Biomass Model Research Articles

Uniformly 13C Labeled Lignin Internal Standards for Quantitative Pyrolysis−GC−MS Analysis of Grass and Wood

With the ever-advancing lignocellulose valorization strategies, lignin analyses need to advance as well. However, lignin quantification still heavily relies on unspecific, time- and sample-consuming gravimetric, and spectrophotometric analyses. Here, we demonstrate that lignin isolates from uniformly 13C-labeled wheat straw, willow, and douglas fir serve as “ideal” internal standards for pyrolysis gas chromatography high-resolution mass spectrometry (py–GC–HR-MS) analyses of plant biomass, allowing the accurate and precise quantification and structural characterization of lignin in grasses, hardwoods, and softwoods. The 13C lignin internal standards were comprehensively structurally characterized by HSQC NMR and py–GC–HR-MS analyses, and their application for lignin quantification was validated in biomass model systems and in actual plant biomass. For all botanical origins and species, the lignin content was determined within 5% relative deviation of the Klason benchmark. These results establish the capab...

Effect of Sample Size on the Precision of Biomass Model of Pinus yunnanensis Seedlings

Effect of Sample Size on the Precision of Biomass Model of Pinus yunnanensis Seedlings

Metal catalysts supported on biochars: Part I synthesis and characterization

In the current study, synthesis and detailed characterization of cellulose biochars as a waste biomass model component and vine shoot biochars as a real waste biomass catalyst was performed. Although initially biochars exhibit poor textural properties, a simple activation process can make them much more suitable as a catalyst supports. A combination of physical (CO2) and chemical activation (ZnCl2) was evaluated. The characterization results indicated that the surface area and pore volume of the biochars have increased significantly by chemical activation treatment with ZnCl2. A series of metal catalysts (Pd, Au and Ru) supported on biochars was prepared and characterized. The prepared materials represent a set of noble metal catalysts supported on biochars with different textural and surface properties, which can be used to evaluate the catalytic role of the active phase and carbon support nature in catalytic reactions of interest, such as hydrodeoxygenation, described in the part II.

Transectos de datos LiDAR: una estrategia de muestreo para estimar biomasa aérea en áreas forestales

La estimación y el mapeo de la biomasa aérea sobre áreas extensas puede realizarse haciendo uso de las herramientas que ofrece la percepción remota. El objetivo de este estudio fue estimar la biomasa aérea de dos tipos de selva mediana: subperennifolia (SMSP) y subcaducifolia (SMSC) en la península de Yucatán, México, empleando métricas generadas a partir de datos Light Detection and Ranging (LiDAR). Se usaron datos de 365 unidades de muestreo del Inventario Nacional Forestal y de Suelos (INFyS) de México para calibrar modelos de biomasa aérea usando regresión lineal múltiple y Random Forest (RF). Con estos modelos se mapeó la biomasa aérea sobre franjas de datos LiDAR. El modelo de regresión transformado logró explicar la varianza en un 62% (RMSE = 41.44 Mg ha-1 para SMSP y 36.60 Mg ha-1 para SMSC) para ambos tipos de vegetación. Los modelos generados a través de RF lograron explicar la varianza en un 57% (RMSE = 40.73 Mg ha-1) para la SMSP y solo de 52% (RMSE = 35.10 Mg Ha-1) para la SMSC. El desfase entre la toma de datos en campo y LiDAR, así como el error en la precisión de las coordenadas de los sitios de inventario, son factores reconocidos que influyeron en los resultados. A pesar de lo anterior, las estimaciones obtenidas podrían servir de base para estimar el inventario completo de biomasa en el área de estudio incorporando datos espectrales derivados de un sensor remoto que cubra la totalidad de esta.

Evapotranspiration and biomass modelling in the Pontal Sul Irrigation Scheme

ABSTRACTIn order to make feasible the use of irrigated areas of the Pontal Sul Irrigation Scheme in Petrolina, State of Pernambuco, Brazil, modelling of real evapotranspiration (ET) and plant biomass production (BIO) at a large scale was performed with associated agro-meteorological data and the algorithm SAFER (Simple Algorithm For Evapotranspiration Retrieving) using MODIS images for the years 2010–2017. The year 2012 presented the highest ET average with 3.15 mm day−1 in the rainy season, reaching a maximum of 6 mm day−1. The lowest mean ET was recorded in the year 2013, with 0.08 mm day−1 in the dry season, and the maximum recorded for the period was 3.40 mm day−1 in the irrigated agricultural area. For BIO, the highest average was 104.82 kg ha−1 day−1, during the rainy season of 2012, reaching a maximum value of 252 kg ha−1 day−1. The lowest average was for the dry period of 2013, with a value of 0.93 kg ha−1 day−1, and a maximum of 112.51 kg ha−1 day−1 in the irrigated agricultural area. The higher ET results represent moisture in the root zone, provided by rainfall and/or irrigation, showing maximum values in the rainy season. The results obtained for BIO suffer greatly from perimeter water availability. The present study becomes very important to the management of water resources, as it provides an increase in water productivity for food production.

