• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Journal of the Korean earth science society
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• v.27 no.6
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• pp.620-642
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• 2006

The Moho structure and its deformation in the southern part of the Korean Peninsula were estimated using gravity and topography data. Gravity signals from the upper and lower crust were separated using a filter that was computed from isostacy and elastic thickness. The result of this study shows three characteristic features of the Moho deformation. First, the Moho folding structure is parallel to SKTL (the South Korean Tectonic Line), which indicates positive association with the collision of the Yeongnam and Gyeonggi Massifs and repeated compression afterwards. In contrast, noticeable deformation of the Moho was not observed along the Imjingang Belt, which is interpreted as another continental collisional belt in the Korean Peninsula. Second, the Moho beneath the Gyeongsang Basin has remarkably risen; this seems to be the result from both the collisional compression and buoyancy caused by magmatic underplating. Third, the Moho deformation is shallowest in the east of the Taebaek Mountains and deepens toward the west, consistent with the topographic characteristic of the Korean Peninsula of "high east and low west". It can be interpreted as the results of the opening of the East Sea and Ulleung Basin. A tectonic explanation for this could be the ascent of the mantle induced by continental rifting and horizontal extension at the early stage of the opening of the East Sea. The Moho deformation model computed in this study correlates well with the earthquake distribution and crustal movement measured by GPS. We suggest that the compression along the SKTL is still exerted, consequently, the Moho deformation is active, although it may be weak.

• Journal of Forest and Environmental Science
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• v.34 no.6
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• pp.461-471
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• 2018

In this study, the deforestation and forest degradation areas have been obtained in Myanmar using a land cover lamp (LCM) and a tree cover map (TCM) to get the $CO_2$ potential reduction and the strength of occurrence was evaluated by using the geostatistical technique. By applying a multiple criteria decision-making method to the regions having high strength of occurrence for the $CO_2$ potential reduction for the deforestation and forest degradation areas, the priority was selected for candidate lands for REDD+ project. The areas of deforestation and forest degradation were 609,690ha and 43,515ha each from 2010 to 2015. By township, Mong Kung had the highest among the area of deforestation with 3,069ha while Thlangtlang had the highest in the area of forest degradation with 9,213 ha. The number of $CO_2$ potential reduction hotspot areas among the deforestation areas was 15, taking up the $CO_2$ potential reduction of 192,000 ton in average, which is 6 times higher than that of all target areas. Especially, the township of Hsipaw inside the Shan region had a $CO_2$ potential reduction of about 772,000 tons, the largest reduction potential among the hotpot areas. There were many $CO_2$ potential reduction hot spot areas among the forest degradation area in the eastern part of the target region and has the $CO_2$ potential reduction of 1,164,000 tons, which was 27 times higher than that of the total area. AHP importance analysis showed that the topographic characteristic was 0.41 (0.40 for height from surface, 0.29 for the slope and 0.31 for the distance from water area) while the geographical characteristic was 0.59 (0.56 for the distance from road, 0.56 for the distance from settlement area and 0.19 for the distance from Capital). Yawunghwe, Kalaw, and Hsi Hseng were selected as the preferred locations for the REDD+ candidate region for the deforestation area while Einme, Tiddim, and Falam were selected as the preferred locations for the forest degradation area.

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.175-195
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• 1998

Automatic lineament extraction algorithms had been developed by various researches for geological purpose using remotely sensed data. However, most of them are designed for a certain topographic model, for instance rugged mountainous region or flat basin. Most of common topographic characteristic in Korea is a mountainous region along with alluvial plain, and consequently it is difficult to apply previous algorithms directly to this area. A new algorithm of automatic lineament extraction from remotely sensed images is developed in this study specifically for geological applications. An algorithm, named as DSTA(Dynamic Segment Tracing Algorithm), is developed to produce binary image composed of linear component and non-linear component. The proposed algorithm effectively reduces the look direction bias associated with sun's azimuth angle and the noise in the low contrast region by utilizing a dynamic sub window. This algorithm can successfully accomodate lineaments in the alluvial plain as well as mountainous region. Two additional algorithms for estimating the individual lineament vector, named as ALEHHT(Automatic Lineament Extraction by Hierarchical Hough Transform) and ALEGHT(Automatic Lineament Extraction by Generalized Hough Transform) which are merging operation steps through the Hierarchical Hough transform and Generalized Hough transform respectively, are also developed to generate geological lineaments. The merging operation proposed in this study is consisted of three parameters: the angle between two lines($\delta$$\beta$), the perpendicular distance($(d_ij)$), and the distance between midpoints of lines(dn). The test result of the developed algorithm using Landsat TM image demonstrates that lineaments in alluvial plain as well as in rugged mountain is extremely well extracted. Even the lineaments parallel to sun's azimuth angle are also well detected by this approach. Further study is, however, required to accommodate the effect of quantization interval(droh) parameter in ALEGHT for optimization.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.450-456
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• 2017

