Tree Biomass Estimation in Central African Forests Using Allometric Models

Abstract Quantifying tree biomass is an important research and management goal across many disciplines. For species that exhibit predictable relationships between structural metrics (e.g. diameter, height, crown breadth) and total weight, allometric calculations produce accurate estimates of above-ground biomass. However, such methods may be insufficient where inter-individual variation is large relative to individual biomass and is itself of interest (for example, variation due to herbivory). In an East African savanna bushland, we analysed photographs of small (<5 m) trees from perpendicular angles and fixed distances to estimate above-ground biomass. Pixel area of trees in photos and diameter were more strongly related to measured, above-ground biomass of destructively sampled trees than biomass estimated using a published allometric relation based on diameter alone (R2 = 0.86 versus R2 = 0.68). When tested on trees in herbivore-exclusion plots versus unfenced (open) plots, our predictive equation based on photos confirmed higher above-ground biomass in the exclusion plots than in unfenced (open) plots (P < 0.001), in contrast to no significant difference based on the allometric equation (P = 0.43). As such, our new technique based on photographs offers an accurate and cost-effective complement to existing methods for tree biomass estimation at small scales with potential application across a wide variety of settings.

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Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

Biogeosciences ◽

10.5194/bg-13-1571-2016 ◽

2016 ◽

Vol 13 (5) ◽

pp. 1571-1585 ◽

Cited By ~ 36

Author(s):

Pierre Ploton ◽

Nicolas Barbier ◽

Stéphane Takoudjou Momo ◽

Maxime Réjou-Méchain ◽

Faustin Boyemba Bosela ◽

...

Keyword(s):

Tropical Forest ◽

Reference Model ◽

Original Data ◽

Carbon Stocks ◽

Forest Carbon ◽

Biomass Estimation ◽

Systematic Bias ◽

Data Set ◽

Allometric Models ◽

Large Trees

Abstract. Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥  45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Taking a crown mass proxy into account in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error (in %) from [−23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

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Assessment of generalized allometric models for aboveground biomass estimation: A case study in Australia

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2020.105610 ◽

2020 ◽

Vol 175 ◽

pp. 105610

Author(s):

Li Liu ◽

Samsung Lim ◽

Xuesong Shen ◽

Marta Yebra

Keyword(s):

Aboveground Biomass ◽

Biomass Estimation ◽

Allometric Models

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Closing a gap in tropical forest biomass estimation: accounting for crown mass variation in pantropical allometries

Biogeosciences Discussions ◽

10.5194/bgd-12-19711-2015 ◽

2015 ◽

Vol 12 (23) ◽

pp. 19711-19750 ◽

Cited By ~ 5

Author(s):

P. Ploton ◽

N. Barbier ◽

S. T. Momo ◽

M. Réjou-Méchain ◽

F. Boyemba Bosela ◽

...

Keyword(s):

Tropical Forest ◽

Reference Model ◽

Central Africa ◽

Carbon Stocks ◽

Forest Carbon ◽

Biomass Estimation ◽

Systematic Bias ◽

Allometric Models ◽

Original Dataset ◽

Large Trees

Abstract. Accurately monitoring tropical forest carbon stocks is an outstanding challenge. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference in the coming years. However, this reference model shows a systematic bias for the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass dataset on 673 trees measured in five tropical countries (101 trees > 100 cm in diameter) and an original dataset of 130 forest plots (1 ha) from central Africa to quantify the error of biomass allometric models at the individual and plot levels when explicitly accounting or not accounting for crown mass variations. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Accounting for a crown mass proxy in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error from −23–16 to 0–10 %. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by accounting for a crown mass proxy for the largest trees in a stand, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

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ALLOMETRIC MODELS FOR ESTIMATING ABOVEGROUND BIOMASS AND BIOMASS ALLOCATION OF CAPIXINGUI TREES (Croton floribundus Spreng.) IN AN AGRISILVICULTURAL SYSTEM

Revista Árvore ◽

10.1590/0100-67622016000200010 ◽

2016 ◽

Vol 40 (2) ◽

pp. 279-288 ◽

Cited By ~ 1

Author(s):

