Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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UAV-based Land Cover Mapping Technique for Monitoring Coastal Sand Dunes

  • Received : 2017.01.26
  • Accepted : 2017.02.27
  • Published : 2017.02.28
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Abstract

In recent years, coastal dune erosion has accelerated as various structures have been developed around the coastal dunes. A land cover map should be developed to identify the characteristics of sand dunes and to monitor the condition of sand dunes. The Korean Ministry of Environment's land cover maps suffer from problems, such as limited classes, target areas, and durations. Thus, this study conducted experiments using RGB and multispectral images based on UAV (Unmanned Aerial Vehicle) over an approximately one-year cycle to create a land cover map of coastal dunes. RF (Random Forest) classifier was used for the analysis in accordance with the experimental region's characteristics. The pixel- and object-based classification results obtained by using RGB and multispectral cameras were evaluated, respectively. The study results showed that object-based classification using multispectral images had the highest accuracy. Our results suggest that constant monitoring of coastal dunes can be performed effectively.

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References

  1. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M. (2004), Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 58, No. 3, pp. 239-258. https://doi.org/10.1016/j.isprsjprs.2003.10.002
  2. Blaschke, T. and Strobl, J. (2001), What's wrong with pixels? Some recent developments interfacing remote sensing and GIS, GeoBIT/GIS, Vol. 6, No. 1, pp. 12-17.
  3. Breiman, L. (2001), Random forests, Machine Learning, Vol. 45, No. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324
  4. Chan, J.C. and Paelinckx, D. (2008), Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery, Remote Sensing of Environment, Vol. 112, No. 6, pp. 2999-3011. https://doi.org/10.1016/j.rse.2008.02.011
  5. Choi, H. (2012), Environmental Changes of the Sand Beach Coasts in Byeonsan Peninsula, Master' s thesis, Korea National University of Education, Cheong-ju, Korea, 191p. (in Korean)
  6. Choi, S.K., Lee, S.K., Jung, S.H., Choi, J.W., Choi, D.Y., and Chun, S.J. (2016), Estimation of fractional vegetation cover in sand dunes using multi-spectral images from fixedwing UAV, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 34, No. 4, pp. 431-441. https://doi.org/10.7848/ksgpc.2016.34.4.431
  7. Giltelson, A.A., Kaufman, Y.J., Stark, R., and Rundquist, D. (2002) Novel algorithm for remote estimation of vegetation fraction, Remote Sensing of Environment, Vol. 80, pp. 76-87. https://doi.org/10.1016/S0034-4257(01)00289-9
  8. Fuyi, T., Chun, B.B., Matjafri, M.Z., San, L.H., Abdullah, K., and Tahrin, M. (2012), Land cover/use mapping using multi-band imageries captured by cropcam unmanned aerial vehicle autopilot(UAV) over Penang Island, Malaysia, 10 September, Edinburgh, United Kingdom, Proceedings of SPIE, Vol. 8540, pp. 1-6.
  9. Giada, S., Degroeve, T., Ehrlich, D., and Soille, P. (2003), Information extraction from very high resolution satellite imagery over Lukole refugee camp, Tanzania, International Journal of Remote Sensing, Vol. 24, No. 22, pp. 4251-4266. https://doi.org/10.1080/0143116021000035021
  10. Hassan, F.M., Lim, H.S., and Matjafri, M.Z. (2011), Cropcam UAV for land use/land cover mapping over Penang island, Malaysia, Pertanika Journal of Science & Technology, Vol. 19, pp. 69-76.
  11. Herold, M., Scepan, J., Muller, A., and Gunther, S. (2002), Object-oriented mapping and analysis of urban land use/cover using IKONOS data, In 22nd Earsel Symposium Geoinformation for European-Wide Integration, 4-6 June, Prague, Czech Republic, pp. 4-6.
  12. Herwitz, S.R., John, L.F., Dunagan, S.E., Higgins, R.G., Sullivan, D.V., Zheng, J., Lobitz, B.M., Leung, J.G., Gallmeyer, B.A., Aoyagi, M., Slye, R.E., and Brass, J.A. (2004), Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support, Computers and Electronics in Agriculture, Vol. 44, No. 1, pp. 49-61. https://doi.org/10.1016/j.compag.2004.02.006
  13. Ho, T.K. (1998), The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp. 832-844. https://doi.org/10.1109/34.709601
  14. Phi, J.H. (2015), Coastal Dune Typology and Management Plan by Ecological Structure Analysis - in Case of the Taean Peninsula, Chungcheongnam-do Province, Ph.D. dissertation, University of Seoul, Seoul, Korea, 244p. (in Korean)
  15. Jensen, J.R. (2015), Introductory Digital Image Processing: A Remote Sensing Perspective, Pearson Education.