A Diaminopropane Diolefin Ru(0) Complex Catalyzes Hydrogenation and Dehydrogenation Reactions

AbstractNew ruthenium (0) complexes with a cooperative diolefin diaminopropane (DAP) or the dehydrogenated iminopropenamide ligand (IPA) were synthesized for comparison with their diaminoethane (DAE)/ diazadiene (DAD) ruthenium analogues. These DAP/IPA complexes are efficient catalysts in dehydrogenation reactions of alkaline aqueous methanol which proceeds under mild conditions (T=70 °C) and of higher alcohols, forming the corresponding carbonate and carboxylates, respectively. The scope of the reaction includes an example of a 1,2‐diol as model for biomass derived alcohols. Their catalytic applications are extended to the atom‐efficient dehydrogenative coupling of alcohols and amines to amides. The reaction proceeds without any additives and is applicable to the synthesis of formamides from methanol. Moreover, DAP/IPA complexes catalyze the hydrogenation of a series of esters, lactone, ketone, activated olefin, aldehyde and imine substrates. The diaminopropane Ru catalyst exhibits higher activity compared to the dehydrogenated β‐ketiminate (IPA) and previously studied DAD/DAE based catalysts. We present studies on their stoichiometric reactivity with relevance to their possible catalytic mechanisms and the isolation and full characterization of key reaction intermediates.

The Variation Driven by Differences between Species and between Sites in Allometric Biomass Models

Background and Objectives: It is commonly assumed that allometric biomass models are species-specific and site-specific. However, the magnitude of species and site dependency in these models is not well-known. This study aims to investigate the variation in allometric models (i.e., aboveground biomass predicted by diameter at breast height and tree height) that has originated from the differences between tree species and between sites, thereby contributing to a better understanding of species and site-specificity issue in these models. Materials and Methods: The study is based on two large biomass datasets of 4921 and 5199 trees, from Eurasia and Canada. Using a nested ANOVA model on relative aboveground biomass residuals (with species and site as random effects), the proportion of variance explained by species or site was assessed by means of Variance Partition Coefficient (VPC). Results: The proportion of variance explained by species (VPCspecies = 42.56%, SE = 6.10% for Dataset 1 and VPCspecies = 47.54%, SE = 6.07% for Dataset 2) was larger than that explained by site (VPCsite = 20.08%, SE = 3.35% for Dataset 1 and VPCsite = 8.27%, SE = 1.38% for Dataset 2). The proportion of variance explained by site decreased by 24%–44% and the proportion of variance explained by species changed only slightly, when height is included in the allometric biomass models (i.e., models based on diameter at breast height alone, compared to models based on diameter at breast height and tree height). Conclusions: Allometric biomass models were more species-specific than they were site-specific. Therefore, the species (i.e., differences between species) seems to be a more important driver of variability in allometric models compared to site (i.e., differences between sites). Including height in allometric biomass models helped reduce the dependency of these models, on sites only.

Effect of measurement errors on the estimation of tree biomass

Diameter at breast height (DBH) is commonly used to predict the aboveground biomass (AGB) of forests and to derive biomass models for single trees; however, there is evidence that measurement errors of DBH have not been previously considered. In this study, two types of measurement errors were evaluated: errors in national forest inventory data (NFID) and errors in a calibration data set (CDS). Using Monte Carlo simulations, the uncertainties arising from these two measurement errors were quantified. In addition, the effects of measurement errors on estimates under different error assumptions were analyzed to determine how these two uncertainties change with increasing errors. The results show that CDS measurement error contributes more to the total uncertainty, whereas NFID measurement error has a negligible effect on estimating the biomass of regional forests. The uncertainties of both types of measurement error increased with increasing error assumptions; however, the uncertainties caused by CDS measurement error were noticeably larger than those caused by NFID measurement error. Thus, the greatest potential for reducing uncertainties caused by measurement error lies in increasing the accuracy of DBH measurements in CDS.

