DOI QR코드

DOI QR Code

Detection of Urchin Barren Using Airborne Hyperspectral Imagery and SAM Technique - Focusing on the West Sea Island Areas

항공 초분광 영상과 SAM 기법을 이용한 백화현상 탐지 -서해 도서 지역을 중심으로-

  • Yong-Suk Kim (Department of Landscape Architecture, Dong-A University)
  • 김용석 (동아대학교 디자인환경대학 조경학과)
  • Received : 2024.06.19
  • Accepted : 2024.07.16
  • Published : 2024.07.31

Abstract

The coastal urchin barren phenomenon in our country began to spread and expand from the 1980s, centering on the southern coast and Jeju Island, and by the 1990s, it appeared along the east coast and nationwide. The urchin barren phenomenon is mainly conducted through field surveys by diving, but recently, various surveying techniques have been applied. In this study, a spectral library for terrestrial and marine areas was established for the identification of urchin barrens using airborne hyperspectral imagery, and the distribution area was analyzed through the SAM (spectral angle mapper) algorithm. An analysis of the urchin barren phenomenon in the five islands of the West Sea revealed that it occurrs in most areas, with the combined severity of the urchin barren phenomenon in Sapsido and Oeyeondo being approximately 19.9%. Hyperspectral imagery is expected to be highly useful not only for detecting the urchin barren phenomenon but also for managing and monitoring marine fishery resources through the classification of seaweeds.

Keywords

Acknowledgement

본 연구는 환경부의 재원으로 한국환경산업기술원의 녹색복원 특성화대학원 사업의 지원을 받아 수행되었습니다.

References

  1. Hwang, S. I., Kim, D. K., Sung, B. J., Jun, S. K., Bae, J. I., Jeon, B. H., 2017, Effects of climate change on whitening event proliferation the coast of Jeju, Korean J. Environ. Ecol., 31(6), 529-536.
  2. Choi, B. G., Na, Y. W., Kim, S. H., Lee, J. I., 2014, A Study on theimprovement classification accuracy of land cover using the aerial hyperspectral image with PCA, Journal of the Korean Society for Geospatial Information System, 22(1), 81-88.
  3. Kim, S. H., Yang, C. S., 2015, Current status of hyperspectral data processing techniques for monitoring coastal waters, Jounal of the Korean Association of Geographic Information Studies, 18(1), 48-63.
  4. Ga, C. O., Kim, D. S, Byun, Y. K., Kim, Y. I., 2004, A Comparison of classification techniqyes in hyperspectal image, Proceeding of 2004 fall conference Korean Society of Surveying, Geodesy, Photogrammetry and Catography, 251-256.
  5. Kim, Y. S., 2021, Detection of ecosystem distribution plants using drone hyperspectral spectrum and spectral angle mapper, Journal of Environmental Science International, 30(1), 1-10.
  6. Park, M. H., 2009, A Study on feature selection and feature extraction for hyperspectral image classification using canonical correlation classifier, Journal of Korean Society of Civil Engineering, 29(3-D), 419-421.
  7. Choi, J. W., Byun, Y. K., Kim, Y. I., Yu, K. Y., 2006, Support vector machine classification of hyperspectral image using spectral similarity kernel, Journal of the Korean Society for Geospatial Information System, 14(4), 71-77.
  8. Lee, J. D., Bhang, K. J., Joo, Y. D., 2016, Atmospheric correction effectiveness analysis and land cover classification using airborne hyperspectral imagery, Jour. of KoCon.a, 16(7), 31-41.
  9. Park, H. L., Choi, J. W., 2017, Accuracy evaluation of supervised classification by using morphological attribute profiles and additional band of hyperspectral imagery, Journal of the Korean Society for Geospatial Information System, 25(1), 9-17.
  10. FIRA, 2016, Identification and countermeasures for green tide (Bak-Hwa) phenomenon enhanced restoration of Bak-Hwa by region, Korea Fisheries Resource Agency, https://fira.or.kr (accessed 5 May 2023).
  11. NIFS, 2022, Annual report for climate change trends in fisheries, Report, 38-40, Korea.