A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR

Forest canopy structure has long been known to be a major driver of the processes regulating the exchange of CO2 and water vapour between terrestrial ecosystems and the atmosphere. It is also an important driver of terrestrial vegetation dynamics. Information about fine-scale ecosystem structure is needed to better understand and predict how terrestrial ecosystems respond to and affect environmental change. LiDAR remote sensing from ground-based instruments is a promising technology for providing such information, and physically-based models are ideally suited to process the data and derive reliable products. While complex ray tracing algorithms have been developed to help in the interpretation of LiDAR data, none of these tools are currently widely available. In this paper we present the VoxLAD model; a parametric model using computational geometry that allows to compute estimates of leaf area density at the voxel scale on the basis of terrestrial LiDAR data. This modelling framework removes the need to compute the exact point of entry and exit into and out of the voxels for all individual laser pulses, and thus allows for easier usage. The model requires that each point in the LiDAR point cloud should be classified as wood, foliage, or noise. Here we provide the algorithmic details of the model, and demonstrate that the output of the model closely fits the output of a model using more complex ray