Abstract
Abstract. 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 (R2 = 0.7 and RMSEmean = 27.6 Mg ha−1). 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 p 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−1 yr−1 ± 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−1 yr−1 with old growth forests losing −0.7 Mg ha−1 yr−1 ± 2.2 (SD), and secondary forests gaining +1.8 Mg ha yr−1 ± 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).
Highlights
Tropical forests are a major focus for research because of their high biodiversity and because of the role they play in the global carbon cycle and recently in climate mitigation policies through REDD (Reduced Emissions from Deforestation and Degradation)
Because intermediate height metrics (RH25, RH50 and RH75) from LVIS and discrete return lidar (DRL) are different (Supplementary Material) and there are potential errors introduced by groundfinding algorithms in both sensors, we developed a footprintlevel analysis with canopy elevation metrics such as RH100E and MCHE, where RH100E is the elevation of the maximum canopy height and MCHE corresponds to the elevation of the mean canopy height
Testing for the relative importance of each height metric, we found that all five metrics (RH25, RH50, RH75, MCH and RH100) together explained about 75 % of the variation in forest biomass at 1 ha scale for both LVIS and DRL, www.biogeosciences.net/10/5421/2013/
Summary
Tropical forests are a major focus for research because of their high biodiversity and because of the role they play in the global carbon cycle and recently in climate mitigation policies through REDD (Reduced Emissions from Deforestation and Degradation). For ground-based data, researchers have developed allometric equations from tree inventory data collected from a range of tropical forest types (Chave et al, 2005; Higuchi et al, 1994; Chambers et al, 2001). These equations are key to converting tree diameter, height, and wood-specific gravity into tree aboveground biomass (AGB, measured in oven-dry mass units) and to inferring stand AGB across spatial scales (Chave et al, 2004). Permanent research plots provide accurate estimates of carbon fluxes at the local scale, but their spatial locations may not be representative of forest landscapes, leading to uncertainty in estimates at larger spatial scales
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