Acta Oceanologica Sinica

, Volume 35, Issue 10, pp 11–19 | Cite as

Survey of reefs based on Landsat 8 operational land imager (OLI) images in the Nansha Islands, South China Sea

  • Yuewei Duan
  • Yongxue Liu
  • Manchun Li
  • Minxi Zhou
  • Yuhao Yang
Article
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Abstract

A detailed survey of the reefs in the Nansha Islands’ sea that are of strategic importance is conducive to their future development. In this study, a total of 50 Landsat 8 operational land imager (OLI) images were used to analyze the geological features and spectral characteristics of the Nansha reefs. The exposed islands/sandbanks, reef flats and sub-tidal reefs were identified with the near-infrared band, the red and green bands, and the blue band, respectively. Based on the relationships among various characteristics and bands illustrated in the OLI images, the maximum between-cluster variance threshold algorithm (OTSU method) and the mathematical morphology were employed to extract characteristic parameters of the spatial geometry of the reefs from top to bottom, which were subsequently operated by a series of post-processing methods such as vectorization, simplification and topological analysis. Among the 132 standalone reefs and 16 atolls that are identified in the study, four reefs have yet to be named in the Standard Names of Various Islands in the South China Sea and the website Nansha Islands Online (http://www.nansha.org/). Another 24 ones are exposed to the atmosphere even during high tides. Taiping Island with an area of 0.57 km2 represents the largest exposed geological feature. This study demonstrated the applicability of medium-resolution satellite images to derive the coral reef information effectively and thus provided information for the related departments that are responsible to manage the coast. An improved resolution of multispectral bands with the panchromatic band in higher spatial resolution (15 m for OLI image) is expected to provide an optimum satellite based approach to map marine habitats.

Key words

remote sensing coral reefs OLI imagery Nansha Islands 
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References

