Mapping an inland wetland complex using hyperspectral imagery

Abstract

The goal is to determine the extent to which heterogeneous inland wetland vegetation communities and their dominant species, as well as adjacent upland vegetation types, can be mapped using 4‐m hyperspectral Compact Airborne Spectrographic Imager (CASI) data. Two classification algorithms, the maximum‐likelihood classifier (MLC) and the spectral angle mapper (SAM), are applied to CASI data acquired over an inland wetland complex located in southern Ontario, Canada. Application of the MLC algorithm to all bands of the CASI data produced better classification results than use of the SAM. Using the MLC, 10 classes were identified with an overall accuracy of 92%. This approach permitted differentiation between areas of shrub‐dominated vegetation communities, floating aquatic communities, emergent aquatics and shallow open water. In the SAM classification, 11 image‐derived spectral endmembers were generated. Wetland classes identified were shrub‐dominated wetlands, floating aquatic vegetation communities, shallow open water and moderately turbid shallow open water. Upland vegetation types were accurately mapped with both algorithms. Reasons why the SAM did not perform as well as the MLC in this complex environment are suggested. It is concluded that high‐resolution hyperspectral data can provide information needed by wetland managers about inland wetland plant communities and their dominant species.

Acknowledgements

Financial support for this study was provided by the Centre for Research in Earth and Space Technology (CRESTech), Project No. LR98LEA22, and a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant awarded to P. Howarth. Gil Henderson, a farmer who is a great supporter of environmental research and education, very kindly permitted us access to his Onondaga Farms property. The assistance of Larry Lamb, University of Waterloo, with species identification was greatly appreciated. The author would like to thank Loris Gasparotto, Brock University, for producing figure 1 for this paper. The authors also acknowledge the anonymous reviewers for their valuable comments.