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Remote Sens. 2017, 9(12), 1206;

Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic

Environment Canada, National Wildlife Research Centre, 1125 Colonel by Drive, Ottawa, ON K1A 0H3, Canada
Defense Research and Development Canada, Ottawa, ON K1A 0K2, Canada
Institute Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, Germany
Canada Centre for Mapping and Earth Observation, 560 Rochester St, Ottawa, ON K1S 5K2, Canada
Author to whom correspondence should be addressed.
Received: 5 October 2017 / Revised: 18 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
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Detailed information on the land cover types present and the horizontal position of the land–water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user’s and producer’s accuracies were 5–24% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71–77%) than models based on quad pol RADARSAT-2 and DEM data (80%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89–91%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic. View Full-Text
Keywords: RADARSAT-2; RADARSAT Constellation Mission; Random Forests; Arctic; shorelines RADARSAT-2; RADARSAT Constellation Mission; Random Forests; Arctic; shorelines

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Banks, S.; Millard, K.; Behnamian, A.; White, L.; Ullmann, T.; Charbonneau, F.; Chen, Z.; Wang, H.; Pasher, J.; Duffe, J. Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic. Remote Sens. 2017, 9, 1206.

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