An Object-Based Assessment of Multi-Wavelength SAR, Optical Imagery and Topographical Datasets for Operational Wetland Mapping in Boreal Yukon, Canada

Abstract

The authors evaluated multiple remotely sensed datasets for their contributions to operational wetland mapping in a subarctic, boreal cordillera study site in Yukon, Canada. They assessed Sentinel-2 optical imagery, Sentinel-1 C-band and ALOS PALSAR L-band synthetic aperture radar (SAR) imagery, and topographical data from the territorial digital elevation model (DEM) using an object-based image analysis (OBIA) approach. Three machine-learning algorithms were tested, namely random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN), using various data combinations (11 model scenarios). RF produced the most accurate results when incorporating all optical, SAR and DEM data (86.5%, kappa 0.84), with open water (100% producer accuracy, PA), marsh (75% PA) and swamps (85.7% PA) being detected most accurately. When assessed in isolation, Sentinel-2 optical data consistently generated more accurate classifications than either SAR platform or DEM data. RF variable importance metrics provided further explanation to these results, indicating the 8 most powerful variables to be optical. Variable reduction tests also produced comparable accuracies, indicating that an optimal RF model can be built based on predictive power rankings. The results can be used to inform resource managers on the efficacy of current datasets and their applications to wetland mapping in northern, subarctic environments.