Error propagation in biomass estimation in tropical forests

Geospatial mapping of carbon estimates for forested areas using the InVEST model and Sentinel-2: A case study in Galicia (NW Spain)

CO2 emissions have increased exponentially in recent years, so measuring and quantifying carbon sequestration is a step towards sustainable forest management and combating climate change. The overall goal of this study is to develop an accurate model for estimating carbon storage and sequestration for forest areas of the Atlantic Biogeographic Region. Specifically, the modelling and field sampling are carried out in the municipality of Baiona (Galicia, NW Spain), which was selected as a representative biome of this region. The methodology consists of carrying out two object-based image analysis (OBIA) classifications in spring and autumn to observe possible stocks of seasonal differences. Two carbon storage and sequestration models are built up (model 1 and model 2): model 1 for forest areas only and model 2 including all other land cover in the study area. Sentinel-2 geospatial data for 2021, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tools and geographic information systems (GIS) are used. A Kappa index of 0.92 is obtained for both classifications, thus ruling out any notable seasonal differences in the images used. The results from both models indicate that it is land covers associated with forest uses which store the most carbon in the study area, accounting for >50 % more than the other land covers. It is concluded that the methodology and data used are very useful for quantifying ecosystem services, which will help the governance of the region by implementing measures to mitigate some of the effects of climate change and help to create silvicultural models for the sustainable management of the Atlantic Biogeographic Region.

Aboveground Biomass Models in the Combretum-Terminalia Woodlands of Ethiopia: Testing Species and Site Variation Effects

The Combretum-Terminalia woodlands and wooded grasslands (CTW) are widely distributed in East Africa. While these landscapes may have the potential to act as key global carbon sinks, relatively little is known about their carbon storage capacity. Here we developed a set of novel aboveground biomass (AGB) models and tested for species and site variation effects to quantify the potential for CTW to store carbon. In total, 321 trees were sampled from 13 dominant tree species, across three sites in the Northwest lowlands of Ethiopia. Overall, fitted species-specific models performed the best, with diameter at breast height explaining 94–99% of the AGB variations. Interspecific tree allometry differences among species were more substantial than intraspecific tree allometry among sites. Incorporating wood density and height in the mixed-species models significantly improved the model performance relative mean absolute error (MAPE) of 2.4–8.0%, while site variation did not affect the model accuracy substantially. Large errors (MAPE%) were observed when using existing pantropical models, indicating that model selection remains an important source of uncertainty. Although the estimates of selected site-specific models were accurate for local sites, mixed-species and species-specific models performed better when validation data collated from different sites were incorporated together. We concluded that including site- and species-level data improved model estimates of AGB for the CTW of Ethiopia.

Dataset on woody aboveground biomass, disturbance losses, and wood density from an African savanna ecosystem

This dataset comprises tree inventories and damage assessments performed in Namibia's semi-arid Zambezi Region. Data were sampled in savannas and savanna woodlands along steep gradients of elephant population densities to capture the effects of those (and other) disturbances on individual-level and stand-level aboveground woody biomass (AGB). The dataset contains raw data on dendrometric measures and processed data on specific wood density (SWD), woody aboveground biomass, and biomass losses through disturbance impacts. Allometric proxies (height, canopy diameters, and in adult trees also stem circumferences) were recorded for n = 6,179 tree and shrub individuals. Wood samples were taken for each encountered species to measure specific wood density.

These measurements have been used to estimate woody aboveground biomass via established allometric models, advanced through our improved methodologies and workflows that accounted for tree and shrub architecture shaped by disturbance impacts. To this end, we performed a detailed damage assessment on each woody individual in the field. In addition to estimations of standing biomass, our new method also delivered data on biomass losses to different disturbance agents (elephants, fire, and others) on the level of plant individuals and stands.

The data presented here have been used within a study published with Ecological Indicators (Kindermann et al., 2022) to evaluate the benefits of our improved methodology in comparison to a standard reference method of aboveground biomass estimations. Additionally, it has been employed in a study on carbon storage and sequestration in vegetation and soils (Sandhage-Hofmann et al., 2021).

The raw data of dendrometric measurements can be subjected to other available allometric models for biomass estimation. The processed data can be used to analyze disturbance impacts on woody aboveground biomass, or for regional carbon storage estimates. The data on species-specific wood density can be used for application to other dendrometric datasets to (re-) estimate biomass through allometric models requiring wood density. It can further be used for plant functional trait analyses.

