• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.164-169
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• 2017

The primary purpose of this study is to determine NDVI analysis methodologies for green algae monitoring system. A fixed wing UAV integrated with multi-spectral sensor has been adopted to capture the images along the watershed in Gumgang River. The study area was near the Baekje water reservoir and the images was captured on July 2016. Pix4D Mapper Pro was used to process the captured images. Through the comparison actual chlorophyll measurement values with NDVI output image, empirical formula was suggested and geo-locational conversion was carried out. As a result of this study chlorophyll image set applied to actual measurement values was able to extracted. For the efficient management of green algae, its monitoring and prevention in terms of disaster management, gathering chlorophyll information using UAV is very beneficial.

• Aerospace Engineering and Technology
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• v.1 no.2
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• pp.132-140
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• 2002

By utilizing OSMI (Ocean Scanning Multi-spectral Imager) onboard KOMPSAT-1, the moon can be imaged. Because the moon has no atmosphere and reflects sun lights at a constant rate, it can be the radiance source for calibration of OSMI. But there are a lot of risks which made KOMPSAT-1 enter into safe-hold mode. So planning the imaging of the moon with OSMI should be determined seriously with consideration to information on KOMPSAT-1 operation, the moon, the sun, etc. But it takes a long time for determining the imaging time of the moon using MCE(Mission Control Element) simulator and there are operational problems to be solved. In this paper, fast simulator for determining imaging time for the moon with OSMI has been developed. The proper timeline for imaging the moon and the position of the moon image in OSMI image coordinates and the phase of the moon are determined. STK was used for acquiring information on KOMPSAT-1, the moon, the sun and the characteristitcs of OSMI are considered. As a result, we can determine imaging time of the moon with OSMI much faster and efficiently.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.10a
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• pp.73-74
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• 2009

This study attempts to establish a system extracting and monitoring cultural grounds of seaweeds (lavers, brown seaweeds and seaweed fulvescens) and abalone on the basis of both KOMPSAT-2 and Terrasar-X data. The study areas are located in the northwest and southwest coast of South Korea, famous for coastal cultural grounds. The northwest site is in a high tidal range area (on the average, 6.1 m in Asan Bay) and has laver cultural grounds for the most. An semi-automatic detection system of laver facilities is described and assessed for spaceborne optic images. On the other hand, the southwest cost is most famous for seaweeds. Aquaculture facilities, which cover extensive portions of this area, can be subdivided into three major groups: brown seaweeds, capsosiphon fulvescens and abalone farms. The study is based on interpretation of optic and SAR satellite data and a detailed image analysis procedure is described here. On May 25 and June 2, 2008 the TerraSAR-X radar satellite took some images of the area. SAR data are unique for mapping those farms. In case of abalone farms, the backscatters from surrounding dykes allows for recognition and separation of abalone ponds from all other water-covered surfaces. But identification of seaweeds such as laver, brown seaweeds and seaweed fulvescens depends on the dampening effect due to the presence of the facilities and is a complex task because objects that resemble seaweeds frequently occur, particularly in low wind or tidal conditions. Lastly, fusion of SAR and optic spatial images is tested to enhance the detection of aquaculture facilities by using the panchromatic image with spatial resolution 1 meter and the corresponding multi-spectral, with spatial resolution 4 meters and 4 spectrum bands, from KOMPSAT-2. The mapping accuracy achieved for farms will be estimated and discussed after field verification of preliminary results.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.249-261
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• 2020

Coastal monitoring using multiple platforms/sensors is a very important tools for accurately understanding the changes in offshore marine environment and disaster with high temporal and spatial resolutions. However, integrated observation studies using multiple platforms and sensors are insufficient, and none of them have been evaluated for efficiency and limitation of convergence. In this study, we aimed to suggest an integrated observation method with multi-remote sensing platform and sensors, and to diagnose the utility and limitation. Integrated in situ surveys were conducted using Rhodamine WT fluorescent dye to simulate various marine disasters. In September 2019, the distribution and movement of RWT dye patches were detected using satellite (Kompsat-2/3/3A, Landsat-8 OLI, Sentinel-3 OLCI and GOCI), unmanned aircraft (Mavic 2 pro and Inspire 2), and manned aircraft platforms after injecting fluorescent dye into the waters of the South Sea-Yeosu Sea. The initial patch size of the RWT dye was 2,600 ㎡ and spread to 62,000 ㎡ about 138 minutes later. The RWT patches gradually moved southwestward from the point where they were first released,similar to the pattern of tidal current flowing southwest as the tides gradually decreased. Unmanned Aerial Vehicles (UAVs) image showed highest resolution in terms of spatial and time resolution, but the coverage area was the narrowest. In the case of satellite images, the coverage area was wide, but there were some limitations compared to other platforms in terms of operability due to the long cycle of revisiting. For Sentinel-3 OLCI and GOCI, the spectral resolution and signal-to-noise ratio (SNR) were the highest, but small fluorescent dye detection was limited in terms of spatial resolution. In the case of hyperspectral sensor mounted on manned aircraft, the spectral resolution was the highest, but this was also somewhat limited in terms of operability. From this simulation approach, multi-platform integrated observation was able to confirm that time,space and spectral resolution could be significantly improved. In the future, if this study results are linked to coastal numerical models, it will be possible to predict the transport and diffusion of contaminants, and it is expected that it can contribute to improving model accuracy by using them as input and verification data of the numerical models.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.735-747
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• 2020

Recent advanced UAV (Unmanned Aerial Vehicle) technology supply new opportunities for estimating crop condition using high resolution imagery. We analyzed the diurnal change of reflectance and NDVI (Normalized Difference Vegetation Index) in UAV imagery for crop monitoring in clear day condition. Multi-spectral images were obtained from a 5-band multi-spectral camera mounted on rotary wing UAV. Reflectance were derived by the direct method using down-welling irradiance measurement. Reflectance using UAV imagery on calibration tarp, concrete and crop experimental sites did not show stable by time and daily reproducible values. But the CV (Coefficient of Variation) of diurnal NDVI on crop experimental sites was less than 5%. As a result of comparing NDVI at the similar time for two day, the daily mean average ratio of error showed a difference of 0.62 to 3.97%. Therefore, it is considered that NDVI using UAV imagery can be used for time series crop monitoring.

