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대한원격탐사학회> 대한원격탐사학회지> Monitoring Mount Sinabung in Indonesia Using Multi- Temporal InSAR

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Monitoring Mount Sinabung in Indonesia Using Multi- Temporal InSAR

Chang-wook Lee , Zhong Lu , Jin Woo Kim
  • : 대한원격탐사학회
  • : 대한원격탐사학회지 33권1호
  • : 연속간행물
  • : 2017년 02월
  • : 37-46(10pages)
대한원격탐사학회지

DOI


목차

1. Introduction
2. Data processing
3. Results
4. Discussion and Conclusion
Acknowledgment
References

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초록 보기

Sinabung volcano in Indonesia was formed due to the subduction between the Eurasian and Indo-Australian plates along the Pacific Ring of Fire. After being dormant for about 400 years, Sinabung volcano erupted on the 29th of August, 2010 and most recently on the 1st of November, 2016. We measured the deformation of Sinabung volcano using Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) interferometric synthetic aperture radar (InSAR) images acquired from February 2007 to January 2011. Based on multi-temporal InSAR processing, we mapped the ground surface deformation before, during, and after the 2010 eruption with time-series InSAR technique. During the 3 years before the 2010 eruption, the volcano inflated at an average rate of ~1.7 cm/yr with a markedly higher rate of 6.6 cm/yr during the 6 months prior to the 2010 eruption. The inflation was constrained to the top of the volcano. From the 2010 eruption to January 2011, the volcano subsided by approximately 3 cm (~6 cm/yr). We interpreted that the inflation was due to magma accumulation in a shallow reservoir beneath Sinabung. The deflation was attributed to magma withdrawal from the shallow reservoir during the eruption as well as thermo-elastic compaction of erupted material. This result demonstrates once again the utility of InSAR for volcano monitoring.

UCI(KEPA)

I410-ECN-0102-2018-400-000455173

간행물정보

  • : 자연과학분야  > 기타(자연과학)
  • : KCI등재
  • :
  • : 격월
  • : 1225-6161
  • : 2287-9307
  • : 학술지
  • : 연속간행물
  • : 1985-2022
  • : 1774


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An attempt to derive the surface temperature from the Korea Multi-purpose Satellite (KOMPSAT)-3A mid-wave infrared (MWIR) data acquired over the southern California on Nov. 14, 2015 has been made using the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model. Since after the successful launch on March 25, 2015, the KOMPSAT-3A spacecraft and its two payload instruments ― the high-resolution multispectral optical sensor and the scanner infrared imaging system (SIIS) ― continue to operate properly. SIIS uses the MWIR spectral band of 3.3-5.2 μm for data acquisition. As input data for the realistic simulation of the KOMPSAT-3A SIIS imaging conditions in the MODTRAN model, we used the National Centers for Environmental Prediction (NCEP) atmospheric profiles, the KOMPSAT-3A sensor response function, the solar and line-of-sight geometry, and the University of Wisconsin emissivity database. The land cover type of the study area includes water, sand, and agricultural (vegetated) land located in the southern California. Results of surface temperature showed the reasonable geographical pattern over water, sand, and agricultural land. It is however worthwhile to note that the surface temperature pattern does not resemble the top-of-atmosphere (TOA) radiance counterpart. This is because MWIR TOA radiances consist of both shortwave (0.2-5 μm) and longwave (5-50 μm) components and the surface temperature depends solely upon the surface emitted radiance of longwave components. We found in our case that the shortwave surface reflection primarily causes the difference of geographical pattern between surface temperature and TOA radiance. Validation of the surface temperature for this study is practically difficult to perform due to the lack of ground truth data. We therefore made simple comparisons with two datasets over Salton Sea: National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) field data and Salton Sea data. The current estimate differs with these datasets by 2.2 K and 1.4 K, respectively, though it seems not possible to quantify factors causing such differences.

