Terrestrial laser scanning for 3D forest modeling and reflectance simulation through radiative transfer

Forests cover more than one third of the world’s land surface and play an important role in capturing solar energy into biomass and regulating climate. Active remote sensing sensors called light detection and ranging (LiDAR) describe forest structural attributes by measuring interceptions of emitted laser pulses with the canopy, while passive, spectral sensors provide for the estimation of leaf and canopy chemical properties. This study investigates the potential of integrating these datasets, which is typically achieved through radiative transfer modelling (RTM). The spatial distribution of crown constituents in the 3D space in combination with radiometric vegetation characteristics influences the radiation interception, emission and scattering by canopy elements. In this study, the Monte Carlo Ray Tracing (MCRT) RTM model librat is used with the purpose of simulating radiometric properties of a 3D-modeled forest based on spectral libraries that characterize bark, soil and leaf spectral signatures. Our findings confirm that TLS is a valid technique that provides an independent and reliable estimation of forest attributes compared to other techniques (ALS underestimates tree heights of 0.65 m compared to TLS and field-measured tree height is user biased). Preliminary reflectance simulations through RTM show that spectral information can be simulated from a virtual forest model, which should stimulate the interest of further research. The estimation of reflectance proprieties from 3D vegetation models combined with radiometric properties is promising and offers prospects for fusion of data from both optical and LiDAR sensors.