DOI QR코드

DOI QR Code

Detection of the Coastal Wetlands Using the Sentinel-2 Satellite Image and the SRTM DEM Acquired in Gomsoman Bay, West Coasts of South Korea

Sentinel-2 위성영상과 SRTM DEM을 활용한 연안습지 탐지: 서해안 곰소만을 사례로

  • 정윤재 ((주)지오씨엔아이 공간정보기술연구소) ;
  • 김경섭 ((주)지오씨엔아이 공간정보기술연구소) ;
  • 박인선 ((주)지오씨엔아이 공간정보기술연구소)
  • Received : 2021.05.17
  • Accepted : 2021.06.11
  • Published : 2021.06.30

Abstract

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.

기존 연구에서는 연안습지를 탐지하기 위해 위성/항공 영상의 다중분광 밴드로부터 산출한 식생지수 또는 토지피복도를 활용하였으나, 단일 센서만을 활용할 경우 토지피복정보와 지형정보를 동시에 고려하는 것에 한계가 있어 높은 정확도의 연안습지 탐지 및 대규모 연안습지 관리 업무 수행에 많은 지장을 초래하였다. 본 연구에서는 우리나라 서해안 곰소만 지역을 촬영한 Sentinel-2 위성영상의 다중분광 밴드와 디지털 지형 모델인 SRTM(Shuttle Radar Topography Mission) DEM(Digital Elevation Model)을 사용하여 서해안 곰소만의 대규모 연안습지를 다음의 과정을 통해 탐지하였다. 우선 Sentinel-2 위성영상의 Green 및 근적외선 밴드를 활용하여 정규수분지수 영상을 제작하였다. 그리고 정규수분지수 영상에서 픽셀의 밝기값 0.2를 임계치로 설정하여 물과 육지를 구분하는 이진화 영상을 제작하였으며, SRTM DEM에서 픽셀의 밝기값 0을 임계치로 설정하여 해수면 아래와 해수면 위를 구분하는 이진화 영상을 제작하였다. 최종적으로는 두 장의 이진화 영상에 중첩 분석을 적용하여 이진화 영상 기반 연안습지 지도를 제작하였다. 본 연구에서 제안한 기술을 활용하여 제작한 이진화 영상 기반 연안습지 지도의 정확도는 94%로서 매우 높은 결과를 보여주었으며, 연안습지가 아닌 내륙습지, 산지습지 등은 탐지되지 않아서 연안습지 관리 업무에 매우 효과적으로 활용될 수 있음을 확인하였다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었습니다(과제번호 21DPIW-C153746-03).

References

  1. Blanchette, M., A. N. Rousseau and M. Poulin. 2018. Mapping wetlands and land cover change with Landsat archives: the added value of geomorphologic data. Canadian Journal of Remote Sensing 44(4):337-356. https://doi.org/10.1080/07038992.2018.1525531
  2. Choung, Y.J., K.S., Kim, I.S. Park and I.Y. Chung. 2021. Detection of surface water bodies in Daegu using various water indices and machine learning technique based on the Landsat-8 satellite image. Journal of the Korean Association of Geographic Information Studies 24(1):1-11 https://doi.org/10.11108/KAGIS.2021.24.1.001
  3. Choung, Y.J. and M.H. Jo. 2016. Shoreline change assessment for various types of coasts using multi-temporal Landsat imagery of the east coast of South Korea. Remote Sensing Letters 7(1):91-100. https://doi.org/10.1080/2150704X.2015.1109157
  4. Choung, Y.J. and M.H. Jo. 2017. Comparison between a machine-learning-based method and a water-index-based method for shoreline mapping using a high-resolution satellite image acquired in Hwado island, South Korea. Journal of Sensors 2017:1-13. https://doi.org/10.1155/2017/8245204
  5. European Space Agency(ESA). 2021. Sentinel-2. https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2 (Accessed June 06, 2021).
  6. Jang, D.H., C. Kim and J.H. Park. The land-cover changes and pattern analysis in the tidal flats using post-classification comparison method: the case of Taean peninsula region. Journal of the Korean Geographical Society 45(2):275-292
  7. Jo, M.H. 2005. Analyzing the spectral characteristics and detecting the change of tidal flat area in Seo han bay, North Korea using satellite images and GIS. Journal of the Korean Association of Geographic Information Studies 8(2):44-54
  8. Korea National Park. 2021. Research for coastal wetlands. https://www.knps.or.kr/portal/main/contents.do?menuNo=8000322# (Accessed May 10, 2021).
  9. Korea Wetland Society. 2016. Wetland. Life Science Publishing Co., Seoul, pp.590
  10. Lee H.R. and J.B. Lee. 2005. Monitoring spatiotemporal changes of tidal flats in Go-gunsan islands by environmental factors using satellite images. Journal of the Korean Association of Geographic Information Studies 8(3):34-43
  11. Lee, J.O., Y.S. Kim and G.J. We. 2010. Change analysis of Eulsukdo wetland using qualitative multi-temporal image data. Journal of the Korean Association of Geographic Information Studies 13(2):64-73 https://doi.org/10.11108/kagis.2010.13.2.064
  12. Ministry of Oceans and Fisheries(MOF). 2021. MOF news. https://www.mof.go.kr/iframe/article/view.do?boardKey=10&menuKey=376&tPageNo=1&articleKey=36580 (Accessed June 06, 2021).
  13. Munyati, C. 2000. Wetland change detection on the kafue flats, Zambia, by classification of a multitemporal remote sensing image dataset. International Journal of Remote Sensing 21(9):1787-1806. https://doi.org/10.1080/014311600209742
  14. Park, J., J. Kim, G. Lee and J.E. Yang. 2017. Vertical accuracy assessment of STRM ver 3.0 and ASTER GDEM ver 2 over Korea. Journal of Soil Groundwater Environment 22(6):120-128 https://doi.org/10.7857/JSGE.2017.22.6.120
  15. Pressian. 2021. Significance of mudflat.https://www.pressian.com/pages/articles/71579#0DKU (Accessed May 10, 2021).
  16. Weng, Q. 2002. Land use change analysis in the Zhujiang delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management 64:273-284. https://doi.org/10.1006/jema.2001.0509