Thanks to increasing urban development, it has become important for municipalities to understand how ecological processes function. In particular, urban wetlands are vital habitats for the people and the animals living amongst them. This is because wetlands provide great services, including water filtration, flood and drought mitigation, and recreational spaces. As such, several recent urban development plans are currently needed to monitor these invaluable ecosystems using time- and cost-efficient approaches. Accordingly, this study is designed to provide an initial response to the need of wetland mapping in the City of St. John's, Newfoundland and Labrador (NL), Canada. Specifically, we produce the first high-resolution wetland map of the City of St. John's using advanced machine learning algorithms, very high-resolution satellite imagery, and airborne LiDAR. An object-based random forest algorithm is applied to features extracted from WorldView-4, GeoEye-1, and LiDAR data to characterize five wetland classes, namely bog, fen, marsh, swamp, and open water, within an urban area. An overall accuracy of 91.12% is obtained for discriminating different wetland types and wetland surface water flow connectivity is also produced using LiDAR data. The resulting wetland classification map and the water surface flow map can help elucidate a greater understanding of the way in which wetlands are connected to the city's landscape and ultimately aid to improve wetland-related conservation and management decisions within the City of St. John's.
Keywords: City; Image classification; LiDAR; Object-based; Random forest; Remote sensing; VHR imagery; Wetland.
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