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The application of spectral unmixing and supervised classification remote sensing techniques to Landsat 7 data for detecting arboreal lichen abundance Norquay, Alan

Abstract

In British Columbia, mountain caribou (Rangifer tarandus caribou) rely on arboreal lichen as a main source of food during the late winter months. The animals spend a majority of the year at these higher elevations where old growth forests provide a suitable microclimate for the lichen genera Bryoria and, to a lesser extent, Alectoria. Caribou migrate there from lower areas when the snowpack solidifies sufficiently to allow efficient travel, possible by their unique hooves that spread to distribute weight. Forestry operations also have an interest in these old growth stands, and harvesting operations have begun to threaten the continued existence of mountain caribou in British Columbia. Efforts to determine caribou habitat, and the extent of that habitat, have been underway for many years. However, there has been an increase in the amount and intensity of research during the past six years, due largely to higher level planning processes that have identified the animals as a priority for inventory research programs. This research was focused on identifying stands of trees that contain arboreal lichen by using remotely - sensed data. While it is known that older stands are required for lichen growth, not all older stands have produced useful quantities of lichen. Since stands containing heavy loadings of arboreal lichen appear different to the naked eye than those with lesser loadings, it is reasonable to assume these differences can be measured with remote sensing techniques. The purpose of this work is to identify these differences using Landsat 7 data. I used a 30-metre pixel resolution Landsat scene collected on August 22 1999. Spectral unmixing, supervised classification and correlation analysis techniques where conducted, but no method was able to distinguish stands by the amount of lichen they contained. The reason this attempt did not succeed is probably due to the pixel size of the data (30 metres) versus the effect small clumps of lichen have on the reflectance recorded for those pixels. If data from a sensor with higher spatial resolution (e.g. IKONOS 4 meter data) where used in future work, the results may be more successful.

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