Michael J Mahoney , Lucas K Johnson, Eddie Bevilacqua, Colin M Beier

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of
forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution
and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is,
excluding returns from LiDAR point clouds based on simple height thresholds – is a common
practice meant to improve the `signal’ content of LiDAR returns by preventing ground returns from
masking useful information about tree size and condition contained within canopy returns.
However, ground returns may be helpful for making accurate aboveground biomass predictions
in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover
types. In this paper, we applied several ground noise filtering thresholds while mapping forest AGB
across New York State (USA), a heterogenous landscape composed of both contiguously forested
and highly fragmented areas with mixed land cover types. We fit random forest models to
predictor sets derived from each filtering intensity threshold and compared model accuracies,
paying attention to how changes in accuracy correlated with landscape structure. We observed
that removing ground noise via any height threshold systematically biases many of the LiDAR-
derived variables used in AGB modeling, with mean correlation (Spearman’s ρ) between variables
increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest
AGB with lower accuracy than models trained using predictors derived from unfiltered point
clouds, with RMSE increasing by up to 2.2 Mg ha-1 statewide. Although we only modeled AGB
for forest cover types, models fit to predictors derived from filtered point clouds performed worse
as landscape heterogeneity (as measured by patch density and edge density) increased, suggest-
ing ground returns are particularly useful when modeling edge forests. Our results suggest that
ground filtering should be a carefully considered decision when mapping forest AGB, particularly
when mapping heterogeneous and highly fragmented landscapes, as ground returns are more
likely to represent useful `signal’ than extraneous `noise’ in these cases.