Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1111-1113, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1111-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1111-1113, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1111-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Jun 2016

24 Jun 2016

MAPPING OF CORAL REEF ENVIRONMENT IN THE ARABIAN GULF USING MULTISPECTRAL REMOTE SENSING

H. Ben-Romdhane, P. R. Marpu, H. Ghedira, and T. B. M. J. Ouarda H. Ben-Romdhane et al.
  • Institute Centre for Water and Environment (iWater) Masdar Institute of Science and Technology, PO Box 54224, Abu Dhabi, United Arab Emirates

Keywords: Mapping, Coral Reef, Benthic Habitats, DubaiSat-2, Arabian Gulf

Abstract. Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.