GEO DATA

Most-read articles are from the articles published in 2023 during the last three month.

Original Paper

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.

Data Article

A Dataset for Species Distribution Modelling of Mangroves in Vietnam: Based on the National Forest Inventory Monitoring: Sungsoo Yoon, Nguyen Duy Liem, Le Hoang Tu, Nguyen Kim Loi; GEO DATA. 2024;6(3):150-158. Published online September 30, 2024; DOI: https://doi.org/10.22761/GD.2024.0022

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Abstract PDF: Mangroves provides essential ecosystem services such as protection of coastal areas, carbon sequestration, and habitat provision for diverse species in coastal ecosystems. Species distribution models (SDMs) are powerful tools for predicting the potential distribution of mangrove species, which support impact assessments of climate changes on biodiversity and ecological functions of mangrove ecosystems. A comprehensive dataset for mangrove occurrence information derived from the Forest Inventory Map of Vietnam was designed to facilitate the building and projection of SDMs. The prediction data designed for training SDMs integrates ecological information including 701 field survey-based mangrove occurrences at the genus level and 21 environmental variables such as bioclimatic variables, digital elevation model and soil properties with 1 km spatial resolution. The projection data for provide sets of predictors aligned with four shared socioeconomic pathways scenarios representing two future periods to support the projection of SDM results under future climate conditions in Vietnam. This dataset serves as a valuable ecological information resource, enabling the modeling and predicting of potential mangrove habitats and distributions for the protection and restoration of mangroves in Vietnam under changing environmental conditions.

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.

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.

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

Data Articles

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):159-174. Published online September 30, 2024; DOI: https://doi.org/10.22761/GD.2024.0011

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Abstract PDF: This study analyzes the geological and geochemical features of surface sediments in the Hwangdo Tidal Flat, located on Korea’s West Coast. The tidal flat experiences semi-diurnal tides, impacting organic matter decomposition and nutrient cycling. Ninety one sediment samples were collected and analyzed for physical and chemical properties including grain size, density, water content, organic carbon, and nitrogen. Sediments consist mainly of sand and silt, with coarser sediments near the main channel and finer sediments towards the west. Sediment grain size averages 4.12 Φ with a sorting coefficient of 1.96 Φ, indicating diverse energy environments. Total organic carbon and nitrogen correlate positively with grain size and density, reflecting sediment origin and environment. Kriging maps sediment grain size distribution, while correlation and linear regression analyses show relationships between variables. High correlations exist between various parameters, aligning with tidal flat characteristics and aiding understanding of sediment transport and deposition. The study provides baseline data for understanding the tidal flat’s geological, geochemical, and physical aspects, valuable for remote sensing validation and environmental monitoring. The dataset is freely available for research and management purposes.

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.

Distribution Status for the Plants of Alien Species on the Baekdudaegan Protected Area, South Korea: Hyungsoo Seo, Hyun-Su Hwang, Hyun-Chul Shin, Daeun Kim, Donghui Choi, Youngjun Park; GEO DATA. 2024;6(3):101-109. Published online September 30, 2024; DOI: https://doi.org/10.22761/GD.2024.0019

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Abstract PDF: This study was conducted to provide information on alien species to the Baekdudaegan Protected Area eco-survey by Ministry of Environment in South Korea from 2015 to 2019. The scope of the survey is based on data from 26 subsections out of 44 subsections in five regions, excluding Korea National Park. In the study area, a total 58 taxa, consisting of 16 family, 48 genera, 56 species, two varieties were found. In addition, five species of ecosystem-disturbing species were identified that Ambrosia artemisiifolia, Aster pilosus, Rumex acetosella, Solanum carolinense, Humulus japonicus. However, the habitat of ecosystem-disturbing species could not be confirmed in the subsections of Dakmokjae-Kubusiryeong (designated number, 13-20), Gisdaebaegibong-Doraegijae (designated number, 23, 24), Ihwaryeong-Haneuljae (designated number, 33), and Neuljae-Miljae (designated number, 37). The spatial status of alien flora on the Baekdudaegan Protected Area monitored by Ministry of Environment in our data can be basic ecological information for the conservation and management of plant species diversity on it.

Original Papers

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.