This study was conducted to develop estimated equation for mortality rates (volume of dead trees, %) on coniferous and broad-leaved forests, representative forest types of South Korea. There were 6 equation models applied for estimating mortality such as a exponential equation, a Hamilton equation and variables using were DBH, basal area, and site index. Raw data used for estimating mortality were $5^{th}$ and $6^{th}$ national forest inventory data, and mortality was calculated with the difference of stocks between lived trees and dead trees by each sample plots. The most applicable equation to describe mortality on coniferous forest and broad-leaved forest was indicated as $P=(1+e^{(a+b{\times}DBH+c{\times}BA+d{\times}no\_ha+e{\times}density)})^{-1}$ and their goodness of fit showed 34% and 51% respectively. Goodness of fit in both equations were not much high because there were various factors which affect the mortality such as topographic conditions, soil characteristic, climatic factors, site quality, and competition. Therefore, it is considered that explaining mortality in forest with only 2 or 3 variables like DBH, basal area used in this analysis could be very difficult facts. However, this study is certainly worth in that there is no useful information on mortality by each forest type throughout the country at the present, and we would make an effort to promote the fitness of estimated equation for mortality adding competition index, tree crown density etc.

• Journal of Korean Society of Rural Planning
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• v.22 no.3
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• pp.63-70
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• 2016

Due to the Korea's topographic characteristic, there are a lot of marine bridges to connect between islands and mainland. In addition, marine bridges play an important role in a regional landscape. For these reasons, landscape design of bridge is necessary in order to improve beautification of region. So, this studies analyzed image and landscape preference of marine bridges in rural area. The main results were summarized as follows: When rating the image of the background in sea and mountain image, 'stable' and 'natural' were rated highly. When rating the image of the arch bridge in sea and mountain image, 'beautiful', and 'attractive' were rated highly. When rating the image of the cable-stayed bridge in sea and mountain image, 'splendid', and 'attractive' were rated highly. When rating the image of the suspension bridge in sea and mountain image, 'beautiful', and 'splendid' were rated highly. Next, When rating the image of the background in sea and building image, 'stable' and 'natural' were rated highly. When rating the image of the arch bridge in sea and building image, 'beautiful', and 'splendid' were rated highly. When rating the image of the cable-stayed bridge in sea and building image, 'beautiful', and 'attractive' were rated highly. When rating the image of the suspension bridge in sea and building image, 'beautiful', and 'attractive' were rated highly. And, The image of suspension bridges in sea and mountain image is more highly preferred than other image. The background in sea and mountain image is landscape of the lowest preference. In the mountain and sea image, the preference of suspension bridge landscape has the highest rating. In the sea and building image, the preference of arch bridge landscape has the highest rating. In conclusion, the results illustrate that the marine bridge's shape and its background in rural area are important elements of a visual preference. When designing the marine bridge, designer have to choose a proper bridge shape for its background. However, this research's limitation is that this research consider only bridge shape and background to analyze landscape preference of marine bridges. Therefore, further research is necessary to consider various elements.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.48-62
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• 2018

In this study, landslide of debris flow occurred at 51 sites around Daeryounsan located in between Chuncheon-si and Hongcheon-gun during July in 2013 were investigated in field and behavior characteristics of debris flow were analyzed on the basis of records of rainfall and site investigation. According to debris flow types of channelized and hill slope, location and slope angle of initiation and deposit zone, and width and depth of erosion were investigated along entire runout of debris flow. DEM(Digital Elevation Model) of Daeryounsan was constructed with digital map of 1:5,000 scale. Land slide hazard was estimated using SINMAP(Stability INdex MAPping) and the predicted results were compared with field sites where debris flow occurred. As analyzed results, for hill slope type of debris flow, predicted sites were quite comparable to actual sites. On the other hand, for channelized type of debris flow, debris flow occurrence sites were predicted by using stability index associated with topographic wetness index. As analyzed results of 4 different conditions with the parameter T/R, Hydraulic transmissivity/Effective recharge rate, proposed by NRCS (Natual Resources Conservation Service), predicted results showed more or less different actual sites and the degree of hazard tended to increase with decrease of T/R value.