Multimodal Remote Sensing of Complex Forests for Height and Biomass Estimation

Abstract:

Spanning over 30% of the planet’s landmass, the global forests play significant roles in local and global ecosystems as well as planetary systems including the global carbon cycle. One of the fundamental technical challenges of any new space-borne vegetation remote sensing mission is the determination of what sensor(s) to place on-board and what, if any, overlapping modes of operation they will employ as each on-board sensor adds significant cost to the overall mission. In this thesis, the strengths of various remote sensing technologies are explored in the context of measuring forest parameters through the fusion of different remote sensing technologies. Polarimetric radar, LiDAR, and near-IR passive optical sensing platforms are employed in conjunction with physics-based models.  It is shown that this proposed method can achieve high accuracy estimates while using minimal ancillary data in the estimation process. It is further shown that this method can be extended to regions lacking a full-suite of remotely sensed measurements. Transitioning from sensor fusion to the effect of phenomenology on SAR and InSAR, this thesis presents a novel approach to accurately gauge the impact of the wind, a temporal decorrelator, on these two technologies.

Chair: Professor Kamal Sarabandi
Co-Chair: Professor Leland Pierce