Polarimetric Decompositions of Temperate Wetlands at C-Band

C-band SAR is well established as a useful sensor for water resources applications. It is commonly accepted that the backscatter from wetlands that consist of many emergent stems over open water (swamps and marshes) is dominated by a double-bounce scattering mechanism. However, recent observations with fully polarimetric data from Radarsat-2 over the extensive wetlands of the Everglades and numerous small wetlands in Ontario appear to be inconsistent with this interpretation of the backscatter physics. In this paper, we use several forms of polarimetric analysis and decomposition. All of these indicate that the backscatter from small marshes and swamps in Ontario is dominated by polarimetric characteristics normally attributed to the odd-bounce mechanism. This anomalous result might be explained as a consequence of changes in the double-bounce reflectance properties of vegetation as a function of the incidence angle. However, detailed electromagnetic backscatter modeling will be needed to provide a more complete and reliable understanding of the details of backscattering from wetlands with emergent vegetation. Additional observational and theoretical work will be required to document and understand the unusual results we report here. If these results are substantiated, the SAR community must re-interpret the generally accepted meanings of the popular decomposition variables, and introduce new terminology to describe them. This would lead to an improved understanding of the backscatter physics and better use of polarimetric SAR for wetland management applications

Combination of optical and SAR remote sensing data for wetland mapping and monitoring

Wetlands provide many services to the environment and humans. They play a pivotal role in water quality, climate change, as well as carbon and hydrological cycles. Wetlands are environmental health indicators because of their contributions to plant and animal habitats. While a large portion of Newfoundland and Labrador (NL) is covered by wetlands, no significant efforts had been conducted to identify and monitor these valuable environments when I initiated this project. At that time, there were only two small areas in NL that had been classified using basic Remote Sensing (RS) methods with low accuracies. There was an immediate need to develop new methods for conserving and managing these vital resources using up-to-date maps of wetland distributions. In this thesis, object- and pixel-based classification methods were compared to show the high potential of the former method when medium or high spatial resolution imagery were used to classify wetlands. The maps produced using several classification algorithms were also compared to select the optimum classifier for future experiments. Moreover, a novel Multiple Classifier System (MCS), which combined several algorithms, was proposed to increase the classification accuracy of complex and similar land covers, such as wetlands. Landsat-8 images captured in different months were also investigated to select the time, for which wetlands had the highest separability using the Random Forest (RF) algorithm. Additionally, various spectral, polarimetric, texture, and ratio features extracted from multi-source optical and Synthetic Aperture Radar (SAR) data were assessed to select the most effective features for discriminating wetland classes. The methods developed during this dissertation were validated in five study areas to show their effectiveness. Finally, in collaboration with a team, a website (http://nlwetlands.ca/) and a software package were developed (named the Advanced Remote Sensing Lab (ARSeL)) to automatically preprocess optical/SAR data and classify wetlands using advanced algorithms. In summary, the outputs of this work are promising and can be incorporated into future studies related to wetlands. The province can also benefit from the results in many ways