• Proceedings of the KSRS Conference
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• v.1
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• pp.34-37
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• 2006

When tsunami waves propagate across open ocean they are steered by Coriolis force and refraction due to gentle gradients in the bathymetry on scales longer than the wavelength. When the wave encounters steep gradients at the edges of continental shelves and at the coast, the wave becomes non-linear and conservation of momentum produces squirts of surface current at the head of submerged canyons and in coastal bays. HF coastal ocean radar is well-conditioned to observe the current bursts at the edge of the continental shelf and give a warning of 40 minutes to 2 hours when the shelf is 50-200km wide. The period of tsunami waves is invariant over changes in bathymetry and is in the range 2-30 minutes. Wavelengths for tsunamis (in 500-3000 m depth) are in the range 8.5 to over 200 km and on a shelf where the depth is about 50 m (as in the Great Barrier Reef) the wavelengths are in the range 2.5 - 30 km. It is shown that the phased array HF ocean surface radar being deployed in the Great Barrier Reef (GBR) and operating in a routine way for mapping surface currents, can resolve surface current squirts from tsunamis in the wave period range 20-30 minutes and in the wavelength range greater than about 6 km. There is a trade-off between resolution of surface current speed and time resolution. If the radar is actively managed with automatic intervention during a tsunami alert period (triggered from the global seismic network) then it is estimated that the time resolution of the GBR radar may be reduced to about 2 minutes, which corresponds to a capability to detect tsunamis at the shelf edge in the period range 5-30 minutes. It is estimated that the lower limit of squirt velocity detection at the shelf edge would correspond to a tsunami with water elevation of less than 5 cm in the open ocean. This means that the GBR HF radar is well-conditioned for use as a monitor of small and medium scale tsunamis, and has the potential to contribute to the understanding of tsunami genesis research.

• Research in Plant Disease
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• v.28 no.1
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• pp.1-18
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• 2022

Volatiles exist ubiquitously in nature. Volatile compounds produced by plants and microorganisms confer inter-kingdom and intra-kingdom communications. Autoinducer signaling molecules from contact-based chemical communication, such as bacterial quorum sensing, are relayed through short distances. By contrast, biogenic volatiles derived from plant-microbe interactions generate long-distance (>20 cm) alarm signals for sensing harmful microorganisms. In this review, we discuss prior work on volatile compound-mediated diagnosis of plant diseases, and the use of volatile packaging and dispensing approaches for the biological control of fungi, bacteria, and viruses. In this regard, recent developments on technologies to analyze and detect microbial volatile compounds are introduced. Furthermore, we survey the chemical encapsulation, slow-release, and bio-nano techniques for volatile formulation and delivery that are expected to overcome limitations in the application of biogenic volatiles to modern agriculture. Collectively, technological advances in volatile compound detection, packaging, and delivery provide great potential for the implementation of ecologically-sound plant disease management strategies. We hope that this review will help farmers and young scientists understand the nature of microbial volatile compounds, and shift paradigms on disease diagnosis and management to aromatic (volatile-based) agriculture.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.725-733
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• 2020

The objectives of this study were (1) to develop a temporal mask map using MCD12Q1 data, and (2) to extract the annual variations in paddy, (3) to investigate the correlation analysis between MYD13Q1 NDVI and rice yield, and (4) to review its applicability. For these purposes, the temporal mask map was created using annual MCD12Q1 PFT data from 2002 to 2019, and compared with the fixed mask map. As a result, it found that the temporal mask map well reflected the variations of the paddy area. In addition, the correlation coefficient between NDVI and rice yield was also high significant as compared to the fixed mask map. Therefore, the temporal mask map will be useful for NDVI extraction, crop monitoring, and estimation of rice yield.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.2
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• pp.151-159
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• 2004

The diversity, expansion of human activity and rapid urbanization make modem society to faced with problems like damage of nature and drain of natural resources. Under these circumstances rapid and accurate change detection techniques, which can detect wide range utilization changes, are needed for efficient management and utilization plan of national territory. In this study to perform change detection from remote sensing images, space analysis technique contained in Geographic Information System is applied. And from this technique, the software. that can execute new change detection algorithm, query, inquiry and analysis, is produced. This software is on the basis of graphic user interface and has many functions such as format conversion, grid calculation, statistical processing, display and reference. In this study, simultaneously change detection for multi-temporal satellite images can be performed and integrated one change image about four different periods was produced. Further more software user can acquire land cover change information for an specific area through querying and questioning about yearly changes. Finally making of every application module for change detection into one window based visual basic program, can be produced user convenience and automatic performances.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.55-70
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• 2021

