Open AccessMapping Canadian boreal forest vegetation using pigment and water absorption features derived from the AVIRIS sensor
Author(s) -
Fuentes David A.,
Gamon John A.,
Qiu Honglie,
Sims Daniel A.,
Roberts Dar A.
Publication year - 2001
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2001jd900110
Subject(s) - vegetation (pathology) , environmental science , taiga , deciduous , remote sensing , vegetation classification , boreal , imaging spectrometer , enhanced vegetation index , normalized difference vegetation index , geology , leaf area index , spectrometer , forestry , ecology , geography , vegetation index , physics , biology , medicine , paleontology , pathology , quantum mechanics
Using imagery of the Canadian boreal forest, we explored the ability of the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) to map vegetation type by taking advantage of pigment and water absorption features. Two techniques were exploited. 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 (SERM), Forestry Branch Inventory Unit (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 and 73.3%, when compared to the same vegetation map. Both of these complementary techniques were able to differentiate important vegetation types such as fen, deciduous trees, and wet and dry conifers at accuracies superior to other well‐established classification methods for this area. This improved vegetation classification can now be used to evaluate regional surface‐atmosphere fluxes of carbon and water vapor.