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

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Subdivision of Water Body in Watersheds Classified by Remote Sensing

  • Choi, Hyun (Dept. of Civil Engineering, Kyungnam University)
  • Received : 2020.02.11
  • Accepted : 2020.03.30
  • Published : 2020.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

South korea has been developing and managing the complete dimensions, around the rivers to rapid economic growth. In Korea, where water resources are scarce, administration and work are complicated and diversified in the computerization of related facilities and hydrologic data due to the indiscriminate development of river facilities. In general, dividing the water system based on object in remote sensing is relatively accurate in the image with the same spectral characteristics. However, the distinction between the reservoir and the river must be made manually due to the characteristics of remote sensing. Therefore, this study performed three classifications using GIS (Geographic Information System) to classify reservoirs and rivers. For the purpose of accuracy analysis, the land cover map provided by EGIS (Environmental Geographic Information Service) was used to evaluate the accuracy, and the average of 85.63% was found to be 75.40% of rivers, 89.50% of reservoirs, and 92.00% of others.

Keywords

References

  1. Blaschke, T. (2010), Object based image analysis for remote sensing, Journal of Photogrammetry and Remote Sensing, Vol. 65, pp. 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
  2. Feyisa, G.L., Meilby, H., Fensholt, R., and Proud, S. R. (2014), Automated water extraction index: A new technique for surface water mapping using Landsat imagery, Remote Sensing of Environment, Vol. 140, pp. 23-35. https://doi.org/10.1016/j.rse.2013.08.029
  3. Frazier, P.S., and Page, K .J. (2000), Water body detection and delineation with Landsat TM data, Photogrammetric Engineering and Remote Sensing, Vol. 66, No. 12, pp. 1461-1468.
  4. Ji, L., Zhang, L., and Wylie, B. (2009), Analysis of dynamic thresholds for the normalized difference water index, Photogrammetric Engineering and Remote Sensing, Vol. 75, No. 11, pp. 1307-1317. https://doi.org/10.14358/PERS.75.11.1307
  5. Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J., and Xiao, T. (2014), An automated method for extracting rivers and lakes from Landsat imagery, Remote Sensing, Vol. 6, No. 6, pp. 5067-5089. https://doi.org/10.3390/rs6065067
  6. Kim, H.J., Kim Y., and Lee B. (2015), A study on the feature extraction using spectral indices from WorldView-2 satellite image, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 5, pp. 363-371. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2015.33.5.363
  7. Sohn, H.G., Song, Y.S., and Jang, H. (2004), Improvement of water area classification during a flood using RADARSAT SAR imagery and terrain informations in mountainous area, Journal of the Korean Society of Civil Engineers, Vol. 24, No. 2D, pp. 293-301.(in Korean with English abstract)
  8. Verpoorter, C., Kutser, T., and Tranvik, L. (2012), Automated mapping of water bodies using Landsat multispectral data, Limnology and Oceanography, Vol. 10, No. 12, pp. 1037-1050. https://doi.org/10.4319/lom.2012.10.1037
  9. White, K. and El Asmar, H.M. (1999), Monitoring changing position of coastlines using the matic mapper imagery, an example from the Nile delta. Geomorphology, Vol. 29, pp. 93-105. https://doi.org/10.1016/S0169-555X(99)00008-2
  10. Xu, H. (2006), Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, International Journal of Remote Sensing, Vol. 27, No.14, pp. 3025-3033. https://doi.org/10.1080/01431160600589179
  11. Yang, K., Li, M., Liu, Y., Cheng, L, Duan, Y., and Zhou, M. (2014), River delineation from remotely sensed imagery using a multiscale classification approach, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 12, pp. 4726-4737. https://doi.org/10.1109/JSTARS.2014.2309707
  12. Yang, Y., Liu, Y., Zhou, M., Zhang, S., Zhan, W., Sun, C., and Duan, Y. (2015), Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach, Remote Sensing of Environment, Vol. 171, No. 75, pp.14-32. https://doi.org/10.1016/j.rse.2015.10.005
  13. Ye, C.S. (2016), water body extraction using block-based image partitioning and extension of water body boundaries, Korean Journal of Remote Sensing, Vol. 32, No. 5, pp. 471-482.(in Korean with English abstract) https://doi.org/10.7780/kjrs.2016.32.5.6
  14. Zhang, F., Tiyip, T., Kung, H., Johnson, V.C., Wang, J., and Nurmemet, I. (2016), Improved water extraction using Landsat TM/ETM+ images in Ebinur Lake, Xinjiang, China, Remote Sensing Applications: Society and Environment, Vol. 4, pp. 109-118. https://doi.org/10.1016/j.rsase.2016.08.001
  15. Zhou, Y., Luo, J., Z. Hu, Shen, X., and Yang, H. (2014), Multiscale water body extraction in urban environments from satellite images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 10, pp. 4301-4312. https://doi.org/10.1109/JSTARS.2014.2360436