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An optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data

  • GCEC 2017
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

This study proposed a workflow for an optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data. The method is validated on a set of data captured over a part of Selangor located in the Peninsular Malaysia. The method comprised four components including image segmentation, Taguchi optimization, attribute selection using random forest, and rule-based feature extraction. Results indicated the robustness of the proposed approach as the area under curve of forest; grassland, old oil palm, rubber, urban tree, and young oil palm were calculated as 0.90, 0.89, 0.87, 0.87, 0.80, and 0.77, respectively. In addition, results showed that SAR data is very useful for extracting rubber and young oil palm trees (given by random forest importance values). Finally, further research is suggested to improve segmentation results and extract more features from the scene.

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Authors and Affiliations

  1. School of Systems, Management and Leadership, Faculty of Engineering and Information Technology, University of Technology Sydney, PO Box 123, Building 11, Level 06, 81 Broadway, Ultimo, NSW, 2007, Australia

    Ahmed Abdulkareem Ahmed, Biswajeet Pradhan, Maher Ibrahim Sameen & Ali Muayad Makky

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  1. Ahmed Abdulkareem Ahmed
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  2. Biswajeet Pradhan
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  3. Maher Ibrahim Sameen
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  4. Ali Muayad Makky
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Correspondence to Biswajeet Pradhan.

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This article is part of the Topical Collection on Global Sustainability through Geosciences and Civil Engineering

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Ahmed, A.A., Pradhan, B., Sameen, M.I. et al. An optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data. Arab J Geosci 11, 280 (2018). https://doi.org/10.1007/s12517-018-3632-1

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