  1. Andréfouët S, Kramer P, Torres-Pulliza D, et al. 2003. Multi-site evaluation of IKONOS data for classification of tropical coral reef environments. Remote Sensing of Environment, 88(1–2): 128–143CrossRefGoogle Scholar
  2. Brock J C, Wright C W, Clayton T D, et al. 2004. LIDAR optical rugosity of coral reefs in Biscayne National Park, Florida. Coral Reefs, 23(1): 48–59CrossRefGoogle Scholar
  3. Call K A, Hardy J T, Wallin D O. 2003. Coral reef habitat discrimination using multivariate spectral analysis and satellite remote sensing. International Journal of Remote Sensing, 24(13): 2627–2639CrossRefGoogle Scholar
  4. Chen Jianyu, Mao Zhihua, Zhang Huaguo, et al. 2007. Analysis on coral reefs mapping using SPOT5 at Dongsha Atoll. Haiyang Xuebao (in Chinese), 29(3): 51–57Google Scholar
  5. Clayton D G. 1971. Algorithm AS 46: Gram-schmidt orthogonalization. Journal of the Royal Statistical Society. Series C—Applied Statistics, 20(3): 335–338Google Scholar
  6. Gao Junguo, Liu Baoyin. 2009. The Nansha Islands Spatial Information Fusion Analysis and Warning: Reefs Development Military Regional Warning System (in Chinese). Beijing: China Ocean Press, 1–3Google Scholar
  7. Hochberg E J, Atkinson M J. 2003. Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra. Remote Sensing of Environment, 85(2): 174–189CrossRefGoogle Scholar
  8. Hochberg E J, Atkinson M J, Andréfouët S. 2003. Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing. Remote Sensing of Environment, 85(2): 159–173CrossRefGoogle Scholar
  9. Holden H, LeDrew E. 1998. Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal components analysis, and derivative spectroscopy. Remote Sensing of Environment, 65(2): 217–224CrossRefGoogle Scholar
  10. Holden H, LeDrew E. 1999. Hyperspectral identification of coral reef features. International Journal of Remote Sensing, 20(13): 2545–2563CrossRefGoogle Scholar
  11. Hu Leiqiu, Liu Yalan, Ren Yuhuan, et al. 2010. Research on the extraction method of coral reef at Spratly Islands using SPOT5. Remote Sensing Technology and Application (in Chinese), 25(4): 493–501Google Scholar
  12. Kutser T, Dekker A G, Skirving W. 2003. Modeling spectral discrimination of Great Barrier Reef benthic communities by remote sensing instruments. Limnology and Oceanography, 48(1): 497–510CrossRefGoogle Scholar
  13. Li Cunjun, Liu Liangyun, Wang Jihua, et al. 2004. Comparison of two methods of fusing remote sensing images with fidelity of spectral information. Journal of Image and Graphics (in Chinese), 9(11): 1376–1385Google Scholar
  14. Lim A, Hedley J D, LeDrew E, et al. 2009. The effects of ecologically determined spatial complexity on the classification accuracy of simulated coral reef images. Remote Sensing of Environment, 113(5): 965–978CrossRefGoogle Scholar
  15. Lubin D, Li W, Dustan P, et al. 2001. Spectral signatures of coral reefs: Features from space. Remote Sensing of Environment, 75(1): 127–137CrossRefGoogle Scholar
  16. Mumby P J, Green E P, Edwards A J, et al. 1997. Coral reef habitat mapping: how much detail can remote sensing provide?. Marine Biology, 130(2): 193–202CrossRefGoogle Scholar
  17. Mumby P J, Skirving W, Strong A E, et al. 2004. Remote sensing of coral reefs and their physical environment. Marine Pollution Bulletin, 48(3–4): 219–228CrossRefGoogle Scholar
  18. Palandro D, Andréfouët S, Muller-Karger F E, et al. 2003. Detection of changes in coral reef communities using Landsat-5 TM and Landsat-7 ETM+ data. Canadian Journal of Remote Sensing, 29(2): 201–209CrossRefGoogle Scholar
  19. Pan Chunmei, Ding Qian, Cao Wenyu. 2002. TM image analysis of island reef topography of The Nansha Islands. Remote Sensing for Land & Resources (in Chinese), (2): 34–37, 60Google Scholar
  20. Smith V E, Rogers R, Reed L. 1975. Automated mapping and inventory of Great Barrier Reef zonation with LANDSAT data. In: OCEAN 75 Conference. San Diego, CA: IEEE, 775–780CrossRefGoogle Scholar
  21. Sun Zongxun, Zhan Wenhuan, Zhu Junjiang. 2004. Rockmass structure and engineering geological zones of coral reefs Nansha Islands of South China Sea. Journal of Tropical Oceanography (in Chinese), 23(3): 11–20Google Scholar
  22. Yu Kefu. 2000. The recent fifty years high-resolution climate of Nansha Islands recorded in reef coral (in Chinese) [dissertation]. Guangzhou: Guangzhou Institute of Geochemistry, Chinese Academy of SciencesGoogle Scholar
  23. Zhao Ziyu, Shen Sanwei. 2011. The rise and hold, highlighting the strategic position of South China. National Think Tank (in Chinese), (1): 7–12Google Scholar
  24. Zou Yarong, Liang Chao, Zhu Haitian. 2012. Research about monitoring the development status of coral reefs based on QuickBird data. Haiyang Xuebao (in Chinese), 34(2): 57–62Google Scholar
  25. Zou Yarong, Lin Mingsen, Wang Hua, et al. 2006. Study on high resolution image classification for Dongsha Island based on fuse information. Haiyang Xuebao (in Chinese), 28(1): 56–61Google Scholar

Copyright information

© The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yuewei Duan
    • 1
  • Yongxue Liu
    • 1
    • 2
  • Manchun Li
    • 1
    • 2
  • Minxi Zhou
    • 1
  • Yuhao Yang
    • 1
  1. 1.Department of Geographic Information ScienceNanjing UniversityNanjingChina
  2. 2.Collaborative Innovation Center for the South China Sea StudiesNanjing UniversityNanjingChina

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