Estimation of crown and stem water content and biomass of boreal forest using polarimetric SAR imagery

Characterization of boreal forests in ecosystem models requires temporal and spatial distributions of water content and biomass over local and regional scales. The authors report on the use of a semi-empirical algorithm for deriving these parameters from polarimetric synthetic aperture radar (SAR) measurements. The algorithm is based on a two layer radar backscatter model that stratifies the forest canopy into crown and stem layers and separates the structural and biometric attributes of forest stands. The structural parameters are estimated by training the model with SAR image data over dominant coniferous and deciduous stands in the boreal forest such as jack pine, black spruce, and aspen. The algorithm is then applied on AIRSAR images collected during the Boreal Ecosystem Atmospheric Study (BOREAS) over the boreal forest of Canada. The results are verified using biometry measurements during BOREAS-intensive field campaigns. Field data relating the water content of tree components to dry biomass are used to modify the coefficients of the algorithm for crown and stem biomass. The algorithm was then applied over the entire image generating biomass maps. A set of 18 test sites within the imaged area was used to assess the accuracy of the biomass maps. The accuracy of biomass estimation is also investigated by choosing different combinations of polarization and frequency channels of the AIRSAR system. It is shown that polarimetric data from P-band and L-band channels provide similar accuracy for estimating the above-ground biomass for boreal forest types. In general, the use of P-band channels can provide better estimates of stem biomass, while L-band channels can estimate the crown biomass more accurately.