Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands

Kuyah, Shem; Sileshi, Gudeta W.; Rosenstock, Todd S.

doi:10.3390/f7020013

Cited by 35 publications

(21 citation statements)

References 33 publications

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“…Allometric models are based on correlations between biomass and morphological characteristics, such as stem diameter and plant height [9]. The choice of an appropriate allometric model is often the most critical step towards minimizing the errors and increasing the accuracy in the estimation of forest biomass [10].…”

Section: Introductionmentioning

confidence: 99%

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

Halin

Zhou

et al. 2019

Forests

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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.

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Section: Introductionmentioning

confidence: 99%

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

Halin

Zhou

et al. 2019

Forests

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“…Thus, using the Bayesian method to estimate the aboveground biomass from small sample sizes is a good direction in biomass estimation. If classical methods are applied, especially with a small sample size, violation of statistical assumptions of error can lead to biased point estimates [20,39]. In addition, in this study, we have not taken into account the error propagation, thus a join model is perhaps a good method, and further studies are needed to solve the join model under the Bayesian framework for reducing the model uncertainties.…”

Section: Discussionmentioning

confidence: 99%

“…presented a comparison of aboveground biomass estimation in different sample sizes using the Bayesian method and the classical method. They obtained a result that the bias in the classical method was always bigger than that in the Bayesian approach.Although LIDAR technology and Bayesian methods are used separately in individual DBH and AGB modeling [6,[13][14][15][17][18][19][20][21], few studies have combined the two methods to estimate DBH and aboveground biomass. Tenneson [22] presented a combination of the Bayesian method and LIDAR to model LIDAR-derived forest inventory, combining the advantages of both DBH and AGB prediction; they used Bayesian model averaging (BMA), while we are using the hierarchical Bayesian method [23,24].…”

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confidence: 99%

Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach

Wang

Liu

et al. 2019

Remote Sensing

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Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements.2 of 12 has been widely applied in forest investigation and yield modeling. It is important to note that DBH measurement in large-scale forest inventory is still time-consuming and costly.LIDAR is a range detection system, which obtains the forest point cloud by LIDAR sensors. The LIDAR system has been used to measure the forest vegetation structure and has been applied to measure forest variables since the mid-1980s [11]. These variables, including tree height, stem volume, crown projection area, diameter at breast height, and other variables, are significantly connected to the forest biomass [3,11,12]. Using these LIDAR variables, individual tree DBH and aboveground biomass can be estimated by developing DBH and AGB models, respectively [3,12,13]. With some studies also using LIDAR data to predict timber volume and discern age class, LIDAR data have been widely used in forestry over the past two decades. The application of LIDAR technology has reduced the cost of forest management and provided a good opportunity for large-scale forest inventory [11,12].The Bayesian method is used to integrate prior information about unknown parameters with the sample information; then, the posterior information is obtained according to the Bayes formula and the method of unknown parameters is further inferred according to the posterior information. Many studies have already used the Bayesian method to develop DBH and AGB models and have compared the Bayesian model with the classical model [14][15][16][17]. presented a comparison of aboveground biomas...

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“…Perennial grasses can be grown by ratooning, a form of zero-tillage harvest that leaves roots and soil undisturbed, and can rapidly increase soil organic C while providing high biomass yields (Matsuoka and Stolf, 2012; Sumiyoshi et al, 2016). Many generalized models predict biomass and C stock in forestry and agroforestry systems (Nair et al, 2009; Chave et al, 2014; Vahedi et al, 2014; Ali et al, 2015; Kuyah and Rosenstock, 2015; Kuyah et al, 2016; Mganga, 2016), but only a few equations have been developed for non-forest crops (Navar et al, 2004; Nafus et al, 2009; Martin et al, 2013; Fard and Heshmati, 2014; Oliveras et al, 2014). However, Martin et al (2013) used stalk base D, stalk H (the length from the base stalk to the base of the forth internode) to predict stalk biomass and soluble sugar concentration of Sweet Sorghum ∗ ( Sorghum bicolor ) in Australia.…”

Section: Introductionmentioning

confidence: 99%

“…Developing new allometric models can improve the accuracy of biomass assessment protocols, and advance our understanding of architectural constraints on plant development (Chave et al, 2014). Allometric models are based on correlations between biomass and morphological characters, such as basal diameter (or area), height, canopy diameter, or canopy volume (Martin et al, 2013; Cornet et al, 2015; Kuyah et al, 2016). These parameters can be used individually, or combined in one allometric model (Brown, 1997, 2002).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands

Cited by 35 publications

References 33 publications

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

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

Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach

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

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