Understory trees in airborne LiDAR data - Selective mapping due to transmission losses and echo-triggering mechanisms

Understory trees in multilayered stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement, and biodiversity management. Cost-efficient inventory methods are needed and airborne LiDAR is a promising addition to fieldwork. The overstory, however, obstructs wall-to-wall sampling of the understory using LiDAR, because transmission losses affect echo-triggering probabilities and intensity (peak amplitude) observations. We examined the potential of LiDAR in mapping of understory trees in pine (Pinus sylvestris L) stands (62 degrees N, 24 degrees E), using careful experimentation. We formulated a conceptual model for the transmission losses and illustrated that loss normalization is highly ill-posed, especially for vegetation. The losses skew the population of targets that produce a subsequent echo. Losses up to 10-15% can occur even if an overstory echo is not triggered. In LiDAR sensors, quantized intensity values start from binary zero, but actually should include an offset, the noise level. We estimated these empirically. Constraining to low-loss pulses and ground data, we estimated parameters for compensation models that were based on the radar equation and employed the geometry of the pulse, as well as the overstory intensity observations as predictors. Intensity variation of second-return data was reduced, but, the intensity data were deemed of low value in species discrimination. Our results highlight differences between sensors in near-ground echo-triggering and height data. Area-based LiDAR height metrics from the understory had reasonable correlation with the density and mean height of the understory trees, whereas tree species seemed out of reach even if the transmission losses were compensated for. We conclude that transmission losses are a general impediment for radiometric analysis of multi-echo pulses in discrete-return and waveform LiDAR data. (C) 2011 Elsevier Inc. All rights reserved.