GEO DATA

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

Original Papers

Detection of Floating Debris in the Lake Using Statistical Properties of Synthetic Aperture Radar Pulses: Donghyeon Yoon, Ha-eun Yu, Moung-Jin Lee; GEO DATA. 2023;5(3):185-194. Published online September 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0032

1,807 View
43 Download

Abstract PDF: This study developed the European Space Agency (ESA) Setinel-1 Ground Range Detected (GRD) time series analysis model for monitoring floating debris in lake areas through Google Earth Engine Application Programming Interface. The study aims to monitor floating debris caused by heavy rainfall efficiently. Regarding water resources and water quality management, floating debris from multipurpose dams requires continuous monitoring from the initial generation stage. In the study, a Synthetic Aperture Radar (SAR) time series analysis model that is easy to identify water bodies was developed due to low accessibility in large areas. Although SAR satellite images could be used to observe inland water environments, debris detection on water surface surfaces has yet to be studied. For the first time, this study detected floating debris patches in a wide range of lakes from GRD imagery acquired by ESA’s Sentinel-1 satellite. It demonstrated the potential to distinguish them from naturally occurring materials such as invasive floating plants. In this study, the case of Daecheong Dam, in which predicted floating debris was detected after heavy rain using Sentinel-1 GRD data, is presented. It could quickly detect various floating debris flowing into dams used as a source of drinking water and serve as a reference for establishing a collection plan.

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

3,585 View
784 Download

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 Articles

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

1,454 View
59 Download

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.

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

533 View
16 Download

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.

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

912 View
67 Download

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.

Original Papers

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

2,545 View
62 Download

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.

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

6,554 View
1,456 Download

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.

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

2,293 View
71 Download
1 Citations

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.

Citations

Citations to this article as recorded by

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

1,544 View
50 Download
2 Citations

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.

Citations

Citations to this article as recorded by

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 Urban Water Body Detection Using TerraSAR-X Satellite Radar Imagery
Eu-Ru Lee, Jun-Hyeok Jung, Ki-Chang Kim, Seong-Jae Yu, Hyung-Sup Jung
GEO DATA.2024; 6(4): 435. CrossRef

Data Articles

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

548 View
39 Download

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.

KOMPSAT-3/3A Image-text Dataset for Training Large Multimodal Models: Han Oh, Dong-Bin Shin, Dae-Won Chung; GEO DATA. 2025;7(1):27-35. Published online March 19, 2025; DOI: https://doi.org/10.22761/GD.2025.0003

374 View
27 Download

Abstract PDF: This study aims to improve the accuracy and interpretability of large multimodal models (LMMs) specialized in satellite image analysis by constructing an image-text dataset based on KOMPSAT-3/3A imagery and presenting the results of training using this dataset. Conventional LMMs are primarily trained on general images, limiting their ability to effectively interpret the specific characteristics of satellite imagery, such as spectral bands, spatial resolution, and viewing angles. To address this limitation, we developed an image-text dataset, divided into pretraining and finetuning stages, based on the existing KOMPSAT object detection dataset. The pretraining dataset consists of captions summarizing the overall theme and key information of each image. The fine-tuning dataset integrates metadata -including acquisition time, sensor type, and coordinates- with detailed object detection labels to generate six types of question-answer pairs: detailed descriptions, conversations with varying answer lengths, bounding box identification, multiple choice questions, and complex reasoning. This structured dataset enables the model to learn not only the general context of satellite images but also fine-grained details such as object quantity, location, and geographic attributes. Training with the new KOMPSAT-based dataset significantly improved the model’s accuracy in recognizing regional information and object characteristics in satellite imagery. Finetuned models achieved substantially higher accuracy than previous models, surpassing even the GPT-4o model and demonstrating the effectiveness of a domain-specific dataset. The findings of this study are expected to contribute to various remote sensing applications, including automated satellite image analysis, change detection, and object detection.

GeoAI Dataset for Industrial Park Segmentation from Sentinel-2 Satellite Imagery and GEMS: Sung-Hyun Gong, Hyung-Sup Jung, Geun-han Kim, Geun-Hyouk Han, Il-Hoon Choi, Jin-Sung Hong; GEO DATA. 2025;7(1):36-44. Published online February 13, 2025; DOI: https://doi.org/10.22761/GD.2024.0054

374 View
43 Download

Abstract PDF: Air pollution in East Asia presents critical environmental and health challenges, particularly in industrial regions affected by domestic and cross-border emissions. This study developed a GEO AI dataset specifically for industrial park segmentation, integrating Sentinel-2 satellite imagery, Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite data, and Air Quality Monitoring Network data. Optimized for semantic segmentation tasks with labeled data specifically for industrial park classification, this dataset serves as a foundational asset for the precise identification and spatial tracking of major air pollution sources. We validated the dataset’s applicability using a modified U-Net model, achieving a mean intersection over union of 0.8146 and pixel accuracy of 0.9608, thereby demonstrating its potential as a tool for detecting and monitoring pollutant sources in industrial areas. With future expansion through additional temporal data and diverse pollutant measurements, this dataset is anticipated to support regional air quality monitoring efforts and inform strategies for pollution control across East Asia.

Constructing the Spatial Data to Forecast Potential Habitat for Amphibians and Reptiles under Climate Change: Man-Seok Shin, Sung-Ryong Kang, Bo-Ra Kim; GEO DATA. 2024;6(4):208-225. Published online December 27, 2024; DOI: https://doi.org/10.22761/GD.2024.0028

499 View
29 Download

Abstract PDF: In this study, distribution data and environmental information for predicting the potential habitat of amphibians and reptiles in South Korea were compiled. The distribution data of amphibians and reptiles include nationwide surveys conducted by the National Institute of Ecology (seven surveys) and the Korea National Park Service (one survey). The distribution data are based on 57,777 locations for 35 species. Environmental information related to the habitat of amphibians and reptiles was constructed from 19 bioclimatic variables related to climate and four spatial variables related to geographic factors, and 19 bioclimatic variables for the future (2020-2090) were constructed using the results of SSP scenarios. In addition, species distribution models (MaxEnt) were used to predict current and future potential habitat for 28 amphibian and reptile species with more than 50 survey sites. The model validation values for the 28 species ranged from 0.717 to 0.987. These data have the potential to inform conservation strategies in response to climate change by spatially identifying current and future potential habitat for amphibians and reptiles.

Original Paper

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

1,563 View
70 Download
1 Citations

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.

Citations

Citations to this article as recorded by

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 Article

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

984 View
56 Download

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.

First
Prev
Page of 8
Next
Last

Most view