• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1299-1310
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• 2018

In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.179-188
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• 2022

Global warming causes climate change and increases extreme weather events worldwide, and the occurrence of heatwaves and droughts is also increasing in Korea. For the monitoring of extreme weather, various satellite data such as LST (Land Surface Temperature), TCI (Temperature Condition Index), NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and VHI (Vegetation Health Index) have been used. VHI, the combination of TCI and VCI, represents the vegetation stress affected by meteorological factors like precipitation and temperature and is frequently used to assess droughts under climate change. TCI and VCI require historical reference values for the LST and NDVI for each date and location. So, it is complicated to produce the VHI from the recent satellite GK2A (Geostationary Korea Multi-Purpose Satellite-2A). This study examined the retrieval of VHI using GK2A AMI (Advanced Meteorological Imager) by referencing the historical data from VIIRS (Visible Infrared Imaging Radiometer Suite) NDVI and LST as a proxy data. We found a close relationship between GK2A and VIIRS data needed for the retrieval of VHI. We produced the TCI, VCI, and VHI for GK2A during 2020-2021 at intervals of 8 days and carried out the interpretations of recent extreme weather events in Korea. GK2A VHI could express the changes in vegetation stress in 2020 due to various extreme weather events such as heatwaves (in March and June) and low temperatures (in April and July), and heavy rainfall (in August), while NOAA (National Oceanic and Atmospheric Administration) VHI could not well represent such characteristics. The GK2A VHI presented in this study can be utilized to monitor the vegetation stress due to heatwaves and droughts if the historical reference values of LST and NDVI can be adjusted in a more statistically significant way in the future work.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1057-1068
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• 2022

This paper compares the combination performance of feature point-based matching algorithms as a study to confirm the matching possibility between image taken by a user and a virtual texture image with the goal of developing mobile-based real-time image positioning technology. The feature based matching algorithm includes process of extracting features, calculating descriptors, matching features from both images, and finally eliminating mismatched features. At this time, for matching algorithm combination, we combined the process of extracting features and the process of calculating descriptors in the same or different matching algorithm respectively. V-World 3D desktop was used for the virtual indoor texture image. Currently, V-World 3D desktop is reinforced with details such as vertical and horizontal protrusions and dents. In addition, levels with real image textures. Using this, we constructed dataset with virtual indoor texture data as a reference image, and real image shooting at the same location as a target image. After constructing dataset, matching success rate and matching processing time were measured, and based on this, matching algorithm combination was determined for matching real image with virtual image. In this study, based on the characteristics of each matching technique, the matching algorithm was combined and applied to the constructed dataset to confirm the applicability, and performance comparison was also performed when the rotation was additionally considered. As a result of study, it was confirmed that the combination of Scale Invariant Feature Transform (SIFT)'s feature and descriptor detection had the highest matching success rate, but matching processing time was longest. And in the case of Features from Accelerated Segment Test (FAST)'s feature detector and Oriented FAST and Rotated BRIEF (ORB)'s descriptor calculation, the matching success rate was similar to that of SIFT-SIFT combination, while matching processing time was short. Furthermore, in case of FAST-ORB, it was confirmed that the matching performance was superior even when 10° rotation was applied to the dataset. Therefore, it was confirmed that the matching algorithm of FAST-ORB combination could be suitable for matching between virtual texture image and real image.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Ecology and Resilient Infrastructure
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• v.8 no.2
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• pp.79-87
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• 2021

Because active interactions occur among vegetation, hydrology, and geomorphology in riparian systems, any changes in one of these factors can significantly affect the other two. In this study, we evaluated these interactions at four sites (two in Gajeong and two in Hahan) along the Seomjin-gang River that was substantially devastated by an extreme flood in 2020. We examined the relationship between the riparian vegetation and the hydraulic characteristics of the flood using remote sensing, hydraulic modeling, and field surveys combined. The evaluation results showed that the floods caused a record-breaking rise of up to 43.1 m above sea level at the Yeseong-bridge stage gauge station (zero elevation 27.4 m) located between the Gajeong and Hahan sites, with the shear stress being four times higher in Hahan than in Gajeong. Additionally, the water level during the flood was estimated to be a maximum of 1 m higher depending on the location in the presence of riparian plants. Furthermore, both sites underwent extensive biological damage due to the flood, with 78-80% loss in vegetation, with preferential damage observed in large willow species, compared to Quercus acutissima. The above findings imply that all plant species exhibit different vulnerabilities towards extreme floods and do not induce similar behavior towards events causing a disturbance. In conclusion, we developed strategies for effectively managing riparian trees by minimizing flood hazards that could inevitably cause damage.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• Journal of the Korean Geographical Society
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• v.40
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• pp.1-13
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• 1989

