This paper develops an algorithm to estimate vegetation canopy gap fraction (GF), taking advantage of the full Terrestrial Laser Scanner (TLS) resolution. After calculating the TLS angular resolution, the algorithm identifies the missing laser hits (gaps) within an angular grid in the azimuthal and zenithal directions. The algorithm was first tested on angular data simulations with random (R), cluster (C) and random and cluster together (RC) gap pattern distributions. Noise introduced in the simulations as a percentage of the resolution accounted for the effect of TLS angular uncertainty. The algorithm performs accurately if angular noise is <6% of the angular resolution. To assess the impact of the change in projection, this study compared these GF estimates from angular grid simulations to their transformation into simulated hemispherical images (SHI). SHI with C patterns perform accurately, but R and RC patterns underestimate GF, especially for GF values below 0.6. The SHI performance to estimate GF was always far below the algorithm developed here with the angular grid simulations. When applied to actual TLS data acquired over individual Quercus ilex L. trees, the algorithm rendered a GF between 0.26 and 0.40. TLS had an angular noise <6%. Converting the angular grid into simulated HI (TLS-SHI) provided a better agreement with actual HI acquired in the same location as the TLS data, since they are in the same projection. The TLS-SHI underestimated GF by an average of 4% compared to HI. HI and TLS-SHI presented 14% and 17% lower values than the GF calculated from the angular grids, respectively. Nevertheless, the results from the simulations indicate that the algorithm based on the angular grid should be closer to the actual GF of the tree canopy.
Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model