Maria Luiza Franceschi Nicodemo ◽

Marcelo Dias Muller ◽

Antônio Aparecido Carpanezzi ◽

Vanderley Porfírio-da-Silva

Keyword(s):

Aboveground Biomass ◽

Stem Diameter ◽

Distribution Model ◽

Biomass Estimation ◽

Total Biomass ◽

Biomass Distribution ◽

Residual Distribution ◽

Allometric Models ◽

Crown Diameter ◽

Bole Biomass

ABSTRACT The objective of this study was to select allometric models to estimate total and pooled aboveground biomass of 4.5-year-old capixingui trees established in an agrisilvicultural system. Aboveground biomass distribution of capixingui was also evaluated. Single- (diameter at breast height [DBH] or crown diameter or stem diameter as the independent variable) and double-entry (DBH or crown diameter or stem diameter and total height as independent variables) models were studied. The estimated total biomass was 17.3 t.ha-1, corresponding to 86.6 kg per tree. All models showed a good fit to the data (R2ad > 0.85) for bole, branches, and total biomass. DBH-based models presented the best residual distribution. Model lnW = b0 + b1* lnDBH can be recommended for aboveground biomass estimation. Lower coefficients were obtained for leaves (R2ad > 82%). Biomass distribution followed the order: bole>branches>leaves. Bole biomass percentage decreased with increasing DBH of the trees, whereas branch biomass increased.

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Tree Biomass Estimation in Karst Forest of West Papua, Indonesia

Jurnal Wasian ◽

10.20886/jwas.v8i2.6400 ◽

2021 ◽

Vol 8 (2) ◽

pp. 75-86

Author(s):

Andes Rozak ◽

◽

Destri Destri ◽

Zaenal Mutaqien

Keyword(s):

Allometric Equations ◽

Biomass Estimation ◽

Total Biomass ◽

Tree Biomass ◽

Karst Forest ◽

Large Trees ◽

West Papua ◽

Reference Equation ◽

Selection Of ◽

Made In

Indonesia is estimated to have 14,5 million hectares of karst areas. The characteristic of karst vegetation is specific, one of which is the dominance of small trees. With all of the potency, their vegetation acts as a significant carbon sequester and store it in biomass. This study aims to estimate and discuss biomass estimation in the karst forest within the Nature Recreational Park of Beriat, a protected area in South Sorong, West Papua. A total of 28 plots were made in the forest using the purposive random sampling method. Tree biomass (DBH ≥10 cm) was estimated using five different allometric equations. The results showed that the biomass was estimated at ca. 264 Mg ha-1 (95 % CI: 135-454 Mg ha-1). While small trees (DBH 10 – 30 cm) only contribute 30 % of the total biomass, about 38 % of the biomass is the contribution of large trees (DBH >50 cm), where Pometia pinnata contributes ca. 39 % of the biomass at plot-level. The use of various allometric equations results in different biomass estimates and biases with deviations ranged from -14.78 % to +17.02 % compared to the reference equation. Therefore, the selection of allometric equations used must be considered carefully to reduce uncertainties in biomass estimation.

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Generalized additive models reveal among-stand variation in live tree biomass equations

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0219 ◽

2020 ◽

Author(s):

Jacob I. Levine ◽

Perry de Valpine ◽

John J. Battles

Keyword(s):

Linear Models ◽

Generalized Additive Models ◽

Additive Models ◽

Carbon Accounting ◽

Stand Age ◽

Accurate Estimation ◽

Biomass Estimation ◽

Allometric Relationships ◽

Tree Biomass ◽

Live Tree

Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees; however, limited datasets, uncertainty in model form, and unaccounted for sources of variation warrant a re-examination of allometric relationships using modern statistical techniques. We asked the following questions: (1) Is there among-stand variation in allometric relationships? (2) Is there nonlinearity in allometric relationships? (3) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (4) What are the implications for biomass estimation? To answer these questions, we synthesized a dataset of small trees from six different studies in the White Mountains of New Hampshire. We compared the performance of generalized additive models (GAMs) and linear models and found that GAMs consistently outperform linear models. The best-fitting model indicates that allometries vary among both stands and species and contain subtle nonlinearities which are themselves variable by species. Using a planned contrasts analysis, we were able to attribute some of the observed among-stand heterogeneity to differences in stand age. However, variability in these results point to additional sources of stand-level heterogeneity, which if identified could improve the accuracy of live-tree biomass estimation.

