Model-based Integration of Insar and Lidar for Canopy Structure

In this work we present a model-based approach to sensor fusion that produces detailed information on vegetation structure and aboveground biomass for terrestrial ecology studies related to the carbon cycle and biodiversity. We integrate lidar data with polarimetric and interferometric SAR data in a joint optimization framework using a combination of physics-based and empirically derived sensor models. While both radar and LIDAR data have been used separately for deriving vegetation parameters, it is widely accepted that a single data modality cannot produce a comprehensive set of these parameters, but rather a combination of data types that are sensitive to different aspects of vegetation structure are needed [1]. The SAR models to be used in the estimation algorithm described here are derived from explicit physical models of radar scattering from forested ecosystems, while the lidar models are derived using a mix of empirical training data sets and physical waveform-based analyses.