Abstract
Abstract. Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.
Highlights
Accurate estimates of tropical tree biomass are essential to determine geographic patterns in carbon stocks, the magnitudes of fluxes due to land-use change, and to quantify avoided carbon emissions via mechanisms such as REDD+ (Reducing Emissions from Deforestation and forest Degradation)
We examine how the selected H models (Table 3) affect biomass estimates (Fig. 3) and uncertainty (Fig. 4) as a result of regional variation in forest structure (Supplement Table S2) and distribution of biomass among diameter classes for trees measured in pantropical permanent sample plots (Supplement Table S1)
We show that (1) including H significantly improves the accuracy of estimation of tropical forest aboveground biomass; (2) failing to include H usually causes an overestimate of biomass; (3) such overestimates may have globally significant implications – here we estimate that carbon storage in tropical forests may be overestimated by 13%; and we recommend that (4) continental or regional-specific asymptotic Weibull H :D functions to be included in future estimates of biomass to reduce uncertainty in aboveground biomass estimates in tropical forests
Summary
Accurate estimates of tropical tree biomass are essential to determine geographic patterns in carbon stocks, the magnitudes of fluxes due to land-use change, and to quantify avoided carbon emissions via mechanisms such as REDD+ (Reducing Emissions from Deforestation and forest Degradation). Global estimates of tree carbon in tropical forests vary between 40 to 50 % of the total carbon in terrestrial vegetation (Watson et al, 2000; Kindermann et al, 2008), indicating considerable uncertainty. Calibration of remotely-sensed biomass requires ground-based biomass estimates derived from stem diameter measurements and allometric equations (either calibrated “on-site” or from the literature to “ground-truth” data) (e.g., Lucas et al, 2002; Mitchard et al, 2009). Both ground- and space-borne biomass estimates have uncertainty, and scaling from plots to regions introduces additional uncertainty. It is necessary to generate accurate allometric models that reduce uncertainty in tree and plot-level estimates
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