• 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.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1037-1052
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• 2019

We had developed in preceding study a classification model for the Korean pine and Larch with an accuracy of 98 percent using Hyperion and Sentinel-2 satellite images, texture information, and geometric information as the first step for tree species mapping in the inaccessible North Korea. Considering a share of major tree species in North Korea, the classification model needs to be expanded as it has a large share of Oak(29.5%), Pine (12.7%), Fir (8.2%), and as well as Larch (17.5%) and Korean pine (5.8%). In order to classify 5 major tree species, national forest type map of South Korea was used to build 11,039 training and 2,330 validation data. Sentinel-2 data was used to derive spectral information, and PlanetScope data was used to generate texture information. Geometric information was built from SRTM DEM data. As a machine learning algorithm, Random forest was used. As a result, the overall accuracy of classification was 80% with 0.80 kappa statistics. Based on the training data and the classification model constructed through this study, we will extend the application to Mt. Baekdu and North and South Goseong areas to confirm the applicability of tree species classification on the Korean Peninsula.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.197-209
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• 2001

TOPMODEL, semi-distributed hydrological model, is frequently applied to predict the amount of discharge, main flow pathways and water quality in a forested catchment, especially in a spatial dimension. TOPMODEL is a kind of conceptual model, not physical one. The main concept of TOPMODEL is constituted by the topographic index and soil transmissivity. Two components can be used for predicting the surface and subsurface contributing area. This study is conducted for the validation of applicability of TOPMODEL at small forested catchments in Korea. The experimental area is located at Gwangneung forest operated by Korea Forest Research Institute, Gyeonggi-do near Seoul metropolitan. Two study catchments in this area have been working since 1979 ; one is the natural mature deciduous forest(22.0 ha) about 80 years old and the other is the planted young coniferous forest(13.6 ha) about 22 years old. The data collected during the two events in July 1995 and June 2000 at the mature deciduous forest and the three events in July 1995 and 1999, August 2000 at the young coniferous forest were used as the observed data set, respectively. The topographic index was calculated using $10m{\times}10m$ resolution raster digital elevation map(DEM). The distribution of the topographic index ranged from 2.6 to 11.1 at the deciduous and 2.7 to 16.0 at the coniferous catchment. The result of the optimization using the forecasting efficiency as the objective function showed that the model parameter, m and the mean catchment value of surface saturated transmissivity, $lnT_0$ had a high sensitivity. The values of the optimized parameters for m and InT_0 were 0.034 and 0.038; 8.672 and 9.475 at the deciduous and 0.031, 0.032 and 0.033; 5.969, 7.129 and 7.575 at the coniferous catchment, respectively. The forecasting efficiencies resulted from the simulation using the optimized parameter were comparatively high ; 0.958 and 0.909 at the deciduous and 0.825, 0.922 and 0.961 at the coniferous catchment. The observed and simulated hyeto-hydrograph shoed that the time of lag to peak coincided well. Though the total runoff and peakflow of some events showed a discrepancy between the observed and simulated output, TOPMODEL could overall predict a hydrologic output at the estimation error less than 10 %. Therefore, TOPMODEL is useful tool for the prediction of runoff at an ungaged forested catchment in Korea.

• Korean Journal of Environment and Ecology
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• v.32 no.5
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• pp.486-496
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• 2018

