• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.9-15
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• 2018

From natural disasters such as floods, heavy rains, and strong winds and social disasters such as 911 U.S. terrorism and cyber attacks that could have a fatal impact on corporate continuity, it is necessary to introduce and implement a Business Continuity Management System (BCMS) within a firm to maintain continuity of business and to change the organizational structure for an emergency state in order to operate and manage it systematically and efficiently. therefore, this study analyzed and verified the impact of introducing a Business Continuity Management System (BCMS) on the change in the recognition structure of an organization in four categories, including personal recognition, organizational culture, organizational structure, and organizational strategy, in order to analyse the impact and effect of introducing a Business Continuity Management System (BCMS) on the change in the recognition structure of each category. through this study, we believe that the introduction of a Business Continuity Management System (BCMS) within a firm could effectively change the organization's perception of an emergency state and help it maintain its continuity as well as improve its value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.545-550
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• 2019

The frequency of natural disasters and the scale of damage are increasing due to the abnormal weather phenomenon occurring all over the world. As a result, as the hydrological aspect of the urban watershed changes, the increase in impervious area leads to serious domestic flood damage due to increased rainfall. In order to minimize the damage of life and property, domestic flooding prediction system is needed. In this study, we developed a flood nomogram capable of predicting flooding only by rainfall intensity and duration. This study suggests a method to set the internal water immersion alarm criterion by analyzing the characteristics of the flooding damage in the flooded area in the metropolitan area where flooding is highly possible and the risk of flooding is high. In addition, based on the manhole and the pipe, the water level was set as follows under the four conditions. 1) When manhole overflows, 2) when manhole is full, 3) when 70% of the pipe is reached, and 4) when 60% of the pipe is reached. Therefore, it can be used as a criterion and a predictive measure to cope with the pre-preparation before the flooding starts, through the rainfall that causes the flooding and the flooding damage.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.253-265
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• 2020

As the risk of landslide is recently increasing due to the typhoons and localized heavy rains, effective techniques for the landslide damage detection are required to support the establishment of the recovery planning. This study describes the analysis of landslide-damaged areas using ISODATA(Iterative Self-Organizing Data Analysis Technique Algorithm) with Sentinel-2 image, regarding the case of Gokseong in August 7, 2020. A total of 4.75 ha of landslide-damaged areas was detected from the Sentinel-2 image using spectral characteristics of red, NIR(Near Infrared), and SWIR(Shortwave Infrared) bands. We made sure that the satellite remote sensing is an effective method to detect the landslide-damaged areas and support the establishment of the recovery planning, followed by the field surveys that require a lot of manpower and time. Also, this study can be used as a reference for the landslide management for the CAS500-1/2(Compact Advanced Satellite) scheduled to launch in 2021 and the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.210-221
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• 2022

In order to promptly respond to disasters, the era of new spaces has opened where satellite images with various characteristics can be used. As the number of satellites in operation at home and abroad increases and the characteristics of satellite sensors vary, it is necessary to find satellite images optimized for disaster types. Disaster types were divided into typhoons, heavy rains, droughts, forest fires, etc., and the optimal satellite images were selected for each type of disaster considering satellite orbits, active/passive sensors, spatial resolution, wavelength bands, and revisit cycles. Each satellite orbit TLE (Two Line Element) information was applied to the SGP4 (Simplified General Perturbations version 4) model to develop a satellite orbit simulation algorithm. The developed algorithm simulated the satellite orbit at 10-second intervals and selected an accurate observation area by considering the angle of incidence of each sensor. The satellite orbit simulation algorithm was applied to the case of Typhoon Mitag in 2019 and compared with the actual satellite list. Through the analyzed results, the time and area of the captured image and the image to be recorded were analyzed within a few seconds to select the optimal satellite image according to the type of disaster. In the future, it is intended to serve as a basis for building a system that can promptly request and secure satellite images in the event of a disaster.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1091-1104
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• 2022

Climate change is predicted to increase the frequency and intensity of rainfall worldwide, and the pattern is changing due to inundation damage in urban areas due to rapid urbanization and industrialization. Accordingly, the impact assessment of climate change is mentioned as a very important factor in urban planning, and the World Meteorological Organization (WMO) is emphasizing the need for an impact forecast that considers the social and economic impacts that may arise from meteorological phenomena. In particular, in terms of traffic, the degradation of transport systems due to urban flooding is the most detrimental factor to society and is estimated to be around £100k per hour per major road affected. However, in the case of Korea, even if accurate forecasts and special warnings on the occurrence of meteorological disasters are currently provided, the effects are not properly conveyed. Therefore, in this study, high-resolution analysis and hydrological factors of each area are reflected in order to suggest the depth of flooding of urban floods and to cope with the damage that may affect vehicles, and the degree of flooding caused by rainfall and its effect on vehicle operation are investigated. decided it was necessary. Therefore, the calculation formula of rainfall-immersion depth-vehicle speed is presented using various machine learning techniques rather than simple linear regression. In addition, by applying the climate change scenario to the rainfall-inundation depth-vehicle speed calculation formula, it predicts the flooding of urban rivers during heavy rain, and evaluates possible traffic network disturbances due to road inundation considering the impact of future climate change. We want to develop technology for use in traffic flow planning.

• Journal of Korean Society of Disaster and Security
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• v.15 no.2
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• pp.37-44
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• 2022

As the frequency of torrential rains and typhoons increases due to climate change, the frequency of occurrence of debris flow is also increasing. In particular, in the case of Kangwon-do, the occurrence of damage caused by mountain disasters is increasing as it has a topographical characteristic where the mountains and the coast are in contact. In order to analyze the flow characteristics in the sedimentary part of the debris flow, input data were constructed through numerical maps and field data, and a two-dimensional model, FLO-2D, was simulated. The damaged area was divided into the inflow part of the debris flow, the village center, and the vicinity of the port, and the flow center and flow velocity of the debris flow were simulated and compared with field survey data. As a result, the maximum flow depth was found to be 2.4 m at the debris flow inlet, 2.7 m at the center of the village, and 1.4 m at the port adjacent to the port so the results were similar when compared to the field survey. And in the case of the maximum flow velocity, it was calculated as 3.6 m/s at the debris flow inlet, 4.9 m/s in the center of the village and 1.2 m/s in the vicinity of the port, so It was confirmed that the maximum flow center occurred in the section where the maximum flow rate appeared.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.386-388
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• 2021

According to data from the World Meteorological Organization (WMO) in 2019, the global average temperature between 2015 and 2019 increased by 1.1℃ compared to the pre-industrial period (1850-1900). If the average temperature rises by 1.5℃, the occurrence of natural disasters such as extreme high temperatures, heavy rains and droughts will increase, and this change will intensify depending on the speed and size of warming. Due to the effects of global warming, global surface temperatures have gradually risen, and tropical fruits, which could only be grown in tropical regions, can be seen in Korea. According to the 5th report released by the IPCC of the Intergovernmental Panel on Climate Change under the United Nations, the world's average temperature will rise 3.7 degrees Celsius at the end of the 21st century (2081-2100). If the temperature rises gradually, it is believed that Korea's current cultivation area, which can produce good quality fruit, could be turned into an unfavorable area in the future. This paper aims to develop a plant cultivation system that utilizes Arduino to provide a customized environment for the growth of plants desired by growers.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.743-754
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• 2023

The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.