Characterizing the tundra taiga interface using Radarsat-2 (Mealy Mountains, Labrador)

Ward, Heather (2012) Characterizing the tundra taiga interface using Radarsat-2 (Mealy Mountains, Labrador). Masters thesis, Memorial University of Newfoundland.

[English] PDF (Migrated (PDF/A Conversion) from original format: (application/pdf)) - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (38MB) [English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. (Original Version)

Abstract

The transition zone between the boreal forest and Arctic tundra, also known as the tundra taiga interface (TTl), is a unique and sensitive ecosystem. A convenient way to monitor and understand TTl changes is through the interpretation and analysis of earth observation satellite images, or remote sensing. Ecosystem monitoring provides useful information about vegetation distribution and global climate. Currently, vegetation is monitored at both global and regional scales through the use of multispectral, light detection and ranging and synthetic aperture radar imagery. Each remote sensing technology offers unique spatial, spectral and radiometric resolution sets. This thesis investigates the use of synthetic aperture radar images from the Canadian Space Agency's RADARSAT-2 satellite to derive an image product discriminating different types of vegetation cover within the TTl region of Labrador. A selection of texture measures was applied to a dataset consisting of six RADARSAT-1 and fourteen RADARSAT-2 images. Statistical parameters were utilized to measure how strongly the radar derived vegetation product correlated with the well established normalized difference vegetation index (NDVI). The analysis was guided and validated by field data describing forest and non-forest land cover types. The results indicate that a mean texture measure with a window size relating to a ground area of 330x330 m (fine mode} and 450x450 m (standard mode) applied to an R-2 HV-polarized image is able to inform on the location of the TTl and also complements the vegetation cover found in NDVl images.

Actions (login required)

View Item