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Sustainability 2018, 10(3), 646;

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

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
Remote Sensing Research Group (RSRG), University of Bonn, 53115 Bonn, Germany
Author to whom correspondence should be addressed.
Received: 30 December 2017 / Revised: 5 February 2018 / Accepted: 27 February 2018 / Published: 28 February 2018
(This article belongs to the Special Issue Sustainability Assessment of Land Use and Land Cover)
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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. View Full-Text
Keywords: mangroves mapping; Polarimetric SAR; ALOS PALSAR; RapidEye; OBIA; Red Sea mangroves mapping; Polarimetric SAR; ALOS PALSAR; RapidEye; OBIA; Red Sea

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Abdel-Hamid, A.; Dubovyk, O.; Abou El-Magd, I.; Menz, G. Mapping Mangroves Extents on the Red Sea Coastline in Egypt using Polarimetric SAR and High Resolution Optical Remote Sensing Data. Sustainability 2018, 10, 646.

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