Allometric Equations to Estimate Aboveground Biomass in Spotted Gum (Corymbia citriodora Subspecies variegata) Plantations in Queensland

Accurate equations are critical for estimating biomass and carbon accumulation for forest carbon projects, bioenergy, and other inventories. Allometric equations can provide a reliable and accurate method for estimating and predicting biomass and carbon sequestration. Cross-validatory assessments are also essential to evaluate the prediction ability of the selected model with satisfactory accuracy. We destructively sampled and weighed 52 sample trees, ranging from 11.8 to 42.0 cm in diameter at breast height from three plantations in Queensland to determine biomass. Weighted nonlinear models were used to explore the influence of different variables using two datasets: the first dataset (52 trees) included diameter at breast height (D), height (H) and wood density (ρ); and the second dataset (40 trees) also included crown diameter (CD) and crown volume (CV). Cross validation of independent data showed that using D alone proved to be the best performing model, with the lowest values of AIC = 434.4, bias = −2.2% and MAPE = 7.2%. Adding H and ρ improved the adjusted. R2 (Δ adj. R2 from 0.099 to 0.135) but did not improve AIC, bias and MAPE. Using the single variable of CV to estimate aboveground biomass (AGB) was better than CD, with smaller AIC and MAPE less than 2.3%. We demonstrated that the allometric equations developed and validated during this study provide reasonable estimates of Corymbia citriodora subspecies variegata (spotted gum) biomass. This equation could be used to estimate AGB and carbon in similar spotted gum plantations. In the context of global forest AGB estimations and monitoring, the CV variable could allow prediction of aboveground biomass using remote sensing datasets.

A 3D approach to model the taper of irregular tree stems: making plots biomass estimates comparable in tropical forests

In tropical forests, the high proportion of trees showing irregularities at the stem base complicates forest monitoring. For example, in the presence of buttresses, the height of the point of measurement (H_POM ) of the stem diameter (D_POM ) is raised from 1.3 m, the standard breast height, up to a regular part of the stem. While D_POM is the most important predictor for tree aboveground biomass (AGB) estimates, the lack of harmonized H_POM for irregular trees in forest inventory increases the uncertainty in plot-level AGB stock and stock change estimates. In this study, we gathered an original non-destructive three-dimensional (3D) data set collected with terrestrial laser scanning and close range terrestrial photogrammetry tools in three sites in central Africa. For the 228 irregularly shaped stems sampled, we developed a set of taper models to harmonize H_POM by predicting the equivalent diameter at breast height (DBH') from a D_POM measured at any height. We analyzed the effect of using DBH' on tree-level and plot-level AGB estimates. To do so, we used destructive AGB data for 140 trees and forest inventory data from eight 1-ha plots in the Republic of Congo. Our results showed that our best simple taper model predicts DBH' with a relative mean absolute error of 3.7% (R²  = 0.98) over a wide D_POM range of 17-249 cm. Based on destructive AGB data, we found that the AGB allometric model calibrated with harmonized H_POM data was more accurate than the conventional local and pantropical models. At the plot level, the comparison of AGB stock estimates with and without H_POM harmonization showed an increasing divergence with the increasing share of irregular stems (up to -15%). The harmonization procedure developed in this study could be implemented as a standard practice for AGB monitoring in tropical forests as no additional forest inventory measurements is required. This would probably lead to important revisions of the AGB stock estimates in regions having a large number of irregular tree stems and increase their carbon sink estimates. The growing use of three-dimensional (3D) data offers new opportunities to extend our approach and further develop general taper models in other tropical regions.

Height–diameter allometry in African monodominant forest close to mixed forest

African monodominant forests are frequently formed by Gilbertiodendron dewevrei (De Wild.) J. Leonard and commonly found close to mixed forests. However, previous studies have ignored differences between these two forest types in height–diameter allometry, which is extremely important for aboveground biomass (AGB) estimates. This study aims to evaluate the performance of height–diameter models and their effects on height attributes and AGB estimations in African monodominant and mixed forests. Four 1-ha plots divided in 16 subplots (0.25 ha) were installed in each forest type in northern Republic of Congo. We measured diameter of all trees ≥ 10 cm diameter for each subplot and we measured the height of 264 trees over a large range of 7–64 m in two forest types. There was a significant difference in height–diameter allometry between two forest types and trees were taller and had greater AGB in monodominant forests than in mixed forests. Two height–diameter models from the literature generated the lowest error values when predicting tree height and AGB in mixed forests, whereas no model derived from the literature was appropriate for monodominant forests. The variation in height–diameter allometry between monodominant and mixed forests influences AGB estimates that have practical implications for carbon monitoring.

