• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.335-346
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• 2010

Recently, ESA (European Space Agency) has launched CryoSAT-2 for polar ice observations. CryoSAT-2 is equipped with a SIRAL (SAR/interferometric radar altimeter), which is a high spatial resolution radar altimeter. Conventional altimeters cannot measure a precise three-dimensional ground position because of the large footprint diameter, while SIRAL altimeter system accomplishes a precise three-dimensional ground positioning by means of interferometric synthetic aperture radar technique. In this study, we developed an efficient SIRAL SARIn mode processing technique to measure a precise three-dimensional ground position. We first simulated SIRAL SARIn RAW data for the ideal target by assuming the flat Earth and linear flight track, and second accessed the precision of three-dimensional geopositioning achieved by the proposed algorithm. The proposed algorithm consists of 1) azimuth processing that determines the squint angle from Doppler centroid, and 2) range processing that estimates the look angle from interferometric phase. In the ideal case, the precisions of look and squint angles achieved by the proposed algorithm were about -2.0 ${\mu}deg$ and 98.0 ${\mu}deg$, respectively, and the three-dimensional geopositioning accuracy was about 1.23 m, -0.02 m, and -0.30 m in X, Y and Z directions, respectively. This means that the SIRAL SARIn mode processing technique enables to measure the three-dimensional ground position with the precision of several meters.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.25-33
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• 2008

To investigate the hydrologic impacts of climate changes on dam inflow for Soyanggangdam watershed $(2694.4km^2)$ of northeastern South Korea, SLURP (Semi-distributed Land Use-based Runoff Process) model and the climate change results of CCCma CGCM2 based on SRES A2 and B2 were adopted. By the CA-Markov technique, future land use changes were estimated using the three land cover maps (1985, 1990, 2000) classified by Landsat TM satellite images. NDVI values for 2050 and 2100 land uses were estimated from the relationship of NDVI-Temperature linear regression derived from the observed data (1998-2002). Before the assessment, the SLURP model was calibrated and verified using 4 years (1998-2001) dam inflow data with the Nash-Sutcliffe efficiencies of 0.61 to 0.77. In case of A2 scenario, the dam inflows of 2050 and 2100 decreased 49.7 % and 25.0 % comparing with the dam inflow of 2000, and in case of B2 scenario, the dam inflows of 2050 and 2100 decreased 45.3 % and 53.0 %, respectively. The results showed that the impact of land use change covered 2.3 % to 4.9 % for the dam inflow change.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.4
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• pp.333-341
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• 2008

A remote sensing technique to identify trajectories of breaking waves in the surf zone using oblique digital nearshore videos is proposed. The noise arising from white foam induced by wave breaking has hindered accurate remote sensing of wave properties in the surf zone. For this reason, this paper focuses on image processing to remove the noise and wave trajectory identification essential for wave property estimation. The nearshore video imagery sampled at 3 Hz are used, covering length scale(100 m). Original image sequences are processed through image frame differencing and directional low-pass image filtering to remove the noise characterized by high frequencies in the video imagery. The extraction of individual wave crest features is conducted using a Radon transform-based line detection algorithm in the processed cross-shore image timestacks having a two-dimensional space-time domain. The number of valid wave crest trajectories identified corresponds to about 2/3 of waves recorded by the in-situ sensors.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.57-68
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• 2013

Large-scale coastal construction projects, such as land reclamation and dykes, were constructed from the late twentieth century in Yoobu-Do region. Land reclamation combined with the dynamics of tidal currents may have accelerated local sedimentation and erosion resulting in rapid reformation of coastal topography. This study presents the results of the topography changes around Yoobu-Do by large-scale coastal constructions using time-series waterline extraction technique of Landsat TM/ETM+ data acquired from 1998 to 2012. Furthermore, the Freeman-Durden decomposition was applied to fully polarimetric RADARSAT-2 SAR data in order to analyze the scattering mechanisms of the deposited surface. According to the case study, the deposition areas were over 4.5 $km^2$ and distributed in the east, northeast, and west of Yoobu-Do. In the eastern deposition area, it was found that the scattering mechanism was difference from other deposition areas possibly indicating that different types of soil were deposited.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.339-351
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• 2011