Similar Impacts of Alien and Native Tree Species on Understory Light Availability in a Temperate Forest

Research Highlights: We evaluated influence of alien and native trees and shrubs on stand leaf area index to basal area ratio, indicating that both groups provide similar amounts of foliage. Background and Objectives: Foliage traits determine tree species effect on understory light availability. Direct comparisons of understory light availability due to different foliage traits of tree species are conducted less often at the stand level. We hypothesized that light availability is driven by canopy leaf area, and alien species contribution to canopy foliage will be similar to native species due to analogous patterns of biomass allocation in tree species. Materials and Methods: We studied forests dominated by alien and native tree species in Wielkopolski National Park (Western Poland). We measured light availability using the LAI-2200 canopy analyzer (Li-Cor Inc., Lincoln, NE, USA) and we calculated leaf area index (LAI) in 170 stands using published models of foliage biomass and data on specific leaf area. Results: Our study confirmed an impact of LAI on light availability in the understory layer. Analyzing the proportion of contribution to stand LAI and basal area (BA) we found that most alien species did not differ in LAI to BA ratio from native species. The exception was Prunus serotina Ehrh., with a LAI to BA ratio higher than all native and alien trees. However, the highest LAI to BA ratios we found were for the alien shrub Cotoneaster lucidus Schltdl. and native shrubs of fertile broadleaved forests. Conclusions: Our study showed that alien species contribution to shading the understory is comparable to native species, with the exception of P. serotina due to its dominance in the higher shrub canopy strata where it exhibits different patterns of biomass allocation than native trees. Our study explained that invasive tree species impact on light availability in forest ecosystems is mainly mediated by the increased quantity of foliage, not by more effective LAI to BA ratio.

Nondestructive Estimation of the Above-Ground Biomass of Multiple Tree Species in Boreal Forests of China Using Terrestrial Laser Scanning

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.

Aboveground Biomass Of Mongolian Larch (Larix Sibirica Ledeb.) Forests In The Eurasian Region

We used our database of tree biomass with a number of 433 sample trees of Larix from different ecoregions of Eurasia, involving 61 trees from Mongolia for developing an additive model of biomass tree components. Our approach solved the combined problem of additivity and regionality of the model. Our additive model of tree aboveground biomass was harmonized in two ways: first, it eliminated the internal contradictions of the component and of the total biomass equations, secondly, it took into account regional (and correspondingly species-specific) differences of trees in its component structure. A significant excess of larch biomass in the forest-tundra is found that may be explained by permafrost conditions, by tree growth in low-yielding stands with a high basic density of stem wood and relatively high developed tree crown in open stands. The aboveground biomass of larch trees in Mongolia does not stand out against the background of the most ecoregions of Eurasia. Based on our results, we conclude that the growing conditions of larch in Mongolia are not as tough as it was suggested earlier by other scientists. Biomass relations between regions may be explained by unknown and unaccounted factors and errors of measurements in all their phases (assessment of age, diameter, height of a tree, the selection of supposedly representative samples of component biomass, their drying, weighing, etc.). The question what explains the regional differences in the structure of biomass of trees with the same linear dimensions of their stems, remains open. Undoubtedly, the differences in tree age here play an important role. Also, important factor is the variation in the morphological structure of stands, which, in turn, is determined by both climatic and edaphic factors. The obtained models allow the determination of larch forest biomass in different ecoregions of Eurasia with the help of height and diameter data.

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.

Comparing independent climate-sensitive models of aboveground biomass and diameter growth with their compatible simultaneous model system for three larch species in China