Evaluation of P-Band SAR Tomography for Mapping Tropical Forest Vertical Backscatter and Tree Height

Low-frequency tomographic synthetic aperture radar (TomoSAR) techniques provide an opportunity for quantifying the dynamics of dense tropical forest vertical structures. Here, we compare the performance of different TomoSAR processing, Back-projection (BP), Capon beamforming (CB), and MUltiple SIgnal Classification (MUSIC), and compensation techniques for estimating forest height (FH) and forest vertical profile from the backscattered echoes. The study also examines how polarimetric measurements in linear, compact, hybrid, and dual circular modes influence parameter estimation. The tomographic analysis was carried out using P-band data acquired over the Paracou study site in French Guiana, and the quantitative evaluation was performed using LiDAR-based canopy height measurements taken during the 2009 TropiSAR campaign. Our results show that the relative root mean squared error (RMSE) of height was less than 10%, with negligible systematic errors across the range, with Capon and MUSIC performing better for height estimates. Radiometric compensation, such as slope correction, does not improve tree height estimation. Further, we compare and analyze the impact of the compensation approach on forest vertical profiles and tomographic metrics and the integrated backscattered power. It is observed that radiometric compensation increases the backscatter values of the vertical profile with a slight shift in local maxima of the canopy layer for both the Capon and the MUSIC estimators. Our results suggest that applying the proper processing and compensation techniques on P-band TomoSAR observations from space will allow the monitoring of forest vertical structure and biomass dynamics.

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6-1.2 m, height: 30-46 m, AGB: 3960-18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.

Allometric equations for estimating above-ground biomass of Nitraria sibirica Pall. in Gobi Desert of Mongolia

Nitraria sibirica Pall. is a shrub species belonging to the family of Nitrariaceae. It plays pivotal role in arid ecosystems since it is tolerant to high salinity and drought. This species is widely distributed throughout Mongolia and it is mostly found in arid ecosystems of Mongolian Gobi Desert. In this study, we developed allometric equations for estimating above-ground biomass of N. sibirica using various structural descriptors and pinpointed the best models. Variables that precisely predicted above-ground biomass were a combination of basal diameter, crown area, and height. The allometric growth equation constructed is not merely helpful to achieve accurate estimations of the above-ground biomass in shrub vegetation in the Gobi Desert of Mongolia, but also can provide a reference for the above-ground biomass of Nitraria species growing in analogous habitats worldwide. Therefore, our research purposes an important advance for biomass estimation in Gobi ecosystems and complements previous studies of shrub biomass worldwide. This study provides reasonable estimates of biomass of N. sibirica, which will be valuable in evaluations of biological resources, especially for quantifying the main summer diet of Gobi bears, and also can be an alternative tool for assessing carbon cycling in Gobi Desert.

AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.

Site-Specific Allometric Models for Prediction of Above-and Belowground Biomass of Subtropical Forests in Guangzhou, Southern China

Tree allometric models that are used to predict the biomass of individual tree are critical to forest carbon accounting and ecosystem service modeling. To enhance the accuracy of such predictions, the development of site-specific, rather than generalized, allometric models is advised whenever possible. Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few biomass models compared to the diversity of forest ecosystems are currently available for the subtropical forests of China. This study developed site-specific allometric models to estimate the aboveground and the belowground biomass for south subtropical humid forest in Guangzhou, Southern China. Destructive methods were used to measure the aboveground biomass with a sample of 144 trees from 26 species, and the belowground biomass was measured with a subsample of 116 of them. Linear regression with logarithmic transformation was used to model biomass according to dendrometric parameters. The mixed-species regressions with diameter at breast height (DBH) as a single predictor were able to adequately estimate aboveground, belowground and total biomass. The coefficients of determination (R2) were 0.955, 0.914 and 0.954, respectively, and the mean prediction errors were −1.96, −5.84 and 2.26%, respectively. Adding tree height (H) compounded with DBH as one variable (DBH2H) did not improve model performance. Using H as a second variable in the equation can improve the model fitness in estimation of belowground biomass, but there are collinearity effects, resulting in an increased standard error of regression coefficients. Therefore, it is not recommended to add H in the allometric models. Adding wood density (WD) compounded with DBH as one variable (DBH2WD) slightly improved model fitness for prediction of belowground biomass, but there was no positive effect on the prediction of aboveground and total biomass. Using WD as a second variable in the equation, the best-fitting allometric relationship for biomass estimation of the aboveground, belowground, and total biomass was given, indicating that WD is a crucial factor in biomass models of subtropical forest. Root-shoot ratio of subtropical forest in this study varies with species and tree size, and it is not suitable to apply it to estimate belowground biomass. These findings are of great significance for accurately measuring regional forest carbon sinks, and having reference value for forest management.

