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Mapping of forest types in Alaskan boreal forests using SAR imagery

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4 Author(s)
E. J. M. Rignot ; Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA ; C. L. Williams ; J. Way ; L. A. Viereck

Mapping of forest types in the Tanana river flood-plain, interior Alaska, is performed using a maximum-a-posteriori Bayesian classifier applied on SAR data acquired by the NASA/JPL three-frequency polarimetric AIRSAR system on several dates. Five vegetation types are separated, dominated by 1) white spruce, 2) balsam poplar, 3) black spruce, 4) alder/willow shrubs, and 5) bog/fen/nonforest vegetation. Open water of rivers and lakes is also separated. Accuracy of forest classification is investigated as a function of frequency and polarization of the radar, as well as the forest seasonal state, which includes winter/frozen, winter/thawed, spring/flooded, spring/unflooded, and summer/dry conditions. Classifications indicate that C-band is a more useful frequency for separating forest types than L- or P-bands, and HV polarization is the most useful polarization at all frequencies. The highest classification accuracy, with 90 percent of forest pixels classified correctly, is obtained by combining L-band HV and C-band HV data acquired in spring as seasonal river flooding recedes and before deciduous tree species have leaves. In 17 forest stands for which actual percentages of each tree species are known, the same radar data are capable of predicting tree species composition with less than 10 percent error. From the authors' classification, they predict that current and future spaceborne SAR systems will have limited mapping capabilities when used alone. Yet, RADARSAT combined with J-ERS-1 and ERS-1 could resolve forest types with 80 percent accuracy, separate nonforest areas resulting from commercial logging or forest wildfire, and map river edges

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:32 ,  Issue: 5 )