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The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America. Despite much progress in targeting habitat management in staging and wintering areas, methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required. This is particularly true for species that breed in remote, inaccessible areas such as the American black duck, an intensively managed game bird in Eastern North America. Although evidence suggests that black ducks prefer productive, nutrient-rich waterbodies, such as beaver ponds, information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge. Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap. In this study, we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario. The use of multi-source data, including a Digital Elevation Model, a Sentinel-2 Multi-Spectral Image, and RadarSat 2 Polarimetric data, enabled us to identify individual beaver ponds on the landscape. Our model correctly identified an average of 83.0% of the known beaver dams and 72.5% of the known beaver ponds based on validation with an independent dataset. This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents. Furthermore, the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife.
Zhang, W., Hu, B., Brown, G., & Meyer, S. (2023). Beaver pond identification from multi-temporal and multi- sourced remote sensing data. Geo-Spatial Information Science. https://doi.org/10.1080/10095020.2023.2183144