Spatial distribution of carbon density in grassland vegetation of the Loess Plateau of China

Jimin, 程积民 Cheng; Jie, 程杰 Cheng; Xiaomei, 杨晓梅 Yang; Wei, 刘伟 Liu; Furong, 陈芙蓉 Chen

doi:10.5846/stxb201011131626

Cited by 8 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatial distribution of the difference in NPP estimated by both the methods shows that the simulated NPP is comparably higher toward the western, eastern, and lower parts of the YRB, while the simulated values are less toward the western parts of the basin center (Figure 5) due to several factors, including vegetation type, precipitation, and temperature. Further, the NPP estimated by the CASA model was compared with the observed NPP values reported by [43]. The model's accuracy was verified by comparing the in situ observed NPP values for grass, shrub, and forest cover at 36 data points with the estimated results of the CASA model, as shown in Figure 6.…”

Section: Validation Of Casa Model Nppmentioning

confidence: 86%

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

Xiao

Liu

2022

Sustainability

View full text Add to dashboard Cite

The net primary productivity (NPP) of vegetation is an essential factor of ecosystem functions, including the biological geochemical carbon cycle, which is often impacted by climate change and human activities. It plays a significant role in comprehending the nature of carbon balance in an ecosystem and demonstrates the global and regional carbon cycle dynamics. The present study used an upgraded CASA model to calculate the NPP in the Yellow River Basin (YRB), China. The model’s simulation ability was improved by changing the model parameters. Further, the CASA model was validated by comparing with MODIS-NPP and in situ observed NPP, wherein the accuracy of the CASA model estimation was found satisfactory to estimate NPP changes in the study area. The simulated results of the improved CASA model showed that the mean annual NPP value of vegetation in the YRB was 283.4 gC m–2 a–1 from 2001 to 2020, with a declining trend in spatial distribution from south to north. In contrast, the NPP appeared as an increasing trend in the YRB temporally from 212 gC m–2 a–1 in 2001 to 342 gC m–2 a–1 in 2020, with a mean annual growth rate of 4.6 gC m–2 a–1. The total NPP in the YRB increased by 40,088.3 GgC between 2001 and 2020, from 226.06 TgC to 266.15 TgC. This rise can be attributed to the increase in forests. The average grassland area has reduced by 4651 km2 during the last two decades, significantly impacting the total NPP of grasslands. Although the increase in NPP in wetlands was minimal, accounting for 815.53 GgC, the highest change percentage of 79.78%, could be observed among the six vegetation types due to the anthropogenic influences and climate change. The conditions favorable for vegetation growth and a sustained environment were enhanced by the increased precipitation and temperature and the reinforced ecological protection by the government.

show abstract

Section: Validation Of Casa Model Nppmentioning

confidence: 86%

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

Xiao

Liu

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…After reviewing the relevant literature, the carbon density (Carbon storage per unit area) data was corrected [ 6 , 31 , 32 ]. SRB's climate conditions were utilized to obtain the final carbon density values for every land use type [ 33 , 34 ].…”

Section: Methodsmentioning

confidence: 99%

“…From calculation (2) , (3) , (4) , (5) , (6) , (7) , (8) , it follows that the value of K BP is 0.037, K BT is 0.967, K B is 0.036 and K S is 0.669. Building on existing references [ 31 , 32 , 36 ], the corrected carbon density data of SRB is obtained by multiplying the carbon density correction coefficient with the national carbon density value ( Table 2 ).…”

Section: Methodsmentioning

confidence: 99%

Impact of agricultural irrigation and resettlement practices on carbon storage in arid inland river basins: A case study of the Shule river basin

Zhu,

Pan,

2024

Heliyon

View full text Add to dashboard Cite

“…(2) The dry weight of grasslands in the aboveground parts was measured by drying the aboveground biomass from the sample sites in a laboratory drying oven (drying at a constant 65 • C until the mass was constant). Then, the aboveground carbon storage of grasslands was obtained by multiplying the dry weight of grasslands in the aboveground parts by the 0.45 coefficient [32,33]. (3) Based on the measured aboveground vegetation carbon (AVC) of grasslands and the NDVI, a linear regression model of AVC = 376.95NDVI−28.20 (R 2 = 0.84) was used to simulate the AVC of grasslands in Yanchi County.…”

Section: Data Sourcesmentioning

confidence: 99%

Evaluating the Impact of Dynamic Changes in Grasslands on the Critical Ecosystem Service Value of Yanchi County in China from 2000 to 2015

Wang

Zhu

et al. 2022

Sustainability

View full text Add to dashboard Cite

Grasslands are the basis for sustainable development in the northern farming-pastoral transition zone of China, with functions of human production, living, and ecology. Large-scale human activities inevitably lead to significant changes in grasslands, resulting in significant impacts on ecosystem services. To this end, we quantitatively estimated the ecosystem services value in each positive succession process (the improvement in the coverage or area of grasslands) and negative succession process (the degradation in the coverage or area of grasslands). The results indicated that (1) grasslands showed an improving trend from 2000 to 2015. The grassland improvement from low to high coverage dominated the positive succession process. Grassland degradation from high to low coverage dominated the negative succession process. (2) The total ecosystem services value increased by 25,294.87 × 104 yuan from 2000 to 2015. The grassland improvement from low to high coverage was the most important process that led to the increase in ecosystem service value. The degradation between grasslands and non-grasslands was the key process that led to the decrement in ecosystem services value. (3) The impact of grassland dynamics on the regional ecosystem service value showed significant spatial heterogeneity at the town scale. The results will provide some implications for the sustainable development of grassland ecosystem services to improve human well-being.

show abstract

Spatial distribution of carbon density in grassland vegetation of the Loess Plateau of China

Cited by 8 publications

References 0 publications

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

Impact of agricultural irrigation and resettlement practices on carbon storage in arid inland river basins: A case study of the Shule river basin

Evaluating the Impact of Dynamic Changes in Grasslands on the Critical Ecosystem Service Value of Yanchi County in China from 2000 to 2015

Contact Info

Product

Resources

About