Canadian Forest Service Publications
Automated mapping of stream features with high-resolution multi-spectral imagery: an example of the capabilities. 2005. Leckie, D.G.; Cloney, E.E.; Jay, C.; Paradine, D. Photogrammetric Engineering and Remote Sensing 71(2): 145-155.
Issued by: Pacific Forestry Centre
Catalog ID: 25174
The capabilities of high resolution multispectral remote sensing imagery to map important stream planform features is investigated. Eighty centimeter resolution casi imagery was acquired in eight spectral bands over Tofino Creek on the west coast of Vancouver Island, British Columbia. A spectral angle mapping algorithm was used to classify stream features such as water depth, substrate material and woody debris. Fine classes were attempted in terms of stream bed material and water depth, but results were not reliable. A classification of deep water, moderate depth water, shallow water, sand, gravel and cobble, and woody debris in sunlit conditions, however, proved accurate (80% on average). Individual logs and piles of woody debris were consistently detected. Silty substrate in a tidal flats zone was also classified, but results indicated that different substrate material beneath the water may require separate classes and can result in problematic water depth classification. Patterns of general classes were reasonably represented within shadowed areas cast by isolated trees or groups of trees. However, problems do arise within lengthy shadowed stretches. Some boundaries of stream features with surrounding forest and between some zones of sand, gravel and cobble were also misclassified. High resolution multispectral imagery in four or more bands combined with good geometric correction, image mosaicking and appropriate automatic classification techniques offer a viable tool for stream planform mapping to meet a variety of important issues and applications. In the future, a powerful suite of stream information may be compiled from multispectral classification combined with high resolution thermal and lidar data.