• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.152-153
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• 2023

AIS has been widely used for maritime traffic assessment for its convenience. However, AIS has problems with position missing due to radio interference and transmission distance limit. On the other hand, satellite radar determines the location of ships over a wide sea regardless of the problems. This study proposes a noble method of stepwise threshold determination to detect ships from Sentinel-1. The proposed method is up to 25 times faster than the existing moving window-based threshold determination method, and the detection accuracy is similar.

• Korean Journal of Geomatics
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• v.3 no.1
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• pp.33-41
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• 2003

Every year, several typhoons hit the Korean peninsula and cause severe damage. For the prevention and accurate estimation of these damages, real time or almost real time flood information is essential. Because of weather conditions, images taken by optic sensors or LIDAR are sometimes not appropriate for an accurate estimation of water areas during typhoon. In this case SAR (Synthetic Aperture Radar) images which are independent of weather condition can be useful for the estimation of flood areas. To get detailed information about floods from satellite imagery, accurate classification of water areas is the most important step. A commonly- and widely-used classification methods is the ML(Maximum Likelihood) method which assumes that the distribution of brightness values of the images follows a Gaussian distribution. The distribution of brightness values of the SAR image, however, usually does not follow a Gaussian distribution. For this reason, in this study the ANN (Artificial Neural Networks) method independent of the statistical characteristics of images is applied to the SAR imagery. RADARS A TSAR images are primarily used for extraction of water areas, and DEM (Digital Elevation Model) is used as supplementary data to evaluate the ground undulation effect. Water areas are also extracted from KOMPSAT image achieved by optic sensors for comparison purpose. Both ANN and ML methods are applied to flat and mountainous areas to extract water areas. The estimated areas from satellite imagery are compared with those of manually extracted results. As a result, the ANN classifier performs better than the ML method when only the SAR image was used as input data, except for mountainous areas. When DEM was used as supplementary data for classification of SAR images, there was a 5.64% accuracy improvement for mountainous area, and a similar result of 0.24% accuracy improvement for flat areas using artificial neural networks.

• Proceedings of the KSRS Conference
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• v.2
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• pp.981-984
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• 2006

Oil pollution of the ocean is a major environmental problem, especially in its coastal zones. Synthetic aperture radar (SAR) flown on satellites, such as ERS-2 and Envisat, has been proved to be a useful tool in oil spill monitoring due to its wide coverage, day and night, and all-weather capability. The total 120 SAR images containing oil spill over the East China Sea were collected and analyzed, ranging in date from July 23, 2002 to November 11, 2005. After preprocessed, SAR images were segmented by adaptive threshold method. The oil spill images were incorporated into GIS after distinguished from look-like phenomena, finally we presented the oil spills distribution map for the East China Sea. The wide-swath and quick-looks SAR imagery for mapping of oil spill distribution over large marine areas were proved to be useful when full resolution data are not available. After the temporal and spatial distribution of the oil spills were analyzed, we found that most of oil spills were distributed along the main ship routes, which means the illegal discharge by ships, and the occurrence of oil spill detected on SAR images acquired during morning and summer is much higher than during evening and winter.

• Korean Journal of Remote Sensing
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• v.13 no.1
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• pp.57-73
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• 1997

This paper is related to the correction of radiometric distortions induced by topographic relief. RADARSAT SAR image data were obtained over the mountainous area near southern part of Seoul. Initially, the SAR data was geometrically corrected and registered to plane rectangular coordinates so that each pixel of the SAR image has known topographic parameters. The topographic parameters (slope and aspect) at each pixel position were calculated from the digital elevation model (DEM) data having a comparable spatial resolution with the SAR data. Local incidence angle between the incoming microwave and the surface normal to terrain slope was selected as a primary geometric factor to analyze and to correct the radiometric distortions. Using digital maps of forest stands, several fields of rather homogeneous forest stands were delineated over the SAR image. Once the effects of local incidence angle on the radar backscatter were defined, the radiometric correction was performed by an empirical fuction that was derived from the relationship between the geometric parameters and mean radar backscatter. The correction effects were examined by ground truth data.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.131-144
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• 2015

