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 the focus of increased mapping activities, which aim to provide better knowledge to assist in making informed decisions for sustainable minerals and energy development, and land-use management. One of the required maps deals with surficial materials. This thesis studies the potential of combining RADARSAT-2 SAR images with Landsat-8 optical data, DEM and slope data to map surficial materials in the region around Wager Bay, Nunavut. Two study areas were selected, one on the northern side of the bay (NTS map areas 046E, K, L, M, 056H, I, J) and another one on the southern side (NTS map sheets 046D, E, 055P, 056A, H). The images were classified using a non-parametric classifier Random Forests. The results show that including RADARSAT-2 images in the classification process increases the overall classification accuracy from 92.8% to 98.1% in the north region and 96.7% to 99.3% in the south region. The classified maps were compared to GPS data sets to determine the mapping accuracy, and there was a similar ncrease in accuracy when RADARSAT-2 data was added. The limitations of the study are also presented, as well as potential improvements. Key words: surficial materials, remote sensing, SAR, RADARSAT-2, Landsat, optical, Arctic mapping.