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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Korean Journal of Remote Sensing
Journal Basic Information
Journal DOI :
The Korean Society of Remote Sensing
Editor in Chief :
Volume & Issues
Volume 30, Issue 6 - Dec 2014
Volume 30, Issue 5 - Oct 2014
Volume 30, Issue 4 - Aug 2014
Volume 30, Issue 3 - Jun 2014
Volume 30, Issue 2 - Apr 2014
Volume 30, Issue 1 - Feb 2014
Selecting the target year
Validation of OMI HCHO with EOF and SVD over Tropical Africa
Kim, J.H. ; Baek, K.H. ; Kim, S.M. ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 417~430
DOI : 10.7780/kjrs.2014.30.4.1
We have found an error in the operational OMI HCHO columns, and corrected it by applying a background parameterization derived on a 4th order polynomial fit to the time series of monthly average OMI HCHO data. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. A new scientific approach, statistical analyses with EOF and SVD, was adapted to reanalyze the consistency of the corrected OMI HCHO with other satellite measurements of HCHO, CO,
, and fire counts over Africa. The EOF and SVD analyses with MOPITT CO, OMI
, SCIAMAHCY, and OMI HCHO show the overall spatial and temporal pattern consistent with those of biomass burning over these regions. However, some discrepancies were observed from OMI HCHO over northern equatorial Africa during the northern biomass burning seasons: The maximum HCHO was found further downwind from where maximum fire counts occur and the minimum was found in January when biomass burning is strongest. The statistical analysis revealed that the influence of biogenic activity on HCHO wasn't strong enough to cause the discrepancies, but it is caused by the error in OMI HCHO from using the wrong Air Mass Factor (AMF) associated with biomass burning aerosol. If the error is properly taken into consideration, the biomass burning is the strongest source of HCHO seasonality over the regions. This study suggested that the statistical tools are a very efficient method for evaluating satellite data.
Matching and Geometric Correction of Multi-Resolution Satellite SAR Images Using SURF Technique
Kim, Ah-Leum ; Song, Jung-Hwan ; Kang, Seo-Li ; Lee, Woo-Kyung ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 431~444
DOI : 10.7780/kjrs.2014.30.4.2
As applications of spaceborne SAR imagery are extended, there are increased demands for accurate registrations for better understanding and fusion of radar images. It becomes common to adopt multi-resolution SAR images to apply for wide area reconnaissance. Geometric correction of the SAR images can be performed by using satellite orbit and attitude information. However, the inherent errors of the SAR sensor's attitude and ground geographical data tend to cause geometric errors in the produced SAR image. These errors should be corrected when the SAR images are applied for multi-temporal analysis, change detection applications and image fusion with other sensor images. The undesirable ground registration errors can be corrected with respect to the true ground control points in order to produce complete SAR products. Speeded Up Robust Feature (SURF) technique is an efficient algorithm to extract ground control points from images but is considered to be inappropriate to apply to SAR images due to high speckle noises. In this paper, an attempt is made to apply SURF algorithm to SAR images for image registration and fusion. Matched points are extracted with respect to the varying parameters of Hessian and SURF matching thresholds, and the performance is analyzed by measuring the imaging matching accuracies. A number of performance measures concerning image registration are suggested to validate the use of SURF for spaceborne SAR images. Various simulations methodologies are suggested the validate the use of SURF for the geometric correction and image registrations and it is shown that a good choice of input parameters to the SURF algorithm should be made to apply for the spaceborne SAR images of moderate resolutions.
