GEO DATA

Most-download articles are from the articles published in 2022 during the last three month.

Original Papers

Quantitative Study of Butterfly Diversity in Wando Quercus acuta Forest Over 5 Years (2017-2021): Sanghun Lee, Na-Hyun Ahn; GEO DATA. 2023;5(2):55-59. Published online June 20, 2023; DOI: https://doi.org/10.22761/GD.2023.0010

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Abstract PDF: This study presents the long-term quantitative data on butterflies in Wando Arboretum, which represents the only warm-temperate forest located in the southernmost part of South Korea. This arboretum has significant academic value as approximately 770 species of rare woody plants or herbs, such as the Japanese evergreen oak (Quercus acuta), found in warm temperate zones grow under natural conditions here. In this project, the butterflies in this region were studied due to their sensitivity to temperature changes. The study was conducted from March-April to October-November over 5 years (2017-2021) in the region dominated by Japanese evergreen oak. We found 1,743 individuals of 47 butterfly species belonging to five families. The acquired butterfly data could serve as a reference for the further development of a network-oriented database for assessing temporal climate changes.

Exploring Wild Bees Diversity in Seocheon Maeul-Soop: A Quantitative Study: Sanghun Lee, Ohchang Kwon, Dong Su Yu, Jeong-Seop An, Na-Hyun Ahn; GEO DATA. 2024;6(1):1-7. Published online March 26, 2024; DOI: https://doi.org/10.22761/GD.2024.0003

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Abstract PDF: Wild bees are important pollinators in the ecosystem, and it is important to monitor their abundance and diversity to characterize and conserve these pollinators. In this study, wild bees were collected from a Maeul-soop in Seocheon-gun, Chungcheongnam-do, Republic of Korea for 2 years from February 2019 to October 2020. From the survey, a total of 3,258 wild bees from 9 families and 57 species were collected over 2 years in the Maeul-soop. The most dominant species was the Andrena kaguya, followed by the Apis mellifera, the Eucera spurcatipes, the Seladonia aeraria, and the Lasioglossum sibiriacum. Monthly changes in the number of species and populations show that the number of species increased from February and peaked in August, and the population peaked in April and then decreased. In addition, in the list of wild bee species collected over the past 2 years, the Apidae was the largest with 16 species, followed by the Halictidae with 13 species and the Megachilidae with nine species. However, although there is only one species of Andrena kaguya in the Andrenidae, its population is 2,084, which is the largest among all wild bees investigated in this study. The results of this study will be useful in understanding the impact of pollinating insects due to climate change in the future.

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.

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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Abstract PDF: 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.

Vegetation Spatial Distribution on Taean Duung Wetland Protect Area: Haeseon Shin, Sanghun Lee, Sangwook Han; GEO DATA. 2024;6(1):8-13. Published online March 28, 2024; DOI: https://doi.org/10.22761/GD.2024.0004

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Abstract PDF: In this study, we conduct for providing information on the status of vegetation space distribution in the Duung wetland protected area and to help manage the wetland protected area. To understand the spatial distribution of vegetation in Duung Wetland, used the results of surveys in 2019 and 2023. As a result of the study, the number of vegetation types increased by 4 from 20 to 24. Four communities were newly investigated, including the Utricularia tenuicaulis community, Pueraria montana var. lobata-Elymus tsukushiensis community, Spiraea prunifolia for. simpliciflora community, and Miscanthus sinensis var. purpurascens community. In accordance with the environment, the range of aquatic plant communities such as Trapa japonica community and Nymphaea tetragona var. angusta community increased, and the succession zone of cultivated land expanded dry grassland. The survey results can be used as basic data for systematic management of the Duung wetland protected area.

Evaluation of Calibration Using Corner Reflector with Ground-Based Interferometric Radar: Je-Yun Lee, Jeong-Heon Ju, Sang-Hoon Hong; GEO DATA. 2024;6(1):32-42. Published online March 28, 2024; DOI: https://doi.org/10.22761/GD.2024.0002

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Abstract PDF: The accuracy of microwave remote sensing relies on the calibration of the radar measurement. It is important to estimate the radar cross-section (RCS) using a passive corner reflector (CR) or active transponder to evaluate the quality of imaging radar data. A strong and consistent RCS can be achieved by the acquisition of radar signals concentrated at specific angles during the CR calibration procedure. There are several types of CR depending on the shape and size such as triangular trihedrals, square trihedrals, dihedrals, spheres, or cylinders. In this study, we examine the RCSs using three types of CR with Ku-band ground-based real aperture radar equipment, the Gamma Portable Radar Interferometer-II. It can be easily deployed to acquire fully polarimetric radar observations. The initial experiment was conducted at Busan Sam-nak Auto Camping Site on November 1, 2023. The amplitude images show much higher backscattered radar signals at the CR location, whereas relatively lower power has been captured in the surrounding areas. The attenuation factors in the radar receivers could be useful to prevent saturation around the CR location at the line-of-sight direction. The experiment indicates that the different levels of the RCS measurements from three types of CRs could be utilized for calibration study with fully polarimetric radar observations.

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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Abstract PDF: 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.

