Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR

There is a public perception that large high severity wildfires decrease
biodiversity and increase fire hazard by homogenising vegetation
composition and increasing the cover of mid-story vegetation. But a
growing literature suggests that vegetation responses are nuanced. LiDAR
technology provides a promising remote sensing tool to test hypotheses
about post-fire vegetation regrowth because vegetation cover can be
quantified within different height strata at fine-scales over large areas.
We assess the usefulness of airborne LiDAR data for measuring post-fire
mid-story vegetation regrowth over a range of spatial resolutions (10x10m,
30x30m, 50x50m, 100x100m cell size) and investigate the effect of fire
severity on regrowth amount and spatial pattern following a mixed severity
wildfire in Warrumbungle National Park, Australia. We predicted that
recovery would be more vigorous in areas of high fire severity, because
park managers observed dense post-fire regrowth in these areas. Moderate
to strong positive associations were observed between LiDAR and field
surveys of mid-story vegetation cover between 0.5–3m. Thus our LiDAR
survey was an apt representation of on-ground vegetation cover.
LiDAR-derived mid-story vegetation cover was 22–40% higher in areas of low
and moderate than high fire severity. Linear mixed-effects models showed
that fire severity was among the strongest biophysical predictors of
mid-story vegetation cover irrespective of spatial resolution. However
much of the variance associated with these models was unexplained,
presumably because soil seedbanks varied at finer-scales than our LiDAR
maps. Dense patches of mid-story vegetation regrowth were small (median
size 0.01ha) and evenly distributed between areas of low, moderate and
high fire severity, demonstrating that high severity fires do not
homogenise vegetation cover. Our results are relevant for ecosystem
conservation and fire management because they: indicate that native
vegetation are responsive and resilient to high severity fire, and show
the usefulness of remote sensing tools such as LiDAR to monitor post-fire
vegetation recovery over large area in situ.