GEO DATA

Most-download articles are from the articles published in 2023 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.

Data Article

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.

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

Dataset Construction for Monitoring the Effects of Ecosystem Restoration Projects: Soyeon Cho, Nahyun Ahn, Jaegyu Cha; GEO DATA. 2024;6(4):197-207. Published online December 3, 2024; DOI: https://doi.org/10.22761/GD.2024.0027

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Abstract PDF: Climate change have increased the necessity for developing climate adaptation strategies. Ecological restoration projects have been implemented to restore the structure and function of degraded natural environments, thus enabling adaptation to the climate crisis. However, due to the lack of subsequent monitoring and evaluation, the effectiveness and success of these restoration efforts remain unverified. Verification of restoration effectiveness is essential for establishing ecological restoration and adaptation policies. Therefore, this study catalogs 338 cases of ecosystem restoration projects (including Ecosystem Conservation Fund Return Projects, Jayeon Madang Restoration Projects, and Urban Ecological Corridor Restoration Projects) conducted between 2010 and 2023 to effectively select monitoring sites. Using satellite-based spatial data, we quantified the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference moisture index (NDMI) and land surface temperature (LST). This study shows that higher NDVI values significantly lower LST and NDMI, indicating that the more vegetation restored in ecosystems, the more effectively it reduces surface temperatures. The NDVI across all land cover types averaged above 0.2, corresponding to a high vegetation cover density. Specifically, forests exhibited significantly higher NDVI and NDMI, whereas bare land and used area showed significantly higher NDBI and LST. Over the time series, NDVI and NDMI increased, and NDBI decreased, suggesting the positive effects of restoration. The ranges of NDVI, NDBI, NDMI and LST values by land cover type for the 338 restoration project sites provided in this study can be utilized for selecting specific monitoring sites and verifying effectiveness.

Editorial Note

Editorial Note: Kidong Kim; GEO DATA. 2024;6(4):187-188. Published online December 27, 2024; DOI: https://doi.org/10.22761/GD.2024.0131

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Data Articles

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.

Unmanned Aerial Vehicle Photogrammetry Based Dataset of Halophyte Distribution in Jujin Estuary: Donguk Lee, Yeongjae Jang, Joo-Hyung Ryu, Hyeong-Tae Jou, Keunyong Kim; GEO DATA. 2024;6(4):505-511. Published online December 4, 2024; DOI: https://doi.org/10.22761/GD.2024.0012

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Abstract PDF: The importance of blue carbon is significant in terms of climate change mitigation and marine ecosystem conservation, and halophyte acts as a crucial reservoir for this blue carbon. Accordingly, this study utilized unmanned aerial vehicle (UAV) optical sensors to create a distribution map of vegetation in the natural salt marsh of the Jujin estuary. The optical images captured from a UAV at an altitude of 50 m provide ultra-high-resolution optical information with a ground sampling distance of 0.6 cm. Based on these images, a U-Net model was trained to classify Phragmites communis and Suaeda maritima, generating a classification map of the mixed habitats of salt marsh plants. The areas of Phragmites communis and Suaeda maritima in the Jujin- Cheon region were found to be 6,653.23 m² and 1,409.08 m², respectively. The classification results were validated using field control point data, confirming an approximate classification accuracy of 92%.

Evaluation of Residual Phase from Orbit Accuracy Using TerraSAR-X/TanDEM-X SAR Observation: Yeojin Kim, Sang-Hoon Hong; GEO DATA. 2024;6(4):487-494. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0039

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Abstract PDF: Interferometric synthetic aperture radar (InSAR) is used to observe precise surface displacement and create digital elevation models by calculating the phase differences between two or more SAR images obtained over the same surface area. The phase of a repeat-pass interferogram can be expressed as the sum of contributions from topography, ground displacement, earth curvature, noise, and the satellite’s orbital phase component. For precise observations, removing unnecessary phase components is essential. Errors owing to the satellite’s orbit accuracy leave residual phases in the interferogram, which become a significant limitation for wide-area ground displacement monitoring using the InSAR technique. This study used four pairs of images acquired by TerraSAR-X in monostatic pursuit mode from October 2014 to February 2015 to analyze the residual phase caused by orbital errors. Since these images were acquired with a 10-second interval between the TerraSAR-X and TanDEM-X satellites, the phase coherence was maintained over time. The Tarim Basin in China was selected as the study area to minimize the impact of terrain distortion. By introducing a 0.5 m error into the x, y, and z components of the satellite position vectors and creating differential interferograms, it was found that the x component’s orbital error caused the largest residual phase, with linear residual phases observed in the north-south direction. Furthermore, various baselines ranging from -29.71 to 263.21 m were used to quantitatively compare the residual phases caused by orbital errors based on the perpendicular baseline. The residual phase was similar across the four differential interferograms, with approximately 3.49 π for the x component, 0.85 π for the y component, and 1.25 π for the z component. The residual phase resulting from simulated orbital errors was effectively mitigated using a 2D quadratic model.

