Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography Vol. 33, No. 1, 23-30,

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1 Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography Vol. 33, No. 1, 23-30, ISSN (Print) ISSN X(Online) Original article KOMPSAT-2 영상과항공 LiDAR 자료를이용한 3 차원해안선매핑 Mapping 3D Shorelines Using KOMPSAT-2 Imagery and Airborne LiDAR Data 정윤재 1) Choung, Yun Jae Abstract A shoreline mapping is essential for describing coastal areas, estimating coastal erosions and managing coastal properties. This study has planned to map the 3D shorelines with the airborne LiDAR(Light Detection and Ranging) data and the KOMPSAT-2 imagery, acquired in Uljin, Korea. Following to the study, the DSM(Digital Surface Model) is generated firstly with the given LiDAR data, while the NDWI(Normalized Difference Water Index) imagery is generated by the given KOMPSAT-2 imagery. The classification method is employed to generate water and land clusters from the NDWI imagery, as the 2D shorelines are selected from the boundaries between the two clusters. Lastly, the 3D shorelines are constructed by adding the elevation information obtained from the DSM into the generated 2D shorelines. As a result, the constructed 3D shorelines have had 0.90m horizontal accuracy and 0.10m vertical accuracy. This statistical results could be concluded in that the generated 3D shorelines shows the relatively high accuracy on classified water and land surfaces, but relatively low accuracies on unclassified water and land surfaces. Keywords : Coastal Zones, Shorelines, NDWI, LiDAR, KOMPSAT-2 Imagery 초록,. LiDAR KOMPSAT-2., LiDAR DSM( ). KOMPSAT-2 NDWI( ), NDWI., 2. DSM m 0.10m., 3,. :,,, LiDAR, KOMPSAT-2 Received , Revised , Accepted ) Member, Research Institute of Spatial Information Technology, GEO C&I Co., Ltd. ( choung12osu@gmail.com) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. 23

2 Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 1, 23-30, 서론 (shorelines) (coastal zones) (Lee 2012; Li et al., 2003; Shalowitz, 1964)., (Wie and Jeong, 2006)., (Liu et al., 2009), 3., (Lee and Kim, 2007). LiDAR, (Lee and Kim, 2007; Liu et al., 2009; Choung et al., 2013).. Kim et al.(2005) Corona. Wie and Jeong(2006) LiDAR. Lee and Kim(2007) RTK-GPS. Kim and Song(2012). Lee(2012) LiDAR. Liu et al.(2009) Choung et al.(2013) LiDAR Lake Erie. Kim et al.(2013) Landsat.,. LiDAR (Liu et al., 2009; Choung et al., 2013)., LiDAR, (Liu et al., 2009)., KOMPSAT-2 2 LiDAR 3., KOMPSAT-2 NDWI( ). ISODATA(Iterative Self-Organizing Data Analysis Technique) NDWI. 2., LiDAR DSM, 2 DSM 연구대상지역과데이터 7km, (coastal erosion) (Eom et al., 2010). KOMPSAT-2 (KARI: Korea Aerospace Research Institute), 4 (Blue: nm, Green: nm, Red: nm, NIR: nm), 1m (Oh et al., 2012). KOMPSAT-2 1m DEM(Digital Elevation Model) Table 1. Attributes of the LiDAR data used in this research Sensor ALTM(Airborne Laser Terrain Mapper) Gemini 167, Optech Data acquisition time January, 2012 Average point density 1.5 points/1 Horizontal datum GRS 80 Vertical datum MSL(Mean Sea Level) at Incheon Bay Horizontal accuracy 15cm Vertical accuracy 5cm 24

Mapping 3D Shorelines Using KOMPSAT-2 Imagery and Airborne LiDAR Data, 2012 7 29, GRS(Geodetic Reference System) 80, georeferencing RMSE(Root Mean Square Error) 0.8m. LiDAR Table 1. 40cm. Eq.

