GEO DATA

From articles published in GEO DATA during the past two years (2023 ~ ).

Original Papers

GeoAI Dataset for Industrial Park and Quarry Classification from KOMPSAT-3/3A Optical Satellite Imagery: Che-Won Park, Hyung-Sup Jung, Won-Jin Lee, Kwang-Jae Lee, Kwan-Young Oh, Jae-Young Chang, Moung-jin Lee, Geun-Hyouk Han, Il-Hoon Choi; GEO DATA. 2023;5(4):238-243. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0052

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Air pollution is a serious problem in the world, and it is necessary to monitor air pollution emission sources in other neighboring countries to respond to the problem of air pollution spreading across borders. In this study, we utilized domestic and international optical images from KOMPSAT-3/3A satellites to build an AI training dataset for classifying industrial parks and quarries, which are representative sources of air pollution emissions. The data can be used to identify the distribution of air pollution emission sources located at home and abroad along with various state-of-the-art models in the image segmentation field, and is expected to contribute to the preservation of Korea’s air environment as a basis for establishing air-related policies.

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GeoAI Dataset for Industrial Park Segmentation from Sentinel-2 Satellite Imagery and GEMS
Sung-Hyun Gong, Hyung-Sup Jung, Geun-han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong
GEO DATA.2025; 7(1): 36. CrossRef
Semantic Segmentation of Urbanized Areas Using Multi-Encoder U-Net Based on Multi-Modal Data
Sung-Hyun Gong, Hyung-Sup Jung, Geun-Han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong
Korean Journal of Remote Sensing.2025; 41(2): 461. CrossRef
Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef

Spatial Distribution Status of Landform in 1st Grade Area of Ecology and Nature Map: Hye-Yeon Yoon, So-Young Hwang, Hyun-Su Park; GEO DATA. 2024;6(2):87-99. Published online June 27, 2024; DOI: https://doi.org/10.22761/GD.2024.0010

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Abstract PDF

In this study, spatial distribution analysis was conducted on the landforms that appear within the 1st grade area using the ecology and nature map of 2023. As a result, a total of 97 landforms including tidal flat and incised meander were identified as unit landforms, and a total of 1,490 sites were distributed. The spatial distribution by administrative region was highest in Gangwon-do with 273 sites (12.8%), and by unit landform, cliff (173 sites), stream cliff (129 sites), and sea cliff (100 sites) were the most distributed. These landforms are cliffs found in mountainous, riverine, and coastal areas, respectively, and are characterized by their high geomorphological conservation value due to their large scale and geometric shape compared to other terrains. In terms of spatial distribution by landform type, stream landforms (501 sites, 33.6%) accounted for the largest proportion, and there were 24 units landforms. The results obtained can be utilized for future designation of expanded ecosystem protection areas or ecosystem monitoring surveys, along with continued landform conservation.

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Potential Habitat and Priority Conservation Areas for Endangered Species in South Korea
Soyeon Park, Hyomin Park, Sangdon Lee
Animals.2025; 15(8): 1158. CrossRef
Preparation an Ecological Map Using Data from the Third Survey on National Environment
Eui-Jeong Ko, Taeho Kang, Hye-Yeon Yoon
GEO DATA.2024; 6(4): 290. CrossRef

The Cheonji Lake GeoAI Dataset Based in Synthetic Aperture Radar Images: TerraSAR-X, Sentinel-1 and ALOS PALSAR-2: Eu-Ru Lee, Ha-Seong Lee, Ji-Min Lee, Sun-Cheon Park, Hyung-Sup Jung; GEO DATA. 2023;5(4):251-261. Published online December 29, 2023; DOI: https://doi.org/10.22761/GD.2023.0056

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Abstract PDF

The fluctuations in the area and level of Cheonji in Baekdu Mountain have been employed as significant indicators of volcanic activity. Monitoring these changes directly in the field is challenging because of the geographical and spatial features of Baekdu Mountain. Therefore, remote sensing technology is crucial. Synthetic aperture radar utilizes high-transmittance microwaves to directly emit and detect the backscattering from objects. This weatherproof approach allows monitoring in every climate. Additionally, it can accurately differentiate between water bodies and land based on their distinct roughness and permittivity characteristics. Therefore, satellite radar is highly suitable for monitoring the water area of Cheonji. The existing algorithms for classifying water bodies using satellite radar images are significantly impacted by speckle noise and shadows, resulting in frequent misclassification. Deep learning techniques are being utilized in algorithms to accurately compute the area and boundary of interest in an image, surpassing the capabilities of previous algorithms. This study involved the creation of an AI dataset specifically designed for detecting water bodies in Cheonji. The dataset was constructed using satellite radar images from TerraSAR-X, Sentinel-1, and ALOS-2 PALSAR-2. The primary objective was to accurately detect the area and level of water bodies. Applying the dataset of this study to deep learning techniques for ongoing monitoring of the water bodies and water levels of Cheonji is anticipated to significantly contribute to a systematic method for monitoring and forecasting volcanic activity in Baekdu Mountain.

