| Summary: | Water temperature is a significant environmental indicator in lakes, rivers and streams. It
is important to many resource management and environmental fields: forestry practices in
adjacent lands modify runoff patterns which in-turn affects water temperatures;
accelerated glacial melting caused by climate change increases the flux of cold water; and
spawning salmon stocks are lethally affected by thermal shock from small (<5°C)
temperatures changes. Quesnel Lake in the interior of British Columbia, Canada, is an
important lake in all of these respects, and is the subject of this study due to recent mass
mortality of spawning salmon believed to be caused by extreme temperature fluctuations.
The conventional means of tracking temperature are in-situ temperature loggers installed
in a small number of critical locations. This approach is costly and prone to data loss
however, and does not provide a spatially comprehensive measure of lake temperature
patterns. Coupled with satellite Thermal Infrared (TIR) imagery, the two measures can
provide a contiguous dataset in space, depth, and time. Achieving an adequate balance
between temporal and spatial coverage in TIR imagery can be difficult. Sensors with a
sufficiently short revisit time often do not provide adequate spatial resolution to resolve
narrow reaches of lakes and rivers. The MODIS sensor provides very high temporal
coverage, up to four visits per day, but at a resolution of 1km, which is insufficient for
Quesnel Lake's narrow reaches. This research presented in this thesis attempts to reconcile
this deficiency in available TIR products, to produce an adequately accurate and fine
spatial and temporal resolution water surface temperature dataset. By using a priori
vectorized water boundary data, we estimate the sub-pixel surface temperature to within
1°C using a gradient descent solution to the mixed pixel at-sensor radiance equation.
Ground-leaving radiance is estimated from standard MODIS temperature and emissivity
data products for pure pixels and a simple regression technique to estimate atmospheric
effects. The resulting algorithm is simple, effective, and useful for scientists without
extensive remote sensing expertise, essentially unlocking a largely untapped resource for
limnological and hydrological studies. === Applied Science, Faculty of === Civil Engineering, Department of === Graduate
|
|---|
