Estimating carbon stock in secondary forests: Decisions and uncertainties associated with allometric biomass models (original) (raw)
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Annals of Forest Science, 2015
& Key message Three options are proposed to improve the accuracy of national forest biomass estimates and decrease the uncertainty related to tree model selection depending on available data and national contexts. & Introduction Different tree volume and biomass equations result in different estimates. At national scale, differences of estimates can be important while they constitute the basis to guide policies and measures, particularly in the context of climate change mitigation. & Method Few countries have developed national tree volume and biomass equation databases and have explored its potential to decrease uncertainty of volume and biomasttags estimates. With the launch of the GlobAllomeTree webplatform, most countries in the world could have access to country-specific databases. The aim of this article is to recommend approaches for assessing tree and forest volume and biomass at national level with the lowest uncertainty. The article highlights the crucial need to link allometric equation development with national forest inventory planning efforts. & Results Models must represent the tree population considered. Data availability; technical, financial, and human Handling editor: Erwin Dreyer Contribution of co-authors Miguel Cifuentes Jara and Matieu Henry organized and facilitated the discussions which produced the ideas and opinions contained in this paper. They also led the writing and editing of the document. Additional authors provided edits and inputs to the final manuscript. Disclaimer The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of FAO.
CAPITALIZING ON THE INFORMATION IN ALLOMETRIC EQUATION DATA BASES FOR FOREST BIOMASS ESTIMATION
In many countries, inventory data or biomass or volume equations are often incomplete or unavailable. Either taxonomic information is not accurate at the species level, or else no literature exist compiling particular allometric equations for some species. On the other hand, some species are represented by many alternative equations in the database. The vast quantity of information that allometric equation databases such as Globallometree can provide, can be capitalized to inform other, non-available species from the ranges and distributions of aboveground biomass estimates that other, better known species provide. In this study we provide an alternative method that takes those elements to estimate overall plot aboveground biomass from bootstrapping different equations belonging to a certain ecozone. Using a real inventory plot as an example, we prove that such estimates present error levels similar to those of generalized pantropical equations when a minimum set of rules for quality control has been added. This opens the possibility to establish more adequate quality control protocols that end up providing even better estimates than those published pantropical equations.
Improving the accuracy of aboveground biomass estimations in secondary tropical dry forests
Forest Ecology and Management, 2020
Biomass estimates in tropical forests are mainly available for old-growth forests, but the expansion of tropical secondary forests urges the development of tools for more accurate estimations of biomass and carbon pools. In this study, we developed local allometric models to estimate aboveground biomass in secondary tropical dry forests of the Chamela region in western Mexico and compared their accuracy to that of non-local ("foreign") allometric models. We harvested 303 trees from 27 woody species contributing ≥75% of total basal area in secondary forest plots (5-45 y-old) distributed across the landscape. Nine to 14 individuals per species, covering the full natural range in stem diameter (DBH) found in an inventory, were measured for DBH and height (H) before harvesting. Subsamples from each stem and branches were used for dry mass and wood specific density (WSD) determinations. Power model functions were fitted to relate tree AGB to one or a multiplicative combination of three predictors (DBH, H, and WSD). Species-specific models with DBH alone explained a high percentage of the variance in tree AGB (R 2 = 0.927 to 0.999). Among our multispecies models, fit and prediction of biomass improved when pooling the species into low and high WSD functional groups. Using local or global WSD data did not affect the accuracy of our multispecies models. In contrast, bias increased in foreign models with the use of global WSD values. We discuss the applicability of our allometric models and foreign models to improve the accuracy of biomass predictions in secondary tropical dry forests.
