New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species

Leaf angle distribution (LAD) is an important property which influences the spectral reflectance and radiation transmission properties of vegetation canopies, and hence interception, absorption and photosynthesis. It is a fundamental parameter of radiative transfer models of vegetation at all scales. Yet, the difficulty in measuring LAD causes it to be also one of the most poorly characterized parameters, and is typically either assumed to be random, or to follow one of a very small number of parametric 'archetype' functions. Terrestrial LiDAR scanning (TLS) is increasingly being used to measure canopy structure, but LAD estimation from TLS has been limited thus far. We introduce a fast and simple method for detection of LAD information from terrestrial LiDAR scanning (TLS) point clouds. Here, it is shown that LAD information can be obtained by simply accumulating all valid planes fitted to points in a leaf point cloud. As points alone do not have any normal vector, subsets of points around each point are used to calculate the normal vectors. Importantly, for the first time we demonstrate the effect of distance on the reliable LAD information retrieval with TLS data. We test, validate, and compare the MS-based method with established leveled digital photography (LDP) approach. We do this using data from both real trees covering the full range of existing leaf angle distribution type, but also from 3D Monte Carlo ray tracing. Crucially, this latter approach allows us to simulate both images and TLS point clouds from the same trees, for which the LAD is known explicitly a priori. This avoids the difficulty of assessing LAD manually, which being a difficult and potentially error-prone process, is an additional source of error in assessing the accuracy of LAD extraction methods from TLS or photography. We show that compared to the LDP measurement technique, MS is not limited by leaf curvature, and depending on the distance of the TLS from the target, is potentially capable of retrieving leaf angle information from more complex, non-flat leaf surfaces. We demonstrate the possible limitation of TLS measurement techniques for the retrieval of LAD information for more distant canopies, or for taller trees (h > 20 m).