• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.298-301
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• 2004

The aim of this study is to cross-validate of spatial probability model, artificial neural network at Boun, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the Boun, Janghung and Youngin areas from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, forest cover and land use were constructed to spatial data-sets. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database. Topographic type, texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter, age and density of forest were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using the landslide­occurrence factors by artificial neural network model. For the validation and cross-validation, the result of the analysis was applied to each study areas. The validation and cross-validate results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

• Spatial Information Research
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• v.8 no.1
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• pp.101-116
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• 2000

Seoul Metropolitan Government(SMG) as well as the other local governments should have a role in managing groundwater according to the Groundwater Law. As a guidance of the management and control of groundwater use, this study emphasizes the necessity of the construction of 1 ; 5,000 hydrogeological maps and proposes possble usages of the maps. As expected to be a part of the groundwater GIS database constructed by SMG, the maps can be used as base maps to facilitate the whole processes related to the groundwater development and preservation. For the purpose of the construction of the 1 ; 5,000 hydrogelogical GIS database, we develop the classification system of the hydro-geological entities and their attributes.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.170-175
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• 2003

Ground and rock mass considered in tunnelling have characteristics such as uncertainty, heterogeneity and structural complexity because they have been formed undergoing various geological events for a long period. So, it is difficult for engineers to predict behaviors of rock mass in tunneling. In the paper the authors describe the development of an integrated expert system prototype for site investigation, design and construction in tunnelling and introduce the case applying this system to the tunnel construction site under construction. Geostructure Research Group in Korea Institute of Construction Technology (KICT) has developed the system during the past 4 years. The system mainly consists of several modules which is related to the design, construction and management of tunnelling. The test site, Neung-dong tunnel is located in Ulsan, Korea. The geology map shows it may confront big fault zone whose width is over kilometres. With the networking system of ITIS, various information of face mapping, monitoring and other construction task can be transmitted into the database and GIS Server at real time. And necessary analyses can be carried out with the modules equipped in the system.

• Journal of Environmental Science International
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• v.25 no.12
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• pp.1653-1660
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• 2016

Drought is a reoccurring worldwide natural hazard that affects not only food production but also economics, health, and infrastructure. Drought monitoring is usually performed with precipitation-based indices without consideration of the actual state and amount of the land surface properties. A drought index based on the actual evapotranspiration can overcome these shortcomings. The severity of a drought can be quantified by making a spatial map. The procedure for estimating actual evapotranspiration is costly and complicated, and requires land surface information. The possibility of utilizing drone-driven remotely sensed data for actual evapotranspiration estimation was analyzed in this study. A drone collected data was used to calculate the normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). The spatial resolution was 10 m with a grid of $404{\times}395$. The collected data were applied and parameterized to an actual evapotranspiration estimation. The result shows that drone-based data is useful for estimating actual evapotranspiration and the corresponding drought indices.

• Journal of Plant Biology
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• v.12 no.4
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• pp.19-29
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• 1969

This study investigated the vertical distribution of the vegetation in Mt. Hanla of Quelpart Island from 1967 to 1969. According to the physiognomy and life form of plants, the following plant communities are observed and arranged tentatively from the foot to the top. A. North-facing slope. (1) Grass land(0∼600m) (2) Deciduous broad-leaved forest(600∼1200m) (3) Mixed forest(broad and needle leaved forest : 1200∼1300m) (4) Evergreen coniferous forest(1300∼1700m) (5) Scrub communities(1700∼1950m) B. South-facing slope. (1) Grass land(0∼700) (2) Deciduous broad-leaved forest(700∼1300m) (3) Mixed forest(1300∼1550m) (4) Evergreen coniferous forest (1550∼850m) (5) Scrub communities(1850∼1950m) D. West-facing slope. (1) Grass land(0∼650) (2) Deciduous brod-leaved forest(650∼1400m) (3) Mixed forest(1400∼1500m) (4) Evergreen coniferous forest(1500∼1750m) (5) Scrub communities(1750∼1950m) The relation of the geological map and vegetation are as follows: (1) In the region of lava of Mt. Hanla are mainly distribut4ed deciduous broad-leaved forest. (2) In the region of Mt. Hanla are mainly distributed scrub. (3) In the region of lava of Gogun-san are involved deciduous broad-leaved forest, scrub and grass land of Gaimi-Dung.