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In this study a tendency of abnormal sea surface temperature (SST) occurrence in the seas around South Korea is analyzed from daily SST data from satellite and 14 buoys from August 2020 to July 2021. As thresholds 28℃ and 4℃ are used to determine marine heatwaves (MHWs) and abnormal low water temperature (ALWT), respectively, because those values are adopted by the National Institute of Fisheries Science for the breaking news of abnormal temperature. In order to calculate frequency of abnormal SST occurrence spatially by using satellite SST, research area was divided into six areas of coast and three open seas. ALWT dominantly appeared over a wide area (7,745 km2) in Gyeonggi Bay for total 94 days and it was also confirmed from buoy temperature showing an occurrence number of 47 days. MHWs tended to be high in frequency in the coastal areas of Chungcheongdo and Jeollabukdo and the south coastal areas while in case of buoy temperature Jupo was the place of high frequency (32 days). This difference was supposed to be due to the low accuracy of satellite SST at the coasts. MHWs are also dominant in offshore waters around Korean Peninsula. Although detecting abnormal SST by using satellite SST has advantage of understanding occurrence from a spatial point of view, we also need to perform detection using buoys to increase detection accuracy along the coast.

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Mining activity causes environmental pollution and geological hazards such as ground subsidence or landslide of which continuous monitoring is necessary. In this study, the activity on the Fushun West Open- Pit Mine (FWOPM), one of the largest open-pit coal mines in Asia located in Fushun, Liaoning Province, China, was analyzed by using a time-series Sentinel-1 InSAR coherence dataset. By using the difference between the two Digital Elevation Models (DEM) of the area, it was possible to confirm that there was a stockpiling activity in the western area of the FWOPM while excavation activity in the eastern area. By using RGB composite images using the yearly-averaged InSAR coherence images, the activity of the mine was confirmed by period, which was confirmed by Google Earth optical images. As a result, it was possible to confirm three landslides and the related activities on the northwest slope and the dumping activity on the west slope of FWOPM.

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The city of Khartoum, the capital of Sudan, was heavily damaged by the flood of the Nile in 2020. Classification using satellite images can define the damaged area and help emergency response. As Synthetic Aperture Radar (SAR) uses microwave that can penetrate cloud, it is suitable to use in the flood study. In this study, Random Forest classifier, one of the supervised classification algorithms, was applied to the flood event in Khartoum with various sizes of the training dataset and number of images using Sentinel-1 SAR. To create a training dataset, we used unsupervised classification and visual inspection. Firstly, Random Forest was performed by reducing the size of each class of the training dataset, but no notable difference was found. Next, we performed Random Forest with various number of images. Accuracy became better as the number of images increased, but converged to a maximum value when the dataset covers the duration from flood to the completion of drainage.

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Coastal upwelling is a significantly imperative process for understanding the interactions between physical and ecological processes and has been investigated incessantly. In this study, we explored the upwelling index, specifically upwelling age (UA). UA enabled us to observe the initiating, sustaining, and decaying upwelling processes. Although the sensitivity of many other geophysical parameters to estimate UA has been investigated, the wind direction has not been evaluated. Thus, we assessed the appropriate wind direction for the UA and obtained efficient upwelling signals from the four coastal stations. Furthermore, we applied the UA and compared it with the satellite sea level anomaly, sea surface temperature, and chlorophyll-a changes to validate how UA depicts their spatial extents. Thus, UA can predict the timing of coastal upwelling events using predicted geophysical parameters.

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The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.

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8An Analysis of Land Use Changes in DPR Korea Using Land Cover Maps from the Late 1980s to the Late 2010s

저자 : Soojeong Myeong

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 38권 4호 발행 연도 : 2022 페이지 : pp. 411-419 (9 pages)

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DPR Korea has been creating cropland across the country due to its chronic food shortage. Cropland was about 17.4% at the end of the 1980s, but it increased steadily to 19.6% at the end of the 1990s, 24.8% at the end of the first decade of 2000s, and 25.4% at the end of the 2010s. On the other hand, the forest land declined from about 74.8% in the late 1980s to 69.5% in the late 2010s. Urbanization is also progressing, increasing from about 1.15% at the end of the 1980s to 1.68% at the end of the 2010s. Most of the deforestation that occurred in DPR Korea was caused by conversion to cropland. These characteristics of land cover changes in DPR Korea provide useful information and implications for international and inter-Korean cooperation for DPR Korea.

