Regression estimators for aboveground biomass and its constituent parts of trees in native southern Brazilian forests

Trautenmüller, Jonathan William; Netto, Sylvio Péllico; Balbinot, Rafaelo; Watzlawick, Luciano Farinha; Corte, Ana Paula Dalla; Sanquetta, Carlos Roberto; Behling, Alexandre

doi:10.1016/j.ecolind.2021.108025

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…Non-additivity fits the total and each component biomass equations separately, which leads to the possibility that the sum of the component equation predictions not being equal to the total biomass equation predictions (Kozak, 1970). Therefore, to satisfy the statistical efficiency of the biomass equations that the sum of the component equation predictions is equal to the total biomass equation predictions, many researchers have offered various approaches to ensure the additivity of the system of biomass equations (Parresol, 1999;Tang et al, 2000;Affleck and Diéguez-Aranda, 2016;Bronisz and Mehtätalo, 2020;Trautenmüller et al, 2021). As Tang et al (2000;2008) proposed a proportional model system (referred to as Adjustment) and a disaggregation model system (referred to as Disaggregation).…”

Section: Introductionmentioning

confidence: 99%

Stand-level biomass estimation for Korean pine plantations based on four additive methods in Heilongjiang province, northeast China

Xin

Mahardika

Jiang

2022

CERNE

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Background: Korean pine (Pinus koraiensis Siebold & Zucc.) is one of the primary plantation conifer species of economic and ecological importance in northeast China. Forest biomass estimation in the broader landscape has been receiving attention from researchers and forest managers. The development of forest stand biomass models is regarded an effective method to estimate forest biomass at large scales. This study was carried out for developing stand-level biomass models for Korean pine plantations. Four additive methods were compared: Aggregation 1, Aggregation 2, Adjustment, and Disaggregation. All the stand biomass additive modeling systems (i.e., total, root, stem, branch, and leaf) included both stand volume and biomass conversion and expansion factors (BCEFs) as predictors. Results:The predictive performance of the four additive methods and Constant BCEFs were ranked as follows: Aggregation 1 > Disaggregation > Adjustment > Aggregation 2 > Constant BCEFs. The prediction accuracy of the four additive methods was not consistent across the stand volume intervals. Conclusion:The model based on the Aggregation 1 method was recommended for predicting stand biomass. However, different additive method should be selected according to the stand volume intervals of the Korean pine plantations.

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Section: Introductionmentioning

confidence: 99%

Stand-level biomass estimation for Korean pine plantations based on four additive methods in Heilongjiang province, northeast China

Xin

Mahardika

Jiang

2022

CERNE

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“…For forest plantations these errors are minor, for obvious reasons, as the only species of cultivation, they are often clones and the trees are the same age. In general, the literature shows that the errors in the biomass equations for native forests (tropical and subtropical) are above 20% (Trautenmüller et al 2021) and for planted forests they are below 20% (Ribeiro et al 2015).…”

Section: Resultsmentioning

confidence: 98%

“…This lack of normality is often found in biomass data, making it a problem for modeling based on linear regression (Gujarati & Porter, 2009;Balbinot et al 2019;Trautenmüller et al, 2019). The ways to overcome the lack of normality are: (i) transformation of the data, but not indicating when the additivity of the biomass component equations must be reached (Sanquetta et al 2015;Behling et al 2018), (ii) the use of non-linear regression (Huy et al 2016;Trautenmüller et al 2021) that employ other forms of adjusting the coefficients other than the Ordinary Least Squares or, (iii) applying stratification to the data, thus, each group presents normality (Behling et al 2018;Balbinot et al 2019).…”

Section: Resultsmentioning

confidence: 99%

Correlations between dendrometric and allometric variables of Eucalyptus benthamii under agroforestry systems in the subtropic of Brazil

William Trautenmüller,

Kruchelski,

Balbinot

et al. 2023

ADV. FOR. SCI.