• Geomechanics and Engineering
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• v.13 no.3
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• pp.475-488
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• 2017

Geotechnical data contributes substantially to the cost of engineering projects due to increasing cost of site investigations. Existing information in the form of soil maps can save considerable time and expenses while deciding the scope and extent of site exploration for a proposed project site. This paper presents spatial interpolation of data obtained from soil investigation reports of different construction sites and development of soil maps for geotechnical characterization of Multan area using ArcGIS. The subsurface conditions of the study area have been examined in terms of soil type and standard penetration resistance. The Inverse Distance Weighting method in the Spatial Analyst extension of ArcMap10 has been employed to develop zonation maps at different depths of the study area. Each depth level has been interpolated as a surface to create zonation maps for soil type and standard penetration resistance. Correlations have been presented based on linear regression of standard penetration resistance values with depth for quick estimation of strength and stiffness of soil during preliminary planning and design stage of a proposed project in the study area. Such information helps engineers to use data derived from nearby sites or sites of similar subsoils subjected to similar geological process to build a preliminary ground model for a new site. Moreover, reliable information on geometry and engineering properties of underground layers would make projects safer and economical.

• Land and Housing Review
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• v.11 no.4
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• pp.67-74
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• 2020

Despite its ecological importance of wetlands in our life, wetlands around cities and villages have been steadily disappearing. It is mainly due to either natural geological changes or man-made causes such as permitted land zone changes for the purpose to convert to agriculture land. This study aims to establish a comprehensive database of wetlands using GIS in a historic conservation city Gongju City. The systemic data management would enable to identify distribution of wetlands and their ecological attributes, and to estimate the conservation value of the wetlands. The data base will be able to use in strategic way to preserve the local ecological areas for local residents and tourists and manage the quality of wetlands in the historic and conservation city. For the purpose, it is suggested organising a wider network with not only local residents but local authorities and relevant stakeholders would be beneficial. This study also recommends that long-term strategic plan to preserve the existing wetlands. This study findings as a seminal case example can be used to apply to other geographical areas of the country and can contribute to create a national ecological nature map as well as evaluate the attributes and quality of natural ecosystem service of the region.

• Geomechanics and Engineering
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• v.37 no.2
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• pp.135-147
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• 2024

A GIS-based landslide susceptibility mapping (LSM) was carried out using frequency ratio (FR), modified frequency ratio (M-FR), analytic hierarchy process (AHP), and modified analytic hierarchy process (M-AHP) methods to identify and delineate the potential failure zones along the Halong - Vandon expressway. The thematic layers of various landslide causative factors were generated for modeling in GIS, including geology, rainfall, distance to fault, distance to road, slope, aspect, landuse, density of landslide, vertical relief, and horizontal relief. In addition, a landslide inventory along the road network was prepared using data provided by the management department during the course of construction and operation from 2017 to 2019, when many landslides were documented. The validation results showed that the M-FR method had the highest AUC value (AUC = 0.971), which was followed by the FR method with AUC = 0.961. The AUC values were 0.939 and 0.892 for the M-AHP and AHP methods, respectively. The generated LSM obtained from M-FR method classified the study area into five susceptibility classes: very low (0), low (0-1), moderate (1-2), high (2-3), and very high (3-4) classes, which could be useful for various stakeholders like planners, engineers, designers, and local public for future construction and maintenance in the study area.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• Korean Journal of Ecology and Environment
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• v.48 no.1
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• pp.61-67
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• 2015

This study, which was conducted from Apr. 2013 to Jan. 2014, was carried out as part of a project of making a more detailed ecological zoning map with 1/5,000 scale. The necessity of electronic vegetation map with large scale has arisen in order to make the best use of basic research findings on resource monitoring of National Parks and to enhance efficiency in National Park management. In order to improve accuracy and speed of vegetation research process, the data base for vegetation research was categorized into five groups, namely broad-leaved forest, coniferous forest, mixed forest, rock vegetation and miscellaneous one. And then a vegetation map for vegetation research was created for the research on the site. What is in the database for vegetation research and the vegetation map reflecting findings from vegetation research showed similar distribution rate for broad-leaved forest with 71.965% and 71.184%, respectively. The distribution rate of coniferous forest (16.010%, 15.747%), mixed forest (10.619%, 12.085%), and rock vegetation (0.015%, 0.002%) did not have much difference. In a detailed vegetation map reflecting vegetation research findings, the broad-leaved mountain forest was the most widely distributed with 60.096% based on the physiognomy classification. It was followed by mountain coniferous forest (16.332%), mountain valley forest (15.887%), and plantation forest (3.558%) As for vegetation conservation classification evaluated in the national park, grade I and grade II areas took up 200.44 km2, 61.80% and 108.80 km2, 33.55% respectively. The combined area of these two amounts to 95.35%, making this area the first grade area in ecological nature status. This means that this area is highly worth preserving its vegetation. The high rate of grade I area such as climax forests, unique vegetation, and subalpine vegetation seems to be attributable to diverse innate characteristics of Odaesan National Park, high altitude, low level of artificial disturbance, the subalpine zone formed on the ridge of the mountain top, and their vegetation formation, which reflects climatic and geological characteristics, despite continuous disturbance by mountain climbing.