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Remote Sens. 2014, 6(10), 9576-9599;

Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests

Interdepartmental Research Center of Geomatics (CIRGEO), University of Padova, Legnaro 35020, Italy
IAFENT Division, Euro-Mediterranean Center on Climate Change (CMCC), via Pacinotti 5, Viterbo 01100, Italy
Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo 01100, Italy
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 20 June 2014 / Revised: 28 September 2014 / Accepted: 29 September 2014 / Published: 10 October 2014
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We tested metrics from full-waveform (FW) LiDAR (light detection and ranging) as predictors for forest basal area (BA) and aboveground biomass (AGB), in a tropical moist forest. Three levels of metrics are tested: (i) peak-level, based on each return echo; (ii) pulse-level, based on the whole return signal from each emitted pulse; and (iii) plot-level, simulating a large footprint LiDAR dataset. Several of the tested metrics have significant correlation, with two predictors, found by stepwise regression, in particular: median distribution of the height above ground (nZmedian) and fifth percentile of total pulse return intensity (i_tot5th). The former contained the most information and explained 58% and 62% of the variance in AGB and BA values; stepwise regression left us with two and four predictors, respectively, explaining 65% and 79% of the variance. For BA, the predictors were standard deviation, median and fifth percentile of total return pulse intensity (i_totstdDev, i_totmedian and i_tot5th) and nZmedian, whereas for AGB, only the last two were used. The plot-based metric showed that the median height of echo count (HOMTC) performs best, with very similar results as nZmedian, as expected. Cross-validation allowed the analysis of residuals and model robustness. We discuss our results considering our specific case scenario of a complex forest structure with a high degree of variability in terms of biomass. View Full-Text
Keywords: biomass; forest; LiDAR; full-waveform; basal area biomass; forest; LiDAR; full-waveform; basal area

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Pirotti, F.; Laurin, G.V.; Vettore, A.; Masiero, A.; Valentini, R. Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests. Remote Sens. 2014, 6, 9576-9599.

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