In this study, we have selected Larix kaempferi as a study area in Woraksan National Park for understanding the ion index according to the difference of topography in national parks. We measured the weather and ion at two fixed points, ridge and valley, where the Larix kaempferi dominates in the same ecological structure in Woraksan National Park. The weather measurement results showed the average, maximum, and minimum temperatures of $28.22^{\circ}C$, $29.9^{\circ}C$, and $26.4^{\circ}C$, respectively at the ridge. The average, maximum, and minimum temperatures at the valley were $27.08^{\circ}C$, $27.8^{\circ}C$, and $25.5^{\circ}C$, respectively. The average, maximum, and minimum relatively humidities at the ridge were 67.02%, 75.25%, and 61.95%, respectively. he average, maximum, and minimum relatively humidities at the valley were 69.74%, 76.8%, and 63.75%, respectively. The average, maximum, and minimum amounts of positive ions generated in the ridge was $698.40{\pm}59.80ea/cm^3$, $885.88ea/cm^3$, and $597.88ea/cm^3$, respectively. The average, maximum, and minimum amounts of negative ions generated in the ridge were $736.07{\pm}83.89ea/cm^3$, $934.53ea/cm^3$, and $599.32ea/cm^3$, respectively. The ion index is calculated to be 1.06. The average, maximum, and minimum amounts of positive ions generated in the valley were $1,732.49{\pm}354.08ea/cm^3$, $2,652.10ea/cm^3$, and $1,110.92ea/cm^3$, respectively. The average, maximum, and minimum amounts of negative ions generated in the valley were $1,990.47{\pm}433.57ea/cm^3$, $3,126.75ea/cm^3$, and the minimum value was $1,352.17ea/cm^3$. The ion index is calculated to be 1.16. The difference in the amount of negative ions generated in ridge and valley was $1089.26ea/cm^3$, and the difference of the calculated ion index between the ridge portion and the valley portion was 0.10. The results of this study were provided as the reference weather data of national parks for health management.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.227-237
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• 2022

The most critical factors for detecting changes in very high-resolution satellite images are building positional inconsistencies and relief displacements caused by satellite side-view. To resolve the above problems, additional processing using a digital elevation model and deep learning approach have been proposed. Unfortunately, these approaches are not sufficiently effective in solving these problems. This study proposed a change detection method that considers both positional and topology information of buildings. Mask R-CNN (Region-based Convolutional Neural Network) was trained on a SpaceNet building detection v2 dataset, and the central points of each building were extracted as building nodes. Then, triangulated irregular network graphs were created on building nodes from temporal images. To extract the area, where there is a structural difference between two graphs, a change index reflecting the similarity of the graphs and differences in the location of building nodes was proposed. Finally, newly changed or deleted buildings were detected by comparing the two graphs. Three pairs of test sites were selected to evaluate the proposed method's effectiveness, and the results showed that changed buildings were detected in the case of side-view satellite images with building positional inconsistencies.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.481-486
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• 2004

본 연구에서는 분석된 산사태 발생원인을 근거로 산사태 발생 가능 지역에 대한 산사태 발생원인에 대한 등급값을 이용하여, 인접한 연구지역에 교차 적용하여 위험성을 평가하여 취약성도를 작성하고 산사태 피해 예방을 위한 방재 사업, 국토개발 계획 및 건설계획을 위한 기초 자료로 적용 및 활용할 수 있도록 하였다. 연구대상 지역은 여름철 집중호우시 산사태가 많이 발생하는 지역으로 정하였으며, 행정구상으로 강원도 강릉시 사천면 사기막리와 주문진읍 삼교리에 해당한다. 산사태가 발생할 수 있는 요인으로 지형도로부터 경사, 경사방향, 곡률, 수계추출을, 정밀토양도로부터 토질, 모재, 배수, 유효토심, 지형을, 임상도로부터 임상, 경급, 영급, 밀도를, 지질도로부터 암상을, Landsat TM 영상으로부터 토지이용도와 추출하여 격자화 하였으며, 아리랑1호 영상으로부터 선구조를 추출하여 l00m 간격으로 버퍼링한 후 격자화 하였다. 이렇게 구축된 산사태 발생 위치 및 발생요인 데이터베이스를 이용, Frequence ratio를 이용하여 각 요소간의 분류를 산사태와의 상관관계를 바탕으로 취약성도를 구하였다. 그리고 계산된 산사태 취약성 지수의 기존 산사태 발생을 설명하는 능력을 정량적으로 표현하기 위하여 추정능력을 계산하였다 또한 이를 교차적용 하여 산사태 취약성도를 각각의 경우에 맞게 만들었다 이러한 평가는 산사태 피해 예방을 위한 방재 사업, 국토개발 계획, 건설계획 등에 기초자료로서 적용 및 활용될 수 있다.