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The growing interest in integrated agricultural production systems has increased the search for information about the arboreal component, thus, information about the correlations and associations between dendrometric and allometric variables are important. This study investigated the results obtained through simple correlation analysis and its breakdown via path analysis of dendrometric and allometric variables of Eucalyptus benthamii under different integrated systems and monoculture. The following variables were evaluated: diameter at 1.3 m above ground, total height, volume with and without bark, crown radius and biomass components stem wood and bark, branches, leaves and total aboveground biomass. Descriptive statistics showed the great variance of the data with coefficients of variation from 18 to 85.08%. All correlations (Spearman) between allometric and dendrometric variables were positive and 89% were statistically significant (p < 0.05). For the total aboveground biomass as dependent variable, the variables with the greatest direct effects were wood biomass (0.26), volume with bark (0.17) and branch biomass (0.15). With the results of the simple correlation any variable can be used in the modeling of the total aboveground biomass. However, the path analysis makes it clear that some variables have weak associations even with significant correlations of great magnitude.

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“…Using a log-linear regression model for estimating aboveground carbon also has several advantages. First, the log-linear regression model can account for the fact that the relationship between carbon and predictor variables is often non-linear, with diminishing returns as trees get larger (Clerici et al, 2016;Trautenmüller et al, 2021). Second, the log transformation can help to reduce the impact of outliers and make the model more robust to deviations from normality in the data (von Eye and Mun, 2013).…”

Section: Log-linear Regression On Estimating Aboveground Carbonmentioning

confidence: 99%

Assessing the Impact of Hurricane Ivan on Aboveground Carbon of Coastal Forests in Perdido Bay, Florida

Alam,

Kadam,

Brandeis

et al. 2023

Preprint

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Hurricanes can physically transform forestry ecosystems, leading to immediate and potentially long-lasting impacts on carbon dynamics. We compared the average carbon in trees (saplings, bole, stump, tops) and foliage pre (2001-2003) and post (2005-2007) Hurricane Ivan for different types of tree categories in the Perdido Bay watershed, Florida. We estimated a decrease of 74.51 and 17.82 metric tons/ha of aboveground carbon in hardwood and mixed plots after Hurricane Ivan, respectively. Softwoods gained 18.22 metric ton/ha of aboveground carbon in the post-hurricane period due to post-hurricane regrowth. Aboveground carbon decreased in stands with large (≥ 0.15m) and medium (≥ 0.12m) diameter at breast height (d.b.h.) by 22.74 and 30.22 metric tons/ha, respectively. Aboveground carbon in young (< 25y) taller trees (> 15m) decreased by 121.55 metric ton/ha of carbon immediately after the hurricane. The results of our log-linear regression model indicate that stand type (softwood/mixed/hardwood), height, physiographic class (deep sands/flatwoods/rolling uplands/small drains/swaps/bottomlands) along with the hurricane-affected status of the FIA plots were statistically significant (p ≤ 0.05) determinants of the carbon loss from the forest stands. Over the study period, there was a decrease of approximately 28.64% of aboveground carbon in trees and 62.15% of aboveground carbon in foliage. Softwood plots had 51.30% less aboveground carbon compared to hardwood plots, while plots in flatwoods and rolling uplands had 66.27% and 52.73% less carbon than bay and wet pocosins, respectively. Post-hurricane, the carbon in the trees and foliage starts to recover gradually, taking about 15 years to come back to the original status at the landscape level. Our study provides a framework for researchers and policymakers to assess the vulnerability of coastal forests in light of extreme weather events.

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Regression estimators for aboveground biomass and its constituent parts of trees in native southern Brazilian forests

Cited by 17 publications

References 29 publications

Stand-level biomass estimation for Korean pine plantations based on four additive methods in Heilongjiang province, northeast China

Stand-level biomass estimation for Korean pine plantations based on four additive methods in Heilongjiang province, northeast China

Correlations between dendrometric and allometric variables of Eucalyptus benthamii under agroforestry systems in the subtropic of Brazil

Assessing the Impact of Hurricane Ivan on Aboveground Carbon of Coastal Forests in Perdido Bay, Florida

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