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Deriving Individual-Tree Biomass from Effective Crown Data Generated by Terrestrial Laser Scanning

Remote Sensing ◽

10.3390/rs11232793 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2793

Author(s):

Yujie Zheng ◽

Weiwei Jia ◽

Qiang Wang ◽

Xu Huang

Keyword(s):

Laser Scanning ◽

Three Dimensional ◽

Terrestrial Laser Scanning ◽

Tree Height ◽

Biomass Estimation ◽

Tree Biomass ◽

Crown Height ◽

Individual Tree ◽

Cloud Data ◽

Biomass Equations

Biomass reflects the state of forest management and is critical for assessing forest benefits and carbon storage. The effective crown is the region above the lower limit of the forest crown that includes the maximum vertical distribution density of branches and leaves; this component plays an important role in tree growth. Adding the effective crown to biomass equations can enhance the accuracy of the derived biomass. Six sample plots in a larch plantation (ranging in area from 0.06 ha to 0.12 ha and in number of trees from 63 to 96) at the Mengjiagang forest farm in Huanan County, Jiamusi City, Heilongjiang Province, China, were analyzed in this study. Terrestrial laser scanning (TLS) was used to obtain three-dimensional point cloud data on the trees, from which crown parameters at different heights were extracted. These parameters were used to determine the position of the effective crown. Moreover, effective crown parameters were added to biomass equations with tree height as the sole variable to improve the accuracy of the derived individual-tree biomass estimates. The results showed that the minimum crown contact height was very similar to the effective crown height, and an increase in model accuracy was apparent (with R a 2 increasing from 0.846 to 0.910 and root-mean-square error (RMSE) decreasing from 0.372 kg to 0.286 kg). The optimal model for deriving biomass included tree height, crown length from minimum contact height, crown height from minimum contact height, and crown surface area from minimum contact height. The novelty of the article is that it improves the fit of individual-tree biomass models by adding crown-related variables and investigates how the accuracy of biomass estimation can be enhanced by using remote sensing methods without obtaining diameter at breast height.

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Climate-based Allometric Biomass Equations for Five Major Canadian Timber Species

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0485 ◽

2021 ◽

Author(s):

Kun Xu ◽

Jinghe Jiang ◽

Fangliang He

Keyword(s):

Aboveground Biomass ◽

Forest Biomass ◽

Allometric Equations ◽

Accurate Estimation ◽

Biomass Estimation ◽

Change Scenario ◽

Mean Annual Temperature ◽

Timber Species ◽

Trembling Aspen ◽

Allometric Models

Accurate estimation of forest biomass is essential to quantify the role forests play at balancing terrestrial carbon. Allometric equations based on tree size have been used for this purpose worldwide. There is little quantitative understanding on how environmental variation may affect tree allometries. Even less known is how to incorporate environmental factors into such equations to improve estimation. Here we tested the effects of climate on tree allometric equations and proposed to model forest biomass by explicitly incorporating climatic factors. Among the five major Canadian timber species tested, the incorporation of climate was not found to improve the allometric models. For trembling aspen and tamarack, the residuals of their conventional allometric models were found strongly related to frost-free period and mean annual temperature, respectively. The predictions of the two best climate-based models were significantly improved, which indicate that trembling aspen and tamarack store more aboveground biomass when growing in warmer than in colder regions. We showed that, under the RCP4.5 modest climate change scenario, there would be a 10% underestimation of aboveground biomass for these two species if the conventional non-climate models would still be in use in 2030. This study suggests the necessity to proactively develop climate-based allometric equations for more accurate and reliable forest biomass estimation.

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