A Performance Test of Mobile Cloud Service for Bayesian Image Fusion
Kang, Sanggoo ; Lee, Kiwon ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 445~454
DOI : 10.7780/kjrs.2014.30.4.3
In recent days, trend technologies for cloud, bigdata, or mobile, as the important marketable keywords or paradigm in Information Communication Technology (ICT), are widely used and interrelated each other in the various types of platforms and web-based services. Especially, the combination of cloud and mobile is recognized as one of a profitable business models, holding benefits of their own. Despite these challenging aspects, there are a few application cases of this model dealing with geo-based data sets or imageries. Among many considering points for geo-based cloud application on mobile, this study focused on a performance test of mobile cloud of Bayesian image fusion algorithm with satellite images. Two kinds of cloud platform of Amazon and OpenStack were built for performance test by CPU time stamp. In fact, the scheme for performance test of mobile cloud is not established yet, so experiment conditions applied in this study are to check time stamp. As the result, it is revealed that performance in two platforms is almost same level. It is implied that open source mobile cloud services based on OpenStack are enough to apply further applications dealing with geo-based data sets.
Development of relative radiometric calibration system for in-situ measurement spectroradiometers
Oh, Eunsong ; Ahn, Ki-Beom ; Kang, Hyukmo ; Cho, Seong-Ick ; Park, Young-Je ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 455~464
DOI : 10.7780/kjrs.2014.30.4.4
After launching the Geostationary Ocean Color Imager (GOCI) on June 2010, field campaigns were performed routinely around Korean peninsula to collect in-situ data for calibration and validation. Key measurements in the campaigns are radiometric ones with field radiometers such as Analytical Spectral Devices FieldSpec3 or TriOS RAMSES. The field radiometers must be regularly calibrated. We, in the paper, introduce the optical laboratory built in KOSC and the relative calibration method for in-situ measurement spectroradiometer. The laboratory is equipped with a 20-inch integrating sphere (USS-2000S, LabSphere) in 98% uniformity, a reference spectrometer (MCPD9800, Photal) covering wavelengths from 360 nm to 1100 nm with 1.6 nm spectral resolution, and an optical table (
) having a flatness of
. Under constant temperature and humidity maintainance in the room, the reference spectrometer and the in-situ measurement instrument are checked with the same light source in the same distance. From the test of FieldSpec3, we figured out a slight difference among in-situ instruments in blue band range, and also confirmed the sensor spectral performance was changed about 4.41% during 1 year. These results show that the regular calibrations are needed to maintain the field measurement accuracy and thus GOCI data reliability.
Characteristics of Infrared and Water Vapor Imagery for the Heavy Rainfall Occurred in the Korean Peninsula
Seong, Min-Gyu ; Suh, Myoung-Seok ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 465~480
DOI : 10.7780/kjrs.2014.30.4.5
In this study, we analyzed the spatio-temporal variations of satellite imagery for the two heavy rainfall cases (21 September, 2010, 9 August, 2011) occurred in the Korean Peninsula. In general, the possibility of strong convection can be increased when the region with plenty of moisture at the lower layer overlapped with the boundary between dark and bright area in the water vapor imagery. And the merging of convective cells caused by the difference in the moving velocities of two cells resulted in the intensification of convective activity and rainfall intensity. The rainfall intensity is more closely linked with the minimum cloud top temperature than the mean cloud top temperature. Also the spatio-temporal variations of rainfall intensity are impacted by the existence of merging processes. The merging can be predicted by the animation of satellite imagery but earlier detection of convective cells is almost impossible by using the infrared and water vapor imagery.
