GEO DATA

Data Articles

Characteristics of Water Quality and Sediment Distributions on the Northeastern Coast of Jeju Island: Taehee Lee, Hyung Jeek Kim; GEO DATA. 2025;7(1):45-54. Published online March 19, 2025; DOI: https://doi.org/10.22761/GD.2024.0050

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Abstract PDF: Since the 1980s, the number of land-based fish farms on Jeju Island has increased rapidly. With increasing land-based fish farms, a large amount of nutrients from fish farm wastewater is discharged off the coast of Jeju. To understand the characteristics of coastal seawater and the ecological environment on the coast of Jeju, the effect of land-based fish farm effluent on coastal seawater should be evaluated. Temperature, salinity, nutrients, and chlorophyll-a concentration were investigated on the northeastern coast of Jeju during June and July 2023. Nitrate, phosphate, and silicate concentrations in the surface waters were significantly higher in coastal stations than in the outer stations. Unlike the surface waters, nutrient concentrations in the bottom waters are distinctly higher in land-based fish farm effluent stations than in the outer stations. Total organic carbon content in surface sediment was significantly higher in land-based fish farm effluent stations than in the outer stations. This study may provide valuable information for evaluating the impact of land-based fish farm effluent on coastal ecosystems on Jeju Island.

GeoAI Dataset for Training a Deep Learning-based GEMS Snow Detection Model: Jin-Woo Yu, Jun-Hyeok Jung, Kyoung-Hee Kang, Yong-Mi Lee, Hyung-Sup Jung; GEO DATA. 2024;6(4):552-560. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0060

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Abstract PDF: The Geostationary Environment Monitoring Spectrometer (GEMS) observes air quality across East Asia from an altitude of approximately 36,000 km, analyzing the spatiotemporal distribution of atmospheric pollutants that spread beyond localized regions. GEMS currently provides 21 core air quality-related products, most of which are derived from Level 1C data, which has undergone geometric and radiometric correction. For enhanced accuracy in air quality analysis, precise surface reflectance estimation is essential. However, high-reflectance elements, such as snow, interfere with the accurate estimation of radiance values, necessitating precise detection of such areas. Despite this, GEMS relies solely on the ultraviolet and partial visible bands, lacking the infrared bands crucial for snow detection, and it has no proprietary snow detection algorithm, instead utilizing near-real-time ice and snow extent data from the U.S. National Snow and Ice Data Center. Recently, deep learning techniques have shown potential in image processing, outperforming traditional algorithms, which could address these limitations. However, there is currently no deep learning training dataset available for snow detection specifically for GEMS. To address this issue, this study developed a GeoAI dataset for training a deep learning-based snow detection model for GEMS. In this research, we constructed input data using GEMS Level 1C data and generated label data based on GEMS, Advanced Meteorological Imager, and MODIS snow cover data. The snow detection dataset developed in this study is expected to address the snow detection limitations of GEMS, providing foundational data to enhance the reliability of future geostationary satellite-based air quality research.

Drone-based 3D High-resolution Imagery for Barrier Island in Nakdong River Estuary: Sung-Hwan Park, Nam-Hoon Kim, Jun-Yong Park, Eui-Young Jeong; GEO DATA. 2024;6(4):512-520. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0040

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Abstract PDF: This study explores the use of drone-based photogrammetry to analyze the 3D topographic changes on Doyodeung, one of the barrier islands in the Nakdong River estuary. These islands act as natural buffers against wave energy from typhoons, providing essential habitats for migratory birds, while being highly dynamic and subject to morphological changes due to both natural processes and human activities. From June 2016 to July 2017, four drone surveys were conducted using the eBee fixed-wing drone, capturing high-resolution imagery of Doyodeung. The photogrammetry workflow involved aligning images, matching them with ground control points, and generating a detailed digital surface model and orthophotos with a high resolution of less than a few centimeters. The results demonstrated topographic changes, particularly after Typhoon Chaba in October 2016, which caused significant erosion along the shoreline. Following the typhoon, Doyodeung’s total area decreased by 2.5%, and its volume shrank by 8.8%. Despite some recovery observed in later months, the island’s geomorphology continued to evolve. This highresolution dataset provides crucial insights into geomorphological changes, coastal management, spatial variation of halophytes and marine environment monitoring, highlighting the effectiveness of drone-based techniques in capturing precise 3D data in dynamic, hard-to-reach environments.

