• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1067-1087
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• 2018

In a study for LiDAR data based building boundary extraction, usually dense point cloud was used to cluster building rooftop area and extract building outline. However, when we used DSM generated from stereo image matching to extract building boundary, it is not trivial to cluster building roof top area automatically due to outliers and large holes of point cloud. Thus, we propose a technique to extract building boundary semi-automatically from the DSM created from stereo images. The technique consists of watershed segmentation for using user input as markers and recursive MBR algorithm. Since the proposed method only inputs simple marker information that represents building areas within the DSM, it can create building boundary efficiently by minimizing user input.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.545-557
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• 2022

This study used high-resolution satellite images and supervised classification technique based on machine learning method in order to detect the areas affected by wildfires in the demilitarized zone (DMZ) where direct access is difficult. Sentinel-2 A/B was used for high-resolution satellite images. Land cover map was calculated based on the SVM supervised classification technique. In order to find the optimal combination to classify the DMZ wildfire damage area, supervised classification according to various kernel and band combinations in the SVM was performed and the accuracy was evaluated through the error matrix. Verification was performed by comparing the results of the wildfire detection based on satellite image and data by the wildfire statistical annual report in 2020 and 2021. Also, wildfire damage areas was detected for which there is no current data in 2022. This is to quickly determine reliable results.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.96-106
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• 2005

The ability of remote sensing and GIS technique, which used to provide valuable informations in the time and space domain, has been known to be very useful in providing permanent records by mapping and monitoring flooded area. In 2000, floods were at the worst stage of devastation in Tonle Sap Lake, Mekong River Basin, for the second time in records during July and October. In this study, Landsat ETM+ and RADARSAT imagery were used to obtain the basic information on computation of the inundation area and volume using ISODATA classifier and segmentation technique. However, the extracted inundatton area showed only a small fraction than the actually inundated area because of clouds in the imagery and complex ground conditions. To overcome these limitations, the cost-distance method of GIS was used to estimate the inundated area at the peak level by integrating the inundated area from satellite imagery in corporation with digital elevation model (DEM). The estimated inundation area was simply converted with the inundation volume using GIS. The inundation volume was compared with the volume based on hydraulic modeling with MIKE 11. which is the most poppular among the dynamic river modeling system. The method is suitable for estimating inundation volume even when Landsat ETM+ has many clouds in the imagery.

• Journal of the Korean earth science society
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• v.38 no.7
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• pp.496-510
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• 2017

This study presents a new nationwide quantitative precipitation estimation (QPE) based on the hybrid surface rainfall (HSR) technique using the weather radar network of Korea Meteorological Administration (KMA). This new nationwide HSR is characterized by the synthesis of reflectivity at the hybrid surface that is not affected by ground clutter, beam blockage, non-meteorological echoes, and bright band. The nationwide HSR is classified into static (STATIC) and dynamic HSR (DYNAMIC) mosaic depending on employing a quality control process, which is based on the fuzzy logic approach for single-polarization radar and the spatial texture technique for dual-polarization radar. The STATIC and DYNAMIC were evaluated by comparing with official and operational radar rainfall mosaic (MOSAIC) of KMA for 10 rainfall events from May to October 2014. The correlation coefficients within the block region of STATIC, DYNAMIC and MOSAIC are 0.52, 0.78, and 0.69, respectively, and their mean relative errors are 34.08, 30.08, and 40.71%.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.227-244
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• 2015

Synthetic Aperture Radar is able to provide images of wide coverage in day, night, and all-weather conditions. Recently, as the SAR image resolution improves up to the sub-meter level, their applications are rapidly expanding accordingly. Especially there is a growing interest in the use of geographic information of high resolution SAR images and the change detection will be one of the most important technique for their applications. In this paper, an automatic threshold tracking and change detection algorithm is proposed applicable to high-resolution SAR images. To detect changes within SAR image, a reference image is generated using log-ratio operator and its amplitude distribution is estimated through K-S test. Assuming SAR image has a non-gaussian amplitude distribution, a generalized thresholding technique is applied using Kittler and Illingworth minimum-error estimation. Also, MoLC parametric estimation method is adopted to improve the algorithm performance on rough ground target. The implemented algorithm is tested and verified on the simulated SAR raw data. Then, it is applied to the spaceborne high-resolution SAR images taken by Cosmo-Skymed and KOMPSAT-5 and the performances are analyzed and compared.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.835-846
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• 2020

