Estimating the changes in tree carbon stocks in Galician forests (NW Spain) between 1972 and 2009

Gómez-García, Esteban

doi:10.1016/j.foreco.2020.118157

Cited by 14 publications

(5 citation statements)

References 37 publications

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“…carbon dynamics in European forests (Gómez-García, 2020;Mateos et al, 2016;van der Plas et al, 2018;Vayreda et al, 2012). However, a comprehensive study that compares carbon storage and sequestration of multiple native and non-native forests at the national scale is lacking in the literature.…”

Section: National Forest Inventories Have Been Previously Used To Assessmentioning

confidence: 99%

“…These inventories can be used to assess the amount of carbon stock per unit of forest area (carbon storage; Mg C ha −1 ) and the changes of carbon stock between consecutive inventories (carbon sequestration; Mg C ha −1 year −1 ) (González‐Díaz et al, 2019; Ruiz‐Benito, Gómez‐Aparicio, et al, 2014; Ruiz‐Benito, Madrigal‐González, et al, 2014). National forest inventories have been previously used to assess carbon dynamics in European forests (Gómez‐García, 2020; Mateos et al, 2016; van der Plas et al, 2018; Vayreda et al, 2012). However, a comprehensive study that compares carbon storage and sequestration of multiple native and non‐native forests at the national scale is lacking in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying carbon storage and sequestration by native and non‐native forests under contrasting climate types

Lázaro-Lobo

Ruiz‐Benito

Cruz‐Alonso

et al. 2023

Global Change Biology

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Non‐native trees may have significant impacts on the carbon sink capacity of forested lands. However, large‐scale patterns of the relative capacity of native and non‐native forests to uptake and store carbon remain poorly described in the literature, and this information is urgently needed to support management decisions. In this study, we analyzed 17,065 plots from the Spanish Forest Inventory (covering c. 30 years) to quantify carbon storage and sequestration of natural forests and plantations of native and non‐native trees under contrasting climate types, while controlling for the effects of environmental factors (forest structure, climate, soil, topography, and management). We found that forest origin (non‐native vs. native) highly influenced carbon storage and sequestration, but such effect was dependent on climate. Carbon storage was greater in non‐native than in native forests in both wet and dry climates. Non‐native forests also had greater carbon sequestration than native ones in the wet climate, due to higher carbon gains by tree growth. However, in the dry climate, native forests had greater carbon gains by tree ingrowth and lower carbon loss by tree mortality than non‐native ones. Furthermore, forest type (classified by the dominant species) and natural forests versus tree plantations were important determinants of carbon storage and sequestration. Native and non‐native Pinus spp. forests had low carbon storage, whereas non‐native Eucalyptus spp. forests and native Quercus spp., Fagus sylvatica, and Eurosiberian mixed forests (especially not planted ones) had high carbon storage. Carbon sequestration was greatest in Eucalyptus globulus, Quercus ilex, and Pinus pinaster forests. Overall, our findings suggest that the relative capacity of native and non‐native forests to uptake and store carbon depends on climate, and that the superiority of non‐native forests over native ones in terms of carbon sequestration declines as the abiotic filters become stronger (i.e., lower water availability and higher climate seasonality).

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Section: National Forest Inventories Have Been Previously Used To Assessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying carbon storage and sequestration by native and non‐native forests under contrasting climate types

Lázaro-Lobo

Ruiz‐Benito

Cruz‐Alonso

et al. 2023

Global Change Biology

View full text Add to dashboard Cite

show abstract

“…Therefore, to a certain extent, using a fixed value for estimating the stand carbon pool will cause a great deviation. Assuming a global carbon content of 50% led to overestimating the total carbon stocks by between 4.1% and 5.6% [15]. One study showed that the generalized assumption of 50% C content is consistently slightly above the real values for the species studied.…”

Section: Introductionmentioning

confidence: 97%

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Meng

Zhang

et al. 2021

Forests

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This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.

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“…However, the volume-derived biomass method has not been able to assess the carbon sink of young trees, which leads to the underestimation of the carbon sink capacity of forest ecosystems [1]. The assessment of the carbon sink capacity of forest ecosystems has received extensive global attention [2][3][4]. To address the limitations of the volume-derived biomass method, it is necessary to evaluate the carbon sink of young trees.…”

Section: Introductionmentioning

confidence: 99%

Construction of Additive Allometric Biomass Models for Young Trees of Two Dominate Species in Beijing, China

Wang,

Feng,

Wang

et al. 2024

Forests

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The traditional volume-derived biomass method is limited because it does not fully consider the carbon sink of young trees, which leads to the underestimation of the carbon sink capacity of a forest ecosystem. Therefore, there is an urgent need to establish an allometric biomass model of young trees to provide a quantitative basis for accurately estimating the carbon storage and carbon sink of young trees. The destructive data that were used in this study included the biomass of the young trees of the two dominant species (Betula pendula subsp. mandshurica (Regel) Ashburner & McAll and Populus × tomentosa Carrière) in China, which was composed of the aboveground biomass (Ba), belowground biomass (Bb), and total biomass (Bt). Univariate and bivariate dimensions were selected and five candidate biomass models were independently tested. Two additive allometric biomass model systems of young trees were established using the proportional function control method and algebraic sum control method, respectively. We found that the logistic function was the most suitable for explaining the allometric growth relationship between the Ba, Bt, and diameter at breast height (D) of young trees; the power function was the most suitable for explaining the allometric growth relationship between the Bb and D of young trees. When compared with the independent fitting model, the two additive allometric biomass model systems provide additive biomass prediction which reflects the conditions in reality. The accuracy of the Bt models and Ba models was higher, while the accuracy of the Bb models was lower. In terms of the two dimensions—univariate and bivariate, we found that the bivariate additive allometric biomass model system was more accurate. In the univariate dimension, the proportional function control method was superior to the algebraic sum control method. In the bivariate dimension, the algebraic sum control method was superior to the proportional function control method. The additive allometric biomass models provide a reliable basis for estimating the biomass of young trees and realizing the additivity of the biomass components, which has broad application prospects, such as the monitoring of carbon stocks and carbon sink evaluation.

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Estimating the changes in tree carbon stocks in Galician forests (NW Spain) between 1972 and 2009

Cited by 14 publications

References 37 publications

Quantifying carbon storage and sequestration by native and non‐native forests under contrasting climate types

Quantifying carbon storage and sequestration by native and non‐native forests under contrasting climate types

Prediction of the Carbon Content of Six Tree Species from Visible-Near-Infrared Spectroscopy

Construction of Additive Allometric Biomass Models for Young Trees of Two Dominate Species in Beijing, China

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