Allometric Models for Estimation of Forest Biomass in North East India

In tropical and sub-tropical regions, biomass carbon (C) losses through forest degradation are recognized as central to global terrestrial carbon cycles. Accurate estimation of forest biomass C is needed to provide information on C fluxes and balances in such systems. The objective of this study was to develop generalized biomass models using harvest data covering tropical semi-evergreen, tropical wet evergreen, sub-tropical broad leaved, and sub-tropical pine forest in North East India (NEI). Among the four biomass estimation models (BEMs) tested AGBest = 0.32(D2Hδ)0.75 × 1.34 and AGBest = 0.18D2.16 × 1.32 were found to be the first and second best models for the different forest types in NEI. The study also revealed that four commonly used generic models developed by Chambers (2001), Brown (1989), Chave (2005) and Chave (2014) overestimated biomass stocks by 300–591 kg tree−1, while our highest rated model overestimated biomass by 197 kg tree−1. We believe the BEMs we developed will be useful for practitioners involved in remote sensing, biomass estimation and in projects on climate change mitigation, and payment for ecosystem services. We recommend future studies to address country scale estimation of forest biomass covering different forest types.

Weighing trees with lasers: advances, challenges and opportunities

Terrestrial laser scanning (TLS) is providing exciting new ways to quantify tree and forest structure, particularly above-ground biomass (AGB). We show how TLS can address some of the key uncertainties and limitations of current approaches to estimating AGB based on empirical allometric scaling equations (ASEs) that underpin all large-scale estimates of AGB. TLS provides extremely detailed non-destructive measurements of tree form independent of tree size and shape. We show examples of three-dimensional (3D) TLS measurements from various tropical and temperate forests and describe how the resulting TLS point clouds can be used to produce quantitative 3D models of branch and trunk size, shape and distribution. These models can drastically improve estimates of AGB, provide new, improved large-scale ASEs, and deliver insights into a range of fundamental tree properties related to structure. Large quantities of detailed measurements of individual 3D tree structure also have the potential to open new and exciting avenues of research in areas where difficulties of measurement have until now prevented statistical approaches to detecting and understanding underlying patterns of scaling, form and function. We discuss these opportunities and some of the challenges that remain to be overcome to enable wider adoption of TLS methods.

Measurements of stem diameter: implications for individual- and stand-level errors

Stem diameter is one of the most common measurements made to assess the growth of woody vegetation, and the commercial and environmental benefits that it provides (e.g. wood or biomass products, carbon sequestration, landscape remediation). Yet inconsistency in its measurement is a continuing source of error in estimates of stand-scale measures such as basal area, biomass, and volume. Here we assessed errors in stem diameter measurement through repeated measurements of individual trees and shrubs of varying size and form (i.e. single- and multi-stemmed) across a range of contrasting stands, from complex mixed-species plantings to commercial single-species plantations. We compared a standard diameter tape with a Stepped Diameter Gauge (SDG) for time efficiency and measurement error. Measurement errors in diameter were slightly (but significantly) influenced by size and form of the tree or shrub, and stem height at which the measurement was made. Compared to standard tape measurement, the mean systematic error with SDG measurement was only -0.17 cm, but varied between -0.10 and -0.52 cm. Similarly, random error was relatively large, with standard deviations (and percentage coefficients of variation) averaging only 0.36 cm (and 3.8%), but varying between 0.14 and 0.61 cm (and 1.9 and 7.1%). However, at the stand scale, sampling errors (i.e. how well individual trees or shrubs selected for measurement of diameter represented the true stand population in terms of the average and distribution of diameter) generally had at least a tenfold greater influence on random errors in basal area estimates than errors in diameter measurements. This supports the use of diameter measurement tools that have high efficiency, such as the SDG. Use of the SDG almost halved the time required for measurements compared to the diameter tape. Based on these findings, recommendations include the following: (i) use of a tape to maximise accuracy when developing allometric models, or when monitoring relatively small changes in permanent sample plots (e.g. National Forest Inventories), noting that care is required in irregular-shaped, large-single-stemmed individuals, and (ii) use of a SDG to maximise efficiency when using inventory methods to assess basal area, and hence biomass or wood volume, at the stand scale (i.e. in studies of impacts of management or site quality) where there are budgetary constraints, noting the importance of sufficient sample sizes to ensure that the population sampled represents the true population.

