• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.4
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• pp.343-355
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• 2001

The choice of map projection method is depend on the desired map purpose because there is no best projection for maps. Previous projection decisions for published paper maps are not necessarily the right decisions for corresponding digital maps. The map grid and index system is simpler than projection system but it can be easily understanded by general users. In this study, through the study for the grid and index systems of the digital and paper map in our and other countries, I suggested the improvement plan on the problems of our digital map. New grid and index system of this paper are presented on behalf of digital geospatial data which as digital topographic map, digital elevation model and other digital maps.

• Korean Journal of Environmental Biology
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• v.23 no.3 s.59
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• pp.215-220
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• 2005

Through multiple stage analysis, the biochores in Korea were classified and organized as a hierarchy system. In the 1st step, the isopleth lines of warmth index $100^{\circ}C,\;85^{\circ}C,\;55^{\circ}C\;\cdot$ month and the coldness index $-8^{\circ}C,\;or-10^{\circ}C\;\cdot$ month, which indicate the boundaries of plant formation zones (Yim and Kira 1975; Yim 1977), were applied in the determination of major biochores. In the 2nd step, these biochores were subdivided into the five classes based on Thornthwaite's moisture index (Im) and Yim and Kira (1976), as follows: $100{\le}Im,100 In the 3 rd step, the analysis of topographic features yielded three categories of flatlands, gentle slope, and steep slope areas. These were obtained by adopting the $100{\times}100-meter$ gridded DEM and by considering the physical features of the Korean Peninsula. The features of relief in mountainous areas, waters, islands, etc. were converted into climatic indices. This grouping of biochores serves as a useful tool for the interpretation of the distributional patterns of vegetation of vascular plants and similar phenomena.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.903-916
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• 2021

An area's topography refers to the shape of the earth's surface, described by its elevation, slope, and aspect, among other features. The topographical conditions determine energy flowsthat move water and energy from higher to lower elevations, such as how much solar energy will be received and how much wind or rain will affect it. Another common factor, the topographic wetness index (TWI), is a calculation in digital elevation models of the tendency to accumulate water per slope and unit area, and is one of the most widely referenced hydrologic topographic factors, which helps explain the location of forest vegetation. Analyses of topographical factors can be calculated using a geographic information system (GIS) program based on digital elevation model (DEM) data. Recently, a large number of free open source software (FOSS) GIS programs are available and developed for researchers, industries, and governments. FOSS GIS programs provide opportunitiesfor flexible algorithms customized forspecific user needs. The majority of biodiversity in island areas exists at about 20% higher elevations than in land ecosystems, playing an important role in ecological processes and therefore of high ecological value. However, island areas are vulnerable to disturbances and damage, such as through climate change, environmental pollution, development, and human intervention, and lacks systematic investigation due to geographical limitations (e.g. remoteness; difficulty to access). More than 4,000 of Korea's islands are within a few hours of its coast, and 88% are uninhabited, with 52% of them forested. The forest ecosystems of islands have fewer encounters with human interaction than on land, and therefore most of the topographical conditions are formed naturally and affected more directly by weather conditions or the environment. Therefore, the analysis of forest topography in island areas can be done more precisely than on its land counterparts, and therefore has become a major focus of attention in Korea. This study is focused on calculating the performance of different topographical factors using FOSS GIS programs. The test area is the island forests in Korea's south and the DEM of the target area was processed with GRASS GIS and SAGA GIS. The final slopes and TWI maps were produced as comparisons of the differences between topographic factor calculations of each respective FOSS GIS program. Finally, the merits of each FOSS GIS program used to calculate the topographic factors is discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.58-67
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• 2008

Topography factors, as homeostasis variables at forest fire, affect the formation of fuel load patterns, atmospheric phenomena and forest fire behavior. Examination of the correlation between landforms and fire severity is important to decision making for fire hazard analysis and fighting strategies. In this study, fire severity was analyzed using Normalized Burn Ratio(NBR) derived from pre- and post-fire Landsat TM/+ETM images and landform were classified based on Topographic Position Index(TPI) in Samcheok(2000), Cheongyang(2002), and Yangyang(2005) forest fire regions. F-tests and Duncan's multi-range test between landform and fire severity showed that fire severities of headwater, high ridges, and upper slopes is higher than ones of local ridges, midslope ridges, and plains. Fire severity were more sensitive in coniferous forest than broadleaf forests.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1233-1242
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• 2017

In this study, we tried to analyze deforestation due to forest fire by using KOMPSAT satellite imagery. For deforestation analysis, unsupervised change detection algorithm is applied to multitemporal images. Through ITPCA (ITerative Principal Component Analysis) of NDVI (Normalized Difference Vegetation Index) generated from multitemporal satellite images before and after forest fire, changed areas due to deforestation are extracted. In addition, a post-processing method using SRTM (Shuttle Radar Topographic Mission) data is involved in order to minimize the error of change detection. As a result of the experiment using KOMPSAT-2 and 3 images, it was confirmed that changed areas due to deforestation can be efficiently extracted.