The brightness temperature from NOAA AVHRR CH 4 images was examined for the metropolitan Seoul area, the capital city of Korea, to detect the characteristics of the urban heat island for this study. Surface data from 21 meteorological stations were compared with the brightness temperatures Through computer enhancement techniques, more than 20 heat islands could be recognized in South Korea, with 1 km spatii resolution at a scale of 1: 200, 00O(Fig. 3, 4 and 6). The result of the analysis of AVHRR CH 4 images over the metropolitan Seoul area can be summerized as follows (1) The pattern of brightness temperature distribution in the metropolitan Seoul area shows a relatively strong temperature contrast between urban and rural areas. There is some indication of the warm brightness temperature zone characterrizing built-up area including CBD, densely populated residential district and industrial zone. The cool brightness temperature is asociaed with the major hills such as Bukhan-san, Nam-san and Kwanak-san or with the major water bodies such as Han-gang, and reservoirs. Although the influence of the river and reservoirs is obvious in the brightness temperauture, that of small-scaled land use features such as parks in the cities is not features such as parks in the cities is not apperent. (2) One can find a linerar relationshop between the brightenss temperature and air temperature for 10 major cities, where the difference between two variables is larger in big cities. Though the coefficient value is 0.82, one can estimate that factors of the heat islands can not be explained only by the size of the cities. The magnitude of the horizontal brightness temperature differences between urban and rural area is found to be greater than that of horizontal air temperature difference in Korea. (3) Also one can find the high heat island intensity in some smaller cities such as Changwon(won(Tu-r=9.0$^{\circ}$C) and Po-hang(Tu-r==7.1$^{\circ}$~)T. he industrial location quotient of Chang-won is the second in the country and Po-hang the third. (4) A comparision of the enhanced thermal infrared imageries in 1986 and 1989, with the map at a scale of 1:200, 000 for the meotropolitan Seoul area showes the extent of possible urbanization changes. In the last three years, the heat islands have been extended in area. zone characterrizing built-up area including (5) Although the overall data base is small, the data in Fig. 3 suggest that brightness tempeautre could ge utilized for the study on the heat island characteristics. Satellite observations are required to study and monitor the impact of urban heat island on the climate and environment on global scale. This type of remote sensing provides a meams of monitoring the growth of urban and suburban aeas and its impact on the environment.

• Journal of the Korean association of regional geographers
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• v.12 no.4
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• pp.449-460
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• 2006

This study aims to analyze distribution of natural lakes including lagoonal lake(lagoon) and tributary dammed lake(tributary lake) and calculate the size, morphology in order to interpret time-serial change of lakes using methodology of remote sensing images(1990s), GIS and topographic maps(1920s) in east coast of Korean Peninsular. Analysis results show that in 1990s, there are 57 natural lakes, with the total size of $75.62km^2$ over size $0.01km^2$. marine-origin lagoons are 48 with total size of $64.85km^2$, composing 85% of total natural lake, and the largest lagoon is Beonpo in Raseon City. Tributary lakes have been formed by damming of tributary channels by fluvial sand bars from main stream, located nearby at coastal zone, similar to lagoon sites. Large tributary lake, Jangyeonho, is developed in lava plateau dissection valley of Eorang Gun, Hamnam Province. There are more distributed at Duman River mouth$\sim$Cheongjin City, Heungnam City$\sim$Hodo Peninsular and Anbyeon Gun$\sim$Gangreung City. Geomorphometrically, correlation of size to circumference is very high, but correlation of size to shape irregularity is very low. The direction of lagoonal coast, NW-SE and NE-SW are predominated due to direction of tectonic structure and longshore currents. The length of the river into lake are generally short, maximum under 15km, and lake size is smaller, degree of size decreasing is higher. Geomorphic patterns of the lake location are classified as coast-hill range, coastal plain, coastal plain-channel valley, coastal plain-hill range and channel valley-hill range. During from 1920s to 1990s, change with lake size decreasing is highest at coastal plain-channel valley, next is coastal plain. Causes of the size decreasing are fluvial deposition from upper rivers and human impacts such as reclamation.