• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1353-1364
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• 2018

This study proposed a method to detect Cochlodinium polykrikoides red tide pixels in satellite images using a logistic regression model of machine learning technique under Imbalanced data. The spectral profiles extracted from red tide, clear water, and turbid water were used as training dataset. 70% of the entire data set was extracted and used for as model training, and the classification accuracy of the model was evaluated using the remaining 30%. At this time, the white noise was added to the spectral profile of the red tide, which has a relatively small number of data compared to the clear water and the turbid water, and over-sampling was performed to solve the unbalanced data problem. As a result of the accuracy evaluation, the proposed algorithm showed about 94% classification accuracy.

• Journal of Space Technology and Applications
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• v.4 no.1
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• pp.74-85
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• 2024

As the limitations of conventional radio communications between satellites and the ground become apparent, various experiments are being conducted around the world to overcome them with space laser communication. In this study, we address the development of our own optical communications terminal (OCT) and optical ground station (OGS) and the experiments of free-space optical communication (FSOC) using them. Using a 30 mm-diameter OCT and a 250 mm-diameter portable OGS telescope, as well as commercial 10 Gbps SFP+ modules and media converters, we successfully transmitted and received 4K high-definition multimedia interface (HDMI) signals through 1,550 nm optical laser beam. The transmission and reception distances of the experiment were 3, 9, and 20 km, respectively, and the received signal strength at each distance was +6.1, -2.8, and -10.9 dBm, respectively. It was demonstrated that the 4K HDMI video lasted for over 10 minutes.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.2
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• pp.52-61
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• 2018

Urban heat islands occur due to increases in the extent of artificial surfaces such as concrete, asphalt and high-rise buildings. In this regard, research into the use of satellite thermal infrared images for thermal environment analysis of urban areas is being carried out. However, such analysis of the characteristics of individual land cover with low-resolution satellite images suffers from limitations because land cover patterns in urban areas are complicated. Recently, UAV has been widely used, which can compensate for this limitation as it is able to acquire high-resolution images. In this paper, the accuracy of UAV infrared images is verified and the applicability of UAV in urban thermal environment analysis is examined by comparing the results with land surface temperatures from Landsat 8 thermal images. The results show a high positive correlation of temperature values at 0.95, and no statistically significant difference between the two groups. Comparisons of land surface temperature according to land cover showed that the largest difference observed was $4.63^{\circ}C$ in the Used area, and UAV images with small cell units reflected various surface temperatures. Furthermore, it was possible to analyze the surface temperatures of various green spaces such as wetlands and street tree areas, which can lower surface temperatures in urban areas, with street tree shadows reducing surface temperatures by about $4-6^{\circ}C$. UAV can easily and rapidly measure the surface temperature of urban areas and is able to analyze various types of green spaces. Thus, this is an effective tool for thermal environment analysis in urban areas to aid in the design or management of urban green spaces, as it can allow for land cover and the effects of the various green spaces.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.85-99
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• 2015

TDI CCD sensors are being used for most of the electro-optical camera mounted on the low earth orbit satellite to meet high performance requirements such as SNR and MTF. However, the CMOS sensors which have a lot of implementation advantages over the CCD, are being upgraded to have the TDI function. A few methods for improving the issue of motion blur which is apparent in the CMOS sensor than the CCD sensor, are being introduced. Each pixel can be divided into a few sub-pixels to be read more than once as is the same case with three or four phased CCDs. The fill factor can be reduced intentionally or even a kind of mask can also be implemented at the edge of pixels to reduce the blur. The motion blur can also be reduced in the TDI CMOS sensor by reducing the integration time from the full line scan time. Because the integration time can be controlled easily by the versatile control electronics, one of two performance parameters, MTF and SNR, can be concentrated dynamically depending on the aim of target imaging. MATLAB simulation has been performed and the results are presented in this paper. The goal of the simulation is to compare dynamic MTFs affected by the different methods for reducing the motion blur in the TDI CMOS sensor.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1245-1255
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• 2022

Terra Nova Bay polynya (TNBP) is a representative coastal polynya in East Antarctica, which is formed by strong katabatic winds. As the TNBP is one of the major sea ice factory in East Antarctica and has a great impact on regional ocean circulation and surrounding marine ecosystem, it is very important to analyze its area change and development characteristics. In this study, we detected the TNBP from synthetic aperture radar (SAR) and optical images obtained from April 2007 to April 2022 by visually analyzing the stripes caused by the Langmuir circulation effect and the boundary between the polynya and surrounding sea ice. Then, we analyzed the area change and development characteristics of the TNBP. The TNBP occurred frequently but in a small size during the Antarctic winter (April-July) when strong katabatic winds blow, whereas it developed in a large size in March and November when sea ice thickness is thin. The 12-hour mean wind speed before the satellite observations showed a correlation coefficient of 0.577 with the TNBP area. This represents that wind has a significant effect on the formation of TNBP, and that other environmental factors might also affect its development process. The direction of TNBP expansion was predominantly determined by the wind direction and was partially influenced by the local ocean current. The results of this study suggest that the influences of environmental factors related to wind, sea ice, ocean, and atmosphere should be analyzed in combination to identify the development characteristics of TNBP.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1031-1035
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• 2019