Using National-Scale Data To Develop Nutrient-Microcystin Relationships That Guide Management Decisions

Quantitative models that predict cyanotoxin concentrations in lakes and reservoirs from nutrient concentrations would facilitate management of these resources for recreation and as sources of drinking water. Development of these models from field data has been hampered by the high proportion of samples in which cyanotoxin concentrations are below detection limits and by the high variability of cyanotoxin concentrations within individual lakes. Here, we describe a national-scale hierarchical Bayesian model that addresses these issues and that predicts microcystin concentrations from summer mean total nitrogen and total phosphorus concentrations. This model accounts for 69% of the variance in mean microcystin concentrations in lakes and reservoirs of the conterminous United States. Mean microcystin concentrations were more strongly associated with differences in total nitrogen than total phosphorus. A general approach for assessing this and similar types of models for their utility for guiding management decisions is also described.

Allometric Models for Predicting Aboveground Biomass and Carbon Stock of Tropical Perennial C4 Grasses in Hawaii

Biomass is a promising renewable energy option that provides a more environmentally sustainable alternative to fossil resources by reducing the net flux of greenhouse gasses to the atmosphere. Yet, allometric models that allow the prediction of aboveground biomass (AGB), biomass carbon (C) stock non-destructively have not yet been developed for tropical perennial C₄ grasses currently under consideration as potential bioenergy feedstock in Hawaii and other subtropical and tropical locations. The objectives of this study were to develop optimal allometric relationships and site-specific models to predict AGB, biomass C stock of napiergrass, energycane, and sugarcane under cultivation practices for renewable energy and validate these site-specific models against independent data sets generated from sites with widely different environments. Several allometric models were developed for each species from data at a low elevation field on the island of Maui, Hawaii. A simple power model with stalk diameter (D) was best related to AGB and biomass C stock for napiergrass, energycane, and sugarcane, (R² = 0.98, 0.96, and 0.97, respectively). The models were then tested against data collected from independent fields across an environmental gradient. For all crops, the models over-predicted AGB in plants with lower stalk D, but AGB was under-predicted in plants with higher stalk D. The models using stalk D were better for biomass prediction compared to dewlap H (Height from the base cut to most recently exposed leaf dewlap) models, which showed weak validation performance. Although stalk D model performed better, however, the mean square error (MSE)-systematic was ranged from 23 to 43 % of MSE for all crops. A strong relationship between model coefficient and rainfall was existed, although these were irrigated systems; suggesting a simple site-specific coefficient modulator for rainfall to reduce systematic errors in water-limited areas. These allometric equations provide a tool for farmers in the tropics to estimate perennial C₄ grass biomass and C stock during decision-making for land management and as an environmental sustainability indicator within a renewable energy system.

Soil carbon and belowground carbon balance of a short-rotation coppice: assessments from three different approaches

Uncertainty in soil carbon (C) fluxes across different land-use transitions is an issue that needs to be addressed for the further deployment of perennial bioenergy crops. A large-scale short-rotation coppice (SRC) site with poplar (Populus) and willow (Salix) was established to examine the land-use transitions of arable and pasture to bioenergy. Soil C pools, output fluxes of soil CO ₂, CH ₄, dissolved organic carbon (DOC) and volatile organic compounds, as well as input fluxes from litter fall and from roots, were measured over a 4-year period, along with environmental parameters. Three approaches were used to estimate changes in the soil C. The largest C pool in the soil was the soil organic carbon (SOC) pool and increased after four years of SRC from 10.9 to 13.9 kg C m^-2. The belowground woody biomass (coarse roots) represented the second largest C pool, followed by the fine roots (Fr). The annual leaf fall represented the largest C input to the soil, followed by weeds and Fr. After the first harvest, we observed a very large C input into the soil from high Fr mortality. The weed inputs decreased as trees grew older and bigger. Soil respiration averaged 568.9 g C m^-2 yr^-1. Leaching of DOC increased over the three years from 7.9 to 14.5 g C m^-2. The pool-based approach indicated an increase of 3360 g C m^-2 in the SOC pool over the 4-year period, which was high when compared with the -27 g C m^-2 estimated by the flux-based approach and the -956 g C m^-2 of the combined eddy-covariance + biometric approach. High uncertainties were associated to the pool-based approach. Our results suggest using the C flux approach for the assessment of the short-/medium-term SOC balance at our site, while SOC pool changes can only be used for long-term C balance assessments.

