WKSS Final Report 1996-2001 - Environment and Natural ...

41
West Kitikmeot/Slave Study Final Report
The researchers also asked elders to identify vegetation types at areas where they had
been in the past. The intent is to verify this information at a later date as a test of the
reliability of these "predictions." They suggest that these sites, whose names are
associated with specific resources, can be monitored for change over time.
Vegetation Classification for the West Kitikmeot / Slave Study Region
Organization / Researcher: Northwest Territories Centre for Remote Sensing, Helmut Epp
Length of Project: 4 years (1997-2001)
Personnel Involved: n/a
Total Project Expenditures: $404,975
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0 100 km
Prepared by the Centre for Remote Sensing
Information from Landsat satellite images was analyzed by a computer to make
vegetation maps of the region. The project worked in cooperation with the Habitat of
Dogrib Traditional Territory: Place names as Indicators of Bio-geographical Knowledge
project (see p.39) to produce a joint habitat classification in the Lac de Gras area. This
information is useful for all researchers doing wildlife studies in the West Kitikmeot /
Slave Study area, as well as for identifying important vegetation / habitat areas and
assessing effects of development.
Eight Landsat scenes have been analyzed covering a total area of about 200,000 square
km. This area includes most of the WKSS region except for the areas immediately north
of Great Slave Lake and parts of the eastern and western edges of the SGP. Nineteen
classifications were used: fourteen vegetation units, three unvegetated and two for
water. Some scenes do not contain all 19 types. Ground data was gathered from 300 to
500 sites for each scene to provide information for testing and increasing the accuracy
of the computer’s classification of the satellite imagery. About 150 hours of helicopter
time was spent over the four years of the project in collecting ground data through onground
observations and photos from the air. The accuracy assessment from scene to
scene varied from 51% to 82% correct.
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Eight overlapping satellite images,
covering approximately 200,000
km 2 were analyzed in order to
develop the classification system.
Satellite images are particularly
useful for studies and planning in
large, remote areas such as the
WKSS area.