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저자 : Chul-hee Lim

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 38권 4호 발행 연도 : 2022 페이지 : pp. 421-434 (14 pages)

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Fine particulate matter (PM2.5) has been the biggest environmental problem in Korea since the 2010s. The present study considers the value of urban forests and green infrastructure as an ecosystem service (ES) concept for PM2.5 reduction based on satellite and spatial data, with a focus on Seoul, Korea. A method for the spatial ES assessment that considers social demand variables such as population and land price is suggested. First, an ES assessment based on natural environment information confirms that, while the vitality of vegetation is relatively low, the ES is high in the city center and residential areas, where the concentration of PM2.5 is high. Then, the ES assessment considering social demand (i.e., the ESS) confirms the existence of higher PM2.5 values in residential areas with high population density, and in main downtown areas. This is because the ESS of urban green infrastructure is high in areas with high land prices, high population density, and above-average PM2.5 concentrations. Further, when a future green infrastructure improvement scenario that considers the urban forest management plan is applied, the area of very high ESS is increased by 74% when the vegetation greenness of the green infrastructure in the residential area is increased by only 20%. This result suggests that green infrastructure and urban forests in the residential area should be continuously expanded and managed in order to maximize the PM2.5 reduction ES.

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10Random Forest Classifier-based Ship Type Prediction with Limited Ship Information of AIS and V-Pass

저자 : Ho-kun Jeon , Jae Rim Han

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 38권 4호 발행 연도 : 2022 페이지 : pp. 435-446 (12 pages)

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Identifying ship types is an important process to prevent illegal activities on territorial waters and assess marine traffic of Vessel Traffic Services Officer (VTSO). However, the Terrestrial Automatic Identification System (T-AIS) collected at the ground station has over 50% of vessels that do not contain the ship type information. Therefore, this study proposes a method of identifying ship types through the Random Forest Classifier (RFC) from dynamic and static data of AIS and V-Pass for one year and the Ulsan waters. With the hypothesis that six features, the speed, course, length, breadth, time, and location, enable to estimate of the ship type, four classification models were generated depending on length or breadth information since 81.9% of ships fully contain the two information. The accuracy were average 96.4% and 77.4% in the presence and absence of size information. The result shows that the proposed method is adaptable to identifying ship types.

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1Analysis of Geometric and Spatial Image Quality of KOMPSAT-3A Imagery in Comparison with KOMPSAT-3 Imagery

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This study investigates the geometric and spatial image quality analysis of KOMPSAT-3A stereo pair. KOMPSAT-3A is, the latest satellite of KOMPSAT family, a Korean earth observation satellite operating in optical bands. A KOMPSAT-3A stereo pair was taken on 23 November, 2015 with 0.55 m ground sampling distance over Terrassa area of Spain. The convergence angle of KOMPSAT-3A stereo pair was estimated as 58.68˚. The investigation was assessed through the evaluation of the geopositioning analysis, image quality estimation and the accuracy of automatic Digital Surface Model (DSM) generation and compared with those of KOMPSAT-3 stereo pair with the convergence angle of 44.80˚ over the same area. First, geopositioning accuracy was tested with initial rational polynomial coefficients (RPCs) and after compensating the biases of the initial RPCs by manually collected ground control points. Then, regarding image quality, relative edge response was estimated for manually selected points visible from two stereo pairs. Both of the initial and biascompensated positioning accuracy and the quality assessment result expressed that KOMPSAT-3A images showed higher performance than those of KOMPSAT-3 images. Finally, the accuracy of DSMs generated from KOMPSAT-3A and KOMPSAT-3 stereo pairs were examined with respect to the reference LiDAR-derived DSM. The various DSMs were generated over the whole coverage of individual stereo pairs with different grid spacing and over three types of terrain; flat, mountainous and urban area. Root mean square errors of DSM from KOMPSAT-3A pair were larger than those for KOMPSAT-3. This seems due to larger convergence angle of the KOMPSAT-3A stereo pair.

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Jellyfish (cnidarian) are conspicuous in many marine ecosystems when in bloom. Despite their importance for the ecosystem structure and function, very few sampling programs are dedicated to sample jellyfish because they are patchily distributed and easily clogged plankton net. Although satellite remote sensing is an excellent observing tool for many phenomena in the ocean, their uses for monitoring jellyfish are not possible due to the coarse spatial resolutions. Hence, we developed the low altitude remote sensing platform to detect jellyfish in high resolutions, which allow us to monitor not only horizontal, but also vertical migration of them. Using low altitude remote sensing platform, we measured the jellyfish from the pier at the Chesapeake Biological Laboratory in Chesapeake Bay. The patterns observed included discrete patches, in rows that were aligned with waves that propagated from deeper regions, and aggregation around physical objects. The corresponding areas of exposed jellyfish on the sea surface were 0.1×104 pixel2, 0.3×104 pixel2, and 2.75×104 pixel2, respectively. Thus, the research result suggested that the migration of the jellyfish was related to the physical forcing in the sea surface.