In this study, in the field of remote sensing, where the scope of application is rapidly expanding to fields such as land monitoring, disaster prediction, facility safety inspection, and maintenance of cultural properties, monitoring of rural space and surrounding environment using UAV is utilized. It was carried out to verify the possibility, and the following main results were derived. First, the aerial image taken with an unmanned aerial vehicle had a much higher image size and spatial resolution than the aerial image provided by the National Geographic Information Service. It was suitable for analysis due to its high accuracy. Second, the more the number of photographed photos and the more complex the terrain features, the more the point cloud included in the aerial image taken with the UAV was extracted. As the amount of point cloud increases, accurate 3D mapping is possible, For accurate 3D mapping, it is judged that a point cloud acquisition method for difficult-to-photograph parts in the air is required. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. Fourth, the digital elevation model(DEM) produced with aerial image taken with an UAV can visually express the altitude and shape of the topography of the study site, so it can be used as data to predict the effects of topographical changes due to changes in rural space. Therefore, it is possible to utilize various results using the data included in the aerial image taken by the UAV. In this study, the superiority of images acquired by UAV was verified by comparison with existing images, and the effect of 3D mapping on rural space monitoring was visually analyzed. If various types of spatial data such as GIS analysis and topographic map production are collected and utilized using data that can be acquired by unmanned aerial vehicles, it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1319-1326
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• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.218-222
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• 2014

In this paper, we introduce an autonomous calibration method for a 2D laser displacement sensor (e.g. laser vision sensor and laser range finder) by matching a single point on a flat structure. Many arc welding robots install a 2D laser displacement sensor to expand their application by recognizing their environment (e.g. base metal and seam). In such systems, sensing data should be transformed to the robot's coordinates, and the geometric relation (i.e. rotation and translation) between the robot's coordinates and sensor coordinates should be known for the transformation. Calibration means the inference process of geometric relation between the sensor and robot. Generally, the matching of more than 3 points is required to infer the geometric relation. However, we introduce a novel method to calibrate using only 1 point matching and use a specific flat structure (i.e. circular hole) which enables us to find the geometric relation with a single point matching. We make the rotation component of the calibration results as a constant to use only a single point by moving a robot to a specific pose. The flat structure can be installed easily in a manufacturing site, because the structure does not have a volume (i.e. almost 2D structure). The calibration process is fully autonomous and does not need any manual operation. A robot which installed the sensor moves to the specific pose by sensing features of the circular hole such as length of chord and center position of the chord. We show the precision of the proposed method by performing repetitive experiments in various situations. Furthermore, we applied the result of the proposed method to sensor based seam tracking with a robot, and report the difference of the robot's TCP (Tool Center Point) trajectory. This experiment shows that the proposed method ensures precision.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.461-476
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• 2013

Carnegie-Ames-Stanford Approach (CASA) model is one of the most quick, convenient and accurate models to estimate the NPP (Net Primary Productivity) of vegetation. The purposes of this study are (1) to examine the spatial and temporal patterns of vegetation NPP of the paddy field area in Korea from 2002 to 2012, and (2) to investigate how the rice productivity responded to inter-annual NPP variability, and (3) to estimate rice yield in Korea using CASA model applied to MOderate Resolution Imaging Spectroradiometer (MODIS) products and solar radiation. MODIS products; MYD09 for NIR and SWIR bands, MYD11 for LST, MYD15 for FPAR, respectively from a NASA web site were used. Finally, (4) its applicability is to be reviewed. For those purposes, correlation coefficients (linear regression for monthly NPP and accumulated NPP with rice yield) were examined to evaluate the spatial and temporal patterns of the relations. As a result, the total accumulated NPP and Sep. NPP tend to have high correlation with rice yield. The rice yield in 2012 was estimated to be 526.93kg/10a by accumulated NPP and 520.32 kg/10a by Sep. NPP. RMSE were 9.46kg/10a and 12.93kg/10a, respectively, compared with the yield forecast of the National Statistical Office. This leads to the conclusion that NPP changes in the paddy field were well reflected rice yield in this study.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.237-247
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• 2020

Here, we generated maps of primary production in the coastal waters of the East Sea using sea surface chlorophyll-a concentrations (CHL), photosynthetically available radiation (PAR), euphotic depth induced by GOCI along with sea surface temperature (SST) from satellites of foreign countries as input parameters, and carried out a sensitivity analysis for each parameters. On 25th of July in 2013 when a wide cold waters appeared and on 13th of August in 2013 when a big harmful algal bloom existed in the study area, it shows high productivities with averages 1,012 and 1,945 mg C m^-2 d^-1, respectively. On August 25, 2013, when the cold waters and red tide retreated, it showed an average of 778 m^-2 d^-1, similar to the results of the previous analysis. As a result of the sensitivity analysis, PAR did not significantly affect the results of the primary production, but the euphotic depth and CHL showed aboveaverage sensitivity. In particular, SST had a large influence to the results, thus we could imply that an error in SST could lead to a large error in the primary production. This study showed that GOCI data was available for primary production study, and the accuracy of input parameters might be improved by applying GOCI, which can acquire images 8 times a day, making it more accurate than foreign polar orbit satellites and consequently, it is possible to estimate highly accurately primary production.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.