Tree height and tropical forest biomass estimation (original) (raw)
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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.
Biomass estimation in the Tapajos National Forest, Brazil
Forest Ecology and Management, 2001
Changes in the biomass of Amazon region forests represent an important component of the global carbon cycle but the biomass of these forests remains poorly quanti®ed. Minimizing the error in forest biomass estimates is necessary in order to reduce the uncertainty in future Amazon carbon budgets. We examined forest survey data for trees with a diameter at breast height (DBH) greater than 35 cm from four plots with a total area of 392 ha in the Tapajos National Forest near Santarem, Para, Brazil (3804 H S, 54895 H W). The average frequency of trees greater than 35 cm DBH was approximately 55 ha À1 . Based on tree diameters, allometric relations, and published relations for biomass in other compartments besides trees of DBH > 35 cm, we estimated a total biomass density of 372 Mg ha À1 . We produced a highly conservative error estimate of about 50% of this value. Trees with diameters greater than 35 cm DBH accounted for about half of the total biomass. This estimate includes all live and dead plant material above-and below-ground with the exception of soil organic matter. We propagated errors in sampling and those associated with allometric relations and other ratios used to estimate biomass of roots, lianas and epiphytes, and necromass. The major sources of uncertainty in our estimate were found in the allometric relations for trees with DBH greater than 35 cm, in the estimates of biomass of trees with DBH less than 35 cm, and in root biomass. Simulated sampling based on our full survey, suggests that we could have estimated mean biomass per hectare for trees DBH ! 35 cm to within 20% (sampling error only) with 95% con®dence by sampling 21 randomly selected 0.25 ha plots in our study area. #
Forest Ecology and Management, 2012
Precise estimation of biomass at a regional scale is required for evaluating forest carbon stocks throughout the Amazon. We examined six types of allometric models to identify the best estimator of biomass in primary forests (terra firme) in the northwestern sector of the Brazilian Amazon. We also tested six regression models for estimating tree height. We developed each allometric model using measurements of 101 trees excavated in a primary forest distributed along the upper Rio Negro. A simple power function with stem diameter at breast height D as a single variable was selected as the best model for estimating each biomass component, i.e. above-ground total mass AGW, below-ground total mass BGW, and whole individual mass. Among models developed to estimate tree height H from D, we selected a regression model with a coefficient corresponding to an asymptotic height as the best fit. The D-AGW relationship at our study site differed significantly from models developed previously for other regions of the Amazon. We explain this regional variation in part by regional differences in D-H relationships of sample trees. The D-BGW relationship at our site also differed significantly from that in the central Amazon. However, AGW-BGW relationships were consistent between the upper Rio Negro forest and other forests in the central Amazon, in that the BGW-AGW ratio was constant as 0.136 regardless of tree size. On the basis of D-based allometry and census data from 23 plots established in the upper Rio Negro region, we estimated a stand-level total biomass (dry mass) of 252.6 Mg ha À1. This estimate is at least 73% lower than the potential stand biomass for the region previously suggested by several meta-analyses.
Uncertainty in the biomass of Amazonian forests: An example from Rondônia, Brazil
Forest Ecology and Management, 1995
A critical factor in estimating the contribution of tropical deforestation to nutrient mobilization and to CO2 build-up in the atmosphere is the amount of biomass available to bum. The biomass data for Brazil, a major site for deforestation, are few and of uncertain accuracy. Recent international agreements, however, require national inventories of sources and sinks for atmospheric greenhouse gases; such inventories will need better estimates of biomass and their uncertainties. To provide additional information on biomass uncertainty and on forest structure in southwestern Amazonia, a region of active deforestation, we measured in 1988 the diameter, bole and canopy heights of 474 trees covering a total of 1 ha ( 10 000 m2) in the Ecological Station of the Samuel Hydroelectric Reservoir in Rondonia (845'S, 63"23'W). Using allometric equations based on destructively sampled trees, we estimated the largest biomass component, standing alive aboveground biomass (SAAB), as 285 Mg (dry weight) ha-I. Fallen trunks and litter were 30 Mg and 10 Mg ha-', respectively. The sum of these components, 325 Mg ha-', is an underestimate of the total biomass because the biomass of roots, vines, shrubs, and small trees was not measured. Measurement error of SAAB is + 20%. k 57 Mg ha-' about the mean (95% confidence interval), as derived by a Monte Carlo simulation. The SAAB distribution among trees is highly skewed: 3% of the trees contain 50% of the SAAB. For forests of similar distributions, sampling units typically used for biomass estimates (less than 2000 m2) will usually produce biomass estimates significantly different from those of larger units. Based on subsamples of our data, sampling units of 1000 m* or smaller had at least a 75% chance of being outside the confidence interval of the global mean (228-342 Mg ha-r) derived from Monte Carlo simulation. To improve estimates of SAAB in similar forests a sampling program should focus on emergent and large canopy trees, the dominant contributors to biomass.
