Improving Forest Attribute Estimation of Airborne Lidar Based on Profile Assimilation from Terrestrial Lidar

Abstract:

The development of high-resolution Terrestrial Laser Scanning (TLS) system can expose sub-canopy details with flexible scanning angles. This advantage of preciseness makes TLS an ideal source to integrate to Airborne Laser Scanning (ALS), with regard to the potential for removing ALS’s penetration bias. The popular treatment of the integration is simply spatial co-registration or TLS-derived inventory statistics, without further concerning the rich geometrical information from TLS. This poster proposes a profile assimilation approach for ALS and TLS integration, in order to improve the plot-level estimation of tree height and biomass. The overlapped ALS and TLS data were first co-registered into compound point clouds and the canopy structure were reconstructed from the compound. The canopy profile of ALS was then calibrated from the reconstructed canopy profile using a Kalman filter. The calibration was applied to the rest ALS canopy profile, from which the new estimation of tree height and biomass can be derived. Our study site is located in Vivian, Toronto. The area was flown with an Optech Titan operating at 532, 1064 and 1550 nm wavelengths one week before the TLS data collection in July, 2015. The trees scans by TLS were halved to calibrate and to validate our proposed integration approach. Additional validation was also conducted via in-situ inventory.