GEO DATA

Data Articles

Data on Time-series Observation of Ground Subsidence in South Korea Using Sentinel-1 SAR Observations: Chanuk Lee, Jeongheon Ju, JangHun Kang, Seowon Kim, HeeJin An, Yuna Hong, Seokyeong Hwang, Sang-Hoon Hong; GEO DATA. 2024;6(4):495-504. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0048

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Abstract PDF: Ground subsidence is a phenomenon where surface materials sink due to a combination of natural and anthropogenic factors. South Korea has experienced human casualties and economic losses due to ground subsidence, such as sinkholes. Moreover, with the recent increase in earthquakes in the country, the importance of collecting and analyzing data for monitoring ground subsidence and surface displacement for disaster prevention is growing. This study monitored ground subsidence that occurred in South Korea from January 1, 2021 to December 31, 2022, while also observing other surface displacements. The study utilized synthetic aperture radar (SAR) satellite data, which, due to its high penetration capabilities of microwaves, is relatively unaffected by weather and day-night conditions, enabling wide-area observation with high spatial resolution, making it suitable for monitoring surface displacements. A total of 321 C-band Sentinel-1 SAR images, obtained between January 1, 2021 and December 31, 2022, were analyzed. Based on a perpendicular baseline distance of 200 meters and a time interval of 100 days, small baseline subset network were created. Time-series surface displacement data and velocity maps were produced to analyze the overall displacement patterns in the study areas.

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):435-450. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0046

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Abstract PDF: This study presents the generation of a GeoAI dataset for urban water body detection using TerraSAR-X satellite synthetic aperture radar (SAR) imagery. The study area includes urban regions in Seoul and Gyeonggi Province, chosen for their complex structures and frequent flooding, which pose challenges for SAR analysis. The data preprocessing involved generating Sigma0 images, image co-registration, median filtering for speckle noise reduction, decibel conversion, and orthorectification using Copernicus DEM for precise geometric correction. Label data were created using the global river widths from Landsat dataset combined with the Otsu thresholding method and fine-tuned with Google Map imagery. Annotation guidelines were meticulously designed to account for SAR-specific phenomena such as layover, corner reflections, and side lobe effects, ensuring consistent and accurate labeling across different orbits and observation conditions. The resulting dataset supports deep learning models in learning geometric characteristics of SAR imagery, enhancing water body detection capabilities. This work provides a foundational resource for future applications in urban water management and climate-resilient disaster response.

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.

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

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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.

Construction of Time-series Displacement Data of Yongdam Dam Based on PSInSAR Analysis of Satellite C-band SAR Images: Taewook Kim, Hyunjin Shin, Jungkyo Jung, Hyangsun Han, Ki-mook Kang, Euiho Hwang; GEO DATA. 2023;5(3):147-154. Published online September 22, 2023; DOI: https://doi.org/10.22761/GD.2023.0024

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Abstract PDF: The increase in water-related disasters due to climate change has a significant impact on the stability of water resource facilities. The displacement of a water resource facility is one of the important indicators to evaluate the stability of the facility. In this study, the time-series displacement of the Yongdam Dam was constructed by applying the persistent scatter interferometric synthetic aperture radar (PSInSAR) technique to the Sentinel-1 C-band SAR images. A sufficient number of persistent scatterers were derived to enable local deformation monitoring of the Yongdam Dam, and the dam showed very small displacement velocity except during the heavy rainfall in August 2020. In the future, C-band SAR imagery from the water resources satellite (Next Generation Medium Satellite 5) is expected to provide accurate displacement data for water resource facilities.

