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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.

This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations of multiple parameters, including solar radiation, air temperature, humidity, and UV exposure. Scarce ground PM10 data were collected and compared against satellite products, yet owed to their discrete nature of availability, our approach made use of the Random Decision Forest (RDF) model to convert satellite-based AOD and other meteorological parameters to predict PM10. The RDF model with inputs from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Global Land Data Assimilation System (GLDAS) datasets improves the performance of using AOD products to estimate PM10 values. The connection between climate variability and aerosol intensity, as well as their impact on health-related PM2.5 over Egypt is also demonstrated.