Mapping Surficial Materials in Nunavut using RADARSAT-2 C-HH and C-HV, Landsat-8 OLI, DEM and Slope Data

Abstract

The Canadian Arctic is currently subject to increased mapping activities for providing better knowledge to assist in making informed decisions for sustainable development. Surficial material maps are one of the required maps. For an area located in Nunavut, we produced a map with 21 surficial material classes by applying a non-parametric classifier, Random Forests (RF), to a combination of RADARSAT-2 C-HH and C-HV with Landsat-8 OLI, digital elevation model, and slope data. We also tested the All-polygon and Sub-polygon scripts of RF. Validation accuracies were determined by comparing the resulting maps to more than 1000 field sites. By adding RADARSAT-2 dual-polarized images, the classification overall accuracy increases from 90.6% to 96.4% with the Sub-polygon script and from 92.8% to 98.1% with the All-polygon script. The overall validation accuracy increases from 76.3% to 88.9% with the Sub-polygon script and from 76.4% to 93.3% with the All-polygon script. With the All-polygon script, the validation accuracies are above 85% for all classes, except the user’s accuracy of gravelly till (76.7%) and the producer’s accuracy of sand and gravel with vegetation (70%), both classes being confused with thin till over bedrock.