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Mapping Mangroves Extents on the Red Sea Coastline in Egypt using Polarimetric SAR and High Resolution Optical Remote Sensing Data

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  • Ayman Abdel-Hamid

    (Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, 53113 Bonn, Germany
    Environmental Studies Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 1564, Egypt)

  • Olena Dubovyk

    (Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, 53113 Bonn, Germany
    Remote Sensing Research Group (RSRG), University of Bonn, 53115 Bonn, Germany)

  • Islam Abou El-Magd

    (Environmental Studies Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 1564, Egypt)

  • Gunter Menz

    (Center for Remote Sensing of Land Surfaces (ZFL), University of Bonn, 53113 Bonn, Germany
    Remote Sensing Research Group (RSRG), University of Bonn, 53115 Bonn, Germany)

Abstract

Mangroves ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They are among the most productive forest ecosystems. They provide various ecological and economic ecosystem services. Despite of their economic and ecological importance, mangroves experience high yearly loss rates. There is a growing demand for mapping and assessing changes in mangroves extents especially in the context of climate change, land use change, and related threats to coastal ecosystems. The main objective of this study is to develop an approach for mapping of mangroves extents on the Red Sea coastline in Egypt, through the integration of both L-band SAR data of ALOS/PALSAR, and high resolution optical data of RapidEye. This was achieved via using object-based image analysis method, through applying different machine learning algorithms, and evaluating various features such as spectral properties, texture features, and SAR derived parameters for discrimination of mangroves ecosystem classes. Three non-parametric machine learning algorithms were tested for mangroves mapping; random forest (RF), support vector machine (SVM), and classification and regression trees (CART). As an input for the classifiers, we tested various features including vegetation indices (VIs) and texture analysis using the gray-level co-occurrence matrix (GLCM). The object-based analysis method allowed clearly discriminating the different land cover classes within mangroves ecosystem. The highest overall accuracy (92.15%) was achieved by the integrated SAR and optical data. Among all classifiers tested, RF performed better than other classifiers. Using L-band SAR data integrated with high resolution optical data was beneficial for mapping and characterization of mangroves growing in small patches. The maps produced represents an important updated reference suitable for developing a regional action plan for conservation and management of mangroves resources along the Red Sea coastline.

Suggested Citation

  • Ayman Abdel-Hamid & Olena Dubovyk & Islam Abou El-Magd & Gunter Menz, 2018. "Mapping Mangroves Extents on the Red Sea Coastline in Egypt using Polarimetric SAR and High Resolution Optical Remote Sensing Data," Sustainability, MDPI, vol. 10(3), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:646-:d:133979
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    References listed on IDEAS

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    1. Nadia S Santini & Quan Hua & Nele Schmitz & Catherine E Lovelock, 2013. "Radiocarbon Dating and Wood Density Chronologies of Mangrove Trees in Arid Western Australia," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-8, November.
    2. Edward B. Barbier & Suthawan Sathirathai (ed.), 2004. "Shrimp Farming and Mangrove Loss in Thailand," Books, Edward Elgar Publishing, number 3252.
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    Cited by:

    1. Shuai Li & Shufang Tian, 2023. "A Deep Feature Fusion Method for Complex Ground Object Classification in the Land Cover Ecosystem Using ZY1-02D and Sentinel-1A," Land, MDPI, vol. 12(5), pages 1-20, May.

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