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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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

Dataset on the Distribution of Ecosystem-Disturbing Plants in the Republic of Korea: Man-Seok Shin, Yu Jin Hong, Sanghun Lee; GEO DATA. 2023;5(2):66-76. Published online June 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0009

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Abstract PDF: In this study, we presented distribution data for 16 plant species designated as ecosystem-disturbing species by the Ministry of Environment of the Republic of Korea. These data include location information for the ecosystem-disturbing plants from four survey projects (Monitoring of invasive alien species designated by the wildlife protection act, Nationwide survey of non-native species in Korea, The 3rd and 4th national ecosystem survey) conducted by two agencies (National Institute of Ecology and National Institute of Environmental Research) between 2014 and 2021. Additionally, the data includes habitat environmental characteristics and administrative district information on the survey sites of the ecosystem-disturbing plants. These data have a high potential for utilization as basic information for natural environmental policies and related research by identifying the habitat characteristics of invasive alien species.

Refined GEDI Level-2A Database Construction: Focused on Gyeonggi Province, Republic of Korea: Kyeong-Hun Cho, Seung-Kuk Lee; GEO DATA. 2024;6(2):77-86. Published online June 28, 2024; DOI: https://doi.org/10.22761/GD.2024.0008

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Abstract PDF: The Global Ecosystem Dynamics Investigation (GEDI), a full-waveform light detection and ranging system, translated the energy into a continuous waveform and recorded the signals chronologically for enabling geometric analysis of the vertical structure of vegetation. The National Aeronautics and Space Administration has used the land, vegetation, and ice sensor (LVIS) airborne laser altimeter system to measure terrain, tree heights, and vegetation carbon stocks in designated areas. The effectiveness of the collected LVIS data has been proven in mapping forest structures and biomass in tropical and temperate systems. Based on the successful achievements of LVIS, the GEDI aimed to establish a global range of forest data needed to analyze and predict the carbon cycle and climate change. The GEDI was launched aboard the SpaceX-16 in 2018 and successfully attached to the International Space Station (ISS) for a 2-year mission, but now extended until March 2023. Since being mounted on the ISS, GEDI measured over 10 billion cloud-free surface observations within the range of 51.6°N to 51.6°S. In this paper, GEDI mission is introduced, and the process of downloading, refining the GEDI level-2A product focused on Gyeonggi Province is outlined.

Data Article

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.

Review Paper

Global Geospatial Data for Flood and Landslide Susceptibility Mapping: Saro Lee, Rezaie Fatemeh; GEO DATA. 2023;5(4):380-393. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0058

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Abstract PDF: Susceptibility mapping is an important component of natural hazard risk assessment and management. Susceptibility maps for floods and landslides, which are particularly damaging to human life and property, can provide a comprehensive understanding of risk areas and factors related to flood and landslide susceptibility. To create a global flood and landslide susceptibility map, global geospatial data for 37,984 landslide and 6,682 flood locations, as well as 11 selected environmental factors were used to construct a geographic information system database. The 11 environmental factors found to influence flood and landslide occurrence were rainfall, slope, terrain position index, plane curvature, terrain wetness index, distance from rivers, land use, soil texture, soil moisture, geology, and temperature. These data were then used directly to create a global flood and landslide susceptibility map.

Data Article

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):144-149. Published online September 27, 2024; DOI: https://doi.org/10.22761/GD.2024.0023

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Abstract PDF: The National Institute of Ecology in Seocheon, Chungcheongnam-do, exhibits and studies Korea’s diverse ecosystems to communicate the importance of biodiversity conservation to the general public. The site faces the challenge of preserving the natural environment while ensuring visitor convenience. This requires systematic data collection to quickly detect and respond to threats from pests and natural disasters. In this study, high-resolution orthoimages were acquired to monitor ecological changes using unmanned aerial vehicles. Images were captured through flights monthly from March to July 2024. Digital elevation models and orthoimages were generated to identify dead trees and assess areas of damage caused by heavy rainfall. The study covered an area of 998,655 m2, and the image resolution was 3.6 cm/pixel. The orthophotos were useful in identifying plant pest damage. They also helped evaluate damage caused by rain, showing that the total area of damage was 29,384 m2, mainly due to soil erosion. Furthermore, it is expected that the accumulation of such unmanned remote sensing image data can also be applied to the safety management of various natural conservation areas and public facilities.

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