• Korean Journal of Remote Sensing
• /
• v.32 no.2
• /
• pp.97-104
• /
• 2016

We present linear particle depolarization ratio at 440, 675, 870, and 1020 nm retrieved from measurements with an AERONET sun/sky radiometer at Osaka, Japan. The retrieved data were compared with lidar derived linear particle depolarization ratio at 532 nm at the same site. We find good agreement between linear particle depolarization ratios derived with Sun photometer and measured by lidar except for those at 440 nm. The coefficients of determination between lidar derived data and sun/sky radiometer derived data were 0.28, 0.81, 0.88, and 0.89 at 440, 675, 870, and 1020 nm, respectively. We find that the linear particle depolarization ratio derived with sun/sky radiometer varies by the mixing between Asian dust and pollution particles. As the mixing ratio of Asian dust and pollution particles is increased, the linear particle depolarization ratio values are lower than the values of pure Asian dust. It was confirmed by the value of single-scattering albedo and particle size distribution.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.6
• /
• pp.641-650
• /
• 2011

Airborne LIDAR system, utilized in various forest studies, provides efficiently spatial information about vertical structures of forest areas. The tree height is one of the most essential measurements to derive forest information such as biomass, which can be estimated from the forest terrain model. As the terrain model is generated by the interpolation of ground points extracted from LIDAR data, filtering methods with high reliability to classify reliably the ground points are required. In this paper, we applied three representative filtering methods to forest LIDAR data with diverse characteristics, measured the errors and performance of these methods, and analyzed the causes of the errors. Based on their complementary characteristics derived from the analysis results, we have attempted to combine the results and checked the performance improvement. In most test areas, the convergence method showed the satisfactory results, where the filtering performance were improved more than 10% in maximum. Also, we have generated DTM using the classified ground points and compared with the verification data. The DTM retains about 17cm RMSE, which can be sufficiently utilized for the derivation of forest information.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2006.04a
• /
• pp.411-417
• /
• 2006

As NGIS project has been implemented in 1995 and effectiveness of spatial information increased, Digital maps serve several purpose in administrative, living, and industrial fields. But, Digital maps have many differences with real world information and difficulties in application of managing urban facilities due to high update cost and long generation period. In this study, We suggest methodology to update a building layer of digital map in urban area as regenerating a building layer by detection of changed buildings in the existing digital map anf extraction using LIDAR data and Aerial images, more easier to acquire data and available to guarantee accuracy. Experimental results are expected to applied for constructing 3D urban model in a broad expensive urban area.

• Korean Journal of Geomatics
• /
• v.3 no.2
• /
• pp.107-114
• /
• 2004

The National Geographic Information Institute (NGII) in korea, through the National Geographic Information System (NGIS) Program, has prepared to generate and disseminate digital elevation data for Korea. This is a pilot research to propose a policy for production, maintenance, and supply of Korea Digital Elevation Data(KDED). Customer demands for accuracy and resolution of DEM was surveyed through a questionnaire. In order to investigate the quality, the technical efficiency and the production cost, a tentative DEM in a small test site was generated based on digital topographic maps (original paper map scale 1:5,000), analytical plotter, and LIDAR. The Accuracy standard for KDED was derived based on source data generation methods. As a result of this research, a uniformly spaced grid model was recommended for KDED. Its preferable grid space is 5m in urban areas and its vicinity, and 10m in field and mountainous area. LIDAR has been valuated as a proper KDED generation method fulfilling customers' demands for the accuracy.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.923-926
• /
• 2003

In this paper we propose a scheme to generate DTM and detect buildings on DSM generated from LIDAR data. Two stages are performed. The first stage is to perform object segmentation by using two morphology operations namely, flattening and H-Dome transformation. After filtering out the object points above the ground, we used the non-object points to generate DTM. The second stage is to detect buildings from the objects by analyzing differential slopes. The test data is in raster form with 1m spacing around Hsin-Chu Scientific Area in Taiwan. The mean error is -0.16m and the RMSE is 0.45m for DTM generation. The successful rate for building detection is 87.7%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.3
• /
• pp.269-277
• /
• 2012