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Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef
GeoAI Dataset for Urban Water Body Detection Using TerraSAR-X Satellite Radar Imagery
Eu-Ru Lee, Jun-Hyeok Jung, Ki-Chang Kim, Seong-Jae Yu, Hyung-Sup Jung
GEO DATA.2024; 6(4): 435. CrossRef

Evaluating the Longitudinal Connectivity of Dorim Stream in Seoul based on Artificial Constructure and Fish Data: Jeong Ho Hwang, Myeong-Hun Ko, Sungmin Jung, Jong-Hak Yun; GEO DATA. 2023;5(4):286-297. Published online December 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0040

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Abstract PDF

The vertical connectivity of the river aquatic ecosystem was evaluated based on fish and artificial structures in Dorim stream, an urban stream in Seoul. As a result of a survey in the downstream area in 100.0 m of a total of 71 artificial structures, 13,728 individuals of fishes belonging to five orders, seven families, and 25 species were investigated, with the dominant species Zacco platypus and the subdominant species Rhynchocypris oxycephalus. As for endemic species, seven species were investigated and in terms of feeding characteristics, omnivorous species were the most common with 17 species (68%). Also an alien species, Poecilia reticulata was found. Fish species tended to decrease as the survey was conducted to upstream. Based on the movement characteristics of the fish species and the features of artificial structure survey results, the longitudinal continuity of each artificial structure was evaluated as 43 continuity, two damaged, 19 discontinuity, and seven absent. In inclined structures, stream velocity was found to be the main factor for discontinuity. In vertical structures, the down depth and head drop appeared to be the main factors for discontinuity. The results of this survey are expected to serve as basic data for the conservation of river aquatic ecosystems in the future.

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Fish Diversity of East Sea Streams in Nakdong River Region
Jeong Ho Hwang, Jong-Hak Yun
GEO DATA.2024; 6(3): 110. CrossRef
Fish Diversity of East Sea Streams in Han River Region
Jeong Ho Hwang, Jong-Hak Yun
GEO DATA.2024; 6(4): 312. CrossRef

Data Articles

GeoAI Dataset for Urbanized Area Segmentation from Landsat 8/9 Satellite Imagery and GEMS: Sung-Hyun Gong, Hyung-Sup Jung, Geun-Han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong; GEO DATA. 2024;6(4):478-486. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0053

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1 Citations

Abstract PDF

In South Korea, air pollution has emerged as a pressing social issue, necessitating data-driven approaches to monitor sources of air pollutants. This study constructed a GEO AI dataset for detecting air pollution sources in urbanized areas, utilizing Landsat 8/9 satellite imagery, Geostationary Environment Monitoring Spectrometer geostationary satellite data, and air quality monitoring network data. The dataset is optimized for semantic segmentation tasks, including labeled data for urban area segmentation, and is designed to enable precise detection of pollution sources within urban regions by integrating satellite imagery and air quality information. Using this dataset, we applied a modified U-Net model to classify pollutant sources in urbanized areas, achieving high performance with an mIoU of 0.8592 and pixel accuracy of 0.9433. These results demonstrate the effectiveness of the GEO AI dataset as a tool for identifying and managing major pollution sources, providing foundational data for air quality monitoring and policy development across South Korea and East Asia. With further integration of additional air pollution data, this dataset is expected to contribute to long-term air quality management and the mitigation of health impacts associated with pollution.

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Semantic Segmentation of Urbanized Areas Using Multi-Encoder U-Net Based on Multi-Modal Data
Sung-Hyun Gong, Hyung-Sup Jung, Geun-Han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong
Korean Journal of Remote Sensing.2025; 41(2): 461. CrossRef

A Study on the Spatial Information Compilation of Inland Wetlands in South Korea: Chang-Su Lee, Haeseon Shin, Hyeongcheol Lee, Yijung Kim, Sanghun Lee; GEO DATA. 2024;6(4):226-234. Published online December 4, 2024; DOI: https://doi.org/10.22761/GD.2024.0034

