New forest biomass carbon stock estimates in Northeast Asia based on multisource data

Luo, Weixue; Kim, Hyun Seok; Zhao, Xiuhai; Ryu, Dong-Woo; Jung, Ilbin; Cho, Hyunkook; Harris, Nancy L.; Ghosh, Sayon; Zhang, Chunyu; Liang, Jingjing

doi:10.1111/gcb.15376

Cited by 22 publications

(17 citation statements)

References 109 publications

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“…As a relatively simple process of parameter optimization that optimizes only two parameters, the RF model has been widely used to reproduce terrestrial carbon and climate data. Importantly, the RF ensemble method can effectively correct underdispersion in terrestrial carbon ensemble forecasts (Luo et al., 2020; Tang et al., 2018). Notably, the state‐of‐the‐art ML technique is an important unequally weighted ensemble method that can take into account model simulation skills and relationships between model results.…”

Section: Methodsmentioning

confidence: 99%

“…A reviewable, consistent field inventory protocol and laboratory methodology were used to grain these datasets, including the biomasses of forest, shrub, grass and crop ecosystems. Valuable TVCD data have previously been used to estimate the carbon pools of Chinese terrestrial ecosystems (Luo et al., 2020; Su et al., 2020; Yu et al., 2020). Terrestrial vegetation biomass carbon compartments include leaves, wood (with the above‐ground biomass being the sum of leaves and wood) and roots (which constitute the below‐ground biomass), and together these compartments describe the total carbon content of the living biomass (including leaves, roots and wood).…”

Section: Data and Preprocessingmentioning

confidence: 99%

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A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density

et al. 2021

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1. Assessing the terrestrial vegetation carbon density (TVCD) is crucial for evaluating the national carbon balance. However, current national-scale TVCD assessments show strong disparities, despite the good estimation method of their underlying models. Here, we attribute this contradiction to a flaw in the methods of using multimodel simulation results, which ignore the connections between results, leading to an overoptimistic evaluation of the multimodel ensemble mean (MMEM) method.2. Thus, using the state-of-the-art multimodel random forest ensemble (MMRFE) method to integrate the results of 10 models, we reproduced Chinese TVCD data during 1982-2010.3. Compared with the nationally averaged TVCD field investigation data (27 ± 26 Mg C/ha), we found that the results of five models were overestimated by 7.4%-85.2%, and the remaining models were underestimated by 3.7%-77.8%. The MMEM TVCD method produced an overestimation of 2%, but the MMRFE method produced an underestimation of only 0.2%. Additionally, the summary Taylor diagrams of the TVCD at the national and ecosystem (forest, shrub, grass and crop ecosystems) scales all showed that the MMRFE TVCD produced the smallest standard deviations and root mean square deviations and the highest correlation coefficients. Furthermore, the MMRFE TVCDs were all significantly positively correlated with the normalized difference vegetation index (NDVI), and they had the same increasing trend, but an opposite variation trend from the MMEM TVCD and NDVI. This result implied that the spatiotemporal variation modes of the MMRFE TVCD were consistent with those of the NDVI. The results suggested that compared with the traditional MMEM method, the MMRFE TVCD and its spatiotemporal variation modes were more similar to the real TVCD. 4. In conclusion, the MMRFE method can effectively improve the accuracy of nationalscale TVCD estimation, and effectively reduce the uncertainty of large-scale terrestrial vegetation carbon estimation processes. Notably, we provide a new method thatThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Methodsmentioning

confidence: 99%

Section: Data and Preprocessingmentioning

confidence: 99%

A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density

et al. 2021

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“…Terrestrial ecosystems play an important role in regulating the global and local climates (Gadow et al, 2021; Hwan & Chun, 2011; Zhou et al, 2013), and carbon cycles (Kuribayashi et al, 2017; Zhao et al, 2012), and maintaining biodiversity (Kitayama et al, 2018; Ren et al, 2017). Among these terrestrial ecosystems, the forest ecosystem is the largest C reservoir, which comprises more than 80% and 40% of the global terrestrial C pools above‐ground and below‐ground, respectively (Dixon et al, 1994; Luo et al, 2020; Pan et al, 2011). Tree trunks and branches contain a massive ratio of these C, which is called above‐ground biomass (AGB) (Fahey et al, 2010; Fotis et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…

Ren et al, 2017). Among these terrestrial ecosystems, the forest ecosystem is the largest C reservoir, which comprises more than 80% and 40% of the global terrestrial C pools above-ground and below-ground, respectively (Dixon et al, 1994;Luo et al, 2020;Pan et al, 2011). Tree trunks and branches contain a massive ratio of these C, which is called above-ground biomass (AGB) (Fahey et al, 2010;Fotis et al, 2018).

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confidence: 99%

Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

Fan

Geng

et al. 2022

Ecology and Evolution

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Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment.

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“…Encompassing approx. 50 million hectares of species‐rich forests, MTF store around 2.5 Pg of carbon, which accounts for 24.1% of the total carbon storage in forests of China (Tang et al, 2018; Luo et al, 2020). Meanwhile, with a total forest stock of 3.4 billion m 3 , MTF provide approx.…”

Section: Introductionmentioning

confidence: 99%

Understanding patterns and potential drivers of forest diversity in northeastern China using machine‐learning algorithms

Luo

Zhang

Zhao

et al. 2021

J Vegetation Science

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Question: Forest ecosystems are the most important global repositories of terrestrial biodiversity. The mixed temperate forests in northeastern China constitute one of the most biodiverse temperate regions globally and provide nearly one-third of China's wood supply. We ask what are the spatial patterns and potential drivers of tree species diversity in mixed temperate forests.Location: Temperate, mixed forests of northeastern China.Methods: Using a large set of ground-source forest inventory data (FIN) and geospatial covariates derived from published raster layers, we compared different machinelearning and statistical models to study spatial patterns of tree species diversity and their underpinning drivers. Results:The spatial distribution of tree species diversity (species richness and evenness) varied greatly across northeastern China. Tree species diversity varied most with climatic (annual precipitation and annual mean temperature), topographic (elevation and slope), and anthropogenic factors. Anthropogenic factors affected tree species evenness (importance value = 13%) more than tree species richness (importance value = 9%). Based on these relationships, we mapped spatial patterns of tree diversity throughout the region at a 1 km × 1 km resolution. Conclusions:Our findings shed light on the processes behind community assembly and biodiversity patterns in mixed temperate forests in northeastern China, and provide a benchmark for future assessment of biodiversity. Our high-resolution tree species diversity maps can be useful to landowners and land management agencies in their decision-making processes about sustainable forest management, biodiversity conservation, and forest restoration -a priority task outlined by the recently implemented 2050 China National Forest Management Plan.

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New forest biomass carbon stock estimates in Northeast Asia based on multisource data

Cited by 22 publications

References 109 publications

A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density

A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density

Assessing scale‐dependent effects on Forest biomass productivity based on machine learning

Understanding patterns and potential drivers of forest diversity in northeastern China using machine‐learning algorithms

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