Quantifying vegetation indices using terrestrial laser scanning: methodological complexities and ecological insights from a Mediterranean forest

Abstract

Abstract. Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as digital hemispherical photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial laser scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard. Using TLS data collected in 33 plots containing 2472 trees of 5 species in Mediterranean forests, we compare three TLS methods (lidar pulse, 2D intensity image and voxel-based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate the ratio of wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density. We find the lidar pulse method agrees most closely with DHP, but it is limited to single-scan data, so it cannot determine individual tree properties, including α. The voxel-based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the voxel-based method, we show that species explain some variation in α; however, height and plot density were better predictors. Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function as well as the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large-scale monitoring of PAI and LAI.