• Korean Journal of Remote Sensing
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• 제37권5_1호
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• pp.847-859
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• 2021

Recently, as the number of high-resolution satellite SAR images increases, the demand for precise matching of SAR imagesin change detection and image fusion is consistently increasing. RMSE (Root Mean Square Error) values using GCPs (Ground Control Points) selected by analysts have been widely used for quantitative evaluation of image registration results, while it is difficult to find an approach for automatically measuring the registration accuracy. In this study, a feasibility analysis was conducted on using the FSIM (Feature Similarity) index as a measure to evaluate the registration accuracy. TerraSAR-X (TSX) staring spotlight data collected from various incidence angles and orbit directions were used for the analysis. FSIM was almost independent on the spatial resolution of the SAR image. Using a single SAR image, the FSIM with respect to registration errors was analyzed, then use it to compare with the value estimated from TSX data with different imaging geometry. FSIM index slightly decreased due to the differencesin imaging geometry such as different look angles, different orbit tracks. As the result of analyzing the FSIM value by land cover type, the change in the FSIM index according to the co-registration error was most evident in the urban area. Therefore, the FSIM index calculated in the urban was mostsuitable for determining the accuracy of image registration. It islikely that the FSIM index has sufficient potential to be used as an index for the co-registration accuracy of SAR image.

• Korean Journal of Remote Sensing
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• 제37권2호
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• pp.359-365
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• 2021

GEO-KOMPSAT-2A (GK2A) AMI (Advanced Meteorological Imager) Best Detector Select (BDS) map is pre-determined and uploaded before the satellite launch. After the launch, there is some possibility of a detector performance change driven by an abrupt temperature variation and thus the status of BDS map needs to be evaluated and updated if necessary. To investigate performance of entire elements of the detectors, AMI BDS analyses were conducted based on a technical note provided from the AMI vendor (L3HARRIS). The concept of the BDS analysis is to investigate the stability of signals from detectors while they are staring at targets (deep space and internal calibration target). For this purpose, Long Time Series (LTS) and Output Voltage vs. Bias Voltage (V-V) methods are used. The LTS for 30 secs and the V-V for two secs are spanned respectively for looking at the targets to compute noise components of detectors. To get the necessary data sets, these activities were conducted during the In-Orbit Test (IOT) period since a normal operation of AMI is stopped and special mission plans are commanded. With collected data sets during the GK2A IOT, AMI BDS map was intensively examined. It was found that about 1% of entire detector elements, which were evaluated at the ground test, showed characteristic changes and those degraded elements are replaced by alternative best ones. The stripping effects on AMI raw images due to the BDS problem were clearly removed when the new BDS map was applied.

• Korean Journal of Remote Sensing
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• 제34권6_2호
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• pp.1251-1260
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• 2018

High-quality sea-ice surface models generated from aerial images can be used effectively as field data for developing satellite-based remote sensing methods but also as analysis data for understanding geometric variations of Arctic sea-ice. However, the lack of texture information on sea-ice surfaces can reduce the accuracy of image matching. In this paper, we analyze the performance of matching cost functions for homogeneous sea-ice surfaces as a part of high-quality sea-ice surface model generation. The matching cost functions include sum of squared differences (SSD), normalized cross-correlation (NCC), and zero-mean normalized cross-correlation (ZNCC) in image domain and phase correlation (PC), orientation correlation (OC), and gradient correlation (GC) in frequency domain. In order to analyze the matching performance for texture changes clearly and objectively, a new evaluation methodology based on the principle of object-space matching technique was introduced. Experimental results showed that it is possible to secure reliability and accuracy of image matching only when optimal search windows are variably applied to each matching point in textureless regions such as sea-ice surfaces. Among the matching cost functions, NCC and ZNCC showed the best performance for texture changes.

• Korean Journal of Remote Sensing
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• 제34권6_3호
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• pp.1399-1414
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• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.

• Journal of Environmental Impact Assessment
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• 제27권6호
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• pp.717-726
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• 2018

PM(Particulate Matter) cause serious diseases of air pollution. Most of the studies have analyzed local distribution trends using satellite images or modeling techniques. However,the method using the spatial interpolation method based on the meteorological value is insufficient in Korea. In this study, monthly spatial distribution of $PM_{10}$ and $PM_{2.5}$ in January, February, March, and April of 2018 Seoul Metropolitan City were analyzed based on 39 PM monitoring networks. In addition, a distribution map showing the difference between $PM_{10}$ and $PM_{2.5}$ was based on the distribution obtained through this study. The regions of high $PM_{10}$ and $PM_{2.5}$ emissions were selected. In addition, the correlation between $PM_{10}$ and $PM_{2.5}$ was confirmed through the distribution map. This study analyzed the spatial distribution variation results of analyzing $PM_{10}$ and $PM_{2.5}$ in Seoulthrough spatial analysis technique. As a result of this study, it was confirmed that $PM_{10}$ shows high measured value on the roadside measurement station.

