Next Article in Journal
Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives
Previous Article in Journal
Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(10), 9576-9599; doi:10.3390/rs6109576

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

1
Interdepartmental Research Center of Geomatics (CIRGEO), University of Padova, Legnaro 35020, Italy
2
IAFENT Division, Euro-Mediterranean Center on Climate Change (CMCC), via Pacinotti 5, Viterbo 01100, Italy
3
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
View Full-Text   |   Download PDF [3306 KB, uploaded 10 October 2014]   |  

Abstract

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
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top