Recent severe climate changes and extreme weather events have caused the uncommon types of forest ecosystem disturbances such as hails and gypsy moths. This paper describes the analysis of the forest ecosystem disturbances using ISODATA (Iterative Self-organizing Data Analysis Technique Algorithm) with the RapidEye and Sentinel-2 images, regarding the cases of the hail damages in Hwasun in 2017 and the gypsy moth damages in the Chiak Mountain in 2020. In the case of hail damages, the comparison of the June image of this study and the July field survey of the previous study showed that the damage severity increased from June to July as the drought overlapped after the trees were injured by the hails. In the case of gypsy moths, significant leaf damages were found from the image of June, and the damages were mainly distributed at the low-altitude slope near Wonju City. We made sure that satellite remote sensing is a very effective method to detect various and unusual forest ecosystem disturbances caused by climate change. Also, it is expected that the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024 can be actively utilized to monitor such forest ecosystem disturbances.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.4
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• pp.447-452
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• 2006

The remote sensing technique was applied to enhance the water qualify model validation in a large water body. Since the satellite image usually covers the wide surface area of a large water body, it can compensate for the lark of measured data points required for model calibration and verification. This paper describes the analysis of Landsat FTM+images collected on April 29th and September 4th in year 2000 to evaluate surface water temperature of Lake Paldang. The water temperature data obtained from the satellite image were compared with model results by estimating three different methods of error criteria. The residual ratios on April 29th and September 4th were 0.13 and 0.04 respectively. This showed that the model result accords with the data obtained from the process of satellite image. Without considering atmospheric interference, however, transformation process of satellite image causes relatively large residual ratio in the surface water temperature distribution pattern on April 29th. In the future study, therefore, the atmospheric properties of image acquisition point needs to be considered for the application of radiance transformation model.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.975-988
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• 2020

Change detection is the process of identifying changes by observing the multi-temporal images at different times, and it is an important technique in remote sensing using satellite images. Among the change detection methods, the unsupervised change detection technique has the advantage of extracting rapidly the change area as a binary image. However, it is difficult to understand the changing pattern of land cover in binary images. This study used grid points generated from seamless digital map to evaluate the satellite image change detection results. The land cover change results were extracted using multi-temporal KOMPSAT-3A (K3A) data taken by Gimje Free Trade Zone and change detection algorithm used Spectral Angle Mapper (SAM). Change detection results were presented as binary images using the methods Otsu, Kittler, Kapur, and Tsai among the automated threshold selection algorithms. To consider the seasonal change of vegetation in the change detection process, we used the threshold of Differenced Normalized Difference Vegetation Index (dNDVI) through the probability density function. The experimental results showed the accuracy of the Otsu and Kapur was the highest at 58.16%, and the accuracy improved to 85.47% when the seasonal effects were removed through dNDVI. The algorithm generated based on this research is considered to be an effective method for accuracy assessment and identifying changes pattern when applied to unsupervised change detection.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.91-102
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• 2004

This paper reflects the estimation of using the EOC(Electro-optical Camera) images supporting GIUH(geomorphological instantaneous unit hydrograph) approach. We have analyzed GIUH in its density and frequency distribution by creating a DEM(digital elevation model) for the sub basin produced from the EOC images and examined topographical and hydrological application possibility of the EOC images. In this process, we have topographical basin characteristic analysis that use the remote sensing technique analyzing the DEM creation process of the EOC stereo images by studying the basic topographical hydrology analysis about abstraction technique since it is flirty complex and is more time-consuming than other method. we executed statistical analysis of a basin size and river length using the frequency function after divided lattice spacing applied have to the sub river basin from the image data and the digital map into 10m intervals ranging from 10m to 100m. After comparing and examining the peak and time to peak of the GIUH, we proceeded with a comparative analysis by lattice concerning the topographical divergence rate, area ratio, length ratio. Accumulating the peak and time to peak of the GIUH is altered to non-linear form in accordance to lattice dimension as well as basin factor. It was proved that the lattice dimension is one of the important factors about the peak and time to peak of the GIUH.