• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.90-104
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• 2008

Laser scanning technology with high positional accuracy and high density will be widely applied to vast range of fields including geomatics. Especially, the development of laser scanning technology enabling long range information extraction is increasing its full use in civil engineering. This study taps into the strengths of a terrestrial laser scanning technique to develop a tunnel cross section management system that can be practically employed for determining the cross section of tunnels more promptly and accurately. Three dimensional data with high density were obtained in a prompt and accurate manner using a terrestrial laser scanner. Data processing was then conducted to promptly determine arbitrary cross sections at 0.1meter, 0.5meter and 1.0meter intervals. A laser scanning technique was also used to quickly and accurately calculate the overbreak and underbreak of both each cross section and the entire tunnel section. As the developed system utilizes vast amounts of data, it was possible to promptly determine the shape of arbitrary cross section and to calculate the overbreak and underbreak more accurately with higher area precision. It is expected, therefore, that the system will not only enable more efficient and cost effective tunnel drilling management and monitoring but also will provide a basis for future construction and management of tunnel cross section.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1187-1195
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• 2019

We, for the first time, developed a Raman lidar system which can remotely detect surface CO₂ volume mixing ratio (VMR). The Raman lidar system consists of the Nd: YAG laser of wavelength 355 nm with 80 mJ, an optical receiver, and detectors. Indoor CO₂ cell measurements show that the accuracy of the Raman lidar system is calculated to be 99.89%. We carried out the field measurement using our Raman lidar at Pukyong National University over a seven-day period in October 2019. The results show good agreement between CO₂ VMRs measured by the Raman lidar (CO₂ Raman Lidar) and those measured by in situ instruments (CO₂ In situ) which located 300 m and 350 m away from the Raman lidar system. The correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) between CO₂ In situ and CO₂ Raman Lidar are 0.67, 2.78 ppm, and 3.26 ppm, respectively.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.653-666
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• 2020

Understanding the vegetation structure is important to manage forest resources for sustainable forest development. With the recent development of technology, it is possible to apply new technologies such as drones and deep learning to forests and use it to estimate the vegetation structure. In this study, the vegetation structure of Gongju, Samchuk, and Seoguipo area was identified by fusion of drone-optical images and LiDAR data using Artificial Neural Networks(ANNs) with the accuracy of 92.62% (Kappa value: 0.59), 91.57% (Kappa value: 0.53), and 86.00% (Kappa value: 0.63), respectively. The vegetation structure analysis technology using deep learning is expected to increase the performance of the model as the amount of information in the optical and LiDAR increases. In the future, if the model is developed with a high-complexity that can reflect various characteristics of vegetation and sufficient sampling, it would be a material that can be used as a reference data to Korea's policies and regulations by constructing a country-level vegetation structure map.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.775-784
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• 2020

Recent global warming and anthropogenic activities have caused more frequent and massive wildfires with longer durations and more significant damages. MODIS has been monitoring global wildfires for almost 20 years, and GK2A and Himawari-8 are observing the wildfires in East Asia 144 times a day. However, the spatial resolution of 1 to 2 km is not sufficient for the detection of small and medium-size active fires, and therefore the studies on the active fire detection using high-resolution images are essential. However, there is no official product for the high-resolution active fire detection. Hence, we implemented the active fire detection algorithm of Landsat 8 and carried out a high-resolution-based detection of active fires in Australia in 2019, followed by the comparisons with the products of Himawari-8 and MODIS. Regarding the intense fires, the three satellites showed similar results, whereas the weak igniting and extinguishing fires or the fires in narrow areas were detected by only Landsat 8 with a 30m resolution. Small-sized fires, which are the majority in Korea, can be detected by the high-resolution satellites such as Landsat 8, Sentinel-2, Kompsat-3A, and the forthcoming Kompsat-7. Also, a comprehensive analysis together with the geostationary satellites in East Asia such as GK2A, Himawari-8, and Fengyun-3 will help the interoperability and the improvement of spatial and temporal resolutions.

• Economic and Environmental Geology
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• v.45 no.4
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• pp.377-384
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• 2012

The main field component of the Earth's magnetic field was modeled from the tri-axial magnetometer onboard KOrean MultiPurpose SATellite-II (KOMPSAT-II) for the purpose of satellite attitude control. The model computed by the KOMPSAT-II magnetometer measurement data is compared with the International Geomagnetic Reference Field (IGRF) model of a degree of up to 13 in spherical harmonic coefficients. The previous study with KOMPSAT-I (Kim et al. 2004) indicated a good correlation of power spectrum of spherical harmonic coefficients with respect to the degree up to 5. This study, however, showed an agreement of the degree up to 8-9 of the coefficient power spectrum and a discrepancy between degrees 10 and 13. We have concluded that relevant data selection process, removal of the external field from the data in the high latitude region, an accuracy of the magnetometer all play an important role in finding a coherence with the IGRF model. This study will be extended to the secular variation model of geomagnetism if longer-period data become available.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.343-353
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• 2013

