Mapping of Taiga Forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

A maximum a posteriori Bayesian classifier is used to perform a supervised classification of multifrequency, polarimetric, airborne, SAR observations of boreal forests from the Bonanza Creek Experimental Forest, near Fairbanks, Alaska, into six categories: 1) white spruce; 2) black spruce; 3) balsam poplar; 4) alder; 5) treeless areas; and 6) open water. Tree classification accuracy is highest (86%) using L- and C-band fully polarimetric combined on a date where the forest just recovered from river flooding. The SAR map compares favorably with a vegetation map obtained from digitized aerial infra-red photos. C-band frequency and HV-polarization are, respectively, the most useful frequency and polarization for mapping tree types using SAR. Combination of multi-date SAR observations does not improve classification accuracy, and SAR data acquired on different dates, under different environmental conditions, yield classification accuracies 16% to 41% lower. Single-frequency, single-polarization, SAR data show limited mapping capability. Multispectral SPOT observations of the same area on a single date yield a classification accuracy of 78%. Combining optical and SAR data is useful for identifying tree species, independent of ground truth verification, using biomass estimates from SAR, at L-band HV-polarization, NDVI from SPOT red and infra-red radiances, and an unsupervised segmentation map of the SAR data.<
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