• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.2
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• pp.73-81
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• 2015

The objective of this study is in investigating the research for more accurately quantify the information on mountain forest by using the data on high density LiDAR. For the quantitative analysis of mountain forest resources, we investigated the method to acquire the data on high density LiDAR and extract mountain forest resources. Consequently, the height and girth of a tree each mountain forest resources could be extracted by using the data on high density LiDAR. When using the data on low density LiDAR of 2.5points/m² in average used to produce digital map, it was difficult to extract the exact height and girth of mountain forest resources. If using the data on high density LiDAR of 7points/m² by considering topography, the property of mountain forest resources, data capacity and process velocity, etc, it was found that multitudinous entities could be extracted. It was found that mountain topography and mixed topography were generally denser than plane topography and multitudinous mountain forest resources could be extracted. Furthermore, it was also found that the entity at the border could not be extracted, when each partition was individually processed and the area should be subdivided and extracted by considering the process time and property of target area rather than processing wide area at once. We expect to be studied more profoundly the absorption quantity of greenhouse gas later by using information on mountain forest resources in the future.

• Spatial Information Research
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• v.16 no.3
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• pp.373-383
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• 2008

LiDAR, unlike satellite imagery and aerial photographs, which provides irregularly distributed three-dimensional coordinates of ground surface, enables three-dimensional modeling. In this study, urban area was classified based on 3D information collected by LiDAR. Morphological and spatial properties are determined by the ratio of ground and non-ground point that are estimated with the number of ground reflected point data of LiDAR raw data. With this information, the residential and forest area could be classified in terms of height and density of trees. The intensity of the signal is distinguished by a statistical method, Jenk's Natural Break. Vegetative area (high or low density) and non-vegetative area (high or low density) are classified with reflective ratio of ground surface.

• Journal of the Korean Geophysical Society
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• v.9 no.2
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• pp.87-97
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• 2006

LIDAR(Light Detection and Ranging) is a new technology for obtaining DEM(Digital Elevation Model)ewith high density and high point acuracy. As LIDAR emerged, DEM could be developed in the earthsurface more efficiently and more economically, compared to the conventional aerial photogrametry.In this study, a digital camera is simultaneously used in combined LIDAR surveying, and acquired digitial image and DEM produce digital orthoimage. In this process, methods of combining sensor andorthoimage, GCPs determined by GPS surveying are used. Two digital orthoimage are produced; onewith a few GCP and the other without them. The produced maps can be used to corect or revised1:1,000 or 1:5,000 scale maps acordingly.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.547-554
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• 2018

Using crowdsourced sensor data collection technique, it is possible to collect high-density ground vibration data which is difficult to obtain by conventional methods. In this study, we have developed a crowdsourced ground vibration data collection system using MEMS sensors mounted on small electronic devices including smartphones, and implemented client and server based on the proposed infrastructure system design. The system is designed to gather vibration data quickly through Android-based smartphones or fixed devices based on Android Things, minimizing the usage of resource like power usage and data transmission traffic of the hardware.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5D
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• pp.527-532
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• 2012

Terrestrial LiDAR emerges as a main mapping technology for indoor 3D cadastre, cultural heritage conservation and, building management in that it provides fast, accurate, and reliable 3D data. In this paper, a new 3D modeling method consisting of segmentation stage and outline extraction stage is proposed to develop indoor 3D model from the terrestrial LiDAR. In the segmentation process, RANSAC and a refinement grid is used to identify points that belong to identical planar planes. In the outline tracing process, a tracing grid and a data conversion method are used to extract outlines of indoor 3D models. However, despite of an improvement of productivity, the proposed approach requires an optimization process to adjust parameters such as a threshold of the RANSAC and sizes of the refinement and outline extraction grids. Furthermore, it is required to model curvilinear and rounded shape of the indoor structures.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.849-858
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• 2023

Investigating disaster sites such as earthquakes and landslides involves significant risks due to potential secondary disasters like facility collapse. In situations where direct access is challenging, there is a need to develop methods for safely acquiring high-precision 3D disaster information using light detection and ranging (LiDAR) equipped drone survey systems. In this study, the feasibility of using drone LiDAR in disaster scenarios was examined, focusing on the collapse accident at Jeongja Bridge in Bundang-gu, Seongnam City, in April 2023. High-density point clouds for the accident bridge were collected, and the bridge's 3D terrain information was reconstructed and compared to the measurement performance of 10 ground control points. The results showed horizontal and vertical root mean square error values of 0.032 m and 0.055 m, respectively. Additionally, when compared to a point cloud generated using ground LiDAR for the same target area, a vertical difference of approximately 0.08 m was observed, but overall shapes showed minimal discrepancies. Moreover, in terms of overall data acquisition and processing time, drone LiDAR was found to be more efficient than ground LiDAR. Therefore, the use of drone LiDAR in disaster sites with significant risks allows for safe and rapid onsite investigations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.2
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• pp.61-69
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• 2003

Surface energy and $CO_2$ fluxes have been measured over a farmland in Haenam, Korea since July 2002. Eddy covariance technique, which is the only direct flux measurement method, was employed to quantitatively understand the interaction between the farmland ecosystem and the atmospheric boundary layer. Maintenance of eddy covariance system was the main concern during the early stage of measurement to minimize gaps and uncertainties in the dataset. Half-hourly averaged $CO_2$ concentration showed distinct diurnal and seasonal variations, which were closely related to changes in net ecosystem exchange (NEE) of $CO_2$. Daytime maximum $CO_2$ uptake was about -1.0 mg $CO_2$ m$^{-2}$ s$^{-1}$ in August whereas nighttime $CO_2$ release was up to 0.3 mg $CO_2$ m$^{-2}$ s$^{-1}$ during the summer. Both daytime $CO_2$ uptake and nighttime release decreased gradually with season. During the winter season, NEE was from near zero to 0.05 mg $CO_2$ m$^{-2}$ s$^{-1}$ . FK site was a moderate sink of atmospheric $CO_2$ until September with daily NEE of 22 g $CO_2$ m$^{-2}$ d$^{-1}$ . In October, it became a weak source of $CO_2$ with an emission rate of 2 g $CO_2$ m$^{-2}$ d$^{-1}$ . Long-term flux measurements will continue at FK site to further investigate inter-annual variability in NEE. to better understand these exchange mechanism and in-depth analysis, process-level field experiments and intensive short-term intercomparisons are also expected to be followed.