Investigation of Wildlife Crossing Structures in South Korea: Euigeun Song, Sooahn Heo, Il Ryong Kim, Sehee Kim, Hanbi Lee; GEO DATA. 2023;5(4):273-276. Published online December 22, 2023; DOI: https://doi.org/10.22761/GD.2023.0041

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Abstract PDF: Roads, railways and infrastructure are constructed with consideration of their environmental impacts, especially habitat fragmentation. Wildlife crossing structures increase the permeability of roads and other linear infrastructures for wildlife by allowing animals to safely cross under or over roads and by reducing the risk of wildlife-vehicle collosions. We investigated the location and type of 564 wildlife crossing structures in South Korea. Between April and October 2023, we identified 365 overpasses and 199 underpasses of wildlife crossing structures respectively. Gyeonggi-do and Gyeongsangbuk-do had the largest number of wildlife crossing structures. This study can provide basic information for the effective management of wildlife crossing structures.

The Study of Distribution for the Flora of Alien Species and Ecosystem Disturbing Species on Coastal Sand Dune in Chungcheong to Jeolla Region, South Korea: Seonghun Lee, Jihyun Kang, Hyun-Su Hwang; GEO DATA. 2023;5(4):262-272. Published online December 20, 2023; DOI: https://doi.org/10.22761/GD.2023.0031

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Abstract PDF: This study was conducted to provide the coastal sand dunes flora of vascular plants in Chungcheong to Jeolla region based national coastal dune natural environment survey from 2018 to 2019. In the study area, a total 631 taxa, consisting of 119 family, 372 genera, 566 species, 8 subspecies, 50 varieties, and 7 forma, were found. Among them, there were 95 taxa with 23 family, 66 genera, 99 species and 5 varieties as alien species. The number of alien species ranged from 7 to 45 on each coastal sand dune. The largest number was recorded in Sinjimyeongsa dune, while the lowest was in Namujeon dune. Moreover, ecosystem disturbing species had mainly existed on Sinhap dune. Japanese hop (Humulus japonicus) were distributed most widely on 17 coastal sand dune, and bur cucumber (Sicyos angulatus) was only found on Sinhap dune. The spatial status of flora of coastal sand dune in our data can be basic ecological information for the conservation and management of the coastal dune plant species diversity.

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.

Evaluation of Slope Stability on Dam Using Ground-based Interferometric Radar: Seongcheon Park, Sanghoon Hong; GEO DATA. 2024;6(1):24-31. Published online March 21, 2024; DOI: https://doi.org/10.22761/GD.2024.0001

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Abstract PDF: Dams are man-made structures built to manage water resources efficiently and prepare for natural disasters such as droughts and floods. It requires careful and continuous inspection to prevent its failure. Research reported to assess dam stability using terrestrial surveys such as ground penetration radar, electrical resistivity tomography, and remote sensing methods such as space-borne synthetic aperture radar (SAR). Differential interferometric SAR (DInSAR) calculates the phase difference between two consecutive images acquired at separate times and has been widely utilized to detect surface displacement from volcanoes, earthquakes, and ground subsidence. However, space-borne InSAR applications have limitations in acquiring flexible data for specific dates or regions due to the revisit cycle of the orbital configuration and the fixed acquisition geometry. In this feasibility study, the slope stability of the dam was evaluated using the Gamma Portable Radar Interferometer-II (GPRI-II) which has the advantage of overcoming the limitation of satellite observations. The GPRI-II is a ground-based real aperture radar that operates in the Ku-band wavelength (~1.7 cm), providing convenient portability and installation for high spatial and temporal resolution. A total of 20 GPRI-II datasets were acquired for 22 minutes on June 7, 2023, at a dam in Jeollanam-do for the DInSAR application. The displacement calculation revealed an average displacement of approximately -0.36 mm at a randomly selected point, which is negligible. The average displacement of -0.17 mm was observed for the entire dam. Our results suggest that ground-based radar interferometry could assess the dam slope stability.

Distribution Characteristics of the Clithon retropictus in the Estuarine Wetland: Yeounsu Chu, Pyoungbeom Kim; GEO DATA. 2023;5(2):60-65. Published online June 12, 2023; DOI: https://doi.org/10.22761/GD.2023.0011

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Abstract PDF Supplementary Material: This study analyzed the distribution characteristics of Clithon retropictus (C. retropictus), an endangered species, using data from the benthic macroinvertebrate survey on estuarine ecosystems conducted in 2021-2022. A total of 5,906 individuals of C. retropictus were identified in 60 estuarine wetlands located along the eastern coast, southern coast, and Jeju area. It was confirmed to be a dominant species in certain estuarine wetlands such as Obangcheon, Gohyeoncheon, and Osucheon. The southern coast of Gyeongsangnam-do was identified as a major distribution area, indicating the need for systematic conservation and management of C. retropictus in this region. Furthermore, as a basic survey of benthic macroinvertebrates is currently being conducted in Jeolla-do, it is expected that nationwide distribution data for C. retropictus will be obtained.

Article

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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Abstract PDF

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".

Citations

Citations to this article as recorded by

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

Original Papers

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.

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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Abstract PDF: 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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