scholarly journals Spatiotemporal Distribution and Driving Factors of Forest Biomass Carbon Storage in China: 1977–2013

Forests ◽  
2017 ◽  
Vol 8 (7) ◽  
pp. 263 ◽  
Author(s):  
Jiameng Yang ◽  
Xiaoxia Ji ◽  
David Deane ◽  
Linyu Wu ◽  
Shulin Chen
Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 443 ◽  
Author(s):  
Lu Liu ◽  
Fuping Zeng ◽  
Tongqing Song ◽  
Kelin Wang ◽  
Hu Du

Understanding the driving factors of forest biomass are critical for further understanding the forest carbon cycle and carbon storage management in karst forests. This study aimed to investigate the distribution of forest aboveground biomass (AGB) and the effects of stand structural and abiotic factors on AGB in karst forests in Southwest China. We established a 25 ha plot and sampled all trees (≥1 cm diameter) in a subtropical mixed evergreen–deciduous broadleaf forest. We mapped the forest biomass distribution and applied a variation of partitioning analysis to examine the topographic, stand structural, and spatial factors. Furthermore, we used structural equation models (SEM) to test how these variables directly and/or indirectly affect AGB. The average AGB of the 25 ha plot was 73.92 Mg/ha, but that varied from 3.22 to 198.11 Mg/ha in the 20 m × 20 m quadrats. Topographic, stand structural, and spatial factors together explained 67.7% of the variation in AGB distribution. The structural variables (including tree density and the diameter at breast height (DBH) diversity) and topographic factors (including elevation, VDCN (vertical distance to channel network), convexity, and slope) were the most crucial driving factors of AGB in the karst forests. Structural equation models indicated that elevation, tree density, and DBH diversity directly affected AGB, and elevation also indirectly affected AGB through tree density and DBH diversity. Meanwhile, AGB was indirectly influenced by VDCN, convexity, and slope. The evaluation of stand structural and abiotic drivers of AGB provides better insights into the mechanisms that play a role in carbon storage in karst forests, which may assist in improving forest carbon management.


PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250073
Author(s):  
Liu Shu-Qin ◽  
Bian Zhen ◽  
Xia Chao-Zong ◽  
Bilal Ahmad ◽  
Zhang Ming ◽  
...  

According to the forest resources inventory data for different periods and the latest estimation parameters of forest carbon reserves in China, the carbon reserves and carbon density of forest biomass in the Tibet Autonomous Region from 1999 to 2019 were estimated using the IPCC international carbon reserves estimation model. The results showed that, during the past 20 years, the forest area, forest stock, and biomass carbon storage in Tibet have been steadily increasing, with an average annual increase of 1.85×104 hm2, 0.033×107 m3, and 0.22×107 t, respectively. Influenced by geographical conditions and the natural environment, the forest area and biomass carbon storage gradually increased from the northwest to the southeast, particularly in Linzhi and Changdu, where there are many primitive forests, which serve as important carbon sinks in Tibet. In terms of the composition of tree species, coniferous forests are dominant in Tibet, particularly those containing Abies fabri, Picea asperata, and Pinus densata, which comprise approximately 45% of the total forest area in Tibet. The ecological location of Tibet has resulted in the area being dominated by shelter forest, comprising 68.76% of the total area, 64.72% of the total forest stock, and 66.34% of the total biomass carbon reserves. The biomass carbon storage was observed to first increase and then decrease with increasing forest age, which is primarily caused by tree growth characteristics. In over-mature forests, trees’ photosynthesis decreases along with their accumulation of organic matter, and the trees can die. In addition, this study also observed that the proportion of mature and over-mature forest in Tibet is excessively large, which is not conducive to the sustainable development of forestry in the region. This problem should be addressed in future management and utilization activities.


2016 ◽  
Vol 32 (4) ◽  
pp. 297-305 ◽  
Author(s):  
Liyun Zhang ◽  
Ming Xu ◽  
Shuai Qiu ◽  
Renqiang Li ◽  
Haifeng Zhao ◽  
...  

BioResources ◽  
2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiameng Yang ◽  
Runying Xu ◽  
Zhijian Cai ◽  
Jun Bi ◽  
Haikun Wang

2013 ◽  
Vol 10 (12) ◽  
pp. 19005-19044 ◽  
Author(s):  
J. Zhang ◽  
S. Huang ◽  
E. H. Hogg ◽  
V. Lieffers ◽  
Y. Qin ◽  
...  

Abstract. Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present biomass carbon storage in Alberta, Canada, by taking advantage of a spatially explicit dataset derived from a combination of forest inventory data from 1968 plots and spaceborne light detection and ranging (LiDAR) canopy height data. Ten climatic variables together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree based modelling with random forests algorithm (a machine-learning technique), were compared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total biomass stock in Alberta forests was estimated to be 3.11 × 109 Mg, with the average biomass density of 77.59 Mg ha−1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.91 × 109 Mg biomass, accounting for 61% of total estimated biomass. Spatial distribution of biomass varied with natural regions, land cover types, and species. And the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne LiDAR data, land cover classification, climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.


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