• Journal of Wetlands Research
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• v.18 no.1
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• pp.45-57
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• 2016

Levee safety is being evaluated using one of the several failure causes including overtopping, infiltration and erosion or 1D hydraulic analysis considering physical characteristics of levee in practical engineering works. However, mentioned evaluation methods are not able to consider various failure causes of levee at the same time and to get reliable results where requires the accurate topographic information. This study proposed the flood hazard index which is able to consider several hazard factors involving overtopping, infiltration and erosion risk simultaneously. The index was generated from results of 2D hydraulic analysis reflecting accurate topographic information. The study areas are the confluences of the Nakdong River and two streams(Gamcheon and Hoecheon). Levee safety was evaluated using results based on 2D hydraulic analysis considering riverbed changes of before and after dredging work in the study area. This study will contribute to estimate the reliable safety evaluation of levee where may have hazards during extreme flood events.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.87-98
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• 2000

The objective of this study is to test the flood forecasting capability of TOPMODEL on a single watershed in Korea. The selected study area is the Soyang River basin with outlet at Soyang Dam site. The three daily hydrographs and the three hourly flood events during 1990~1996 are selected for model calibrations and performance tests. The model parameters are estimated on 1990 daily event by manual fitting technique and the effects of topographic index distribution to river flow simulations are investigated on the study area. The model performance on correlation coefficient between the observed and the simulated flows for the verification periods are above 0.77 on the 95-, 96-daily events, while above 0.87 for 90-, 95-, 96-hourly events. By the consideration of flood flow characteristics in Korea, the physical interpretation of the model concept, and the model performance, it can be concluded that the TOPMODEL is feasible as a flood forecasting model in Korea. Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.298-298
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• 2011

다양한 수문학적 순환 성분들의 영향을 받는 유역의 포화과정은 토양층의 공간적 이질성과 맞물려 매우 복잡한 거동특성을 보이는 것으로 알려져 왔다. 이와 관련하여 주목해 볼 만한 지형인자 중의 하나로서 지형지수(topographic index)를 들 수 있다. 지형지수는 TOPMODEL의 주요한 입력변수 중의 하나로서 그 공간적 분포에 따라 유역의 상대적인 포화도를 용이하게 평가할 수 있는 객관적인 수단을 제공한다. 하지만 신뢰성 있는 지형지수의 산정은 상당히 까다로운 작업으로서 정확한 지형지수를 산정할 수 있는 절대적인 방법은 존재하지 않는 것으로 알려져 있다. 이는 지금까지 개발된 DEM을 기반으로 한 각종 흐름방향 모의기술의 수준이 아직 자연적인 지형형상을 완전히 반영하지 못함에 기인한다. 본 연구에서는 8-흐름방향방법의 단점을 보완하기 위하여 개발된 바 있는 $\infty$-흐름방향방법에 따라 지형지수를 산정하여 유역이 공간적으로 포화되어 가는 과정을 모의하여 보았다. $\infty$-흐름방향방법은 각도의 형태로 흐름방향을 산정하여 고정된 흐름방향의 구속에서 벗어남과 동시에 최소한의 흐름분산(flow dispersion) 모의를 허용하여 유역규모의 포화과정을 효율적으로 모의할 수 있는 수단으로 판단된다. 대상유역으로는 설마천 유역을 선택하였으며 지형분석 과정에는 Arc GIS를 기반으로 운용되는 software 중의 하나인 TauDEM을 적용하였다. 아래 그림은 각각 포화도 20%, 50%, 90%에 해당하는 포화역을 도시한 것으로 하천망으로 중심으로 유역이 공간적으로 포화되어 가는 과정을 효율적으로 나타내고 있음을 확인할 수 있다. 본 연구를 통하여 제시되는 방법론은 자연유역의 유출응답 속에 내재하는 동적특성이나 비선형특성에 대한 체계적인 접근을 가능하게 하는 효율적인 수단을 제공할 수 있을 것으로 기대된다.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.499-506
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the newly developed techniques for assessment of landslide susceptibility to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial Photographs and field survey data, and a spatial database of the topography, soil type and timber cover were constructed. The landslide-related factors such as topographic slope, topographic curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter were extracted from the spatial database. Using those factors, landslide susceptibility and weights of each factor were analyzed by two artificial neural network methods. In the first method, the landslide susceptibility index was calculated by the back propagation method, which is a type of artificial neural network method. Then, the susceptibility map was made with a GIS program. The results of the landslide susceptibility analysis were verified using landslide location data. The verification results show satisfactory agreement between the susceptibility index and existing landslide location data. In the second method, weights of each factor were determinated. The weights, relative importance of each factor, were calculated using importance-free characteristics method of artificial neural networks.