Accurate estimate of tree biomass is essential for forest management. In recent years, several climate-sensitive allometric biomass models with diameter at breast height [Formula: see text] as a predictor have been proposed for various tree species and climate zones to estimate tree aboveground biomass (AGB). But the allometric models only account for the potential effects of climate on tree biomass and do not simultaneously explain the influence of climate on [Formula: see text] growth. In this study, based on the AGB data from 256 destructively sampled trees of three larch species randomly distributed across the five secondary climate zones in northeastern and northern China, we first developed a climate-sensitive AGB base model and a climate-sensitive [Formula: see text] growth base model using a nonlinear least square regression separately. A compatible simultaneous model system was then developed with the climate-sensitive AGB and [Formula: see text] growth models using a nonlinear seemingly unrelated regression. The potential effects of several temperature and precipitation variables on AGB and [Formula: see text] growth were evaluated. The fitting results of climatic sensitive base models were compared against those of their compatible simultaneous model system. It was found that a decreased isothermality ([mean of monthly (maximum temperature-minimum temperature)]/(Maximum temperature of the warmest month-Minimum temperature of the coldest month)) and total growing season precipitation, and increased annual precipitation significantly increased the values of AGB; an increase of temperature seasonality (a standard deviation of the mean monthly temperature) and precipitation seasonality (a standard deviation of the mean monthly precipitation) could lead to the increase of [Formula: see text]. The differences of the model fitting results between the compatible simultaneous system with the consideration of climate effects on both AGB and [Formula: see text] growth and its corresponding climate-sensitive AGB and [Formula: see text] growth base models were very small and insignificant [Formula: see text]. Compared to the base models, the inherent correlation of AGB with [Formula: see text] was taken into account effectively by the proposed compatible model system developed with the climate-sensitive AGB and [Formula: see text] growth models. In addition, the compatible properties of the estimated AGB and [Formula: see text] were also addressed substantially in the proposed model system.

Applying a Sustainable Development Lens to Global Biomass Potentials

The Sustainable Development Goals (SDGs), adopted by all UN Member States in 2015, guide societies to achieve a better and more sustainable future. Depleting fossil fuels and climate change will strongly increase the demand for biomass, as governments shift towards bioeconomies. Though research has estimated future biomass availability for bioenergetic uses, the implications for sustainable development have hardly been discussed; e.g., how far the estimates account for food security, sustainability and the satisfaction of basic human needs, and what this implies for intragenerational equity. This research addresses the gap through a systematic literature review and our own modeling. It shows that the biomass models insufficiently account for food security; e.g., by modeling future food consumption below current levels. The available biomass, if fairly distributed, can globally replace fossil fuels required for future material needs but hardly any additional energy needs. To satisfy basic human needs, the material use of biomass should, therefore, be prioritized over bioenergy. The different possibilities for biomass allocation and distribution need to be analyzed for their potential negative implications, especially for the poorer regions of the world. Research, society, business and politicians have to address those to ensure the ’leave no one behind´ commitment of the SDGs.

Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China

Human activities alter the growth of coastal wetland vegetation. In the present study, we used a spectrometer and hyperspectral data to determine and compare the biomass of Suaeda salsa in a coastal wetland under protective and destructive activities. Using typical discriminants, the hyperspectral data of Suaeda salsa were distinguished under the influence of two kinds of human activity, and the accuracy of the inversion model of biomass was established following improved differentiation of the data under the influence of human activities. The original spectral reflectance and vegetation index were selected, and the biomass-inversion model was established by linear regression and partial least-squares regression. The model established by partial least-squares regression had a good precision (R2>0.85, RMSE%<5.6%). Hyperspectral technology can accurately show plant biomass and the indirect effects of interference by human activities of different intensity on coastal wetlands. The accuracy of the models can be improved by distinguishing the vegetation patterns under the influence of different types of human activity, and then constructing the biomass models. This study provides technical support for the use of quantitative remote sensing-based methods to monitor the fragile ecology of coastal wetlands under the influence of human activities.

Optimal Model to Estimate Biomass and Carbon Stock at Agroforestry Stand Cempaka (Elmerillia Sp) in Minahasa District

Management of stands in community forests such as agroforestry and stands outside forest areas can reduce greenhouse gas emissions. The agroforestry system is a good choices in reducing climate change compared to other options in terrestrial ecosystems. This study aimed at obtaining the most optimal model to estimating the biomass of Cempaka tree (Elmerillia Sp) in community forest stands in Minahasa Regency. The sample was selected through stratified random sampling from two locations. The first location represents the stand of the Cempaka tree community forest, and the second location represents mixed community forest stands. 35 trees were selected for felling, and measurements of wet weight and biomass were carried out based on tree parts. The model to be developed is an allometric regression model of 35 selected trees, and a previously published model. The estimation model obtained is the Cempaka tree biomass estimator model according to tree dimensions such as stems, branches, twigs, leaves, and roots. The results showed that the allometric regression model in the form of logarithmic regression with one independent variable, i.e. diameter of the tree, was quite good in predicting the Cempaka tree biomass. The accuracy of the estimator model for total tree biomass shows R2 of 99.5% with MSE 0.0023 in pure cempaka tree stands. At the second location the coefficient R2 is 98.3% with MSE 0.0038. The predictive results show that the cempaka tree in the community forest stands has a biomass content of 62% - 72%, and the stem part is the largest content.

Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R 2). A forest AGB map of the study area was generated using the optimal model.

Modeling biomass of white birch (Betula platyphylla) in the Lesser Khingan Range of China based on terrestrial 3D laser scanning system

AbstractThe three‐dimensional (3D) terrestrial laser scanning (TLS) system was used to obtain the point cloud data of Betula platyphylla sample trees and the diameter at breast height (DBH), tree height, and biomass were measured for these trees. A 3D point cloud model was constructed to obtain the parameters, such as trunk volume, crown volume, and crown surface area. Then, the TLS‐based parameters were introduced into the traditional constant allometric ratio (CAR) biomass models. The fitting precision of the modified models was compared with the traditional CAR models. Results showed that (a) the total biomass model and the biomass allocation models by introducing trunk volume and crown volume were optimal in terms of coefficient of determination (R2). The R2 of the total, trunk, branch, and leaf biomass models were 0.990, 0.966, 0.960, and 0.936, respectively. (b) The errors for the modified biomass models with trunk volume and crown volume were the smallest.Recommendations for Resource Managers Forest biomass represents the primary productivity of forests, which plays an important role in the study of the relationship between forest ecosystems and climate. The traditional constant allometric ratio biomass models were modified by introducing some key parameters from three‐dimensional point cloud data, such as trunk volume, crown volume, or crown surface area to improve the fitting precision. Betula platyphylla biomass can be well estimated by the modified biomass models, which can significantly increase the reliability of tracking the changes of carbon stocks of B. platyphylla natural secondary forest in the Lesser Khingan Range of China.

A newly developed method to extract the optimal hyperspectral feature for monitoring leaf biomass in wheat

Hyperspectral image provides a plethora of information, some of which may be redundant. Therefore, reducing information’s dimensionality using feature selection methods becomes essential. Here, we proposed a new technique, named synergy interval partial least squares (SIPLS) with successive projections algorithm (SPA) (SIPLS-SPA), combining SIPLS and SPA, to efficiently extract optimal spectral features of wheat biomass from hyperspectral image data. In this study, hyperspectral images and leaf biomass were acquired from two-year wheat field experiments with varied nitrogen rates, planting densities, and cultivars. The results showed that eight wavelengths (706, 724, 734, 806, 808, 810, 812, and 816 nm) were selected as the sensitive input variables to establish partial least squares regression (PLSR) model for wheat leaf biomass. The SIPLS-SPA biomass model performed better with higher Rc2 (0.79) in calibration, lower RMSEv (0.059 kg/m2) and RRMSEv (38.55%) in validation. Compared with other state-of-the-art feature selection techniques, the SIPLS-SPA method provided significantly fewer unrelated, collinear spectral variables, and showed a promising application in terms of lower overall complexity, reduced computational complexity, and shorter running time. Furthermore, this work demonstrates the potential of SIPLS-SPA method for extracting other plant traits from hyperspectral data in future agricultural applications.

A practical measure for determining if diameter (D) and height (H) should be combined into D2H in allometric biomass models

AbstractTree diameter at breast height (D) and tree height (H) are often used as predictors of individual tree biomass. Because D and H are correlated, the combined variable D2H is frequently used in regression models instead of two separate independent variables, to avoid collinearity related issues. The justification for D2H is that aboveground biomass is proportional to the volume of a cylinder of diameter, D, and height, H. However, the D2H predictor constrains the model to produce parameter estimates for D and H that have a fixed ratio, in this case, 2.0. In this paper we investigate the degree to which the D2H predictor reduces prediction accuracy relative to D and H separately and propose a practical measure, Q-ratio, to guide the decision as to whether D and H should or should not be combined into D2H. Using five training biomass datasets and two fitting approaches, weighted nonlinear regression and linear regression following logarithmic transformations, we showed that the D2H predictor becomes less efficient in predicting aboveground biomass as the Q-ratio deviates from 2.0. Because of the model constraint, the D2H-based model performed less well than the separate variable model by as much as 12 per cent with regard to mean absolute percentage residual and as much as 18 per cent with regard to sum of squares of log accuracy ratios. For the analysed datasets, we observed a wide variation in Q-ratios, ranging from 2.5 to 5.1, and a large decrease in efficiency for the combined variable model. Therefore, we recommend using the Q-ratio as a measure to guide the decision as to whether D and H may be combined further into D2H without the adverse effects of loss in biomass prediction accuracy.