Altitudinal filtering of large-tree species explains above-ground biomass variation in an Atlantic Central African rain forest

Patterns in above-ground biomass of tropical forests over short altitudinal gradients are poorly known. The aim of this study was to investigate the variation of above-ground biomass with altitude in old-growth forests and determine the importance of changes in floristic composition as a cause of this variation. We used a dataset from 15 1-ha permanent plots established from lowland (200 m asl) to submontane forests (900 m asl) in the Ngovayang Massif, south-western Cameroon. We analysed variation over altitude in two specific functional traits, the potential maximum tree height and the wood density. Forest above-ground biomass decreased from 500–600 Mg ha−1 in lowland plots to around 260 Mg ha−1 at the highest altitudes. The contribution to above-ground biomass of large-tree species (dbh ≥ 70 cm) decreased with altitude, while the contribution of smaller trees was constant. Contribution of the Fabaceae subfamily Caesalpinioideae decreased with altitude, while those of Clusiaceae, Phyllanthaceae and Burseraceae increased. While potential maximum tree height significantly decreased, wood specific gravity displayed no trend along the gradient. Finally, the decrease in above-ground biomass along the short altitudinal gradient can be at least partially explained by a shift in species composition, with large-tree species being filtered out at the highest altitudes. These results suggest that global change could lead to significant shifts in the properties of montane forests over time.

Sensitivity of Above-Ground Biomass Estimates to Height-Diameter Modelling in Mixed-Species West African Woodlands

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.

Testing the generality of above-ground biomass allometry across plant functional types at the continent scale

Accurate ground-based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost-effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above-ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power-law models explained 84-95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand-based biomass from allometric models of varying levels of generalization (species-specific, plant functional type) were validated using whole-plot harvest data from 17 contrasting stands (range: 9-356 Mg ha(-1) ). Losses in efficiency of prediction were <1% if generalized models were used in place of species-specific models. Furthermore, application of generalized multispecies models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency of stand-level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost-effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species-specific models is only warranted when gains in accuracy of stand-based predictions are relatively high (e.g. high-value monocultures).

Community assessment of tropical tree biomass: challenges and opportunities for REDD

BACKGROUND

REDD+ programs rely on accurate forest carbon monitoring. Several REDD+ projects have recently shown that local communities can monitor above ground biomass as well as external professionals, but at lower costs. However, the precision and accuracy of carbon monitoring conducted by local communities have rarely been assessed in the tropics. The aim of this study was to investigate different sources of error in tree biomass measurements conducted by community monitors and determine the effect on biomass estimates. Furthermore, we explored the potential of local ecological knowledge to assess wood density and botanical identification of trees.

RESULTS

Community monitors were able to measure tree DBH accurately, but some large errors were found in girth measurements of large and odd-shaped trees. Monitors with experience from the logging industry performed better than monitors without previous experience. Indeed, only experienced monitors were able to discriminate trees with low wood densities. Local ecological knowledge did not allow consistent tree identification across monitors.

CONCLUSION

Future REDD+ programmes may benefit from the systematic training of local monitors in tree DBH measurement, with special attention given to large and odd-shaped trees. A better understanding of traditional classification systems and concepts is required for local tree identifications and wood density estimates to become useful in monitoring of biomass and tree diversity.

Wood Specific Gravity Variations and Biomass of Central African Tree Species: The Simple Choice of the Outer Wood

CONTEXT

Wood specific gravity is a key element in tropical forest ecology. It integrates many aspects of tree mechanical properties and functioning and is an important predictor of tree biomass. Wood specific gravity varies widely among and within species and also within individual trees. Notably, contrasted patterns of radial variation of wood specific gravity have been demonstrated and related to regeneration guilds (light demanding vs. shade-bearing). However, although being repeatedly invoked as a potential source of error when estimating the biomass of trees, both intraspecific and radial variations remain little studied. In this study we characterized detailed pith-to-bark wood specific gravity profiles among contrasted species prominently contributing to the biomass of the forest, i.e., the dominant species, and we quantified the consequences of such variations on the biomass.

