cultural value of Guryong valley, it was considered that active density control of lower layer vegetation would be necessary. Accordingly, to conserve P. densiflora landscape, Whangchangkumpyo(?腸禁標), that area was separated into Pinus densiflora lower layer forest management area($84,029m^2$) and Pinus densiflora seedling conservation management area($21,443m^2$). In understory of Pinus densiflora lower layer flora, the target tree species for elimination and management were Quercus serrata and Quercus mongolica. They were $4{\sim}6trees/100m^2$ and their average diameter was 7.1cm. To preserve Pinus densiflora seedlings, areas with Sasa borealis, the ground vegetation of Pinus densiflora community, rate of 80% or more should be selected as priority management areas and concentrated elimination and management of Sasa borealis should be implemented. Likewise, traditional Pinus densiflora forest is a historically cultural heritage to preserve with sustainable interest and survey. Efficient management method through systematic monitoring system should be made.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.27-34
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• 2013

Due to the increased interest in global warming, interest in forest resources aimed towards reducing greenhouse gases have subsequently increased. Thus far, data related to forest resources have been obtained, through the employment of aerial photographs or satellite images, by means of plotting. However, the use of imaging data is disadvantageous; merely, due to the fact that recorded measurements such as the height of trees, in dense forest areas, lack accuracy. Within such context, the authors of this study have presented a method of data processing in which an individual tree is isolated within forested areas through the use of LIDAR data and ortho-images. Such isolation resulted in the provision of more efficient and accurate data in regards to the height of trees. As for the data processing of LIDAR, the authors have generated a normalized digital surface model to extract tree points via local maxima filtering, and have additionally, with motives to extract forest areas, applied object oriented image classifications to the processing of data using ortho-images. The final tree point was then given a figure derived from the combination of LIDAR and ortho-images results. Based from an experiment conducted in the Yongin area, the authors have analyzed the merits and demerits of methods that either employ LIDAR data or ortho-images and have thereby obtained information of individual trees within forested areas by combining the two data; thus verifying the efficiency of the above presented method.

• Korean Journal of Environment and Ecology
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• v.5 no.1
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• pp.42-53
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• 1991

The Hwaomsa valley forest and the Piagol valley rarest in Mt. Chiri were studied to investigate forest structure and succession. Thirty plots in the Hwaomsa valley forest and thirty-nine plots in the Piagol valley forest were set up, and vegetation analysis of TWINSPAN classification and DCA ordination was carried out. The size of each plot was 20m $\times$ 25m, and the trees above 2cm DBH in each plot were measured. The Hwaomsa valley forest and the Piagol valley forest were classified into four communities and three communities by the altitude, respectively. The successional trends of major tree species seem to be from Pinus densiflora and Quercus mangalica through Quercus serrata to Carpinus spp. in the Hwaomsa valley forest. and from Quercus mongalica through Quercus serrata to Carpinus Spp. in the Piagol valley forest. The Hwaomsa valley is assumed to be interfered by the man more, and develop into the climax less than the Piagol rarest.

• Korean Journal of Environment and Ecology
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• v.25 no.2
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• pp.175-188
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• 2011

The purpose of this study was to investigate the vegetation structure of Daphniphyllum macropodum community in the Geumseon Valley area of Naejangsan National Park. To do so, thirty-seven plots($100m^2$) were set up and surveyed. The surveyed plots were divided into four groups according to the analysis of classification by TWINSPAN; (I)Carpinus laxiflora community (II)Carpinus laxiflora community (III)Deciduous Broad-leaved community and (IV)Zelkova serrata community. The results of vegetation structure analysis were; Daphniphyllum macropodum did not appeared in the canopy later but in understory and shrub layer. Even though Daphniphyllum macropodum will not be dominant species in the canopy later, but it was expected that Daphniphyllum macropodum will be major species in understory and shrub layer. The expected age of forest of the Geumseon Valley where Carpinus laxiflora and Zelkova serrata were dominant trees in canopy layer, was about 50 years old while that of Daphniphyllum macropodum in understory layer was 20 years old.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.403-406
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• 2005

In order to evaluate the Capability of ETM+ remotely- sensed data to provide 'Forest-shrub land-Rangeland' cover type map in areas near the timberline of northern forests of Iran, the data were analyzed in a portion of nearly 790 ha located in Neka-Zalemroud region. First, ortho-rectification process was used to correct the geometric errors of the image, yielding 0/68 and 0/69 pixels of RMS. error in X and Y axis, respectively. The original and panchromatic bands were fused using PANSHARP Statistical module. The ground truth map was made using 1 ha field plots in a systematic-random sampling grid, and vegetative form of trees, shrubs and rangelands was recorded as a criteria to name the plots. A set of channels including original bands, NDVI and IR/R indices and first components of PCI from visible and infrared bands, was used for classification procedure. Pair-wise divergence through CHNSEL command was used, In order to evaluate the separability of classes and selection of optimal channels. Classification was performed using ML classifier, on both original and fused data sets. Showing the best results of $67\%$ of overall accuracy, and 0/43 of Kappa coefficient in original data set. Due to the results represented above, it's concluded that ETM+ data has an intermediate capability to fulfill the spectral variations of three form- based classes over the study area.