Spatiotemporal Assessment of Forest Biomass Carbon Sinks: The Relative Roles of Forest Expansion and Growth in Sichuan Province, China

Li, Renqiang; Shu-jie, Zhao; Zhao, Haifeng; Xu, Minggang; Zhang, Liyun; Huo, Wen; Sheng, Qingkai

doi:10.2134/jeq2016.07.0261

Cited by 6 publications

(1 citation statement)

References 36 publications

(36 reference statements)

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“…Lastly, RF can produce the important and partial dependence plots of each predictor, thereby greatly improving model interpretability [39]. Li et al [40] applied RF for carbon density estimation based on forest inventory plot data in south China. The input variables to their model included the distribution of tree species, geographical coordinates, topographical factors, human disturbance, and climate factors.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

Lei

Zeng

et al. 2022

Sustainability

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The accurate estimation of forest biomass is crucial for supporting climate change mitigation efforts such as sustainable forest management. Although traditional regression models have been widely used to link stand biomass with biotic and abiotic predictors, this approach has several disadvantages, including the difficulty in dealing with data autocorrelation, model selection, and convergence. While machine learning can overcome these challenges, the application remains limited, particularly at a large scale with consideration of climate variables. This study used the random forests (RF) algorithm to estimate stand aboveground biomass (AGB) and total biomass (TB) of larch (Larix spp.) plantations in north and northeast China and quantified the contributions of different predictors. The data for modelling biomass were collected from 445 sample plots of the National Forest Inventory (NFI). A total of 22 independent variables (6 stand and 16 climate variables) were used to develop and train climate-sensitive stand biomass models. Optimization of hyper parameters was implemented using grid search and 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) of the RF models were 0.9845 and 3.8008 t ha−1 for AGB, and 0.9836 and 5.1963 t ha−1 for TB. The cumulative contributions of stand and climate factors to stand biomass were >98% and <2%, respectively. The most crucial stand and climate variables were stand volume and annual heat-moisture index (AHM), with relative importance values of >60% and ~0.25%, respectively. The partial dependence plots illustrated the complicated relationships between climate factors and stand biomass. This study illustrated the power of RF for estimating stand biomass and understanding the effects of stand and climate factors on forest biomass. The application of RF can be useful for mapping of large-scale carbon stock.

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Section: Introductionmentioning

confidence: 99%

Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

Lei

Zeng

et al. 2022

Sustainability

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Understanding the contribution of structural diversity to stand biomass for carbon management of mixed forests using machine learning algorithms

He,

Lei,

Liu

et al. 2024

Eur J Forest Res

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Quantifying the evidence for co-benefits between species conservation and climate change mitigation in giant panda habitats

Powers

et al. 2017

Sci Rep

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Conservationists strive for practical, cost-effective management solutions to forest-based species conservation and climate change mitigation. However, this is compromised by insufficient information about the effectiveness of protected areas in increasing carbon storage, and the co-benefits of species and carbon conservation remain poorly understood. Here, we present the first rigorous quantitative assessment of the roles of giant panda nature reserves (NRs) in carbon sequestration, and explore the co-benefits of habitat conservation and climate change mitigation. Results show that more than 90% of the studied panda NRs are effective in increasing carbon storage, with the mean biomass carbon density of the whole NRs exhibiting a 4.2% higher growth rate compared with lands not declared as NRs over the period 1988–2012, while this effectiveness in carbon storage masks important patterns of spatial heterogeneity across the giant panda habitats. Moreover, the significant associations have been identified between biomass carbon density and panda’s habitat suitability in ~85% NRs and at the NR level. These findings suggest that the planning for carbon and species conservation co-benefits would enhance the greatest return on limited conservation investments, which is a critical need for the giant panda after its conservation status has been downgraded from “endangered” to “vulnerable”.

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Spatiotemporal Assessment of Forest Biomass Carbon Sinks: The Relative Roles of Forest Expansion and Growth in Sichuan Province, China

Cited by 6 publications

References 36 publications

Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

Understanding the contribution of structural diversity to stand biomass for carbon management of mixed forests using machine learning algorithms

Quantifying the evidence for co-benefits between species conservation and climate change mitigation in giant panda habitats

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