Accelerated increase in vegetation carbon sequestration in China after 2010: A turning point resulting from climate and human interaction

2021 ◽  
Author(s):  
Yongzhe Chen ◽  
Xiaoming Feng ◽  
Hanqin Tian ◽  
Xutong Wu ◽  
Zhen Gao ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
Turning Point ◽  
Human Interaction ◽  
Vegetation Carbon

scholarly journals Factors Affecting Spatial Variation in Vegetation Carbon Density in Pinus massoniana Lamb. Forest in Subtropical China

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 880 ◽  
Author(s):  
Pan ◽  
Sun ◽  
Ouyang ◽  
Zang ◽  
Rao ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
Forest Management ◽  
Spatial Variation ◽  
Pinus Massoniana ◽  
Moran’S I ◽  
Spatial Clusters ◽  
Carbon Density ◽  
Moran's I ◽  
Pinus Massoniana Lamb ◽  
Vegetation Carbon

Carbon density is an important indicator of carbon sequestration capacity in forest ecosystems. We investigated the vegetation carbon density of Pinus massoniana Lamb. forest in the Jiangxi Province. Based on plots investigation and measurement of the carbon content of the samples, the influencing factors and spatial variation of vegetation carbon density (including the tree layer, understory vegetation layer and litter layer) were analysed. The results showed that the average vegetation carbon density value of P. massoniana forest was 52 Mg·ha−1. The vegetation carbon density was significantly (p < 0.01) and positively correlated with the stand age, mean annual precipitation, elevation and stand density and negatively correlated with the slope and mean annual temperature. Forest management had a significant impact on vegetation carbon density. To manage P. massoniana forest for carbon sequestration as the primary objective, near-natural forest management theory should be followed, e.g., replanting broadleaf trees. These measures would promote positive succession and improve the vegetation carbon sequestration capacity of forests. The results from the global Moran’s I showed that the vegetation carbon density of P. massoniana forest had significant positive spatial autocorrelation. The results of local Moran’s I showed that the high-high spatial clusters were mainly distributed in the southern, western and eastern parts of the province. The low-low spatial clusters were distributed in the Yushan Mountains and in the northern part of the province. The fitting results of the semivariogram models showed that the spherical model was the best fitting model for vegetation carbon density. The ratio of nugget to sill was 0.45, indicating a moderate spatial correlation of carbon density. The vegetation carbon density based on kriging spatial interpolation was mainly concentrated in the range of 32.5–69.8 Mg·ha−1. The spatial distribution of vegetation carbon density regularity was generally low in the middle region and high in the peripheral region, which was consistent with the terrain characteristics of the study area.

Driving factors of global carbon footprint pressure: Based on vegetation carbon sequestration

2020 ◽  
Vol 267 ◽  
pp. 114914 ◽  
Author(s):  
Jiandong Chen ◽  
Wei Fan ◽  
Ding Li ◽  
Xin Liu ◽  
Malin Song
Keyword(s):  
Carbon Sequestration ◽  
Carbon Footprint ◽  
Driving Factors ◽  
Vegetation Carbon ◽  
Global Carbon

scholarly journals Study on the Relationship between Topological Characteristics of Vegetation Ecospatial Network and Carbon Sequestration Capacity in the Yellow River Basin, China

2021 ◽  
Vol 13 (23) ◽  
pp. 4926
Author(s):  
Minzhe Fang ◽  
Guoxin Si ◽  
Qiang Yu ◽  
Huaguo Huang ◽  
Yuan Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Sequestration ◽  
River Basin ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Carbon Neutrality ◽  
Vegetation Carbon ◽  
The Yellow River Basin ◽  
The Relationship

Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (p < 0.05, R2 = 0.59). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (p < 0.01, R2 = 0.49). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting.

scholarly journals Vegetation Carbon Sequestration Mapping in Herbaceous Wetlands by Using a MODIS EVI Time-Series Data Set: A Case in Poyang Lake Wetland, China

2020 ◽  
Vol 12 (18) ◽  
pp. 3000
Author(s):  
Xue Dai ◽  
Guishan Yang ◽  
Desheng Liu ◽  
Rongrong Wan
Keyword(s):  
Time Series ◽  
Carbon Sequestration ◽  
Vegetation Index ◽  
Time Series Data ◽  
Poyang Lake ◽  
Carbon Stocks ◽  
Series Data ◽  
Biomass Carbon ◽  
Vegetation Carbon ◽  
Poyang Lake Wetland

The carbon sequestration capacity of wetland vegetation determines carbon stocks and changes in wetlands. However, modeling vegetation carbon sequestration of herbaceous wetlands is still problematic due to complex hydroecological processes and rapidly changing biomass carbon stocks. Theoretically, a vegetation index (VI) time series can retrieve the dynamic of biomass carbon stocks and could be used to calculate the cumulative composite of biomass carbon stocks during a given interval, i.e., vegetation carbon sequestration. Hence, we explored the potential for mapping vegetation carbon sequestration in herbaceous wetlands in this study by using a combination of remotely sensed VI time series and field observation data. This method was exemplarily applied for Poyang Lake wetland in 2016 by using a 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) time series. Results show that the vegetation carbon sequestration in this area was in the range of 193–1221 g C m−2 year−1 with a mean of 401 g C m−2 year−1 and a standard deviation of 172 g C m−2 year−1 in 2016. The approach has wider spatial applicability in wetlands than the currently used global map of vegetation production (MOD17A3) because our carbon estimation in areas depicted by ‘no data’ in the MOD17A3 product is considerable, which accounts for 91.2–91.5% of the total vegetation carbon sequestration of the wetland. Thus, we determined that VI time series data shows great potential for estimating vegetation carbon sequestration in herbaceous wetlands, especially with the continuously improving quality and frequency of satellite VI images.

Changes of vegetation carbon sequestration in the tableland of Loess Plateau and its influencing factors

2019 ◽  
Vol 26 (22) ◽  
pp. 22160-22172
Author(s):  
Jie Zhang ◽  
Mengyun Liu ◽  
Mengmeng Zhang ◽  
Jinghan Yang ◽  
Runshan Cao ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Vegetation Carbon

Vegetation carbon sequestration in Chinese forests from 2010 to 2050

2016 ◽  
Vol 23 (4) ◽  
pp. 1575-1584 ◽  
Author(s):  
Nianpeng He ◽  
Ding Wen ◽  
Jianxing Zhu ◽  
Xuli Tang ◽  
Li Xu ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
Vegetation Carbon
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