• Journal of the Korean Society of Marine Environment & Safety
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• 제23권7호
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• pp.909-917
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• 2017

In this study, we investigated the relationship between catches of yellowtail and water temperature at the set net fisheries of Gyeongsangbuk-do (Gyeongbuk) and Gangwon-do (Gangwon) off the east coast of Korea from May-November, 1980-2010. We also analyzed the water temperature and fluctuation of catches during the northward moving period (May-August) and southward moving period (September-November) using NOAA satellite images. Although the total catches fluctuated year by year, catches increased during the southward moving period when the water alongside the coast warmed. Yellowtail catches during the southward period in fall were higher than those during the northward period in spring or summer. The catch when the water temperature decreased was larger in Gyeongbuk than in Gangwon for all periods However, the catch when the water temperature increased was much larger in Gangwon than in Gyeongbuk. This indicates that high summer temperatures (> $25^{\circ}C$) affected the coast of Gangwon, with high water temperature ($20^{\circ}C$) maintained in the region during fall. Therefore, the increased catch in Gangwon was due to longer yellowtail presence caused by increased water temperature.

• Journal of Korean Society of Forest Science
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• 제108권1호
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• pp.10-20
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• 2019

The purpose of this study is to identify the long-term area changes in the subalpine coniferous forests in Korea in order to understand the changes in the subalpine forest ecosystems vulnerable to climate change. We analyzed 20 years of time-series Landsat satellite images (mid 1990s, mid 2010s) for change detection of coniferous forests and compared with the long term changes of climate information to identify their relationship in the study area. As a result, the area of coniferous forests in the study region decreased by 25% over 20 years. The regions with largest changes are Seoraksan, Baegunsan-Hambaeksan-Jangsan, Jirisan, and Hallasan. The region with the largest decrease in area was Baegunsan (reduced area: 542 ha), and the region with large decrease in area and the largest rate of decrease was Hallasan (rate of decrease: 33.3%). As the Jeju region has the most rapid temperature rise, it is projected that Hallasan is the most vulnerable forest ecosystem affected by climate change. The result of this study shows that from a long-term perspective the overall coniferous forests in the subalpine region are declining, but the trend varies in each region. This national and long-term information on the change of coniferous forests in the subalpine region can be utilized as baseline data for the detailed survey of endangered subalpine coniferous trees in the future.

• Journal of Convergence for Information Technology
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• 제10권12호
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• pp.201-207
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• 2020

In this study, the practicality of unmanned aerial vehicle photography information was identified. Therefore, a total of four consecutive surveys were conducted on the field-level survey areas among the areas subject to photography using unmanned aerial vehicles, and the changes in crop conditions were analyzed using pictures of unmanned aerial vehicles taken during each survey. It is appropriate to collect and utilize photographic information by directly taking pictures of the survey area according to the time of the on-site survey using unmanned aerial vehicles in the field layer, which is an area where many changes in topography, crop vegetation, and crop types are expected. And it turned out that it was appropriate to utilize satellite images in consideration of economic and efficient aspects in relatively unchanged rice paddies and facilities. If the survey area is well equipped with systems for crop cultivation, deep learning can be utilized in real time by utilizing libraries after obtaining photographic data for a certain area using unmanned aircraft in the future. Through this process, it is believed that it can be used to analyze the overall crop and shipment volume by identifying the crop status and surveying the quantity per unit area.

• The Journal of the Korea institute of electronic communication sciences
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• 제15권6호
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제39권2호
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• pp.93-101
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• 2021

Recently, with the urban redevelopment and the spread of the planned cities, there is increasing interest in the wind environment, which is related not only to design of buildings and landscaping but also to the comfortability of pedestrians. Numerical analysis for wind environment prediction is underway in many fields, such as dense areas of high-rise building or composition of the apartment complexes, a precisive 3D building model is essentially required in this process. Many studies conducted for wind environment analysis have typically used the method of creating a 3D model by utilizing the building layer included in the GIS (Geographic Information System) data. These data can easily and quickly observe the flow of atmosphere in a wide urban environment, but cannot be suitable for observing precisive flow of atmosphere, and in particular, the effect of a complicated structure of a single building on the flow of atmosphere cannot be calculated. Recently, drone photogrammetry has shown the advantage of being able to automatically perform building modeling based on a large number of images. In this study, we applied photogrammetry technology using a drone to evaluate the flow of atmosphere around two buildings located close to each other. Two 3D models were made into an automatic modeling technique and manual modeling technique. Auto-modeling technique is using an automatically generates a point cloud through photogrammetry and generating models through interpolation, and manual-modeling technique is a manually operated technique that individually generates 3D models based on point clouds. And then the flow of atmosphere for the two models was compared and analyzed. As a result, the wind environment of the two models showed a clear difference, and the model created by auto-modeling showed faster flow of atmosphere than the model created by manual modeling. Also in the case of the 3D mesh generated by auto-modeling showed the limitation of not proceeding an accurate analysis because the precise 3D shape was not reproduced in the closed area such as the porch of the building or the bridge between buildings.