@article{fdi:010091348,
  title = {{R}obust characterisation of forest structure from airborne laser scanning : a systematic assessment and sample workflow for ecologists},
  author = {{F}ischer, {F}. {J}. and {J}ackson, {T}. and {V}incent, {G}r{\'e}goire and {J}ucker, {T}.},
  editor = {},
  language = {{ENG}},
  abstract = {1. {F}orests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. {A}n important tool for forest structure assessments are canopy height models ({CHM}s): high resolution maps of canopy height obtained using airborne laser scanning ({ALS}). {CHM}s are widely used for monitoring canopy dynamics, mapping forest biomass and calibrating satellite products, but surprisingly little is known about how differences between {CHM} algorithms impact ecological analyses. 2. {H}ere, we used high-quality {ALS} data from nine sites in {A}ustralia, ranging from semi-arid shrublands to 90-m tall {M}ountain {A}sh canopies, to comprehensively assess {CHM} algorithms. {T}his included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure. 3. {W}e 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). {I}mpacts of point cloud properties on {CHM}-derived metrics varied, from robust inference for height percentiles, to considerable errors in above-ground biomass estimates (similar to 50 {M}g ha(-1), or 10% of total) and high volatility in metrics that quantify spatial associations in canopies (e.g. gaps). {H}owever, we also found that two {CHM} algorithms-a variation on a 'spikefree' algorithm that adapts to local pulse densities and a simple {D}elaunay triangulation of first returns-allowed for robust canopy characterisation and should thus create a secure foundation for ecological comparisons in space and time. 4. {W}e show that {CHM} choice has a strong impact on forest structural characterisation that has previously been largely overlooked. {T}o address this, we provide a sample workflow to create robust {CHM}s and best-practice guidelines to minimise biases and uncertainty in downstream analyses. {I}n doing so, our study paves the way for more rigorous large-scale assessments of forest structure and dynamics from airborne laser scanning.},
  keywords = {airborne laser scanning ; canopy gaps ; canopy height model ; forest ; structure ; lidar ; pitfree ; spikefree ; structural complexity},
  booktitle = {},
  journal = {{M}ethods in {E}cology and {E}volution},
  volume = {15},
  numero = {},
  pages = {1873--1888},
  ISSN = {2041-210{X}},
  year = {2024},
  DOI = {10.1111/2041-210x.14416},
  URL = {https://www.documentation.ird.fr/hor/fdi:010091348},
}