3 Mapping 3D Shorelines Using KOMPSAT-2 Imagery and Airborne LiDAR Data, , GRS(Geodetic Reference System) 80, georeferencing RMSE(Root Mean Square Error) 0.8m. LiDAR Table 1. 40cm. Eq. (1) Green Green, NIR NIR(Near Infra-Red). Eq. (1) KOMPSAT-2 Green NIR NDWI. NDWI Fig 차원해안선매핑 3 KOMPSAT-2 NDWI, ISODATA, 2, LiDAR DSM, DSM Fig. 1. Fig. 2. NDWI imagery generated from KOMPSAT-2 imagery Fig. 1. Flow chart for mapping 3D shorelines 3.1 NDWI 영상생성 NDWI Gao(1996), (Xu, 2006). NDWI, (McFeeters, 1996), Eq. (1) (McFeeters, 1996; Xu, 2006). (1) Fig. 2, NDWI,. 3.2 물과육지클러스터의분할 NDWI 2,. NDWI. (Jensen, 2004). (unsupervised classification) 25

Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 1, 23-30, 2015 (supervised classification), (Jensen, 2004; Wharton and Turner, 1981). ISODATA NDWI,,.

4 Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 1, 23-30, 2015 (supervised classification), (Jensen, 2004; Wharton and Turner, 1981). ISODATA NDWI,,. NDWI Fig. 3. Fig. 4. Generated 2D shorelines Fig. 3. Water and land clusters generated from the NDWI imagery 차원해안선추출 NDWI, 2. ArcGIS 10.1 Conversion Tools (polygon), (polyline) 2. 2 Fig DSM 생성 LiDAR, , LiDAR DSM. (MOLIT(Ministry of Land Infrastructure and Transport), 2010). LiDAR (Choung et al., 2012; Lee et al., 2004)., LiDAR DSM., LiDAR (1.5points/ ) DSM 1m. LiDAR DSM Fig

Mapping 3D Shorelines Using KOMPSAT-2 Imagery and Airborne LiDAR

5 3차원해안선구축 KOMPSAT-2 2 LiDAR DSM 3. Fig. 7.

5 Mapping 3D Shorelines Using KOMPSAT-2 Imagery and Airborne LiDAR Data 3 Fig 연구결과및논의 3 70 (checkpoints). 100m. LiDAR Appendix 1. 3 Fig. 7. Fig. 5. One section of DSM generated using LiDAR point cloud 3.5 3차원해안선구축 KOMPSAT-2 2 LiDAR DSM 3. Fig. 7. Checkpoints and reference shorelines for measuring the accuracies of the generated 3D shorelines Fig. 6. Constructed 3D shorelines Table

Mean 0.90 0.10 Max 3.71 1.91 Standard Deviation 0.76 0.15 Table 2, 3 0.90m, 0.10m. Fig. 8(a), (beach coast) 3 NDWI,. Fig. 8(b), (bluff coast) 3 NDWI,.

6 Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 1, 23-30, 2015 Table 2. Statistical results showing the vertical and horizontal accuracies of the constructed 3D shorelines Horizontal accuracies (m) Vertical accuracies (m) Mean Max Standard Deviation Table 2, m, 0.10m. Fig. 8(a), (beach coast) 3 NDWI,. Fig. 8(b), (bluff coast) 3 NDWI,. Fig. 8, 3,. 5. 결론 ( (a) (b) Fig. 8. Examples showing the 3D shorelines in the various coastal areas LiDAR ) 3., 2, LiDAR 2 LiDAR 3. KOMPSAT-2 LiDAR 3, m 0.10m,,