Articles

Kompsat-5 Image Data Provision and Quality Management: Dochul Ynag, Horyung Jeong, Doochun Seo; GEO DATA. 2022;4(4):13-19. Published online December 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.4.002

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Abstract PDF: The Korea Aerospace Research Institute is conducting continuous quality management to provide reliable Kompsat-5 SAR image products to users. In this paper, the Kompsat-5 satellite operation, data processing, quality management, and data provision were described. The operation and image mode characteristics of the Kompsat-5 satellite from the image point of view were described, and the classification and characteristics of image products provided to users were explained. In addition, image data acquisition, quality index measurement, and its results are described for quality management of SAR images. Finally, it explains how to search for and order Kompsat image product through the ARIRANG system to quickly provide users with image products whose quality has been confirmed through quality management. Kompsat product can be searched and ordered from the ARIRANG Satellite Search and Order System (https://ksatdb.kari.re.kr/arirang/).

Radiometric Distortion Corrected Radar Backscattering Coefficient Data over Ilam, Iran using Kompsat-5 SAR Image: Dochul Yang; GEO DATA. 2021;3(4):28-31. Published online December 31, 2021; DOI: https://doi.org/10.22761/DJ2021.3.4.004

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Abstract PDF: Flattening gamma naught was calculated using Korea Multipurpose Satellite 5 (KOMPSAT-5, K5) by correcting the radiometric distortion caused by geometric distortion over Ilam, Iran. The flattening gamma naught is not only the SAR core observation of Analysis Ready Data (ARD), which is utilized for artificial intelligence and big data, but also the basis for all fields of application that use the SAR brightness by providing the backscattering values only from surface characteristics. The flattening gamma naught data is provided with the same resolution as that of the K5 SAR image, so the data over the Ilam, Iran have the spatial resolution of the K5 Wide Swath mode of 20 m. Shuttle Radar Topography Mission (SRTM) DEM with a resolution of 30 m was oversampled to generate the flattening gamma naught, and shadow areas where flattening gamma naught generation was not possible were identified using GIM layer information provided with the K5 image. In order to determine the reliability of the calculated flattening gamma naught, histogram analysis and tendency according to the incident angle were investigated, and the performance was verified by comparing it with other backscattering coefficients. Details of the algorithm and procedure are presented in previous studies and reference papers.

Dataset for Water Body Detection Using Satellite SAR Images: SeungJae Lee, Han Oh; GEO DATA. 2021;3(2):12-19. Published online July 21, 2021; DOI: https://doi.org/10.22761/DJ2021.3.2.002

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Abstract PDF: Satellite synthetic aperture radar (SAR) generates valid image information in all-weather. Thus, it can be effectively used for near real-time monitoring and damage analysis of flood areas which always involve overcast skies. Water body detection (WBD) using SAR images can be implemented by various techniques which discriminate electromagnetic characteristics between water and non-water areas. Especially, semantic segmentation exploiting artificial intelligence techniques can be used to develop a high-performance WBD model. To this end, Korea Aerospace Research Institute has built an WBD dataset using KOMPSAT-5 images. The dataset is currently available through the website, aihub.or.kr.

Ground-based data from wheat cropping fields in Australia for development of soil moisture retrieval algorithm using satellite images: SeungJae Lee, SunGu Lee, Dongryeol Ryu; GEO DATA. 2020;2(2):1-4. Published online December 30, 2020; DOI: https://doi.org/10.22761/DJ2020.2.2.001

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Abstract PDF: Soil moisture is an important data which can be used for crop growth estimation, drought prediction, irrigation, and development of hydrological model. However, it is difficult to obtain soil moisture data from inaccessible area or very large area using only general field campaign. For this reason, many soil moisture retrieval algorithms have been developed based on satellite remote sensing technique. It should be noted that both satellite images and ground-based data for the region of interest are required to effectively develop the soil moisture retrieval algorithm using satellite images. Thus, Korea aerospace research institute, KARI, have collected ground-based data containing soil moisture, soil temperature, and crop height in collaboration with the university of Melbourne from wheat cropping fields in Australia which are suitable for the development of soil moisture retrieval algorithm. The ground-based data was collected from wheat cropping fields containing various types of soils for about 7 months from May 2019 to November 2019.

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