scholarly journals Spatial pattern and driving factors of biomass carbon density for natural and planted coniferous forests in mountainous terrain, eastern Loess Plateau of China

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Lina Sun ◽  
Mengben Wang ◽  
Xiaohui Fan
2021 ◽  
Author(s):  
Lina Sun ◽  
Qixiang Wang ◽  
Xiaohui Fan

Abstract Background Mountain forests in China are an integral part of the country’s natural vegetation. Understanding the spatial variability and control mechanisms for biomass carbon density of mountain forests is necessary to make full use of the carbon sequestration potential for climate change mitigation. Based on the 9th national forest inventory data in Shanxi Province, which is mountainous terrain, eastern Loess Plateau of China, we characterized the spatial pattern of biomass carbon density for natural coniferous and broad-leaved forests using Local Getis-ord G* and proposed an integrative framework to evaluate the direct and indirect effects of stand, geographical and climatic factors on biomass carbon density for the two types of forests using structural equation modeling. Results There was no significant difference between the mean biomass carbon densities of the natural coniferous and broad-leaved forests. The number of spots with a spatial autocorrelation accounted for 51.6% of all plots of the natural forests. Compared with the broad-leaved forests, the hot spots at the 1% significance level for the coniferous forests were distributed in areas with higher latitude, higher elevation, lower temperature, and lower precipitation. Geographical factors affected biomass carbon density positively and indirectly, via the stand and climatic factors, with larger effects for the natural coniferous than broad-leaved forests. Latitude and elevation are the most crucial driving factors for coniferous forests, but stand age and forest coverage are for broad-leaved forests. Climatic factors had weaker effects than other factors, with negative effects of temperature for coniferous and no effects for broad-leaved forests. Conclusions The effects of stand, geographical and climatic factors on biomass carbon density are different between natural coniferous and broad-leaved forests, respectively. Employing the integrative framework can improve the prediction of the impact of stand, geographical and climatic factors on natural forests in mountainous areas.


Author(s):  
Li Dai ◽  
Yufang Zhang ◽  
Lei Wang ◽  
Shuanli Zheng ◽  
Wenqiang Xu

The natural mountain forests in northwest China are recognized as a substantial carbon pool and play an important role in local fragile ecosystems. This study used inventory data and detailed field measurements covering different forest age groups (young, middle-aged, near-mature, mature, old-growth forest), structure of forest (tree, herb, litter and soil layer) and trees (leaves, branches, trunks and root) to estimate biomass, carbon content ratio, carbon density and carbon storage in Altai forest ecosystems. The results showed that the average biomass of the Altai Mountains forest ecosystems was 126.67 t·hm−2, and the descending order of the value was tree layer (120.84 t·hm−2) > herb layer (4.22 t·hm−2) > litter layer (1.61 t·hm−2). Among the tree parts, trunks, roots, leaves and branches accounted for 50%, 22%, 16% and 12% of the total tree biomass, respectively. The average carbon content ratio was 0.49 (range: 0.41–0.52). The average carbon density of forest ecosystems was 205.72 t·hm−2, and the carbon storage of the forest ecosystems was 131.35 Tg (standard deviation: 31.01) inside study area. Soil had the highest carbon storage (65.98%), followed by tree (32.81%), herb (0.78%) and litter (0.43%) layers. Forest age has significant effect on biomass, carbon content ratio, carbon density and carbon storage. The carbon density of forest ecosystems in study area was spatially distributed higher in the south and lower in north, which is influenced by climate, topography, soil types and dominant tree species.


2020 ◽  
Vol 728 ◽  
pp. 138582
Author(s):  
Fengjiao Wang ◽  
Wei Liang ◽  
Bojie Fu ◽  
Zhao Jin ◽  
Jianwu Yan ◽  
...  

2019 ◽  
Vol 57 (6) ◽  
pp. 461-469
Author(s):  
Pengyu Zhao ◽  
Jinxian Liu ◽  
Tong Jia ◽  
Zhengming Luo ◽  
Cui Li ◽  
...  

2015 ◽  
Vol 35 (9) ◽  
Author(s):  
薛志婧 XUE Zhijing ◽  
马露莎 MA Lusha ◽  
安韶山 AN Shaoshan ◽  
王万忠 WANG Wanzhong

2015 ◽  
Vol 39 (2) ◽  
pp. 140-158 ◽  
Author(s):  
HU Hai-Qing ◽  
◽  
LUO Bi-Zhen ◽  
WEI Shu-Jing ◽  
WEI Shu-Wei ◽  
...  

2017 ◽  
Vol 53 ◽  
pp. 309-321 ◽  
Author(s):  
Xiaofeng Wang ◽  
Feiyan Xiao ◽  
Yuan Zhang ◽  
Lichang Yin ◽  
Muchu Lesi ◽  
...  

2015 ◽  
Vol 529 ◽  
pp. 685-695 ◽  
Author(s):  
Xuezhang Li ◽  
Ming’an Shao ◽  
Xiaoxu Jia ◽  
Xiaorong Wei ◽  
Liang He

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