Surficial Materials Mapping and Surface Lineaments Analysis in the Umiujalik Lake area, Nunavut, Using RADARSAT-2 Polarimetric SAR, LANDSAT-7, and DEM Images.

摘要

This thesis is focused on the utilization of RADARSAT-2 polarimetric SAR data for mapping two surficial aspects of the Umiujalik Lake area, Nunavut, Canada: i) materials, such as bedrock, boulders, organic material, sand and gravel, thick and thin till; and ii) lineaments. To achieve these tasks, RADARSAT-2 polarimetric SAR images with three west-looking, increasing incidence angles (FQ1, FQ12, and FQ20, respectively) were used alone and in combination with LANDSAT-7 ETM+ and Digital Elevation Model (DEM) image data.;Three main conclusions were reached: i) high incidence angle greatly decreases classification accuracy for the HH polarized image when used alone, but incidence angle has little effect when the HV polarization is added; ii) combining images with three incidence angles (FQ1, FQ12, and FQ20) gives higher accuracy with the maximum likelihood classifier; and iii) the medium incidence angle image (FQ12) produces the best classification accuracy using polarimetric classifiers.;In the second part of the study, surface lineaments were mapped using RADARSAT-2 SAR single-polarized images, RGB HH, HV, VV composites, polarimetric total power images, and LANDSAT -7 ETM+ principal component images. Polarization effect analysis showed that regardless of beam mode, more lineaments were identified on the HH image than on the HV image, and the maximum number of lineaments was identified on the multi-polarized RGB composite. Incidence angle effects results showed that regardless of polarization modes, the FQ12 image yielded more lineaments than the FQ1 or FQ20 images. The majority of lineaments are oriented in NW and NNW directions, which correspond to the ice flow direction during the last glaciation.;The surficial materials mapping study tested: i) the effects of incidence angles on mapping accuracy; and ii) non-polarimetric and polarimetric classifiers. For non-polarimetric analysis, a Maximum Likelihood Classification (MLC) algorithm was applied to different combinations of RADARSAT-2, LANDSAT-7 ETM+, and DEM images, achieving a maximum overall classification accuracy of 85%. Polarimetric analyses first included computation of polarimetric signatures to understand the scattering mechanisms of the considered surficial materials, i.e., surface, volume, and multiple scatterings. It also tested three polarimetric classifiers: supervised Wishart (overall accuracy of 48.7% from FQ12 image), and unsupervised Freeman-Wishart, and Wishart-H/alpha/A.