Allometric regressions for improved estimate of secondary forest biomass in the central Amazon
Forest Ecology and Management, 1999
Estimates of the sequestering of carbon by secondary forests ± which occupy almost half the deforested area of the Brazilian Amazon ± will be improved by the use of accurate allometric relationships for non-destructive measurement of standing biomass and by an evaluation of the suitability of existing equations for application in secondary forest. Species-speci®c and mixed-species regressions for estimating total above-ground dry weight (DW) were therefore developed using eight abundant secondary forest tree species in the central Amazon. Using only DBH as the input variable, the species-speci®c equations estimated DW of individual trees with an average error of 10±15%. For the mixed-species equations, developed using 132 trees from seven of the eight species (excluding Cecropia), average error in estimating DW of individual trees was 19.8% using only DBH and 15.0% using DBH plus speci®c density of the wood (SD). Average SD for each species can be substituted without increasing the error of the estimate. Adding total tree height (H) as an input variable provided only a slight reduction in error to 14.0%. Previously published mixed-species biomass regression models, based on primary and secondary forest trees of the Amazon, were also cross-validated against the trees of this study. Two of these models, based on primary forest plots and using only DBH as an input, overestimated biomass by 10±60% for central Amazonian secondary forest trees in the size range 5±25 cm. The overestimate was greatest for the larger trees. Including Cecropia in the test group will make the overestimate even greater. Those published equations using DBH, H and SD as inputs, whether from secondary or primary forest plots, showed better agreement with the sample-derived regressions and lower average errors in estimation of individual tree dry weights. #
In this study, we analyzed the above-ground biomass data for 631 trees with a diameter P10 cm from different biogeographical regions in Colombia. The aims of this research were (1) to evaluate the accuracy of the most commonly employed pantropical allometric models for the estimation of above-ground bio- mass of natural forests in different sites located along a complex environmental gradient, (2) to develop new models that enable more precise estimations of current carbon stores in the above-ground biomass of natural forest ecosystems in Colombia, and (3) to evaluate the effect on allometric models of forest type classifications as determinants of above-ground biomass variation. The Brown et al. (1989) model for moist forests, which includes diameter, height, and wood density, showed the overall best perfor- mance in Colombian sites. The Type II models of Chave et al. (2005; hereafter Chave II), which include diameter and wood density but not height, tended to strikingly overestimate the above-ground biomass (54.7 ± 135.7%) in the studied Colombian sites. The use of forest classification based on the life zone sys- tem systematically led to better statistical models to estimate AGB at the individual scale and site scale than the use of Chave’s classification. Our results propose that Chave II models should be evaluated prior to their use for a given ecosystem. For Colombia, the new allometric models developed, which employed diameter, wood density, and height, could help improving our understanding of the carbon cycle. Forest type classification was found to be an important determinant of the above-ground biomass estimation when altitudinal and other complex environmental gradients are included. The new models presented here can be considered as an alternative option for assessing carbon stocks in the above-ground biomass of natural forests in neotropical countries.
The tropical biomass & carbon project-An application for forest biomass and carbon estimates
This article introduces the Tropical Biomass & Carbon Applicationthe 'TB&C App', a web application available on the permanent link www.tropicalbiomass.com. The TB&C App requires as input attributes 'the smallest and largest diameters', 'number of trees ha − 1' , basal area ha − 1 , and 'parameters of the diameter (beta) distribution' describing stand structure. The App delivers outputs at two levels: (1) Stand level, including mean aboveground biomass (AGB) and carbon (AGC), in Mg ha − 1 , along with confidence intervals (CIs) as measures of uncertainty, and; (2) Tree level estimates, with AGB and diameter for every simulated tree. Phase 1 of the project TB&C comprises four Brazilian forest (and non-forest) formations: Campinarana, Floresta estacional, Floresta ombrofila, and Savana. This article aims to (i) describe the algorithm written for the TB&C App, and (ii) present results of Phase 1. This first phase counts on a standardized database of 1,428 trees with field-measured dry AGB, from plots across the different formations, which is the largest tree-biomass database compiled so far in Brazil. Model uncertainties were incorporated into the modeling process, allowing computation of CIs through an uncertainty approach. The total variance of residuals of AGB was also modeled, aiming at predicting CIs as a function of the quantity of AGB. An analysis of reliability of the equations implemented in the TB&C App indicates that more than 95% (n = 64,000) of the true AGB's fit into the CI outputted by the TB&C App. A comparison with other approaches in the literature shows significant agreement with previous estimates and more conservative estimates where previously-published estimates disagreed with the TB&C App. We cite as advantages of the TB&C App; (i) reliability of the outputs, (ii) a user-friendly layout, (iii) AGB and AGC estimates provided along with robust CIs, and (iv) estimates at the stand and tree levels with consistent totals. A biomass dataset containing information on 64,000 plots is also delivered as supplement of this paper.
