remote sensing Article Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands Lori White 1,*, Koreen Millard 2,3, Sarah Banks 1, Murray Richardson 2, Jon Pasher 1 and Jason Duffe 1 1 Environment and Climate Change Canada, National Wildlife Research Centre, 1125 Colonel by Drive, Ottawa, ON K1S 5B6, Canada;
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[email protected] (J.P.);
[email protected] (J.D.) 2 Department of Geography and Environmental Studies, Carleton University, 1125 colonel By Drive, Ottawa, ON K1S 5B6, Canada;
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[email protected] (M.R.) 3 Defence Research and Development Canada (DRDC), Ottawa Research Center, 3701 Carling Ave., Ottawa, ON K2K 2Y7, Canada * Correspondence:
[email protected]; Tel.: +1-613-990-9952 Academic Editors: Deepak R. Mishra and Prasad S. Thenkabail Received: 7 April 2017; Accepted: 4 June 2017; Published: 7 June 2017 Abstract: For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal of this research was to prepare for the launch of the upcoming RCM by evaluating three simulated RCM polarizations for mapping landcover within peatlands. We examined (1) if a lower RCM noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important for classification, and (3) whether classification accuracy is affected by the use of simulated RCM data in place of the fully polarimetric RADARSAT-2.