BG Biogeosciences BG Biogeosciences 1726-4189 Copernicus Publications Göttingen, Germany 10.5194/bg-10-5421-2013 Detecting tropical forest biomass dynamics from repeated airborne lidar measurements Meyer V. 1 Saatchi S. S. 1 Chave J. 2 Dalling J. W. 3 Bohlman S. 4 Fricker G. A. 5 Robinson C. 5 Neumann M. 1 Hubbell S. 6 7 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA CNRS, Laboratoire Evolution et Diversité Biologique, UMR5174, Université Paul Sabatier, 31062 Toulouse cedex, France Department of Plant Biology, University of Illinois, Urbana, IL 61801, USA School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA Department of Geography, University of California, Los Angeles, Los Angeles, CA 90095, USA Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA Center for Tropical Forest Science, Smithsonian Tropical Research Institute, Attn: 9100 Panama City, PL, Washington, DC 20521-9100, USA 14 08 2013 10 8 5421 5438 Copyright: © 2013 V. Meyer et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://bg.copernicus.org/articles/10/5421/2013/bg-10-5421-2013.html The full text article is available as a PDF file from https://bg.copernicus.org/articles/10/5421/2013/bg-10-5421-2013.pdf

Reducing uncertainty of terrestrial carbon cycle depends strongly on the accurate estimation of changes of global forest carbon stock. However, this is a challenging problem from either ground surveys or remote sensing techniques in tropical forests. Here, we examine the feasibility of estimating changes of tropical forest biomass from two airborne lidar measurements of forest height acquired about 10 yr apart over Barro Colorado Island (BCI), Panama. We used the forest inventory data from the 50 ha Center for Tropical Forest Science (CTFS) plot collected every 5 yr during the study period to calibrate the estimation. We compared two approaches for detecting changes in forest aboveground biomass (AGB): (1) relating changes in lidar height metrics from two sensors directly to changes in ground-estimated biomass; and (2) estimating biomass from each lidar sensor and then computing changes in biomass from the difference of two biomass estimates, using two models, namely one model based on five relative height metrics and the other based only on mean canopy height (MCH). We performed the analysis at different spatial scales from 0.04 ha to 10 ha. Method (1) had large uncertainty in directly detecting biomass changes at scales smaller than 10 ha, but provided detailed information about changes of forest structure. The magnitude of error associated with both the mean biomass stock and mean biomass change declined with increasing spatial scales. Method (2) was accurate at the 1 ha scale to estimate AGB stocks (<i>R</i><sup>2</sup> = 0.7 and RMSE<sub>mean</sub> = 27.6 Mg ha<sup>−1</sup>). However, to predict biomass changes, errors became comparable to ground estimates only at a spatial scale of about 10 ha or more. Biomass changes were in the same direction at the spatial scale of 1 ha in 60 to 64% of the subplots, corresponding to <i>p</i> values of respectively 0.1 and 0.033. Large errors in estimating biomass changes from lidar data resulted from the uncertainty in detecting changes at 1 ha from ground census data, differences of approximately one year between the ground census and lidar measurements, and differences in sensor characteristics. Our results indicate that the 50 ha BCI plot lost a significant amount of biomass (−0.8 Mg ha<sup>−1</sup> yr<sup>−1</sup> ± 2.2(SD)) over the past decade (2000–2010). Over the entire island and during the same period, mean AGB change was 0.2 ± 2.4 Mg ha<sup>−1</sup> yr<sup>−1</sup> with old growth forests losing −0.7 Mg ha<sup>−1</sup> yr<sup>−1</sup> ± 2.2 (SD), and secondary forests gaining +1.8 Mg ha yr<sup>−1</sup> ± 3.4 (SD) biomass. Our analysis suggests that repeated lidar surveys, despite taking measurement with different sensors, can estimate biomass changes in old-growth tropical forests at landscape scales (>10 ha).

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