• Korean Journal of Environment and Ecology
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• v.3 no.1
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• pp.51-69
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• 1989

To investigate edge vegetation structure and edge species in Kaya Mountain National Park, field survey was executed from July to August, 1989 and the result are as follows. Cantilevered and advancing types of edge vegetation were observed on site, The relative importance values of major species were changed along distance from edge to forest interior and were seemed to be affected by aspect, soil moisture and present tree layer vegetation. Especially, light-oriented species were observed as a codominant species under pine tree canopy due to selective allelopathy effect and thin canopy. Ecological indices according to the distance from edge to forest interior did not show regular pattern, but edge depth was estimated as 15-20m, approximately, Dominant species of edge seemed to be affected by soil moisture rather than altitude and aspect, but floristic similarities seemed to be affected by altitude. Frequency classes of edge species were different by aspect, altitude and physiogra-phical location. Lespedeza maximowiczii, Weigela subsessilis and Fraxinus rhynchophylla showed high frequency class in all environment conditions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.281-292
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• 2014

This study examined the accuracy that produced using various types and combinations of landslide-related factors from landslide susceptibility index maps. A database of landslide-related factors was adopted by the landslide locations that obtained from aerial photographs, and the topographic factors that derived from airborne LiDAR observations and digital maps, and various soil, forest, and land cover. Landslide susceptibility index maps were calculated by logistic regression and frequency ratio from the landslide susceptibility index. The correlation between airborne LiDAR data and digital map was shown strong similarities with one another. Landslide susceptibility index maps indicated the existence of a strong correlation and high prediction accuracy, especially when the frequency ratio and airborne LiDAR were used. Therefore, we concluded that the Airborne LiDAR will contribute to the development of effective landslide prediction methods and damage reduction measures.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.288-288
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• 2019

지구온난화로 인해 해수면이 지속적으로 상승하고 있으며, 이에 따라 연안인근 지역은 복합원인에 의한 홍수피해가 빈번히 발생하고 있다. 우리나라는 반도 지형으로 해수면 상승에 따라 침수피해 발생 시 피해규모가 클 것으로 예상되어 이에 적극적으로 대처할 필요가 있다. 복합원인에 의한 침수예상도는 해수위를 고려한 내외수 침수피해 발생 시 침수의 범위 및 양상을 예측한다. 먼저 침수발생 시 피해규모가 클 것으로 예상되는 연안인근의 도심지역을 위주로 대상지역을 선정하였으며, 침수발생 원인별 침수예상도를 작성하였다. 작성된 침수예상도를 바탕으로 상세 홍수취약성을 평가하였으며, 이를 바탕으로 주요 시설물의 위치 선정, 관거 개량의 우선순위 선정 등에 활용할 수 있다. 먼저 도상조사를 통해 침수발생 후보지역을 선정하고, 현장답사를 통해 현장 변경사항, 재해원인 및 재해발생가능성을 검토하여 대상지역으로 여수시 연등천 인근을 선정하였다. 모의 방법으로는 HEC-HMS 및 XP-SWMM 등 강우-유출 모형에 의해 침수해석을 실시하고, 하류단 경계조건의 변화에 따른 기점수위를 산정하여 해수위를 고려하였다. 하류단 경계조건으로는 대상지역의 폭풍해일에 의한 해수위 상승고를 적용하였다. 배수토구가 하천으로 연결된 경우에는 해당 하천의 홍수위 산정이 필요하며 홍수위 산정에는 HEC-RAS 모형을 사용하였다. 작성된 침수예상도를 통해 상세 홍수취약성 분석을 실시하였으며, 상세 홍수취약성 지수는 "기후변화 적응을 위한 연안도시지역별 복합원인의 홍수 취약성 평가기술 개발 및 대응방안 연구"에서 개발된 지표를 기반으로 산정하였다. 본 연구에서는 강우-유출 모형의 하류단 경계조건 변화를 통해 해수위 상승을 고려하여 연안도시 지역의 침수예상도를 작성하였으며, 침수발생 예상도를 통해 상세 홍수취약성을 분석하였다. 이는 침수발생에 따른 대피지도 개발, 주요 시설물의 계획, 침수피해 예방을 위한 구조적 대책 수립을 위한 기초자료로 활용될 수 있다.