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.
Remote Sensing, 2017
Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.
South African Journal of Science, 2011
The need for accurate quantification of the amount of carbon stored in the environment has never been greater. Carbon sequestration has become a vital component of the battle against global climate change, and monitoring and quantifying this process are major challenges for policymakers. Plant allometric equations allow managers and scientists to quantify the biomass contained in a tree without cutting it down, and therefore can play a pivotal role in measuring carbon sequestration in forests and savannahs. These equations have been available since the beginning of the 20th century, but their usefulness depends on the ability to estimate the error associated with the equations -something which has received scant attention in the past. This paper provides a method based on the theory of linear regression and the lognormal distribution to derive confidence limits for estimates of biomass derived from plant allometric equations. Allometric equations for several southern African savannah species are provided, as well as the parameters and equations required to calculate the confidence intervals. This method was applied to data collected from a sampling campaign carried out in a savannah landscape at the Skukuza flux site, Kruger National Park, South Africa. Here the error was 10% of the total site biomass for the woody biomass and 2% for the leaf biomass. When the data were split into individual plots and used to estimate site biomass (as would occur in most sampling schemes) the error increased to 16% and 12% of the woody and leaf biomasses, respectively, as the sampling errors were added to the errors in the allometric equation. These methods can be used in any discipline that applies allometric equations, such as health sciences and animal physiology.
Tree height integrated into pan-tropical forest biomass estimates (Discussion)
2012
Above-ground tropical tree biomass and carbon storage estimates commonly ignore tree height. We estimate the effect of incorporating height (H) on forest biomass estimates using 37 625 concomitant H and diameter measurements (n = 327 plots) and 1816 harvested trees (n = 21 plots) tropics-wide to answer the following questions: 1. For trees of known biomass (from destructive harvests) which H-model form and geographic scale (plot, region, and continent) most reduces biomass estimate uncertainty? 2. How much does including H relationship estimates derived in (1) reduce uncertainty in biomass estimates across 327 plots spanning four continents? 3. What effect does the inclusion of H in biomass estimates have on plot-and continental-scale forest biomass estimates? The mean relative error in biomass estimates of the destructively harvested trees was half (mean 0.06) when including H, compared to excluding H (mean 0.13). The powerand Weibull-H asymptotic model provided the greatest reduction in uncertainty, with the regional Weibull-H model preferred because it reduces uncertainty in smaller-diameter classes that contain the bulk 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 errors are reduced from 41.8 Mg ha −1 (range 6.6 to 112.4) to 8.0 Mg ha −1 (−2.5 to 23.0) when including H. For all plots, above-ground live biomass was 52.2±17.3 Mg ha −1 lower when including H estimates (13 %), with the greatest reductions in estimated biomass in Brazilian Shield forests and relatively no change in the Guyana Shield, central Africa and southeast Asia. We show fundamentally different stand structure across the four forested tropical continents, which affects biomass reductions due to H. African forests store a greater portion of total biomass in largediameter trees and trees are on average larger in diameter. This contrasts to forests 2571 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | on all other continents where smaller-diameter trees contain the greatest fractions of total biomass. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guyana Shield, and Asia and lowest in W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if closed canopy tropical forests span 1668 million km 2 and store 285 Pg C, then the overestimate is 35 Pg C if H is ignored, and 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 pantropical carbon stocks and emissions due to deforestation.
Tree height integrated into pan-tropical forest biomass estimates
2012
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: