Additive biomass equations for slash pine trees: comparing three modeling approaches

Zhao, Dehai; Westfall, James A.; Coulston, John W.; Lynch, Thomas B.; Bullock, Bronson P.; Montes, Cristián R.

doi:10.1139/cjfr-2018-0246

Cited by 36 publications

(33 citation statements)

References 19 publications

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“…Among these methods of parameter estimation, nonlinear seemingly unrelated regression (NSUR) and seemingly unrelated regression (SUR) are the most widely used. An advantage of SUR and NSUR is the low variance of the total stand biomass model because of their own predictor variables and weighting function account for heteroscedasticity, which makes SUR and NSUR popular methods for parameter estimation in nonlinear and linear stand biomass equations [11][12][13][14]30]. Although several researchers have proposed the inclusion of additivity, it has often been ignored in some stand biomass models [8,10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Stand Biomass Estimation Methods for Major Forest Types in the Eastern Da Xing’an Mountains, Northeast China

Dong

Zhang

2019

Forests

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Currently, forest biomass estimation methods at the regional scale have attracted the greatest attention from researchers, and the development of stand biomass models has become popular a trend. In this study, a total of 5074 measurements on 1053 permanent sample plots were obtained in the Eastern Da Xing’an Mountains, and three additive systems of stand biomass equations were developed. The first additive system (M-1) used stand variables as the predictors (i.e., stand basal area and average height), the second additive system (M-2) utilized stand volume as the sole predictor, and the third additive system (M-3) included both stand volume and biomass expansion and conversion factors (BCEFs) as the predictors. The coefficients of the three model systems were estimated with nonlinear seemingly unrelated regression (NSUR), while the heteroscedasticity of the model residuals was solved with the weight function. The jackknifing technique was used on the residuals, and several statistics were used to assess the prediction performance of each model. We comprehensively evaluated four stand biomass estimation methods (i.e., M-1, M-2, M-3 and a constant BCEF (M-4)). Here, we showed that the (1) three additive systems of stand biomass equations showed good model fitting and prediction performance, (2) M-3 significantly improved the model fitting and performance and provided the most accurate predictions for most stand biomass components, and (3) the ranking of the four stand biomass estimation methods followed the order of M-3 > M-2 > M-4 > M-1. Our results demonstrated these additive stand biomass models could be used to estimate the stand aboveground and belowground biomass for the major forest types in the Eastern Da Xing’an Mountains, although the most appropriate method depends on the available data and forest type.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Stand Biomass Estimation Methods for Major Forest Types in the Eastern Da Xing’an Mountains, Northeast China

Dong

Zhang

2019

Forests

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“…In the aggregative approach, the total tree biomass equation is specifi ed by aggregating the expectations of component biomass models, and their parameters were estimated by a joint selection of the equations for all the components and the total biomass using a weighted nonlinear, apparently unrelated regression (NSUR), or by a joint selection of the equations of the component biomass using weighted NSUR. In addition, several researches (Parresol, 1999(Parresol, , 2001Usoltsev et al, 2016Usoltsev et al, , 2017Zhao et al, 2019) noted that there is no single system to predict all tree biomass components and total tree biomass. Consequently, comparative analyses between the estimated biomass means of the total AGB using our predicted best models for Scots pine showed rather similar means, where diff erence between the two means amounted to only 24.7 kg (8.5%).…”

Section: Discussionmentioning

confidence: 99%

Tree-Level Above-Ground Biomass Equations for Pinus sylvestris L. in Mongolia

Tungalag¹,

Altanzagas²,

Sukhbaatar³

et al. 2020

Mong. J. Biol. Sci.

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Allometric equations have been used as a main tool to quantify forest above-ground biomass. Therefore, the development of species-specifi c allometric equations is essential to accurately estimate forest biomass. The present study aims at developing species-specifi c equations for above-ground biomass (AGB) estimation for Pinus sylvestris L. in northern Mongolia. A total of 35 sample trees were harvested from natural Scots pine forests in the northeastern Khangai and western Khentii forest regions. Evaluating the statistical relationships of AGB against predictor variables, three allometric equations were formulated. Stem diameter at breast height (D) and height (H) were measured, and the biomass of stem, branches and needles were weighed, separately. AGB was regressed against stem D and H individually, and in combination. The best-fi tting equations with a higher coeffi cient of determination and lower residual standard error were selected using model performance statistics. The most well performing biomass model was the logarithmic equation lnŶ = lna + b x lnD + c x lnH for the estimation of the total AGB, the stem, branch and needle masses. The biomass model is the important tool for accurate estimation of Scots pine forest biomass and carbon stocks in Mongolia.

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“…We used allometric equations (Lambert et al 2005) to obtain estimates of biomass that were then converted to carbon. Component ratios (Heath et al 2009) have been shown to be less variable than the use of allometric equations (however, see the recent work by Zhao et al (2018), which indicated that component ratio equation did not prove to be less variable than allometric equations for components). Incorporating a component ratio approach into our bigBAF sampling scheme may reduce the CVs associated with the various carbon components and reduce the costs required to estimate these at the accuracies required by carbon offset programs.…”

Section: Discussionmentioning

confidence: 99%

Application of big BAF sampling for estimating carbon on small woodlots

et al. 2019

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Background: To accurately and efficiently quantify forest carbon stocks, a good forest inventory using appropriate sampling that minimizes costs and human effort is needed for landowners who want to enter carbon offset markets. The most commonly used sampling unit is the fixed-area plot; however, it is time consuming, expensive, and is often less accurate than variable probability methods when resources are limited. Previous studies show that big BAF sampling is efficient at estimating volume, therefore, it is interesting to explore whether the efficiency can be extended to carbon. The study is conducted at Noonan Research Forest, which located 30 km northwest of Fredericton, New Brunswick, Canada. In this study, we compared count BAF effects and measure BAF effects on the overall sampling outcome and sampling error for total aboveground C and each C component (wood, bark, branches, and foliage) and explored the minimum sample size requirements and costs for different combinations of count and measure BAFs. Results: From our research, we found that the efficiency gained from estimating volume using big BAF sampling can be extended to carbon estimation. The minimum overall inventory cost from this study is $3500 Canadian, compared to a full Noonan inventory costs of $40,000 with 2% standard error. We also found that, similar to volume, count BAF has a larger effect on carbon estimation than measure BAF and the optimum choice of measure BAF depends on the choice of count BAF. The optimal count BAF and measure BAF combination for Noonan Research Forest was 2/24. Conclusion: Our results show that big BAF sampling was a very efficient sampling design for estimating carbon and significantly reduces overall inventory costs. Although big BAF sampling is not widely used in forest inventory, it should be considered by landowners facing the cost constraint barrier for entering carbon market and seeking a cost-effective inventory system for estimating carbon.

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Additive biomass equations for slash pine trees: comparing three modeling approaches

Cited by 36 publications

References 19 publications

Evaluation of Stand Biomass Estimation Methods for Major Forest Types in the Eastern Da Xing’an Mountains, Northeast China

Evaluation of Stand Biomass Estimation Methods for Major Forest Types in the Eastern Da Xing’an Mountains, Northeast China

Tree-Level Above-Ground Biomass Equations for Pinus sylvestris L. in Mongolia

Application of big BAF sampling for estimating carbon on small woodlots

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