This research proposes a prototype for visualizing radar rainfall data using the unity 3D engine. The mashup of radar data with topographic information is necessary for the 3D visualization of the radar data with high quality. However, the mashup of a huge amount of radar data and topographic data causes the overload of data processing and low quality of the visualization results. This research utilized the Unitiy 3D engine, a widely used engine in the game industry, for visualizing the 3D topographic data such as the satellite imagery/the DEM(Digital Elevation Model) and radar rainfall data. The satellite image segmentation technique and the image texture layer mashup technique are employed to construct the 3D visualization system prototype based on the topographic information. The developed protype will be applied to the disaster-prevention works by providing the radar rainfall data with the 3D visualization based on the topographic information.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.547-550
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• 2001

Through matched filtering synthetic aperture radar (SAR) produces high-resolution imagery from data collected by a relative small antenna. While the impulse response obtained by the matched filter approach produces the best achievable signal-to-noise ratio, large sidelobes must be reduced to obtain higher-resolution SAR images. So, many enhancement methods of SAR imagery have been proposed. As a deconvolution method, the phase-extension inverse filtering is based on the characteristics of the matched filtering used in SAR imaging. It improves spatial resolution as well as effectively suppresses the sidelobes with low computational complexity. In the phase-extension inverse filtering, the impulse response is obtained from simulation with a point target. But in a real SAR environment, for example ERS-1, the impulse response is distorted by many non-ideal factors. So, in the phase-extension inverse filtering for a real SAR processing, the magnitudes of the frequency transfer function have to be compensated to produce more desirable results. In this paper, an estimation method to obtain a more accurate impulse response from a real SAR image is studied. And a compensation scheme to produce better performance of the phase-extension inverse filtering is also introduced.

• Journal of Satellite, Information and Communications
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• v.2 no.2
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• pp.75-79
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• 2007

Satellite imagery was applied to locating archeological sites and remains around northeastern areas of China, called as Manchuria, and Korean peninsular, such as Mountain Fortress of Goguryeo Dynasty era($37BC{\sim}771AD$), and firing torch and smoke beacon signal sites at mountain tops in Josun Dynasty era($1392{\sim}1910AD$) as well as burial sites below the ground level in the modern era. Information on archeological sites, fire posts and burial places could be found in various literatures, but real figures of such cultural assets have been disappearing due to land development programs and human activities in recent years. Some of these historical sites were identified in satellite images using GPS(Geographical Positioning System). Real locations of these sites would be further necessary to be verified.

• Aerospace Engineering and Technology
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• v.10 no.1
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• pp.149-155
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• 2011

A fast-changing agriculture environment induced by global warming and abnormal climate conditions demands scientific systems for monitoring and predicting crop conditions as well as crop yields at national level. Remote sensing opens up a new application field for precision agriculture with the help of commercial use of high resolution optical as well as radar satellite data. In this study, we investigated the multi-temporal spectral characteristics relative to different agricultural land use types in Korea using RapidEye satellite imagery. There were explicit differences between vegetation and non-vegetation land use types. Also, within the vegetation group spectral vegetation indices represented differences in temporal changing trends as to plant species and paddy types.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.13-22
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• 2021

The ML 5.8 earthquake shocked Gyeongju, Korea, at 11:32:55 UTC on September 12, 2016. One year later, on the afternoon of November 15, 2017, the ML 5.4 earthquake occurred in Pohang, South Korea. The earthquakes injured many residents, damaged buildings, and affected the economy of Gyeongju and Pohang. The damage proxy maps (DPMs) were generated from Sentinel-1 synthetic aperture radar (SAR) imagery by comparing pre- and co-events interferometric coherences to identify anomalous changes that indicate damaged by the earthquakes. DPMs manage to detect coherence loss in residential and commercial areas in both Gyeongju and Pohang earthquakes. We found that our results show a good correlation with the Korea Meteorological Administration (KMA) report with Modified Mercalli Intensity (MMI) scale values of more than VII (seven). The color scale of Sentinel-1 DPMs indicates an increasingly significant change in the area covered by the pixel, delineating collapsed walls and roofs from the official report. The resulting maps can be used to assess the distribution of seismic damage after the Gyeongju and Pohang earthquakes and can also be used as inventory data of damaged buildings to map seismic vulnerability using machine learning in Gyeongju or Pohang.