This study investigates the potentials of satellite, ground measurement data, and geo-spatial information within an urban area for the mapping of the Urban Environmental Quality (UEQ) parameters. The UEQ indicates a complex and various parameters resulting from both human and natural factors, which are greenness, climate, air pollution, the urban infrastructure, and etc. Multi-spectral remote sensing data from the Landsat ETM and TM sensors for the mapping of air pollution by the Haze Optimized Transform (HOT) technique, Urban Heat Island (UHO using the emissivity-fusion method in Seoul from 2000 to 2006 in fine resolution (30m) were analyzed for the estimation of UEQ index. Although the UHI values are similar ($8.4^{\circ}C{\sim}9.1^{\circ}C$) during these years, the spatial coverage of "hot" surface temperature (> $24^{\circ}C$) significantly increased from 2000 to 2006 due to the rapid urban development. Furthermore, high correlations between vegetation index and land surface temperature were achieved with a correlation coefficients of 0.85 (2000), 0.81 (2001), 0.84 (2002), and 0.89 (2006), respectively. It was found that the proposed method was successfully analyzed spatial structure of the UEQ and the scenarios of the best and worst areas within the city were also identified. Based on the quantifiable fine resolution satellite image parameters, UEQ can promote the understanding of the complex and dynamic factors controlling urban environment.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.119-124
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• 2007

The few observed data related snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) building for 5 major watersheds in South Korea. Especially SDC is important parameter of snowmelt model.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.941-946
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• 2019

A wildfire could significantly alter the local hydrological regime, depending on the area and severity, and thus it is critical to understand its effect and feedback using data and simulation. For the wildfire in Gangwon-do on April 4-5, 2019, South Korea, we retrieved the Normalized-Burned Ratio (NBR) index using remote-sensing data (500-m 8-day MODIS surface reflectance data), and detect the damaged-area based on the difference in the NBR (dNBR) before and after the fire. The damaged area was $29.50km^2$ in total, taking up 1.00-6.19% of five catchments. We then used remote-sensing data (500-m 8-day MODIS evapotranspiration data) and estimated that annual evapotranspiration (AET) would decrease as 0.05-1.56% over the five catchments, as compared to the pre-fire AET (2004-2018). This study highlights the importance of improving our understanding about the impact of wildfire on the local hydrological cycle.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1071-1081
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• 2021

Remote Sensing using unmanned aerial vehicles(UAV) can acquire images with higher time resolution and spatial resolution than aerial and satellite remote sensing. However, UAV images are photographed at low altitude and the area covered by one image isrelatively narrow. Therefore multiple images must be processed to monitor large area. Since UAV images are photographed under different exposure conditions, there is difference in brightness values between adjacent images. When images are mosaicked, unnatural seamlines are generated because of the brightness difference. Therefore, in order to generate seamless mosaic image, a radiometric processing for correcting difference in brightness value between images is essential. This paper proposes a relative radiometric calibration and image blending technique. In order to analyze performance of the proposed method, mosaic images of UAV images in agricultural and mountainous areas were generated. As a result, mosaic images with mean brightness difference of 5 and root mean square difference of 7 were avchieved.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.141-151
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• 2006

UV-visible absorption measurement techniques using several horizone viewing directions in addition to the traditional zenith-sky pointing have been recently developed in ground-based remote sensing of atmospheric constituents. The spatial distribution of various trace gases close to the instrument can be derived by combing several viewing directions. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) technique, one of the remote sensing techniques for air quality measurements, uses the scattered sunlight as a light source and measures it at various elevation angles (corresponding to the viewing directions) by sequential scanning with a stepper motor. A MAX-DOAS system developed by GIST/ADEMRC has been applied to measuring trace gases in urban air and plumes of the volcano and fossil fuel power plant in January, May, and October 2004, respectively. MAX-DOAS spectra were analyzed to identify and quantify $SO_2,\;NO_2,\;BrO,\;and\;O_4$ (based on Slant Column Densities, SCD) in the urban air, volcanic plume, and fossil fuel power plant utilizing theirs specific structured absorption features in the UV-visible region. Vertical scan through the multiple elevation angles was performed at different directions perpendicular to the plume dispersion to retrieve cross-sectional distribution of $SO_2\;or\;NO_2$ in the plumes of the volcano and fossil fuel power plant. Based on the estimated cross sections of the plumes the mixing ratios were estimated to 580 $SO_2$ ppbv in the volcanic Plume, and 337 $NO_2\;and\;227\;SO_2$ ppbv in the plume of the fossil fuel power plant, respectively.