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Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models, residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

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4Monitoring Mount Sinabung in Indonesia Using Multi- Temporal InSAR

저자 : Chang-wook Lee , Zhong Lu , Jin Woo Kim

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 33권 1호 발행 연도 : 2017 페이지 : pp. 37-46 (10 pages)

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Sinabung volcano in Indonesia was formed due to the subduction between the Eurasian and Indo-Australian plates along the Pacific Ring of Fire. After being dormant for about 400 years, Sinabung volcano erupted on the 29th of August, 2010 and most recently on the 1st of November, 2016. We measured the deformation of Sinabung volcano using Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) interferometric synthetic aperture radar (InSAR) images acquired from February 2007 to January 2011. Based on multi-temporal InSAR processing, we mapped the ground surface deformation before, during, and after the 2010 eruption with time-series InSAR technique. During the 3 years before the 2010 eruption, the volcano inflated at an average rate of ~1.7 cm/yr with a markedly higher rate of 6.6 cm/yr during the 6 months prior to the 2010 eruption. The inflation was constrained to the top of the volcano. From the 2010 eruption to January 2011, the volcano subsided by approximately 3 cm (~6 cm/yr). We interpreted that the inflation was due to magma accumulation in a shallow reservoir beneath Sinabung. The deflation was attributed to magma withdrawal from the shallow reservoir during the eruption as well as thermo-elastic compaction of erupted material. This result demonstrates once again the utility of InSAR for volcano monitoring.

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5On the Spatial and Temporal Variability of L-band Polarimetric SAR Observations of Permafrost Environment in Central Yakutia

저자 : Sang-eun Park

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 33권 1호 발행 연도 : 2017 페이지 : pp. 47-60 (14 pages)

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The permafrost active layer plays an important role in permafrost dynamics. Ecological patterns, processes, and water and ice contents in the active layer are spatially and temporally complex depending on landscape heterogeneity and local-scale variations in hydrological processes. Although there has been emerging interest in the application of optical remote sensing techniques to permafrost environments, optical sensors are significantly limited in accessing information on near surface geo-cryological conditions. The primary objective of this study was to investigate capability of L-band SAR data for monitoring spatio-temporal variability of permafrost ecosystems and underlying soil conditions. This study exploits information from different polarimetric SAR observables in relation to permafrost environmental conditions. Experimental results show that each polarimetric radar observable conveys different information on permafrost environments. In the case of the dual-pol mode, the radar observables consist of two backscattering powers and one correlation coefficient between polarimetric channels. Among them, the dual-pol scattering powers are highly sensitive to freeze/thaw transition and can discriminate grasslands or ponds in thermokarst area from other permafrost ecosystems. However, it is difficult to identify the ground conditions with dual-pol observables. Additional backscattering powers and correlation coefficients obtained from quad-pol mode help understanding seasonal variations of radar scattering and assessing geo-cryological information on soil layers. In particular, co-pol coherences at HV-basis and circular-basis were found to be very useful tools for mapping and monitoring near surface soil properties.

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6Oil Spill Detection from RADARSAT-2 SAR Image Using Non-Local Means Filter

저자 : Daeseong Kim , Hyung-sup Jung

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 33권 1호 발행 연도 : 2017 페이지 : pp. 61-67 (7 pages)

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The detection of oil spills using radar image has been studied extensively. However, most of the proposed techniques have been focused on improving detection accuracy through the advancement of algorithms. In this study, research has been conducted to improve the accuracy of oil spill detection by improving the quality of radar images, which are used as input data to detect oil spills. Thresholding algorithms were used to measure the image improvement both before and after processing. The overall accuracy increased by approximately 16%, the producer accuracy increased by 40%, and the user accuracy increased by 1.5%. The kappa coefficient also increased significantly, from 0.48 to 0.92.

