GEO DATA

From articles published in GEO DATA during the past two years (2022 ~ ).

Review

Data used for GIS-based Flood Susceptibility Mapping: Saro Lee, Fatemeh Rezaie; GEO DATA. 2022;4(1):1-15. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.001

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The dramatic increase in flood incidents as a significant threat to human life and property, environment, and infrastructure indicates the necessity of mapping spatial distribution of flood susceptible areas to reduce destructive effects of flooding. During the last decade, the integration of the geographic information system (GIS) with the remote sensing data provide efficient means to generate a more reliable and precise flood susceptibility map. The present study contains a review of 200 articles on the application of GIS-based methods in indicating flood vulnerable areas. The papers were reviewed in terms of influential variables, study area, and the number of articles published in the last 10 years. The review shows that the number of studies has increased since 2012. The total study areas covered 39 countries that were mostly located in Asia where the major developments and infrastructures have been constructed in the floodplains. The most common study areas was Iran (44 articles, 22%), followed by India (26 articles, 13%), China (26 articles, 11%), and Vietnam (15 articles, 7.5%). More than 90 variables were considered to map flood susceptible areas that the top 5 widely used flood conditioning factor are slope (98% of total articles), followed by elevation (92% of total articles), land use/land cover (79.5% of total articles), distance to the river (76.5% of total articles), and rainfall (73% of total articles). The review implies that many natural and anthropogenic factors affect flooding and the combination of both groups of factors is necessary to accurately detect and map flood-prone parts of the study area.

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Taşkın Duyarlılığının Best-Worst Yöntemiyle Değerlendirilmesi: Suluca Deresi Havzası (Tokat) Örneği
Kemal Ersayın, Mustafa Yaş
Black Sea Journal of Engineering and Science.2024; 7(4): 683. CrossRef
Geo-Spatial Based Cyclone Shelter Suitability Assessment Using Analytical Hierarchy Process (AHP) in the Coastal Region of Bangladesh
Irteja Hasan, Md. Omar Faruk, Zarin Tasnim Katha, Md. Osman Goni, Md Shafiqul Islam, Tapas Ranjan Chakraborty, Sheikh Fahim Faysal Sowrav, Md Shakhawat Hossain
Heliyon.2024; : e39831. CrossRef
A systematic flood risk assessment of Bloemfontein Watershed, South Africa
Zachariah H. Mshelia, Johanes A. Belle
Geomatics, Natural Hazards and Risk.2024;[Epub] CrossRef

Article

A Geological Environment Characteristics Dataset of Tidal Flat Surface Sediments: A 2021 Pilot Study of the Gomso Bay Tidal Flat Area to Use of Sediment Type Data: Kyoungkyu Park, Han Jun Woo, Hoi-Soo Jung, Joo Bong Jeong, Joo-Hyung Ryu, Jun-Ho Lee; GEO DATA. 2022;4(2):9-22. Published online June 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.2.002

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The Gomso Bay tidal flat is located between Buan-gun and Gochang-gun in Jeollabuk-do, Korea; it is a semi-closed bay in an area where tides prevail over waves. Tidal flats are mainly found south of Gochang-gun, and the main stream located north of the tidal flats is about 15 m deep and 900 m wide at low tide. Limited direct sampling is necessary for analyzing the geological environment of intertidal tidal flats, depending on the expected ebb-tide time and the number of survey items allowed for tidal flat access. This study assessed field measurement and laboratory analysis items for obtaining and establishing geological environment data to use of sediment type data in a pilot research area in the Gomso Bay tidal flat. Thirty sites were examined on June 22 and 24, 2021 (survey time about 3.5 hours for the 2 days). The field measurements were the sample date (year/month/day/hour/minute), ellipsoid height using a real-time kinematics global positioning system (RTK GPS) (m), shear strength (kg/cm²), and Munsell color. Samples for particle size (phi, Φ), specific density, porosity (%), moisture content (%), total organic carbon (%), total carbon (%) and total nitrogen (%) were placed in zipper bags and polypropylene (PP) bottles. The sedimentary phases were classified following Folk and Ward (1957), the organic matter was characterized based on particle size analysis and each experimental result was verified. In the future, a geological environment characteristics dataset based on this pilot study will be used as basic data to assess changes in the tidal flat topography and sedimentation environment. It should be useful data for research, tidal flat environment conservation management and free open data for users of related researchers.

