Modeling gross primary productivity of alpine meadow in the northern Tibet Plateau by using MODIS images and climate data

2010 ◽  
Vol 30 (5) ◽  
pp. 264-269 ◽  
Author(s):  
Gang Fu ◽  
Zhenxi Shen ◽  
Xianzhou Zhang ◽  
Songcai You ◽  
Jianshuang Wu ◽  
...  
Sensors ◽  
2007 ◽  
Vol 7 (12) ◽  
pp. 3312-3328 ◽  
Author(s):  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lingyun Zheng ◽  
Limin Yang

2017 ◽  
Vol 81 ◽  
pp. 285-294 ◽  
Author(s):  
Xi Chai ◽  
Peili Shi ◽  
Ning Zong ◽  
Yongtao He ◽  
Xianzhou Zhang ◽  
...  

2019 ◽  
Vol 11 (10) ◽  
pp. 1183 ◽  
Author(s):  
Qinwei Ran ◽  
Yanbin Hao ◽  
Anquan Xia ◽  
Wenjun Liu ◽  
Ronghai Hu ◽  
...  

The alpine grassland on the Qinghai-Tibet Plateau covers an area of about 1/3 of China’s total grassland area and plays a crucial role in regulating grassland ecological functions. Both environmental changes and irrational use of the grassland can result in severe grassland degradation in some areas of the Qinghai-Tibet Plateau. However, the magnitude and patterns of the physical and anthropogenic factors in driving grassland variation over northern Tibet remain debatable, and the interactive influences among those factors are still unclear. In this study, we employed a geographical detector model to quantify the primary and interactive impacts of both the physical factors (precipitation, temperature, sunshine duration, soil type, elevation, slope, and aspect) and the anthropogenic factors (population density, road density, residential density, grazing density, per capita GDP, and land use type) on vegetation variation from 2000 to 2015 in northern Tibet. Our results show that the vegetation index in northern Tibet significantly decreased from 2000 to 2015. Overall, the stability of vegetation types was sorted as follows: the alpine scrub > the alpine steppe > the alpine meadow. The physical factors, rather than the anthropogenic factors, have been the primary driving factors for vegetation dynamics in northern Tibet. Specifically, meteorological factors best explained the alpine meadow and alpine steppe variation. Precipitation was the key factor that influenced the alpine meadow variation, whereas temperature was the key factor that contributed to the alpine steppe variation. The anthropogenic factors, such as population density, grazing density and per capita GDP, influenced the alpine scrub variation most. The influence of population density is highly similar to that of grazing density, which may provide convenient access to simplify the study of the anthropogenic activities in the Tibet plateau. The interactions between the driving factors had larger effects on vegetation than any single factor. In the alpine meadow, the interaction between precipitation and temperature can explain 44.6% of the vegetation variation. In the alpine scrub, the interaction between temperature and GDP was the highest, accounting for 27.5% of vegetation variation. For the alpine steppe, the interaction between soil type and population density can explain 29.4% of the vegetation variation. The highest value of vegetation degradation occurred in the range of 448–469 mm rainfall in the alpine meadow, 0.61–1.23 people/km2 in the alpine scrub and –0.83–0.15 °C in the alpine steppe, respectively. These findings could contribute to a better understanding of degradation prevention and sustainable development of the alpine grassland ecosystem in northern Tibet.


2017 ◽  
Vol 350 ◽  
pp. 55-68 ◽  
Author(s):  
Qingling Sun ◽  
Baolin Li ◽  
Tao Zhang ◽  
Yecheng Yuan ◽  
Xizhang Gao ◽  
...  

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7272 ◽  
Author(s):  
Biying Liu ◽  
Jian Sun ◽  
Miao Liu ◽  
Tao Zeng ◽  
Juntao Zhu

The vegetation dynamic (e.g., community productivity) is an important index used to evaluate the ecosystem function of grassland ecosystem. However, the critical factors that affect vegetation biomass are disputed continuously, and most of the debates focus on mean annual precipitation (MAP) or temperature (MAT). This article integrated these two factors, used the aridity index (AI) to describe the dynamics of MAP and MAT, and tested the hypothesis that vegetation traits are influenced primarily by the AI. We sampled 275 plots at 55 sites (five plots at each site, including alpine steppe and meadow) across an alpine grassland of the northern Tibet Plateau, used correlation analysis and redundancy analysis (RDA) to explore which key factors determine the biomass dynamic, and explained the mechanism by which they affect the vegetation biomass in different vegetation types via structural equation modelling (SEM). The results supported our hypothesis, in all of the environmental factors collected, the AI made the greatest contribution to biomass variations in RDA , and the correlation between the AI and biomass was the largest (R = 0.85, p < 0.05). The final SEM also validated our hypothesis that the AI explained 79.3% and 84.4% of the biomass variations in the alpine steppe and the meadow, respectively. Furthermore, we found that the soils with higher carbon to nitrogen ratio and soil total nitrogen had larger biomass, whereas soil organic carbon had a negative effect on biomass in alpine steppe; however, opposite effects of soil factors on biomass were observed in an alpine meadow. The findings demonstrated that the AI was the most critical factor affecting biomass in the alpine grasslands, and different reaction mechanisms of biomass response to the AI existed in the alpine steppe and alpine meadow.


2021 ◽  
Vol 307 ◽  
pp. 108456
Author(s):  
Marcelo Sacardi Biudes ◽  
George Louis Vourlitis ◽  
Maísa Caldas Souza Velasque ◽  
Nadja Gomes Machado ◽  
Victor Hugo de Morais Danelichen ◽  
...  

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