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Abstract PDF

Wetlands offer numerous benefits, including improving water quality, providing habitats for wildlife, and storing water. They are areas where water either covers the soil or is just below the surface for extended periods. Wetlands play a crucial role in maintaining environmental balance and ecological stability. In South Korea, the Wetlands Conservation Act was established in 1999 to protect these vital ecosystems and their biodiversity. The law defines inland wetlands as lakes, ponds, swamps, rivers, and estuaries. However, the boundaries of these areas are often unclear, creating challenges for conservation and research. This ambiguity complicates effective management and the implementation of necessary protective measures. This study utilized topographic and aerial images to gather spatial information about inland wetlands and assess their areas. It identified the boundaries of inland wetlands in South Korea, revealing a total area of 3,833.452 km², which is 3.8% of the country’s total land area. The classified the spatial data, showing that vegetated areas cover 1,355.666 km², or 35.4% of the total area, with woody plants covering 102.987 km² and herbaceous plants 1,252.679 km². Non-vegetated areas account for 2,477.786 km², or 64.6%, with open water 2,206.615 km², natural land 160.995 km², artificial land 72.343 km², and Agricultural land 37.833 km². Clearly defining wetland boundaries is essential for effective conservation and protection. Accurate boundary definitions facilitate legal protection and help prevent damage to wetlands. The results provide quantitative data that can inform future wetland conservation planning and management. And enhance our understanding of the size and changes in South Korea’s inland wetlands, supporting their preservation and protection.

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Improving Inland Wetland Classification Performance of Drone Imagery-Based TransUNet Model Using Multi-Class Data Balancing Technique
Eu-Ru Lee, Jin-Sik Bong, Kyu-Ri Choi, Hyung-Sup Jung
Korean Journal of Remote Sensing.2025; 41(2): 447. CrossRef

Dataset for Deep Learning-based GEMS Asian Dust Detection: Jin-Woo Yu, Che-Won Park, Won-Jin Lee, Yong-Mi Lee, Yu-Ha Kim, Hyung-Sup Jung; GEO DATA. 2024;6(3):175-185. Published online September 27, 2024; DOI: https://doi.org/10.22761/GD.2023.0049

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Abstract PDF

In South Korea, Asian dust frequently occurs during the spring, causing various health issues, including respiratory diseases. Consequently, public awareness and concern about air pollutants have increased, leading to demands for improved air quality and accurate forecasting. To meet these demands, the Ministry of Environment has deployed the Geostationary Environment Monitoring Spectrometer (GEMS) on the GK2B satellite to monitor atmospheric pollutants and climate change-inducing substances in real-time. The current GEMS dust product, generated using thresholds of the UV-aerosol index and visible-aerosol index, has shown limitations in accurately detecting suspended particulate matter. This study aims to develop a comprehensive AI dataset for improving GEMS Asian dust detection. Data were collected from January to May 2021, focusing on dates with significant dust events. Label data were meticulously generated through annotations based on outputs from various satellites and groundbased observations. Subsequent data preprocessing and augmentation techniques, including normalization and cut-mix, were applied to enhance the dataset’s robustness and generalizability. To evaluate the dataset, model training was conducted. The results predicted by the model showed improvements over the detection results of existing algorithms. Future datasets will be developed with improved labeling methods and accuracy verification techniques. These dataset improvements are expected to contribute to the development of deep learning models with superior predictive performance compared to current dust detection algorithms.

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GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model
Jin-Woo Yu, Jun-Hyeok Jung, Kyoung-Hee Kang, Yong-Mi Lee, Hyung-Sup Jung
GEO DATA.2024; 6(4): 552. CrossRef

Original Papers

The Cheonji Lake GeoAI Dataset based in Optical Satellite Imagery: Landsat-5/-7/-8 and Sentinel-2: Eu-Ru Lee, Ha-Seong Lee, Sun-Cheon Park, Hyung-Sup Jung; GEO DATA. 2024;6(1):14-23. Published online March 28, 2024; DOI: https://doi.org/10.22761/GD.2023.0055

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Abstract PDF

The variations in the water area and water level of Cheonji, the caldera lake of Baekdu Mountain, serve as reliable indicators of volcanic precursors. However, the geographical and spatial features of Baekdusan make it impossible to directly observe the water area and water level. Therefore, it is crucial to rely on remote sensing data for monitoring purposes. Optical satellite imagery employs different spectral bands to accurately delineate the boundaries between water bodies and non-water bodies. Conventional methods for classifying water bodies using optical satellite images are significantly influenced by the surrounding environment, including factors like terrain and shadows. As a result, these methods often misclassify the boundaries. To address these limitations, deep learning techniques have been employed in recent times. Hence, this study aimed to create an AI dataset using Landsat-5/-7/-8 and Sentinel-2 optical satellite images to accurately detect the water body area and water level of Cheonji lake. By utilizing deep learning methods on the dataset, it is reasonable to consistently observe the area and level of water in Cheonji lake. Furthermore, by integrating additional volcanic precursor monitoring factors, a more accurate volcano monitoring system can be established.