Nowadays, the utilization infrastructure of domestic satellite information is expanding rapidly. Especially, the development of agriculture and forestry satellite is expected to drastically change the utilization of satellite information in the forest sector. The launch of the satellite is expected in 2023. Therefore, NIFoS and academic experts in forest sectors have prepared "Special Issue on Forest Management Research using Optical Sensors and Remote Sensing Technologies" in order to understand new remote sensing technologies and suggest the future direction of forest research and decision-making. This special issue is focused on a variety of fields in forest remote sensing research, including forest resources survey, forest disaster detection, and forest ecosystem monitoring. The new research topics for remote sensing technologies in forest sector focuses on three points: development of new indicators and information for accurate detection of forest conditions and changes, the use of new information sources such as UAV and new satellites, and techniques for improving accuracy through the use of artificial intelligence techniques.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.267-282
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• 2018

Despite the continuous development of phenology detection studies using satellite imagery, verification through comparison with the field observed data is insufficient. Especially, in the case of Korean forests patching in various forms, it is difficult to estimate the start of season (SOS) by using only satellite images due to resolution difference. To improve the accuracy of vegetation phenology estimation, this study reconstructed the large scaled forest type map (1:5,000) with MODIS pixel resolution and produced time series vegetation phenology curves from Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from MODIS images. Based on the field observed data, extraction methods for the vegetation indices and SOS for Korean forests were compared and evaluated. We also analyzed the correlation between the composition ratio of forest types in each pixel and phenology extraction from the vegetation indices. When we compared NDVI and EVI with the field observed SOS data from the Korea National Arboretum, EVI was more accurate for Korean forests, and the first derivative was most suitable for extracting SOS in the phenology curve from the vegetation index. When the eight pixels neighboring the pixels of 7 broadleaved trees with field SOS data (center pixel) were compared to field SOS, the forest types of the best pixels with the highest correlation with the field data were deciduous forest by 67.9%, coniferous forest by 14.3%, and mixed forest by 7.7%, and the mean coefficient of determination ($R^2$) was 0.64. The average national SOS extracted from MODIS EVI were DOY 112.9 in 2014 at the earliest and DOY 129.1 in 2010 at the latest, which is about 0.16 days faster since 2003. In future research, it is necessary to expand the analysis of deciduous and mixed forests' SOS into the extraction of coniferous forest's SOS in order to understand the various climate and geomorphic factors. As such, comprehensive study should be carried out considering the diversity of forest ecosystems in Korea.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1657-1667
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• 2021

Anthropogenic activities and natural processes have been causes of land subsidence which is sudden sinking or gradual settlement of the earth's solid surface. Mexico City, the capital of Mexico, is one of the most severe land subsidence areas which are resulted from excessive groundwater extraction. Because groundwater is the primary water resource occupies almost 70% of total water usage in the city. Traditional terrestrial observations like the Global Navigation Satellite System (GNSS) or leveling survey have been preferred to measure land subsidence accurately. Although the GNSS observations have highly accurate information of the surfaces' displacement with a very high temporal resolution, it has often been limited due to its sparse spatial resolution and highly time-consuming and high cost. However, space-based synthetic aperture radar (SAR) interferometry has been widely used as a powerful tool to monitor surfaces' displacement with high spatial resolution and high accuracy from mm to cm-scale, regardless of day-or-night and weather conditions. In this paper, advanced interferometric approaches have been applied to get a time-series of land subsidence of Mexico City using four-year-long twenty ALOS PALSAR L-band observations acquired from Feb-11, 2007 to Feb-22, 2011. We utilized persistent scatterer interferometry (PSI) and small baseline subset (SBAS) techniques to suppress atmospheric artifacts and topography errors. The results show that the maximum subsidence rates of the PSI and SBAS method were -29.5 cm/year and -27.0 cm/year, respectively. In addition, we discuss the different subsidence rates where the study area is discriminated into three districts according to distinctive geotechnical characteristics. The significant subsidence rate occurred in the lacustrine sediments with higher compressibility than harder bedrock.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1141-1180
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• 2022

The application of satellite imageries has increased in the field of hydrology and water resources in recent years. However, challenges have been encountered on obtaining accurate evapotranspiration and soil moisture. Therefore, present researches have emphasized the necessity to obtain estimations of satellite-based evapotranspiration and soil moisture with related development researches. In this study, we presented the research status in Korea by investigating the current trends and methodologies for evapotranspiration and soil moisture. As a result of examining the detailed methodologies, we have ascertained that, in general, evapotranspiration is estimated using Energy balance models, such as Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration (METRIC). In addition, Penman-Monteith and Priestley-Taylor equations are also used to estimate evapotranspiration. In the case of soil moisture, in general, active (AMSR-E, AMSR2, MIRAS, and SMAP) and passive (ASCAT and SAR)sensors are used for estimation. In terms of statistics, deep learning, as well as linear regression equations and artificial neural networks, are used for estimating these parameters. There were a number of research cases in which various indices were calculated using satellite-based data and applied to the characterization of drought. In some cases, hydrological cycle factors of evapotranspiration and soil moisture were calculated based on the Land Surface Model (LSM). Through this process, by comparing, reviewing, and presenting major detailed methodologies, we intend to use these references in related research, and lay the foundation for the advancement of researches on the calculation of satellite-based hydrological cycle data in the future.