|The effects of topography on the radiometric properties of multispectral scanner (MSS) data are examined in the context of the remote sensing of forests in mountainous regions. The two test areas
considered for this study are located in the coastal mountains of British Colombia, one at the Anderson River near Boston Bar and the other at Gun Lake near Bralorne. The predominant forest type at the lodgepole pine and ponderosa pine. Both regions
have rugged topography, with elevations ranging from 330 to 1100 metres above sea level at Anderson River and from 750 to 1300 metres above sea level at Gun Lake. |
Lambertian and non-Lambertian illumination corrections are formulated, taking into
account atmospheric affects as well as topographic variations. Terrain slope and aspect values are determined from a digital elevation model and atmospheric parameters are obtained from a model atmosphere computation for the irradiance and
atmospheric path radiance are neglected, one is left with a cosine lumination transformations of images of horizontal terrain. However, this extension of the simple cosine correction to the case of sloped terrain is shown to be inadequate, especially
for larger angles of incidence.
Attempts are also made to remove the effect of topography by means of semi-empirical functions primarily based on cosines of the incident illumination angles. In this approach, correlations and linear regressions
between topographic parameters and MSS radiance values are investigated for the different forest types under consideration at each site.
The analysis encompasses LANDSAT MSS and 11-channel airborne MSS data at a resolution of 50 metres.
Slope-aspect correction algorithms for both of these types of data are implemented in software on the image analysis system at the Canada Centre for Remote Sensing. Geometric rectification is also a prerequisite in order to relate image geometry to
the map co-ordinates on which the digital terrain data are based. A special technique involving flight line modelling is used to accomplish this in the case of aircraft data since prior knowledge of the terrain elevation is needed for each image
pixel in order to establish the correct transformation.
Feature selection based on divergence criteria indicates that terrain elevation data compare favorably with the MSS data in terms of ability to separate forest classes. However, maximum
likelihood classification results for MSS data, corrected for slope-aspect effects using a variety of functions, show little or no significant improvement over results