Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists

Abstract Forests display tremendous structural diversity, shaping carbon cycling, microclimates, and terrestrial habitats. One of the most common tools for forest structure assessments are canopy height models (CHMs): maps of canopy height obtained at high resolution and large scale from airborne laser scanning (ALS). CHMs can be computed in many ways, but little is known about the robustness of different CHM algorithms and how they affect ecological analyses. Here, we used high-quality ALS data from nine sites in Australia, ranging from semi-arid shrublands to 90-m tall Mountain Ash canopies, to comprehensively assess CHM algorithms. This included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure. We found that CHM algorithms varied widely both in their height predictions (differences up to 10 m, or 60% of canopy height) and in their sensitivity to point cloud characteristics (biases of up to ∼5 m or 40% of canopy height). Impacts of point cloud properties on CHM-derived metrics varied, from robust inference for height percentiles, to considerable errors in aboveground biomass estimates (∼50 Mg ha −1 , or 10% of total), and high volatility in metrics that quantify spatial associations in canopies (e.g., gaps or spatial autocorrelation). In some cases, biases exceeded ecological variation across sites by a factor of 2. However, we also found that two CHM algorithms – a variation on a “spikefree” algorithm that adapts to local pulse densities and a simple Delaunay triangulation of first returns – allowed for robust canopy characterization and should thus create a secure foundation for ecological comparisons in space and time. Canopy height models are a widely used tool in ecology, but their derivation is not trivial. Our study provides a best-practice guideline and a sample workflow to create robust CHMs and minimize biases and uncertainty in downstream analyses. In doing so we pave the way for global-scale comparisons of forest structural complexity from airborne laser scanning.