Solar Energy Datasets of Deep Learning Models Incorporating with GK-2A and ASOS Ground Measurements: Jong-Sung Ha, Seungtaek Jeong, Seyun Min, Yejin Lee, Suhwan Kim, Doehee Han, Jong-Min Yeom; GEO DATA. 2024;6(4):471-477. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0036

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Abstract PDF: This study presents the construction and evaluation of a dataset for estimating solar energy using the GK^-2A satellite and deep learning. The GK^-2A is currently utilized in real-time for weather observations over the Korean Peninsula. The GK^-2A satellite features 16 channels, producing radiative channel images at spatial resolutions ranging from 500 m to 2 km, with temporal intervals as short as 2 minutes depending on the area. These satellite data are used in various fields, including meteorology, oceanography, vegetation monitoring, and renewable energy. In this study, we used spectral channel data from the GK^-2A expended local area satellite from January 2021 to December 2022. For training and evaluating the accuracy of the deep learning model, we utilized data from 98 automated synoptic observing system ground observation sites operated by the Korea Meteorological Administration. A back-propagation neural network model, which showed meaningful results in estimating solar energy, was applied. Various hyperparameters were optimized, and data preprocessing and separation were conducted to optimize the model. The study also compared the performance of the deep learning model with physical models. The BPNN deep learning model achieved a statistical accuracy of root mean squared error (RMSE) 77.32 Wm^-2, mean bias error (MBE) -0.48 Wm^-2, and R² 0.91, indicating high accuracy. In contrast, the physical model showed an RMSE of 132.01 Wm^-2, MBE -76.51 Wm^-2, and an R² of 0.74, displaying relatively lower accuracy compared to the deep learning model. Additionally, the spatio-temporal map of solar energy generated by the deep learning model successfully captured the attenuation of radiation due to clouds and the variation in solar energy based on the position of the sun. The solar energy data produced in this study are expected to be useful as input data for various fields such as meteorology, agriculture, environmental monitoring, and marine sciences.

Expanded Bioclimatic Variables Extracted from Monthly Climate Predictions under the SSP Climate Scenarios over South Korea: Jieun Oh, Ah Reum Han, Yeong-cheol Kim, Seungbum Hong; GEO DATA. 2024;6(4):235-247. Published online December 3, 2024; DOI: https://doi.org/10.22761/GD.2024.0018

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Abstract PDF: Numerous studies, including the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report, have documented species habitat shifts caused by climate change. These shifts lead to transformations in ecosystem structure, components, and functions. Exploring the connections between species and climate change is essential for developing adaptation strategies. Many studies use species distribution models (SDMs), which are based on the correlation between species habitats and climatic surroundings, to predict ecological shifts under climate change. The primary climate variables for these models are the only 19 variables whose concepts are based on monthly average temperature and precipitation from the BIOCLIM package developed in 1984. These 19 bioclimatic variables usually are obtained from WorldClim data set and other datasets. However, they have limitations in reflecting local climate characteristics and their association with ecology. Firstly, future projection data from global dataset including WorldClim dataset is derived directly from global climate models rather than regional climate models. Secondly, the 19 variables based on monthly temperature and precipitation do not adequately express hydrological characteristics of terrestrial ecosystem which are crucial for species habitats. Lastly, although there are various biogeographical indices excepts the 19 bioclimatic variables, there have been just a few cases that they were applied to SDMs for Korea. To overcome these limitations, this study expands the various bioclimatic variables, using regionally specialized climate data from Korea Meteorology Administration (KMA). The newly extended indices, which can reflect water availability, are expected to improve the prediction of SDMs, enabling more precise assessment of ecological risks due to climate change and effective adaptation strategies to mitigate the impacts of climate change on ecosystems.

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.

The Spatial Distribution of Wetland Preference of Vegetation in River Type Wetland Protected Area: Jong-Hak Yun, Yeonhui Jang, Jeong Ho Hwang, Haeseon Shin; GEO DATA. 2024;6(4):189-196. Published online December 16, 2024; DOI: https://doi.org/10.22761/GD.2024.0025

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Abstract PDF: The spatial distribution of wetland preference of vegetation was analyzed based on the results of actual vegetation maps for six river type wetland protected areas. There are nine to 23 plant communities distributed in the six wetlands. In wetland preference plant communities, there are 16 obligate wetland plant communities (OBW), 10 facultative wetland plant communities (FACW), seven facultative plant communities (FAC), five factultative upland plant communities (FACU), and 15 obligate upland plant communities (OBU). In the central part of the wetland protected area, OBW and FACW are mainly distributed, and in the peripheral area, FACU and OBU are widely distributed. Therefore, in order to continuously maintain wetland vegetation, it is necessary to prevent drying out by managing inflowing water, removing sediment, and securing water flow.

Insect Fauna of Estuary Wetlands in Sacheon City: Ga-Hwa Cheon, Gon-Yang Cheon: Minhyeok Won, Yeounsu Chu, Sanghun Lee; GEO DATA. 2024;6(4):263-270. Published online December 3, 2024; DOI: https://doi.org/10.22761/GD.2024.0026

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Abstract PDF: Estuaries provide beneficial ecosystem services such as providing habitats for various species, and continuous monitoring of species, including insects, is necessary to prevent the destruction of estuaries. In this study, we analyzed the status and aspect of insect fauna in two estuary wetlands based on the results of Survey on Estuarine Ecosystem conducted in Ga-Hwa Cheon in 2013 and 2021 and Gon-Yang Cheon in 2023. A total of 464 insect species were found in the Ga-hwa Cheon and 753 species were found in the Gon-Yang Cheon. At the species level, Coleoptera (159 species), Hemiptera (101 species), and Hymenoptera (50 species) were dominant in the GaHwa Cheon, while Lepidoptera (478 species), Coleoptera (89 species), and Hemiptera (62 species) were dominant in the Gon-Yang Cheon. In the case of invasive species, Ricania sublimata and Vespa velutina nigrithorax were found in both two sites, and Eurema hecabe and Hierodula patellifera were found in both two sites as climate-sensitive biologocal indicator species. In the Ga-Hwa Cheon, Coenonympha hero was found, which corresponds to the vulnerable species of the International Union for Conservation of Nature (IUCN) Red List. Through this study, we analyzed the status and aspect of insect fauna in two estuary wetlands located in Sacheon-si, and it can be used as important basic data for establishing wetland conservation policies and plans, such as controlling invasive species.

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.

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