• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.29-38
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• 2014

UAV(Unmanned Aerial vehicle) could be effectively applied in a field of land monitoring for analyzing disaster area and mapping, because it can quickly acquire image data at low costs. For this reason, we reviewed the legal system related to mapping, and proposed suggestions for improving in legal system, due to introducing the UAV to Korean land-monitoring through this paper. Also, we evaluated spatial and time accuracy of the digital map, which are generated from UAV images that were taken for occasional map updates and disaster detections. As a result, the mean error is about 10m if only GPS/INS data used, while using GCP(Ground Control Points) it is about 10cm. Therefore, we conclude that the UAV could be effective method in korea land-monitoring field.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.545-548
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• 2006

It's very difficult to acquire the accurate GCP(Ground Control Point) in the urban center and forest aerial photo because of occurring of irregular multi-path error. Thr purpose of this study is to apply the GPS and the EDM system for 3D orthophoto in the small areas. GCPs surveyed by accuracy triangulation from EDM after from triangulation points to a fiducial point at study area used to GPS. And I have a comment on how to use areal orthophoto for future 3D-GIS after 3D-Modelling using areal orthophoto. As the results, EDM surveying could resolve multi-path error according to GPS surveying and It is possible for using aerial orthophoto on the basis of the 3D-GIS database.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.15-26
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• 2017

The decrease of green space according to the urbanization has caused many environmental problems as the destruction of habitat, air pollution, heat island effect. With interest growing in natural view recently, proper management of evergreen tree which is lived even the winter season has been on the rise importantly. This study analyzed the distribution area of evergreen tree using vegetation index based on unmanned aerial vehicle (UAV). Firstly, RGB and NIR+RG camera were loaded in fixed-wing UAV and image mosaic was achieved using GCPs based on Pix4d SW. And normalized differences vegetation index (NDVI) and soil adjusted vegetation index (SAVI) was calculated by band math function from acquired ortho mosaic image. validation points were applied to evaluate accuracy of the distribution of evergreen tree for each range value and analysis showed that kappa coefficient marked the highest as 0.822 and 0.816 respectively in "NDVI > 0.5" and "SAVI > 0.7". The area of evergreen tree in "NDVI > 0.5" and "SAVI > 0.7" was $11,824m^2$ and $15,648m^2$ respectively, that was ratio of 4.8% and 6.3% compared to total area. It was judged that UAV could supply the latest and high resolution information to vegetation works as urban environment, air pollution, climate change, and heat island effect.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.158-174
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• 2018

The leaf area index (LAI) is a major factor explaining the photosynthesis of vegetation, evapotranspiration, and energy exchange between the earth surface and atmosphere, and there have been studies on accurate and applicable LAI estimation methods. This study aimed to investigate the relationship between the actual LAI data, UAV image-based vegetation index, canopy height and satellite image (Sentinel-2) LAI and to present an effective LAI estimation method using UAV. As a result, among the six vegetation indices in this study, NDRE ($R^2=0.496$) and CIRE ($R^2=0.443$), which contained red-edge band, showed a high correlation. The application of the canopy height model data to the vegetation index improved the explanatory power of the LAI. In addition, in the case of NDVI, the saturation problem caused by the linear relationship with LAI was addressed. In this study, it was possible to estimate high resolution LAI using UAV images. It is expected that the applicability of such data will be improved if calibration and correction steps are carried out for various vegetation and seasonal images.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.237-250
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• 2023

Orthophotos and DEMs were generated by UAV-based aerial photogrammetry and an attempt was made to apply them to detailed investigations for the production of flood traces. The cultivated area located in Goa-eup, Gumi, where the embankment collapsed and inundated inundation occurred due to the impact of 6th Typhoon Sanba in 2012, was selected as rhe target area. To obtain optimal accuracy of UAV photogrammetry performance, the UAV images were taken under the optimal placement of 19 GCPs and then point cloud, DEM, and orthoimages were generated through image processing using Pix4Dmapper software. After applying CloudCompare's CSF Filtering to separate the point cloud into ground elements and non-ground elements, a finally corrected DEM was created using only non-ground elements in GRASS GIS software. The flood level and flood depth data extracted from the final generated DEM were compared and presented with the flood level and flood depth data from existing data as of 2012 provided through the public data portal site of the Korea Land and Geospatial Informatix Corporation(LX).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.299-309
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• 2014

This paper evaluated the possibility for 1/5,000 digital topographic mapping by using PLEIADES images of 0.5m GSD(Ground Sampling Distance) resolution that has recently launched. Those results of check points by applying the initial RPC(Rational Polynomial Coefficient) of PLEIADES images came out as; RMSE of those were $X={\pm}1.806m$, $Y={\pm}2.132m$, $Z={\pm}1.973m$. Also, if we corrected geometric correction using 16 GCP(Ground Control Point)s, the results of RMSE became $X={\pm}0.104m$, $Y={\pm}0.171m$, $Z={\pm}0.036m$, and t he RMSE of check points were $X={\pm}0.357m$, $Y={\pm}0.239m$, $Z={\pm}0.188m$; which of those results indicated the accuracy of standard adjustment complied in error tolerances of the 1/5,000 scale. Additionally, we converted coordinates of points, obtained by TerraSAR. for comparing with measurements from GPS(Global Positioning System) surveying. The RMSE of comparing converted and GPS points were $X={\pm}0.818m$, $Y={\pm}0.200m$, $Z={\pm}0.265m$, which confirmed the possibility for 1/5,000 digital topographic mapping with PLEIADES images and GCPs. As method of obtaining GCPs in unaccessible area, however, the outcome evaluation of GCPs extracted from TerraSAR images was not acceptable for 1/5,000 digital topographic mapping. Therefore, we considered that further researches are needed on applicability of GCPs extracted from TerraSAR images for future alternative method.