Fusion of AIRSAR and TM data for parameter classification and estimation in dense and hilly forests

Radar and optical remote sensing data are used to develop a parameter classification algorithm based on nonlinear estimation theory. The study site is the H. J. Andrews forest in Oregon, USA, which has significant topography and several old-growth conifer stands with biomass values sometimes exceeding 1000 tons/hectare. Polarimetric C-band, C-band, and P-band AIRSAR data, interferometric C-band TOPSAR data, and six channels of Landsat TM data are used in a regression analysis that relates them to several measurements of one or more forest parameters. Parametric expressions are derived and used to estimate the same parameter(s) at other locations from the combination of AIRSAR and TM data. Statistical characteristics of the parameter estimates are derived and used to define parameter classes. These classes are the basis for future analytic parameter estimation algorithms