Object-based Wetland Characterization Using Radarsat-2 Quad-Polarimetric SAR Data, Landsat-8 OLI Imagery, and Airborne Lidar-Derived Geomorphometric Variables

The goal of this research was to classify four wetland types in the Hudson Bay Lowlands in northern Canada using Radarsat-2 quad-polarization and Landsat-8 satellite sensor data and geomorphometric variables extracted from an airborne lidar digital elevation model. Segmentation was followed by object-based image classification implemented with a Radom Forest machine learning algorithm. The classification accuracy was determined to be approximately 91 percent. This is a significant improvement over the accuracy that was obtained using the Radarsat-2 (80 percent) or Landsat-8 sensor data alone (84 percent). Variable importance (VI) was measured for geomorphometric measures related to the gravity-, wind- and solar-fields, which were developed to explain eco-hydrological differences and increase the separability of wetland classes. Further research will consider additional geomorphometric and spectral response variables that are useful in more detailed boreal wetland classifications and analysis of wetland characteristics over time.