• Journal of Korea Water Resources Association
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• v.56 no.3
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• pp.211-224
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• 2023

In this study, a soil moisture estimation was performed using the Water Cloud Model (WCM), a backscatter model that considers vegetation based on SAR (Synthetic Aperture Radar). Sentinel-1 SAR and Sentinel-2 MSI (Multi-Spectral Instrument) images of a 40 × 50 km² area including the Yongdam Dam watershed of the Geum River were collected for this study. As vegetation descriptor of WCM, Sentinel-1 based vegetation index RVI (Radar Vegetation Index), depolarization ratio (DR), and Sentinel-2 based NDVI (Normalized Difference Vegetation Index) were used, respectively. Forward modeling of WCM was performed by 3 groups, which were divided by the characteristics between backscattering coefficient and soil moisture. The clearer the linear relationship between soil moisture and the backscattering coefficient, the higher the simulation performance. To estimate the soil moisture, the simulated backscattering coefficient was inverted. The simulation performance was proportional to the forward modeling result. The WCM simulation error showed an increasing pattern from about -12dB based on the observed backscattering coefficient.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.435-444
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• 2011

The land cover map derived from spectral features of high resolution optical images has low spectral resolution and heterogeneity in the same land cover class. For this reason, despite the same land cover class, the land cover can be classified into various land cover classes especially in vegetation area. In order to overcome these problems, detailed vegetation classification is applied to optical satellite image and SAR(Synthetic Aperture Radar) integrated data in vegetation area which is the result of pre-classification from optical image. The pre-classification and vegetation classification were performed with MLC(Maximum Likelihood Classification) method. The hierarchical land cover classification was proposed from fusion of detailed vegetation classes and non-vegetation classes of pre-classification. We can verify the facts that the proposed method has higher accuracy than not only general SAR data and GLCM(Gray Level Co-occurrence Matrix) texture integrated methods but also hierarchical GLCM integrated method. Especially the proposed method has high accuracy with respect to both vegetation and non-vegetation classification.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.4
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• pp.65-73
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• 2018

Although the road construction in forest is increasing and there is a need for development ecological restoration on deforest area, no consideration has been given to individual trees in there. This study analyzed aerial photographs of deforest area before and after road construction for determining the degree of forest destruction by extracting individual trees. Study area was selected in the sites where are damaged by road construction in GongJu-si, YuSung-gu, and YeongDong-gun. The aerial photograph taken 1979 before construction is panchromatic image of 80cm in GSD (Ground Sample Distance) and other photograph taken 2016 after construction is multi-spectral image of 10cm in GSD. In order to minimize the difference of GSD, we conducted image re-sampling process for setting to same GSD for the two photographs. After that we carried out visual interpretation method for determining to change of individual tree. The result found that for GongJu-si of the number of individual tree was 1,014 in 1979 and 886 in 2016, which decreased by 128 (12.6%) and the average width of those decreased from 5.77m to 5.75m by 0.47%. In case of YoungDong-gun, the number of it was 761 in 1979 and 746 in 2016, which decreased by 2.0% and the average width of it decreased from 8.99mm to 8.90m by 1.1%. Lastly in case of YuSung-gu, the number of it was 1,578 in 1979 and 988 in 2016, which decreased by 37.4% and the average width of it decreased from 7.09m to 6.65m by 6.21%. these result imply that road construction causes destruction of forests. Since there are limitations such as errors due to researcher, it is necessary to construct a quantitative analysis method for the change of the deforest area. It is need to study the method of extracting individual tree in deforest area more accurately using high-resolution image of GSD 10cm or more as well. This study can be used as a basic data for the ecological restoration of the deforest area considering characteristics of individual tree such as height, diameter at breast height, and biomass.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.455-461
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• 2013

Kompsat-3 is an optical high-resolution earth observation satellite launched in May 2012. The AEISS sensor of the Korean satellite provides 0.7m panchromatic and 2.8m multi-spectral images with 16.8km swath width from the sun-synchronous near-circular orbit of 685km altitude. Kompsat-3 is more advanced than Kompsat-2 and the improvements include better agility such as in-track stereo acquisition capability. This study investigated the characteristic of the epipolar curves of in-track Kompsat-3 stereo images. To this end we used the RPCs(Rational Polynomial Coefficients) to derive the epipolar curves over the entire image area and found out that the third order polynomial equation is required to model the curves. In addition, we could observe two different groups of curve patterns due to the dual CCDs of AEISS sensor. From the experiment we concluded that the third order polynomial-based RPCs update is required to minimize the sample direction image distortion. Finally we carried out the experiment on the epipolar resampling and the result showed the third order polynomial image transformation produced less than 0.7 pixels level of y-parallax.