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Open Access 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.

Document Type: Research Article

Publication date: January 1, 2017

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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