Development of Normalized Difference Blue-ice Index (NDBI) of Glaciers and Analysis of Its Variational Factors by using MODIS Images
Han, Hyangsun ; Ji, Younghun ; Kim, Yeonchun ; Lee, Hoonyol ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 481~491
DOI : 10.7780/kjrs.2014.30.4.6
Blue-ice area is a glacial ice field in ice sheet, ice shelf and glaciers where snow ablation and sublimation is larger than snowfall. As the blue-ice area has large influences on the meteorite concentration mechanism and ice mass balance, it is required to quantify the concentration of blue-ice. We analyzed spectral reflectance characteristics of blue-ice, snow and cloud by using MODIS images obtained over blue-ice areas in McMurdo Dry Valleys, East Antarctica, from 2007 to 2012. We then developed Normalized Difference Blue-ice Index (NDBI) algorithm which quantifies the concentration of blue-ice. Snow and cloud have a high reflectance in visible and near-infrared (NIR) bands. Reflectance of blue-ice is high in blue band, while that lowers in the NIR band. NDBI is calculated by dividing the difference of reflectance in the blue and NIR bands by the sum of reflectances in the two bands so that NDBI = (Blue-NIR)/(Blue + NIR). NDBI calculated from the MODIS images showed that the blue-ice areas have values ranging from 0.2 to 0.5, depending on the exposure and concentration of blue-ice. It is obviously different from that of snow and cloud that has values less than 0.2 or rocks with negative values. The change of NDBI values in the blue-ice area has higher correlation with snow depth (
) than wind speed (
) or air temperature (
), all measured at a meteorological station installed in McMurdo Dry Valleys. As the snow depth increased, the NDBI value decreased, which suggests that snow depth can be estimated from NDBI values over blue-ice areas. The NDBI algorithm developed in this study will be useful for various polar research fields such as meteorite exploration, analysis of ice mass balance as well as the snow depth estimation.
Early Production of Large-area Crop Classification Map using Time-series Vegetation Index and Past Crop Cultivation Patterns - A Case Study in Iowa State, USA -
Kim, Yeseul ; Park, No-Wook ; Hong, Sukyoung ; Lee, Kyungdo ; Yoo, Hee Young ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 493~503
DOI : 10.7780/kjrs.2014.30.4.7
A hierarchical classification scheme, which can reduce the spectral ambiguity and also reflect crop cultivation patterns from past land-cover maps, is presented for the purpose of the early production of crop classification maps in large-scale crop areas. Specifically, the effects of mixed pixels are minimized not only by applying a hierarchical classification approach based on different spectral characteristics from crop growth cycles, but also by considering temporal contextual information derived from past crop cultivation patterns. The applicability of the presented classification scheme was evaluated by a case study of Iowa State in USA with time-series MODIS 250 m Normalized Difference Vegetation Index(NDVI) data sets and past Cropland Data Layers(CDLs). Corn and soybean, which are major crop types in the study area and also display spectral similarity, could be properly classified by applying different classification stages and accounting for past crop cultivation patterns. The classification result by the presented scheme showed increases of minimum 7.68%p and maximum 20.96%p in overall accuracy, compared with one based on purely spectral information. In addition, the combination of temporal contextual information during classification was less affected by the number of NDVI data sets and the best overall accuracy of 86.63% was achieved. Thus, it is expected that this classification scheme can be effectively used for the early production of large-area crop classification maps in major feed-grain importing countries.
An improved method of NDVI correction through pattern-response low-peak detection on time series
Lee, Kyeong-Sang ; Han, Kyung-Soo ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 505~510
DOI : 10.7780/kjrs.2014.30.4.8
Normalized Difference Vegetation Index (NDVI) is a major indicator for monitoring climate change and detecting vegetation coverage. In order to retrieve NDVI, it is preprocessed using cloud masking and atmospheric correction. However, the preprocessed NDVI still has abnormally low values known as noise which appears in the long-term time series due to rainfall, snow and incomplete cloud masking. An existing method of using polynomial regression has some problems such as overestimation and noise detectability. Thereby, this study suggests a simple method using amoving average approach for correcting NDVI noises using SPOT/VEGETATION S10 Product. The results of the moving average method were compared with those of the polynomial regression. The results showed that the moving average method is better than the former approach in correcting NDVI noise.
