[en] Canopy gaps are small-scale openings in forest canopies which offer suitable
micro-climatic conditions for tree regeneration. Field mapping of gaps is complex and
time-consuming. Several studies have used Canopy Height Models (CHM) derived from
airborne laser scanning (ALS) to delineate gaps but limited accuracy assessment has
been carried out, especially regarding the gap geometry. In this study, we investigate
three mapping methods based on raster layers produced from ALS leaf-off and leaf-on
datasets: thresholding, per-pixel and per-object supervised classifications with Random
Forest. In addition to the CHM, other metrics related to the canopy porosity are tested.
The gap detection is good, with a global accuracy up to 82% and consumer’s accuracy often
exceeding 90%. The Geometric Accuracy (GAc) was analyzed with the gap area, main
orientation, gap shape-complexity index and a quantitative assessment index of the matching
with reference gaps polygons. The GAc assessment shows difficulties in identifying a
method which properly delineates gaps. The performance of CHM-based thresholding was
exceeded by that of other methods, especially thresholding of canopy porosity rasters and the
per-pixel supervised classification. Beyond assessing the methods performance, we argue the
critical need for future ALS-based gap studies to consider the geometric accuracy of results.