7 Mapping 3D Shorelines Using KOMPSAT-2 Imagery and Airborne LiDAR Data, 3. 3, 3.,.,. 감사의글 / (NRF-2014M1A3A3A ). References Choung, Y., Li, R., and Jo, M. (2013), Development of a vector-based method for coastal bluffline mapping using LiDAR data and a comparison study in the area of Lake Erie, Marine Geodesy, Vol. 36, No. 3, pp Choung, Y., Park, H., and Jo, M. (2012), A study on mapping 3-d river boundary using the spatial information datasets, Journal of the Korean Association of Geographic Information Studies, Vol. 15, No. 1, pp (in Korean with English abstract) Eom J., Choi, J., Ryu, J., and Won, J. (2010), Monitoring of shoreline change using satellite imagery and aerial photograph: for the Jukbyeon, Uljin, Korean Journal of Remote Sensing, Vol. 26, No. 5, pp (in Korean with English abstract) Gao, B. (1996), NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, Vol. 58, No. 3, pp Jensen, J. (2004), Introductory Digital Image Processing (3rd Edition), Prentice Hall, Upper Saddle River, NJ. Kim, G., Choi, S., Yook, W., and Song, Y. (2005), Coastline change detection using CORONA imagery, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, Vol. 23, No. 4, pp (in Korean with English abstract) Kim, M., Sohn, H., Kim, S., and Jang, H. (2013), Automatic coastline extraction and change detection monitoring using Landsat imagery, Journal of the Korean Society for Geospatial Information System, Vol. 21, No, 4, pp (in Korean with English abstract) Kim, I. and Song, D. (2012), Investigation of coastal erosion status in Geojin Port Area, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, Vol. 30, No. 1, pp (in Korean with English abstract) Lee, I. (2012), Instaneous Shoreline Extraction Utilizing Integrated Spectrum and Shadow Analysis From LiDAR Data and High-resolution Satellite Imagery, Ph.D. dissertation, The Ohio State University, Columbus, OH, USA, 224p. Lee, J., and Kim, Y. (2007), Coastline change analysis using RTK-GPS and aerial photo, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, Vol. 25, No. 3, pp (in Korean with English abstract) Lee, G., Koh, D., and Kim, W. (2004), Efficient construction of topographic data for flood mapping using digital map, Journal of the Korean Association of Geographic Information Studies, Vol. 7, No. 1, pp (in Korean with English abstract) Li, R., Di, K., and Ma. R. (2003), 3d shoreline extraction from IKONOS satellite imagery, Marine Geodesy, Vol. 26, No. (1-2), pp Liu, J., Li, R., Deshapnde, S., Niu, X., and Shih, T. (2009), Estimation of blufflines using topographic LiDAR data and orthoimages, PE&RS, Vol. 75, No. 1, pp Mcfeeters, S. (1996), The use of normalized difference water index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, Vol. 17, No. 7, pp MOLIT (2010), Guidebook for Management of Coastal Areas, Publication No , MOLIT, 29

8 Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 1, 23-30, 2015 Seoul, 207p. Oh, K., Jung, H., and Lee, K. (2012), Comparison of image fusion methods to merge KOMPSAT-2 panchromatic and multispectral images, Korean Journal of Remote Sensing, Vol. 28, No. 1, pp Shalowitz, A. (1964), Shore and Sea Boundaries, Volume 2, National Oceanic and Atmospheric Administration, National Ocean Service, Washington, D.C., USA, 631p. Wharton, S. and Turner, B. (1981), ICAP: an interactive cluster analysis procedure for analyzing remotely sensed data, Remote Sensing of Environment, Vol. 11, pp Wie, K. and Jeong, J. (2006), Development of shoreline extraction algorithm using airborne LiDAR data, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, Vol. 24, No. 2, pp (in Korean with English abstract) Xu, H. (2006), Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery, International Journal of Remote Sensing, Vol. 27, No. 14, pp Appendix 1. 3D coordinates of the 70 checkpoints Point ID X(m) Y(m) Z(m)

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03-16-김용일.indd Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography Vol. 33, No. 2, 95-101, 2015 http://dx.doi.org/10.7848/ksgpc.2015.33.2.95 ISSN 1598-4850(Print) ISSN 2288-260X(Online)