Characteristics of MODIS land-cover data sets over Northeast Asia for the recent 12 years(2001-2012)
Park, Ji-Yeol ; Suh, Myoung-Seok ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 511~524
DOI : 10.7780/kjrs.2014.30.4.9
In this study, we investigated the statistical occupations and interannual variations of land cover types over Northeast Asian region using the 12 years (2001-2012) MODerate Resolution Imaging Spectroradiometer(MODIS) land cover data sets. The spatial resolution and land cover types of MODIS land cover data sets are 500 m and 17, respectively. The 12-year average shows that more than 80% of the analysis region is covered by only 3 types of land cover, cropland (36.96%), grasslands (23.14%) and mixed forests (22.97%). Whereas, only minor portion is covered by cropland/natural vegetation mosaics (6.09%), deciduous broadleaf forests (4.26%), urban and built-up (2.46%) and savannas (1.54%). Although sampling period is small, the regression analysis showed that the occupations of evergreen needleleaf forests, deciduous broadleaf forests and mixed forests are increasing but the occupations of woody savannas and savannas are decreasing. In general, the pixels where the land cover types are classified differently with year are amount to more than 10%. And the interannual variations in the occupations of land cover types are most prominent in cropland (1.41%), mixed forests (0.82%) and grasslands (0.73%). In addition, the percentage of pixels classified as 1 type for 12 years is only 57% and the other pixels are classified as more than 2 types, even 9 types. The annual changes in the classification of land cover types are mainly occurred at the almost entire region, except for the eastern and northwestern parts of China, where the single type of land cover located. When we take into consider the time scale needed for the land cover changes, the results indicate that the MODIS land cover data sets over the Northeast Asian region should be used with caution.
Hydrological Drought Assessment and Monitoring Based on Remote Sensing for Ungauged Areas
Rhee, Jinyoung ; Im, Jungho ; Kim, Jongpil ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 525~536
DOI : 10.7780/kjrs.2014.30.4.10
In this study, a method to assess and monitor hydrological drought using remote sensing was investigated for use in regions with limited observation data, and was applied to the Upper Namhangang basin in South Korea, which was seriously affected by the 2008-2009 drought. Drought information may be obtained more easily from meteorological data based on water balance than hydrological data that are hard to estimate. Air temperature data at 2 m above ground level (AGL) were estimated using remotely sensed data, evapotranspiration was estimated from the air temperature, and the correlations between precipitation minus evapotranspiration (P-PET) and streamflow percentiles were examined. Land Surface Temperature data with
spatial resolution as well as Atmospheric Profile data with
spatial resolution from MODIS sensor on board Aqua satellite were used to estimate monthly maximum and minimum air temperature in South Korea. Evapotranspiration was estimated from the maximum and minimum air temperature using the Hargreaves method and the estimates were compared to existing data of the University of Montana based on Penman-Monteith method showing smaller coefficient of determination values but smaller error values. Precipitation was obtained from TRMM monthly rainfall data, and the correlations of 1-, 3-, 6-, and 12-month P-PET percentiles with streamflow percentiles were analyzed for the Upper Namhan-gang basin in South Korea. The 1-month P-PET percentile during JJA (r = 0.89, tau = 0.71) and SON (r = 0.63, tau = 0.47) in the Upper Namhan-gang basin are highly correlated with the streamflow percentile with 95% confidence level. Since the effect of precipitation in the basin is especially high, the correlation between evapotranspiration percentile and streamflow percentile is positive. These results indicate that remote sensing-based P-PET estimates can be used for the assessment and monitoring of hydrological drought. The high spatial resolution estimates can be used in the decision-making process to minimize the adverse impacts of hydrological drought and to establish differentiated measures coping with drought.
Detection method of objects with a special pattern in satellite images using Histogram Of Gradients (HOG) feature and Support Vector Machine (SVM) classifier
Lim, Ingeun ; Kim, Suhwan ; Choi, Jonggook ;
Korean Journal of Remote Sensing, volume 30, issue 4, 2014, Pages 537~546
DOI : 10.7780/kjrs.2014.30.4.11
In this paper, we propose a method to detect interesting objects in inaccessible areas using high resolution satellite images. We define the interesting objects as a set of objects which have conceptually similar image patterns, not having exact sizes or shapes. In this paper, we developed a learning and classifier of Support Vector Machine (SVM) that extracts characteristic data for inputted images using Histogram of Gradients (HOG) feature and detects similar objects in other images using the characteristic data. As automatic search of interesting objects in our proposed method, we identify that our method provides reduced time and efforts for manual searching similar objects.