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7Accuracy Evaluation of DEM generated from Satellite Images Using Automated Geo-positioning Approach

저자 : Kwan-young Oh , Hyung-sup Jung , Moung-jin Lee

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 33권 1호 발행 연도 : 2017 페이지 : pp. 69-77 (9 pages)

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S The need for an automated geo-positioning approach for near real-time results and to boost cost-effectiveness has become increasingly urgent. Following this trend, a new approach to automatically compensate for the bias of the rational function model (RFM) was proposed. The core idea of this approach is to remove the bias of RFM only using tie points, which are corrected by matching with the digital elevation model (DEM) without any additional ground control points (GCPs). However, there has to be a additional evaluation according to the quality of DEM because DEM is used as a core element in this approach. To address this issue, this paper compared the quality effects of DEM in the conduct of the this approach using the Shuttle Radar Topographic Mission (SRTM) DEM with the spatial resolution of 90m. and the National Geographic Information Institute (NGII) DEM with the spatial resolution of 5m. One KOMPSAT-2 stereo-pair image acquired at Busan, Korea was used as experimental data. The accuracy was compared to 29 check points acquired by GPS surveying. After bias-compensation using the two DEMs, the Root Mean Square (RMS) errors were less than 6 m in all coordinate components. When SRTM DEM was used, the RMSE vector was about 11.2m. On the other hand, when NGII DEM was used, the RMSE vector was about 7.8 m. The experimental results showed that automated geo-positioning approach can be accomplished more effectively by using NGII DEM with higher resolution than SRTM DEM.

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8Land Cover Classification of RapidEye Satellite Images Using Tesseled Cap Transformation (TCT)

저자 : Hogyung Moon , Taeyoung Choi , Guhyeok Kim , Nyunghee Park , Honglyun Park , Jaewan Choi

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 33권 1호 발행 연도 : 2017 페이지 : pp. 79-88 (10 pages)

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The RapidEye satellite sensor has various spectral wavelength bands, and it can capture large areas with high temporal resolution. Therefore, it affords advantages in generating various types of thematic maps, including land cover maps. In this study, we applied a supervised classification scheme to generate highresolution land cover maps using RapidEye images. To improve the classification accuracy, object-based classification was performed by adding brightness, yellowness, and greenness bands by Tasseled Cap Transformation (TCT) and Normalized Difference Water Index (NDWI) bands. It was experimentally confirmed that the classification results obtained by adding TCT and NDWI bands as input data showed high classification accuracy compared with the land cover map generated using the original RapidEye images.

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9An efficient ship detection method for KOMPSAT-5 synthetic aperture radar imagery based on adaptive filtering approach

저자 : Jeongin Hwang , Daeseong Kim , Hyung-sup Jung

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 33권 1호 발행 연도 : 2017 페이지 : pp. 89-95 (7 pages)

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Ship detection in synthetic aperture radar (SAR) imagery has long been an active research topic and has many applications. In this paper, we propose an efficient method for detecting ships from SAR imagery using filtering. This method exploits ship masking using a median filter that considers maximum ship sizes and detects ships from the reference image, to which a Non-Local means (NL-means) filter is applied for speckle de-noising and a differential image created from the difference between the reference image and the median filtered image. As the pixels of the ship in the SAR imagery have sufficiently higher values than the surrounding sea, the ship detection process is composed primarily of filtering based on this characteristic. The performance test for this method is validated using KOMPSAT-5 (Korea Multi-Purpose Satellite-5) SAR imagery. According to the accuracy assessment, the overall accuracy of the region that does not include land is 76.79%, and user accuracy is 71.31%. It is demonstrated that the proposed detection method is suitable to detect ships in SAR imagery and enables us to detect ships more easily and efficiently.

KCI등재

10Tracking the Movement and Distribution of Green Tides on the Yellow Sea in 2015 Based on GOCI and Landsat Images

저자 : Seung-hwan Min , Hyun-ju Oh , Jae-dong Hwang , Young-sang Suh , Mi-ok Park , Ji-sun Shin , Wonkook Kim

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 33권 1호 발행 연도 : 2017 페이지 : pp. 97-109 (13 pages)

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Green tides that developed along the coast of China in 2015 were detected and tracked using vegetation indices from GOCI and Landsat images. Green tides first appeared near the Jiangsu Province on May 14 before increasing in size and number and moving northward to the Shandong Peninsula in mid-June. Typhoon Cham-hom passed through the Yellow Sea on July 12, significantly decreasing the algal population. An algae patch moved east toward Korea and on June 18 and July 4, several masses were found between the southwestern shores of Korea and Jeju Island. The floating masses found in Korean waters were concentrated at the boundary of the open sea and the Jindo cold pool, a phenomenon also observed at the boundary of coastal and offshore waters in China. Sea surface temperatures, derived from NOAA SST data, were found to play a role in generation of the green tides.

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