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Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef

GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery: Sung-Hyun Gong, Hyung-Sup Jung, Moung-Jin Lee, Kwang-Jae Lee, Kwan-Young Oh, Jae-Young Chang; GEO DATA. 2023;5(4):231-237. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0054

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Abstract PDF

In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning.

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Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef

GeoAI Dataset for Training Deep Learning-Based Optical Satellite Image Matching Model: Jin-Woo Yu, Che-Won Park, Hyung-Sup Jung; GEO DATA. 2023;5(4):244-250. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0048

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Satellite imagery is being used to monitor the Earth, as it allows for the continuous provision of multi-temporal observations with consistent quality. To analyze time series remote sensing data with high accuracy, the process of image registration must be conducted beforehand. Image registration techniques are mainly divided into region-based registration and feature-based registration, and both techniques extract the same points based on the similarity of spectral characteristics and object shapes between master and slave images. In addition, recently, deep learning-based siamese neural network and convolutional neural network models have been utilized to match images. This has high performance compared to previous non-deep learning algorithms, but a very large amount of data is required to train a deep learning-based image registration model. In this study, we aim to generate a dataset for training a deep learning-based optical image registration model. To build the data, we acquired Satellite Side-Looking (S2Looking) data, an open dataset, and performed preprocessing and data augmentation on the data to create input data. After that, we added offsets to the X and Y directions between the master and slave images to create label data. The preprocessed input data and labeled data were used to build a dataset suitable for image registration. The data is expected to be useful for training deep learning-based satellite image registration models.

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Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef

High-Resolution Bioclimatic Variables in Mt. Jirisan and Hallasan under Climate Change Scenario: Sanghun Lee, Seungbum Hong, Kyungeun Lee; GEO DATA. 2023;5(4):314-320. Published online December 20, 2023; DOI: https://doi.org/10.22761/GD.2023.0039

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Many endemic and rare species live in Korea’s subalpine zone, but there have been many research results showing that alpine creatures are disappearing due to recent climate change. Therefore, in this study, bioclimatic variables with 100 m resolution were created for Mt. Jirisan and Mt. Hallasan, representative mountainous regions in Korea. Nineteen high-resolution bioclimatic variables were created for the current and 4 future periods, and the generated data is believed to represent topographical characteristics well. This data is expected to be useful to predict potential habitats through species distribution modeling and the impact of climate change on organisms limited to alpine regions.

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Assessment of extinction risk of the endemic plant Coreanomecon hylomeconoides by species distribution modeling and climate change scenarios
Jaewon SEOL, Songhie JUNG, Yong-Chan CHO
Korean Journal of Plant Taxonomy.2024; 54(4): 247. CrossRef

A Study on the Development of Biotope Type and Evaluation Map of Gochang-gun: Jeong-Cheol Kim, Chang-Hoon You, Dong-Wook Kim, WooSeok Oh; GEO DATA. 2023;5(4):277-285. Published online December 20, 2023; DOI: https://doi.org/10.22761/GD.2023.0034

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Gochang-gun, situated in Korea, has achieved the distinction of being the second city in the country to have all three UNESCO-designated natural environmentrelated World Heritage Sites, following in the footsteps of Jeju Island. UNESCO has conferred upon Gochang-gun the prestigious designations of a biosphere reserve, World Natural Heritage (Gochang-Buan mudflat), and World Geopark (Jeonbuk West Coast Geopark). Notably, the entire administrative district has been designated as a UNESCO Biosphere Reserve, signifying its role as a meticulously preserved region of outstanding natural beauty and ecological significance. Within this UNESCO Biosphere Reserve, the core areas encompass remarkable features, including the Gochang-Buan Mudflat, Ungok Wetland, Dolmen World Cultural Heritage sites, Seonunsan Provincial Park, and Dongrim Reservoir. In pursuit of a comprehensive ecological map of Gochang-gun, the National Institute of Ecology (NIE) conducted an extensive two-year ecological survey and biotope survey from 2021 to 2022. Ecological spatial data was meticulously compiled based on the results of these surveys. The resulting Biotope map provides detailed information on various attributes, encompassing Biotope types, Biotope grades, land cover status, land use status, and topographic details. This dataset is formally registered and rigorously managed, employing the Digital Object Identifier (DOI) system. The primary aim of this paper is to provide a comprehensive introduction to each attribute of the Gochang-gun Biotope map, which represents a detailed collection of spatial ecology data for the region. The intent is to make this data readily accessible for future research and studies, thereby advancing our understanding of Gochang-gun’s distinctive ecological and cultural heritage.