  16. Kim, K.H., Yoo, H.S., and Joung, E.J. (2008), Disaster overall prevention system for beach erosion its applications, Journal of Korean Society of Coastal and Ocean Engineers, Vol. 20, No. 6, pp. 602-610. (in Korean with English abstract)
  17. Kim, S.M. (2014), Study of the UAV for application plans and landscape analysis, Journal of Korean Institute of Traditional Landscape Architecture, Vol. 32, No. 3, pp. 213-220. (in Korean with English abstract) https://doi.org/10.14700/KITLA.2014.32.3.213
  18. Laliberte, A.S., Rango, A., Havstad, K.M., Paris, J.F., Beck, R.F., McNeely, R., and Gonzalez, A.L. (2004), Objectoriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico, Remote Sensing of Environment, Vol. 93, No. 1, pp. 198-210. https://doi.org/10.1016/j.rse.2004.07.011
  19. Lee. Y.K. (2011), Study on Changes in the Coastal Environment due to Human Interference : A Case Study of Sand Beach Coast in Gangneung, Master's thesis, Korea National University of Education, Cheong-ju, Korea, 168p. (in Korean)
  20. Lee, K.S. and Kim, S.H. (2000), East sea coast ecosystem in the ecological perspective conservation and utilization of sea coast forest : In Korean East Sea Rim, 2000 International Symposium, Vol. 2000, No. 1, pp 13-45. (in Korean)
  21. Lucas, N.S., Shanmugam, S., and Barnsley, M. (2002), Subpixel habitat mapping of a costal dune ecosystem, Applied Geography, Vol. 22, No. 3, pp. 253-270. https://doi.org/10.1016/S0143-6228(02)00007-3
  22. Mao, W. and Wang, Y. (2003), Real-time detection of between-row weeds using machine vision, 2003 ASABE Annual Meeting, 27-30 July, Las Vegas, NV, Vol. 1, No. 031004, pp. 1-6.
  23. Moffett, K. and Gorelick, S. (2013), Distinguishing wetland vegetation and channel features with object-based image segmentation, International Journal of Remote Sensing, Vol. 34, No. 4, pp. 1332-1354. https://doi.org/10.1080/01431161.2012.718463
  24. Na, S.I., Baek, S.C., Hong, S.Y., Lee, K.D., and Jang, K.C. (2015), A Study on the application of UAV for the onion and garlic growth monitoring, Korea, The Korean Society of Soil Science and Fertilizer spring conference, 4 December, Seoul, Korea, 225p.
  25. Pix4D. (2016), Drone mapping software for desktop + cloud + mobile, Pix4D, Switzerland, https://pix4d.com (last date accessed: 25 January 2017).
  26. Saberioon, M.M., Amin, M.S.M., Anuar, A.R., Gholizadeh, A., Wayayok, A., and Khairunniza-Bejo, S. (2014), Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation, Vol. 32, pp. 35-45. https://doi.org/10.1016/j.jag.2014.03.018
  27. SenseFly. (2016), Drones for professionals, mapping & photogrammetry, flight planning & control software, senseFly SA, Switzerland, https://www.sensefly.com (last date accessed: 25 January 2017).
  28. Shanmugam, S., Lucas, N., Phipps, P., Richards, A., and Barnsley, M. (2003), Assessment of remote sensing techniques for habitat mapping in coastal dune ecosystems, Journal of Coastal Research, Vol. 19, No. 1, pp. 64-75.
  29. Shin, J.S., Lee, T.H., Jung, P.M., and Kwon, H.S. (2015), A study on land cover map of UAV imagery using an objectbased classification method, Journal of The Korea Society for Geospatial Information System, Vol. 23, No. 4, pp. 25-33. (in Korean with English abstract) https://doi.org/10.7319/kogsis.2015.23.4.025
  30. Shin. S. (1999), Establishment of Shore-zone Ecosystem Management Unit using Classification of Shoreline Types : A Case Study of Taean National Park, Master's thesis, Seoul National University, Seoul, Korea, 76p. (in Korean)
  31. Song, J.H., Kang, I.J., Hong, S.H., and Park, D.H. (2014), Construction of vegetation information management system using GIS, Journal of The Korea Society for Geospatial Information System, Vol. 22, No. 4, pp. 99-106. (in Korean with English abstract) https://doi.org/10.7319/KOGSIS.2014.22.4.099
  32. Timm, B.C. and McGarigal, K. (2012), Fine-scale remotelysensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using random forests, Remote Sensing of Environment, Vol. 127, pp. 106-117. https://doi.org/10.1016/j.rse.2012.08.033
  33. Woebbecke, D.M., Meyer, G.E., Von, B.K., and Mortensen, D.A. (2012), Color indices for weed identification under various soil, residue and lighting conditions, Transactions of the ASAE, Vol. 38, pp. 259-269.
  34. Zhang, L. and Baas, A.C. (2012), Mapping functional vegetation abundance in a coastal dune environment using a combination of LSMA and MLC: a case study at Kenfig NNR, Wales, International Journal of Remote Sensing, Vol. 33, No. 16, pp. 5043-5071. https://doi.org/10.1080/01431161.2012.657369

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