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.

Original Papers

Distribution Status of Invasive Alien Species in the Estuarine Wetlands in East and South Coast, Korea: Yeounsu Chu, Pyoungbeom Kim, Minhyeok Won; GEO DATA. 2024;6(2):56-62. Published online June 14, 2024; DOI: https://doi.org/10.22761/GD.2024.0007

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Abstract PDF Supplementary Material: Based on the results of survey on estuarine ecosystem conducted from 2021 to 2023, this study analyzed the distribution pattern of invasive alien species. A total of six invasive alien species (Micropterus salmoides [M. salmoides], Lepomis macrochirus [L. macrochirus], Lithobates catesbeianus [L. catesbeianus], Trachemys scripta [T. scripta], Pseudemys concinna [P. concinna], and Pseudemys nelsoni [P. nelsoni]) were identified across 118 estuarine wetlands. The survey revealed 960 individuals of M. salmoides, 2,596 individuals of L. macrochirus, 1,088 individuals of L. catesbeianus, and 44 individuals of T. scripta, while 1 individual of P. concinna and P. nelsoni were found. These invasive species were found to be densely distributed in estuarine wetlands in the Haenam and Goheung regions of the southern coast. Furthermore, the distribution density varied depending on the characteristics and locations of the estuarine wetlands. It was observed that the distribution density was higher in closed type than in open type, in riverine than in lacustrine, and in the south coast than in the east coast. Once the basic survey of estuarine wetlands located on the west coast is completed, a clearer understanding of the nationwide distribution characteristics of invasive alien species in estuarine wetlands can be achieved. This information will be crucial for formulating appropriate management strategies tailored to the characteristics of wetlands or coastal areas in the future.

Improvement of Algal Bloom Identification Using Satellite Images by the Algal Spatial Monitoring and Machine Learning Analysis in a New Dam Reservoir: Hye-Suk Yi, Sunghwa Choi, Dong-Kyun Kim, Hojoon Kim; GEO DATA. 2023;5(3):126-136. Published online September 25, 2023; DOI: https://doi.org/10.22761/GD.2023.0021

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Algal blooms are major issues and an ongoing cause of water quality problems in inland waters globally. In the case of harmful algal blooms, the water temperature rises after nitrogen and phosphorus inflow, which occurs in the summer, is the main cause of the algae bloom. In South Korea, algae monitoring methods have been performed by collecting water in point monitoring stations. Recently, in order to overcome the limitations of these existing monitoring methods, spatial monitoring methods using hyperspectral images and satellite images has been researched. We used satellite images for analysis of the spatial algal variation. The accuracy of algal identification is imperative for effective spatial monitoring of algal blooms in the context of ecological health and assessment. In this study, we generated algal big-data with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement and predicted chlorophyll-a concentrations using 13- band satellite images derived from Sentinel-2. In order to validate the values from the satellite images, we compared them with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement. The goal of this study is to improve the accuracy of predictions induced from satellite images. The analytical techniques were comparatively evaluated. The results showed that Artificial Neural Networks exhibited the best performance among them, improving more than 30% accuracy compared to that of multiple linear regression. Furthermore, the accuracy of identifying algal blooms has been shown to increase at high algal concentrations. In the end, it was successful to create algal bloom maps using a new algorithm to analyze algal bloom management.

Citations

Citations to this article as recorded by

Assessment of the Usability of the Linkage between GLORIA and Sentinel-2 Imagery for the Surveillance of Algal Blooms in Freshwater Ecosystems
Gibeom Nam, Sunghwa Choi, Euiho Hwang, Kimook Kang, JinGyeom Kim, DongHyeon Yoon
GEO DATA.2024; 6(4): 451. CrossRef

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.

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