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Occurrences Status of Biota in Gochang-gun, South Korea
Dong-Uk Kim, Jeong-Cheol Kim, Chang-Hoon You, WooSeok Oh
GEO DATA.2024; 6(3): 123. CrossRef

Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion: Joonhyuk Choi, Ki-mook Kang, Euiho Hwang; GEO DATA. 2023;5(3):177-184. Published online September 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0029

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For the spatiotemporal analysis of water resources and disasters, water body detection using satellite imagery is crucial. Recently, AI-based methods have been widely employed in water body detection using satellite imagery. To use these AI techniques, a substantial amount of training data is required. When creating training data for water body detection, optical imagery and synthetic aperture radar (SAR) imagery have their respective strengths and weaknesses. To use the advantages of both, this study proposes a water body detection method through the fusion of optical and SAR imagery. The results of the proposed model show an Intersection over Union of 0.612 and an F1 score of 0.759, which is better compared to using either optical or SAR imagery alone. This research presents a method that can easily generate a large amount of water body data, making it promising for use as AI training data for water body detection.

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A Comprehensive Review of Remote Sensing for Water-Related Disaster Management in South Korea: Focus on Floods and Droughts
Eui-Ho Hwang, Jin-Gyeom Kim, Jang-Yong Sung, Ki-Mook Kang
Korean Journal of Remote Sensing.2024; 40(5-2): 833. CrossRef

Estimation of Equivalent Rainfall for Ungauged Reservoir Using Satellite-Based High-Resolution Terrain Data: Jin Gyeom Kim, Kimook Kang, Chanyoung Son, Gibeom Nam, Euiho Hwang; GEO DATA. 2023;5(3):170-176. Published online September 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0028

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Equivalent rainfall refers to the amount of precipitation required to reach a specific water level from the current water level in a reservoir. It serves as a flood forecasting and warning system that allows for the rapid assessment of the reservoir’s maximum water level at the moment of rainfall forecast. In reservoirs where terrain and survey data can be obtained, deriving equivalent rainfall is not difficult. However, without terrain data, satellite imagery and global topographic data are the only available options. In this study, high-resolution topographic data based on satellites were utilized to estimate the equivalent rainfall in the ungauged reservoir, Hwanggang Dam, located in the upper stream of the Imjin River in North Korea. To calculate the inflow into the reservoir, the Natural Resources Conservation Service-Curve Number method was used to determine the effective rainfall, taking into account the antecedent conditions, as the inflow into the reservoir can be changed for the same amount of rainfall depending on the soil moisture content of the watershed.

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A Study on the Rainfall-Storage Volume-Target Water Level Curve for Flood Control on the Small Size Dam: Case study for Goesan Dam
Soojun Kim, Jaewon Kwak, Hui-Seong Noh, Narae Kang, Seokhwan Hwang
Journal of the Korean Society of Hazard Mitigation.2024; 24(2): 105. CrossRef

Estimation of Coastal Area and Lake Water Level Changes Using High-Resolution Altimetry: Do-Hyun Hwang, Hahn Chul Jung, Hyongki Lee; GEO DATA. 2023;5(3):155-160. Published online September 26, 2023; DOI: https://doi.org/10.22761/GD.2023.0030

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Abstract PDF Supplementary Material

Radar satellite altimeters are widely used in offshore areas, whereas they are underutilized in coastal areas due to a number of interference factors. Altimeter satellite data can be used to summarize elevation information at 1 Hz for offshore areas, but for areas close to land, it is more effective to utilize imagery with a resolution of 20 Hz to provide a more detailed representation. The use of highresolution satellite altimeter data is expected to increase the amount of data available for hydrological data such as complex coastlines and small lakes. Therefore, in this study, we investigated the applicability of 20 Hz altimeter data in the Korean Peninsula. First, the accuracy was analyzed by comparing the 20 Hz altimeter data from the Jason-3 satellite with the Ulleungdo tide data. Second, we compared the 20 Hz altimeter data from the Sentinel-3A satellite with the water level data of Soyang Lake to see if it can be applied to land areas. In the case of inland lakes, the water level is estimated to be affected by the discharge volume due to heavy rainfall in summer, and it was determined that the satellite altimeter data can be utilized. Therefore, utilizing the data from this study is expected to improve the accuracy of hydrological analysis in coastal and lake environments.

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Hydrological and Mathematical Framework of Ensemble Learning Regression for Estimating Discharge in Ungauged River Basins
Donghwan Kim
Korean Journal of Remote Sensing.2025; 41(1): 11. CrossRef

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