Street trees are one of useful urban facilities that reduce carbon dioxide and provide green space in urban areas. They are usually managed by local government, and it is effective to use aerial LIDAR data in order to acquire information such as the location, height and crown width of street tree systematically. In this research, algorithm was proposed that improves the accuracy of extracting top points of street trees and separates the region of individual street trees from aerial LIDAR data. In order to verify the proposed algorithm, a simulated aerial LIDAR data that exactly knows the number, height and crown width of street trees was created. As for the procedure of data processing, filtering that separates ground and non-ground points from LIDAR data was first conducted in order to separate the region of individual street trees. An estimated non-street tree points were then removed from non-ground points, and the top points of street trees were estimated. Region of individual street trees was determined by using the intersecting point of straight line that connects top point and ground point of street tree. Through the experiment by using simulated data, it was possible to refine wrongly estimated points occurred by determining tree tops and to determine the positional information, height, crown width of street trees through the determination of region of street trees.

• Atmosphere
• /
• v.25 no.1
• /
• pp.169-177
• /
• 2015

Vertical distribution of particle mass concentrations was estimated from 8-year elastic-backscatter lidar and sky radiometer data, and from ground-level PM10 concentrations measured in Seoul. Lidar ratio and mass extinction efficiency were determined from aerosol optical depth (AOD) and ground-level PM10 concentrations, which were used as constraints to estimate particle mass concentration. The mean lidar ratio (with standard deviation) and mass extinction efficiency for the entire 8-year study period were $60.44{\pm}23.17$ sr and $3.69{\pm}3.00m^2g^{-1}$, respectively. The lidar ratio did not vary significantly with the ${\AA}ngstr{\ddot{o}}m$ exponent (less than ${\pm}10%$); however, the mass extinction efficiency decreases to $1.82{\pm}1.67m^2g^{-1}$ (51% less than the mean value) when the ${\AA}ngstr{\ddot{o}}m$ exponent is less than 0.5. This result implies that the particle mass concentration from lidar measurements can be underestimated for dust events. Seasonal variation of the particle mass concentration estimated from lidar measurements for the boundary layer, was quite different from ground-level PM10 measurements. This can be attributable to an inhomogeneous vertical distribution of aerosol in the boundary layer.

• Korean Journal of Remote Sensing
• /
• v.31 no.3
• /
• pp.267-279
• /
• 2015

In the development of autonomous vehicles, the importance of LIDAR sensors becomes larger. For sensor selection or algorithm development, it is difficult to test expensive LIDAR sensors mounted on a vehicle under various driving environment. In this study, we developed a simulator that is generally applicable for various vehicle LIDAR sensors based on the generalized geometric modeling of the common processes associated with vehicle LIDAR sensors. By configuring this simulator with the specific sensors being widely used, we performed the data simulation and quality analysis. Also, we applied the simulation data to obstacle detection and evaluated the applicability of the selected sensor. The developed simulator enables various experiments and algorithm development in parallel with hardware implementation prior to the deployment and operation of a sensor.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.9 no.1
• /
• pp.46-55
• /
• 2006

The purpose of this study is a development of an automatic mosaicing for applying to large number of airborne multispectral images, which reduces manual operation by human. 2436 airborne multispectral images were acquired from DuncanTech MS4100 camera with three bands; green, red and near infrared. LIDAR(LIght Detection And Ranging) data and GPS/INS(global positioning system/inertial navigation system) data were collected with the multispectral images. First, the multispectral images were converted to image patterns by unsupervised classification. Their patterns were compared with those of adjacent images to derive relative spatial position between images. Relative spatial positions were derived for 80% of the whole images. Second, it accomplished an automatic mosaicing using GPS/INS data and unsupervised classification. Since the time of GPS/INS data did not synchronized the time of readout images, synchronized GPS/INS data with the time of readout image were selected in consecutive data by comparing unsupervised classified images. This method realized mosaicing automatically for 96% images and RMSE (root mean square error) for the spatial precision of mosaiced images was only 1.44 m by validation with LIDAR data.

• Proceedings of the Korea Air Pollution Research Association Conference
• /
• 2010.04a
• /
• pp.498-499
• /
• 2010