Improved allometric models to estimate the aboveground biomass of tropical trees
2014
Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven-dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter-height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter-height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development.
Error propagation in biomass estimation in tropical forests
Methods in Ecology and Evolution, 2013
1. Reliable above-ground biomass (AGB) estimates are required for studies of carbon fluxes and stocks. However, there is a huge lack of knowledge concerning the precision of AGB estimates and the sources of this uncertainty. At the tree level, the tree height is predicted using the tree diameter at breast height (DBH) and a height sub-model. The wood-specific gravity (WSG) is predicted with taxonomic information and a WSG sub-model. The tree mass is predicted using the predicted height, the predicted WSG and the biomass sub-model. 2. Our models were inferred with Bayesian methods and the uncertainty propagated with a Monte Carlo scheme. The uncertainties in the predictions of tree height, tree WSG and tree mass were neglected sequentially to quantify their contributions to the uncertainty in AGB. The study was conducted in French Guiana where long-term research on forest ecosystems provided an outstanding data collection on tree height, tree dynamics, tree mass and species WSG. 3. We found that the uncertainty in the AGB estimates was found to derive primarily from the biomass sub-model. The models used to predict the tree heights and WSG contributed negligible uncertainty to the final estimate. 4. Considering our results, a poor knowledge of WSG and the height-diameter relationship does not increase the uncertainty in AGB estimates. However, it could lead to bias. Therefore, models and databases should be used with care. 5. This study provides a methodological framework that can be broadly used by foresters and plant ecologist. It provides the accurate confidence intervals associated with forest AGB estimates made from inventory data. When estimating region-scale AGB values (through spatial interpolation, spatial modelling or satellite signal treatment), the uncertainty of the forest AGB value in the reference forest plots has to be taken in account. We believe that in the light of the Reducing Emissions from Deforestation and Degradation debate, our method is a crucial step in monitoring carbon stocks and their spatio-temporal evolution.
Tre allometry and improved estimation of carbon stocks and balance in tropical forests
Oecologia
Tropical forests hold large stores of carbon, yet uncertainty remains regarding their quantitative contribution to the global carbon cycle. One approach to quantifying carbon biomass stores consists in inferring changes from long-term forest inventory plots. Regression models are used to convert inventory data into an estimate of aboveground biomass (AGB). We provide a critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees ‡ 5 cm diameter, directly harvested in 27 study sites across the tropics. Proportional relationships between aboveground biomass and the prod-uct of wood density, trunk cross-sectional area, and total height are constructed. We also develop a regression model involving wood density and stem diameter only. Our models were tested for secondary and oldgrowth forests, for dry, moist and wet forests, for lowland and montane forests, and for mangrove forests. The most important predictors of AGB of a tree were, in decreasing order of importance, its trunk diameter, wood specific gravity, total height, and forest type (dry, moist, or wet). Overestimates prevailed, giving a bias of 0.5-6.5% when errors were averaged across all stands. Our regression models can be used reliably to predict aboveground tree biomass across a broad range of tropical forests. Because they are based on an unprecedented dataset, these models should improve the quality Electronic Supplementary Material Supplementary material is available for this article at http://dx.of tropical biomass estimates, and bring consensus about the contribution of the tropical forest biome and tropical deforestation to the global carbon cycle.