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 13 publications

(1 citation statement)

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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