MAPPING VEGETATION COVER TYPES IN THE CANADIAN BOREAL FOREST USING PIGMENT AND WATER ABSORPTION FEATURES DERIVED FROM AVIRIS

Using AVIRIS imagery of the Canadian boreal forest, we explored new methods of mapping vegetation type using pigment and water absorption features.  Two techniques were developed.  In the first classification routine, laboratory acquired leaf spectra representing different “pigment classes” were used in a spectral unmixing procedure to map the relative abundance of pigments in the landscape.  The resulting images were then used in a maximum likelihood routine to map the distribution of vegetation cover types.  Accuracies for this method range between 66.6-80.1%, when compared to a vegetation map prepared by the Saskatchewan Environment and Resource Management, Forestry Branch-Inventory Unit (SERM-FBIU).  In the second approach, seven indices of vegetation structure and physiological function were calculated from AVIRIS.  Cover types were then derived using the index images as inputs in a maximum likelihood classification.  Levels of accuracy for this method were between 56.6-73.3%, when compared to the vegetation map.  Both of these techniques were able to differentiate important vegetation types, e.g. fen and wet conifers, at accuracies superior to other established classification methods for this area.  Furthermore, both methods worked well across seasons.  These results suggest that pigment and water content expressions can be applied to detect functionally significant cover types in the landscape.  Further investigation is focusing on developing more quantitative derivations of pigment and water content using laboratory acquired leaf and pigment spectra and statistical data reduction techniques.  The vegetation classifications derived from these methods can also be used for the purpose of modeling carbon dioxide and water vapor fluxes