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Study on Grain Size, Physical Properties and Organic Matter Characteristics of Tidal Flat Surface Sediments: May 2022 Hwangdo Tidal Flat Dataset, Cheonsu Bay
Jun-Ho Lee, Hoi-Soo Jung, Huigyeong Ryu, Keunyong Kim, Joo-Hyung Ryu, Yeongjae Jang
GEO DATA.2024; 6(3): 159. CrossRef
Characteristics of temporal-spatial variations of zooplankton community in Gomso Bay in the Yellow Sea, South Korea
Young Seok Jeong, Min Ho Seo, Seo Yeol Choi, Seohwi Choo, Dong Young Kim, Sung-Hun Lee, Kyeong-Ho Han, Ho Young Soh
Environmental Biology Research.2023; 41(4): 720. 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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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

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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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 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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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 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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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

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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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

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

Climate Characteristics and Distribution of Native Organisms in Living Modified Organism Confined Field under the Ministry of Environment, Republic of Korea: Sung Min Han, Jung Ro Lee, Kyong-Hee Nam; GEO DATA. 2023;5(3):213-221. Published online September 26, 2023; DOI: https://doi.org/10.22761/GD.2023.0022

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In this study, climate variables and distribution patterns of native organisms were investigated in an enclosed field within the institute of living modified organism (LMO) risk assessment designated by the Ministry of Environment (MOE) of South Korea. The data includes changes in temperature, precipitation, humidity, sunlight, wind direction, and velocity within the LMO confined field from 2019 to 2023 as well as information on plant diversity and soil microbial communities. This data can be used as the basic data when establishing LMO safety management policies by the MOE such as preparing guidelines for systematic LMO risk assessment reflecting domestic environmental characteristics and risk assessment of LMOs for environmental remediation and natural ecosystem. In addition, this data can be usefully used as the comparative data for LMO risk assessment by other ministries such as the Ministry of Agriculture, Food and Rural Affairs, the Ministry of Trade, Industry and Energy, and the Ministry of Oceans and Fisheries.

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Pest Control and Safety Management Monitoring in Outdoor Plantation Using Unmanned Aerial Vehicle Captured Images
Sung Min Han, Kyong-Hee Nam
GEO DATA.2024; 6(3): 144. CrossRef

Articles

UAV Photogrammetry and LiDAR Based Dataset of Spartina anglica Distribution and High-resolution Topographic Map in Ganghwado: Keunyong Kim, Yeongjae Jang, Jingyo Lee, Joo-Hyung Ryu; GEO DATA. 2022;4(2):1-8. Published online June 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.2.001

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The Spartina anglica in the tidal flat at the southern part of Ganghwado, it is known that the distribution area has gradually expanded since it was officially announced as invasive alien species in 2015. The government and local governments are continuing their efforts to remove the S. anglica, and for this, continuous distribution change monitoring is required. This study extracted the data of distribution and extent area of S. anglica from Zenmuse P1 sensor, and generated the high-resolution Digital Elevation Model (DEM) from Zenmuse L1 sensor. Optical and Lidar images were photographed at an altitude of 70 m, and Ground Sampling Distance (GSD) of optical images was obtained at 0.9 cm and GSD of lidar images at 5 cm spatial resolution. However, the data are resampled and provided in GSD 25 cm to comply with the "National Spatial Information Security Management Regulations of the Ministry of Land, Infrastructure and Transport" and "Security Business Regulations of the National Intelligence Service".

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Spartina anglica-Derived Carbon-Coated PE Separator for Physically Restraining Polysulfide Migration in Lithium-Sulfur Batteries
Ye Jin Jeon, Yuna Ha, Jang Kyun Kim, Youn-Jung Kim, Taeeun Yim
Korean Journal of Chemical Engineering.2024; 41(4): 1187. CrossRef

The Dataset of UAV Based High-resolution Tidal Topography at the Nakdong Estuary: Focusing on Jin-u Island and Shin-ja Island: Yeongjae Jang, Jingyo Lee, Joo-Hyung Ryu, Kye-Lim Kim, Hahn Chul Jung, Keunyong Kim; GEO DATA. 2022;4(1):27-36. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.003

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In the tidal flats of the Nakdong Estuary, eight weirs were installed as part of the Four Major River Restoration Project in 2011, and the environment changed from a flowing stream to a still water stream. As the Nakdong River’s weir was permanently opened in February 2022, the topography and ecological environment are expected to large change. In this study, Unmanned Aerial Vehicle (UAV) photogrammetry was conducted on the tidal flats of the Nakdong Estuary in November 2021, the environment before the Nakdong River floodgates were opened. The study area was surveyed using the Network-RTK (Real-Time Kinematic) method to obtain Ground Control Point (GCP), and using an UAV, orthographic image and digital elevation model were generated for an area of 3.47 ㎢ near Jin-u island and 2.75 ㎢ near Shin-ja island. A result of spatial resolution of 1.8 cm was obtained, the result was verified using checkpoints, and results with accuracy exceeding 1 cm were obtained in both Sin-u Island and Jin-woo Island. In the future, changes in the topography and sedimentation environment of this area are expected, so it will be useful data for various research and conservation management.

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Influence of Precipitation Conditions and Discharge Rates of River Estuary Barrages on Geomorphological Changes in an Estuarine Area
Sung-Bo Kim, Doo-Pyo Kim
Applied Sciences.2023; 13(17): 9661. CrossRef

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