METHODS

Radial profiles of wood density at 8% moisture content were compiled for 14 dominant species in the Democratic Republic of Congo, adapting a unique 3D X-ray scanning technique at very high spatial resolution on core samples. Mean wood density estimates were validated by water displacement measurements. Wood density profiles were converted to wood specific gravity and linear mixed models were used to decompose the radial variance. Potential errors in biomass estimation were assessed by comparing the biomass estimated from the wood specific gravity measured from pith-to-bark profiles, from global repositories, and from partial information (outer wood or inner wood).

RESULTS

Wood specific gravity profiles from pith-to-bark presented positive, neutral and negative trends. Positive trends mainly characterized light-demanding species, increasing up to 1.8 g.cm-3 per meter for Piptadeniastrum africanum, and negative trends characterized shade-bearing species, decreasing up to 1 g.cm-3 per meter for Strombosia pustulata. The linear mixed model showed the greater part of wood specific gravity variance was explained by species only (45%) followed by a redundant part between species and regeneration guilds (36%). Despite substantial variation in wood specific gravity profiles among species and regeneration guilds, we found that values from the outer wood were strongly correlated to values from the whole profile, without any significant bias. In addition, we found that wood specific gravity from the DRYAD global repository may strongly differ depending on the species (up to 40% for Dialium pachyphyllum).

MAIN CONCLUSION

Therefore, when estimating forest biomass in specific sites, we recommend the systematic collection of outer wood samples on dominant species. This should prevent the main errors in biomass estimations resulting from wood specific gravity and allow for the collection of new information to explore the intraspecific variation of mechanical properties of trees.

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha^-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha^-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha^-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

Validating Community-Led Forest Biomass Assessments

The lack of capacity to monitor forest carbon stocks in developing countries is undermining global efforts to reduce carbon emissions. Involving local people in monitoring forest carbon stocks could potentially address this capacity gap. This study conducts a complete expert remeasurement of community-led biomass inventories in remote tropical forests of Papua New Guinea. By fully remeasuring and isolating the effects of 4,481 field measurements, we demonstrate that programmes employing local people (non-experts) can produce forest monitoring data as reliable as those produced by scientists (experts). Overall, non-experts reported lower biomass estimates by an average of 9.1%, equivalent to 55.2 fewer tonnes of biomass ha^-1, which could have important financial implications for communities. However, there were no significant differences between forest biomass estimates of expert and non-expert, nor were there significant differences in some of the components used to calculate these estimates, such as tree diameter at breast height (DBH), tree counts and plot surface area, but were significant differences between tree heights. At the landscape level, the greatest biomass discrepancies resulted from height measurements (41%) and, unexpectedly, a few large missing trees contributing to a third of the overall discrepancies. We show that 85% of the biomass discrepancies at the tree level were caused by measurement taken on large trees (DBH ≥50cm), even though they consisted of only 14% of the stems. We demonstrate that programmes that engage local people can provide high-quality forest carbon data that could help overcome barriers to reducing forest carbon emissions in developing countries. Nonetheless, community-based monitoring programmes should prioritise reducing errors in the field that lead to the most important discrepancies, notably; overcoming challenges to accurately measure large trees.

Carbon accounting and economic model uncertainty of emissions from biofuels-induced land use change

Few of the numerous published studies of the emissions from biofuels-induced "indirect" land use change (ILUC) attempt to propagate and quantify uncertainty, and those that have done so have restricted their analysis to a portion of the modeling systems used. In this study, we pair a global, computable general equilibrium model with a model of greenhouse gas emissions from land-use change to quantify the parametric uncertainty in the paired modeling system's estimates of greenhouse gas emissions from ILUC induced by expanded production of three biofuels. We find that for the three fuel systems examined--US corn ethanol, Brazilian sugar cane ethanol, and US soybean biodiesel--95% of the results occurred within ±20 g CO2e MJ(-1) of the mean (coefficient of variation of 20-45%), with economic model parameters related to crop yield and the productivity of newly converted cropland (from forestry and pasture) contributing most of the variance in estimated ILUC emissions intensity. Although the experiments performed here allow us to characterize parametric uncertainty, changes to the model structure have the potential to shift the mean by tens of grams of CO2e per megajoule and further broaden distributions for ILUC emission intensities.