The fractional vegetation cover (FVC) data measured on the ground is the main source for the calibration and verification of FVC remote sensing inversion, and its accuracy directly affects the accuracy of remote sensing inversion results. However, the existing research on the evaluation of the accuracy of the field quadrat survey of FVC based on the satellite remote sensing pixel scale is inadequate, especially in the alpine grassland of the Qinghai-Tibet Plateau. In this paper, five different alpine grasslands were examined, the accuracy of the FVC obtained by the photography method was analyzed, and the influence of the number of samples on the field survey results was studied. First, the results show that the threshold method could accurately extract the vegetation information in the photos and obtain the FVC with high accuracy and little subjective interference. Second, the number of samples measured on the ground was logarithmically related to the accuracy of the FVC of the sample plot (p < 0.001). When the number of samples was larger, the accuracy of the FVC of the sample plot was higher and closer to the real value, and the stability of data also increased with the increase of the number of samples. Third, the average FVC of the measured quadrats on the ground was able to represent the FVC of the sample plot, but on the basis that there were enough measured quadrats. Finally, the results revealed that the degree of fragmentation reflecting the state of ground vegetation affects the acquisition accuracy of FVC. When the degree of fragmentation of the sample plot is higher, the number of samples needed to achieve the accuracy index is higher. Our results suggest that when obtaining the FVC on the satellite remote sensing pixel scale, the number of samples measured on the ground is an important factor affecting the accuracy, which cannot be ignored.

Abstract. China's economy has experienced rapid development in the past few decades, and economic development has also brought serious pollution problems, which has attracted wide attention of scholars at home and abroad. Based on the data of global PM2.5 remote sensing products and China's economic development from 1998 to 2016, the temporal and spatial variations of PM2.5 concentration in China from 1998 to 2016 were analyzed, and the response of PM2.5 concentration in China to economic development was studied. The results showed that the average annual PM2.5 concentration in 1998–2016 showed the spatial distribution characteristics of high in the East and low in the west; during 1998–2016, PM2.5 increased significantly in most regions, but decreased significantly in Inner Mongolia, Shaanxi, Ningxia and Gansu, while PM2.5 did not change significantly in some parts of the central region; during 1998–2007, PM2.5 concentration in most regions of China experienced rapid economic development. The concentration of PM2.5 in a few areas such as Inner Mongolia decreased significantly, while that in Yunnan, Sichuan and Inner Mongolia did not change significantly. During the 10 years of economic slowdown in China (2008–2016), the downward trend of PM2.5 concentration in China was expanding. The concentration of PM2.5 in the central and southern regions decreased or did not change significantly, except in the northwest and a few northeast regions. The change of PM2.5 concentration responds obviously to economic development, but the response of different regional economic development to the change of PM2.5 concentration is different.

Abstract. The source region of the Yellow River is an important source of water conservation in the Qinghai-Tibet Plateau. It is also an animal husbandry base that has a major impact on China. Its ecological environment changes will have a major impact on the safe and sustainable development of water use in Asia. The alpine grassland is an important part of the ecosystem of the Yellow River source area, and the spatial differentiation characteristics of the plaque have important indication significance for the ecological environment succession. In this paper, the alpine grassland in the source region of the Yellow River is taken as the research object. Based on the aerial image of the unmanned aerial vehicle, the EGI threshold method is used to extract the vegetation patches and the bare plaques to obtain high-precision field monitoring data, and the landscape ecology principle is used to analyze the four types of alpine grassland. The morphological characteristics of plaques in different grassland types are studied, and the spatial indications of spatial sequences are used to study the ecological significance of plaque succession. The results show that the number of plaques in swamp meadows, alpine meadows, degraded meadows and alpine grassland increase significantly, the degree of fragmentation increase, and the area of vegetation patches decrease significantly. The area of bare plaque increase significantly as the main trend; the distribution and dominance of landscape plaques are analyzed; the swamp meadow have the highest spread, the best aggregation, and the largest proportion of vegetation plaque, the highest degree of dominance, alpine grassland Conversely; in terms of the complexity of landscape patch shape; the area-weighted shape index and the area-weighted fractal dimension increase with the order of swamp meadows, alpine meadows, degraded meadows and alpine grasslands, tending to be complex plaque space. The ecological risk intensity index of alpine grassland in the source region of the Yellow River varies greatly, and the ecological risk of alpine grassland is high. The results of this study provide data support for elucidating the mechanism of spatial differentiation of alpine grass plaques, provide scientific assistance for grassland monitoring and management in the source area, and it provides an important basis for further discussion on ecological system protection, animal husbandry economy and sustainable development of alpine grassland in China. At the same time, it provides important theoretical support and ecological indication significance for the understanding of the alpine grassland ecosystem succession in the source area of the Yellow River.