Recently, the landscape change by development has become more important and it's been stipulated that environmental effects evaluation must include LIA which reflects the said issue. Even though the selection of viewpoints which have the biggest impact on LIA result is stipulated in accordance with related rules, the criteria are inconsistent. Therefore, LIA experts' or project developers' subjective opinions have been very influential. In this case, wrong viewpoints which do not meet the selection criteria could be selected based on a discretionary interpretation on them, which could in turn substantially reduce an accuracy and reliability of the LIA results. Therefore, this research suggested the reverse-viewshed frequency technique as method that can be verified accuracy and reliability of viewpoints. And it has comparatively analyzed effective viewpoints using reverse-viewshed frequency analysis on viewpoints which were selected for LIA. As a result, the average validity was just 58% of total viewpoints used in construction sites. And the validity of viewpoints decreased as viewshed frequency increased. Based on results above, it was able to verify that the unreasonable points of a substantial proportion have been used as viewpoints in LIA process.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.97-110
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• 2007

A laser scanning technique has been attracting much attention as a new technology to acquire location information. This technique might be applicable to a wide range of areas, most notably in geomatics, due to its high accuracy of location and automation of high-density data acquisition. A alignment information extraction system on highway has been developed in this study by utilizing the advantages of the laser scanning technique. The system can accurately interpret the alignment information of highway and can be applied to actual works. To develop the alignment information extraction system on highway, an algorithm that can automatically separate a horizontal alignment into a straight line, a transition curve, and a circular curve was developed. It can increase its efficiency compared to the conventional methods. In addition, an algorithm that can automatically extract design elements of horizontal and vertical alignments of highway was developed and applied to an object highway. This yielded higher practicality with more accurate values compared to those from previous studies on the extraction of design elements of highway alignment. Furthermore, the extracted design elements were used to perform a virtual driving simulation on the object highway. Through this, data were provided for a visual judgment for judging visually whether the topography and structures were harmonized in a three-dimensional manner or not. The study also presents data that can serve as a basis to determine highway surface freezing sections and to analyze three-dimensional sight distance models. Through the establishment of a systematic database for diverse data on highway and the development of web-based operating programs, an efficient highway maintenance can be ensured and also they can provide important information to be used when estimating a highway safety in the future.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.395-408
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• 2015

High-resolution satellite image mosaics are becoming increasingly important in the field of remote sensing image analysis as an essential image processing to create a large image constructed from several smaller images. In this paper, we present an automatic seamline extraction technique and the procedure to generate a mosaic image by this technique. For more effective seamline extraction in the overlap region of adjacent images, an NDVI-based seamline extraction technique is developed, which takes advantage of the computational time and memory. The Normalized Difference Vegetation Index(NDVI) is an index of plant "greeness" or photosynthetic activity that is employed to extract the initial seamline. The NDVI can divide into manmade region and natural region. The cost image is obtained by the canny edge detector and the buffering technique is used to extract the ranging cost image. The seamline is extracted by applying the Dijkstra algorithm to a cost image generated through the labeling process of the extracted edge information. Histogram matching is also conducted to alleviate radiometric distortion between adjacent images acquired at different time. In the experimental results using the KOMPSAT-2/3 satellite imagery, it is confirmed that the proposed method greatly reduces the visual discontinuity caused by geometric difference of adjacent images and the computation time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.5
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• pp.545-553
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• 2009

Digital Elevation Models (DEM) is widely used in establishing the topographic profile in nation spatial information. Aerial Light Detection And Ranging (LiDAR) system is one of the well-known means to produce DEM. The system has fast data acquisition procedures and less weather-dependent restrictions compared to photogrammetric approaches. In this regards, LiDAR has been widely utilized and accepted in the process of nation spatial information generation due to its sufficient positional accuracy. However, the investigation of the accuracy of aerial LiDAR data over the area of forestation with various kinds of vegetations has been barely implemented in Korea. Hence, this research focuses on the investigation of the accuracy of aerial LiDAR data over the area of forestation and the evaluation of the acquired accuracy according to the characteristics of the vegetations. The study areas include land with shrubs and its adjacent forest area with mixed tree species. The spots for the investigation have been selected to be well-distributed over the whole study areas and their coordinates are surveyed by Global Positioning Systems (GPS). Then, the surveyed information and aerial LiDAR data have been compared with each other and the result accuracy has been evaluated. Conclusively, it is recommended that LiDAR data collection to be conducted after defoliation period, especially over the areas with broadleaf trees due to the possibility of significant outliers.