Estimation of Coral Rubble Quantity using Edge Detection and Morphological- Based Methods

Authors

  • Nursyahirah Hafiz School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
  • Muhammad Suzuri Hitam School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
  • Zainuddin Bachok School of Marine and Environmental Sciences, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
  • Mohd Safuan Che Din Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia

DOI:

https://doi.org/10.46754/umtjur.v1i3.84

Keywords:

Reef rubble, Edge detection, Morphological method, Image

Abstract

Reef rubble represents the broken components of the coral and reef structure which could be in the form of dead, broken or other fragmented coral. The process to estimate the distribution of reef rubble is currently done manually and thus takes a long time to complete and is laborious. This paper presents an image-processing-based method to estimate the distribution of reef rubbles in a coral reef environment from a still image. The method is basically a series of image processing steps including image complement, image binarization, edge detection, smoothing by Weiner filter and followed by erosion and dilation process. The experimental results showed that the system was able to roughly estimate the distribution of reef rubble.

References

Alcala, A. C., & Gomez, E. D. 1982. Coral growth-rates on a reef flat in Sumilon Island, Central Philippines. Kalikasan-The Philippine Journal of Biology, 11(1), 179-184.

Benediktsson, J. A., Pesaresi, M., & Amason, K. 2003. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing, 41(9), 1940-1949.

Bierwirth, P. N., Lee, T. J., & Burne, R. V. 1993. Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery. Photogrammetric Engineering and Remote Sensing ;( United States), 59(3).

Carleton, J. H., & Done, T. J. 1995. Quantitative video sampling of coral reef benthos: large-scale application. Coral Reefs, 14(1), 35-46.

Carper, W., Lillesand, T., & Kiefer, R. 1990. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and remote sensing, 56(4), 459-467.

Chavez, P., Sides, S. C., & Anderson, J. A. 1991. Comparison of three different methods to merge multiresolution and multispectral data- Landsat TM and SPOT panchromatic. Photogrammetric Engineering and remote sensing, 57(3), 295-303.

Green, E. P., Mumby, P. J., Edwards, A. J., & Clark, C. D. 1996. A review of remote sensing for the assessment and management of tropical coastal resources. Coastal management, 24(1), 1-40.

Haralick, R. M. 1979. Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5), 786-804.

Hussain, Z., & Agarwal, D. 2015. A comparative analysis of edge detection techniques used in flame image processing. International Journal of Advance Research in Science and Engineering IJARSE, (4).

Kohler, K. E., & Gill, S. M. 2006. Coral Point Count with Excel extensions (CPCe): A Visual basic program for the determination of coral and substrate coverage using random point count methodology. Computers & Geosciences, 32(9), 1259-1269.

Lenth, R. V. 2001. Some practical guidelines for effective sample size determination. The American Statistician, 55(3), 187-193.

Mausel, P. W., Kramber, W. J., & Lee, J. K. 1990. Optimum band selection for supervised classification of multispectral data. Photogrammetric Engineering and Remote Sensing, 56, 55-60.

Mumby, P. J., & Harborne, A. R. 1999. Development of a systematic classification scheme of marine habitats to facilitate regional management and mapping of Caribbean coral reefs. Biological conservatio, 88(2), 155-163.

Rasser, M., & Riegl, B. 2002. Holocene coral reef rubble and its binding agents. Coral Reefs, 21(1), 57-72.

Soriano, M., Marcos, S., Saloma, C., Quibilan, M., & Alino, P. 2001. Image classification of coral reef components from underwater color video. In Oceans, 2001. MTS/IEEE Conference and Exhibition (Vol. 2, pp. 1008-1013). IEEE.

MathWork. 1994 – 2018. Morphological Dilation and Erosion. https://www.mathworks.com/help/images/morphological-dilation-and-erosion.html [25 April 2018]

Vogt, P., Riitters, K. H., Estreguil, C., Kozak, J., Wade, T. G., & Wickham, J. D. 2007. Mapping spatial patterns with morphological image processing. Landscape ecology, 22(2), 171-177.

Woodley, J., Chornesky, E., Cliffo, P., Jackson, J., Kaufman, L., Knowlton, N., & Rylaarsdz, K. W. 1981. Hurricane Allen’s impact on a Jamaican coral reef. Science, 214, 13.

Additional Files

Published

2019-07-31

How to Cite

Nursyahirah Hafiz, Muhammad Suzuri Hitam, Zainuddin Bachok, & Mohd Safuan Che Din. (2019). Estimation of Coral Rubble Quantity using Edge Detection and Morphological- Based Methods. Universiti Malaysia Terengganu Journal of Undergraduate Research, 1(3), 105–119. https://doi.org/10.46754/umtjur.v1i3.84