Abstract
It has been suggested that above-ground biomass (AGB) inventories should include tree height (H), in addition to diameter (D). As H is a difficult variable to measure, H-D models are commonly used to predict H. We tested a number of approaches for H-D modelling, including additive terms which increased the complexity of the model, and observed how differences in tree-level predictions of H propagated to plot-level AGB estimations. We were especially interested in detecting whether the choice of method can lead to bias. The compared approaches listed in the order of increasing complexity were: (B0) AGB estimations from D-only; (B1) involving also H obtained from a fixed-effects H-D model; (B2) involving also species; (B3) including also between-plot variability as random effects; and (B4) involving multilevel nested random effects for grouping plots in clusters. In light of the results, the modelling approach affected the AGB estimation significantly in some cases, although differences were negligible for some of the alternatives. The most important differences were found between including H or not in the AGB estimation. We observed that AGB predictions without H information were very sensitive to the environmental stress parameter (E), which can induce a critical bias. Regarding the H-D modelling, the most relevant effect was found when species was included as an additive term. We presented a two-step methodology, which succeeded in identifying the species for which the general H-D relation was relevant to modify. Based on the results, our final choice was the single-level mixed-effects model (B3), which accounts for the species but also for the plot random effects reflecting site-specific factors such as soil properties and degree of disturbance.
Highlights
Forest is the main terrestrial biotic reservoir for above-ground carbon stock [1]
In the absence of above-ground biomass (AGB) data obtained by destructive sampling which would allow an independent validation of the final AGB estimations, we considered that accounting for differences between the modelling approaches can provide an idea on how differences in H-D modelling can propagate to the final AGB predictions [11,20]
Using the two-step approach to determining significant species effects, as detailed in sub-section “modelling steps”, the following species were selected for species effects: A. leiocarpus, B. africana and C. molle (Fig 4)
Summary
Forest is the main terrestrial biotic reservoir for above-ground carbon stock [1]. For this reason, accurate estimates of forest above-ground biomass (AGB) are required to understand the global carbon cycle, thereby to implement climate change mitigation policies [2], and support projectPLOS ONE | DOI:10.1371/journal.pone.0158198 July 1, 2016Sensitivity of Biomass Estimates to H-D Models activities such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation in developing countries + conservation of forest carbon stocks, sustainable management of forest and enhancement of forest carbon stocks) [3]. Forest is the main terrestrial biotic reservoir for above-ground carbon stock [1]. For this reason, accurate estimates of forest above-ground biomass (AGB) are required to understand the global carbon cycle, thereby to implement climate change mitigation policies [2], and support project. The accuracy in the estimation of AGB still lags behind the required level, and uncertainties in the terrestrial carbon storage are large in the tropical areas [5]. In comparison to other regions of the world, the AGB estimates of the African forests and woodlands, remain scarce [6,7]. In terms of REDD+, large areas of savanna woodlands, shrublands and agroforestry parklands qualify as forests Biomass typically constitutes the largest fraction of the primary energy consumption in these areas [7], and AGB estimates are important part of fuelwood resource assessments [9]
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