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.
Highlights
Mangroves are highly productive ecosystems located in the intertidal tropical and subtropical regions
The current study shows that using L-band Synthetic Aperture Radar (SAR) data provided by Advanced Land Observing Satellite (ALOS)/Phased Array L-band Synthetic Aperture Radar (PALSAR) combined with the high-resolution optical data of RapidEye is very useful in mapping of mangroves extents growing in fractions and small patches on the Red Sea coastline, as it provides information on the spatial distribution of mangroves and mangroves ecosystem, which is required for conservation and management of mangroves in this area
In this study we investigated the contribution of the dual-polarimetric L-band SAR data of ALOS/PALSAR and the high resolution optical data of RapidEye for mapping mangroves extents on the Red Sea coastline
Summary
Mangroves are highly productive ecosystems located in the intertidal tropical and subtropical regions. Mangroves support the conservation of biological diversity by providing habitats, nurseries, and nutrients for a number of animals and marine organisms [2] They are considered of a great ecological importance in coastlines stabilization, reduction of coastal erosion, sediment, and nutrient retention, flood and flow control, and water quality [3,4,5]. They are of a high economic importance and often provide valuable ecosystem goods and services for local communities [6]. Degradation and loss of these coastal buffering systems due to climate change and direct human impacts contradict the coastal protection they provide and increases their vulnerability, with significant environmental, economic and social consequences for indigenous people in the coastal areas [13]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
