| As an important part of the terrestrial ecosystems,grassland not only provides the material basis for livestock rearing by pastoralists,but also plays an important role in nourishing water and maintaining soil and water.As the largest natural ecosystem in Qinghai province,grassland ecosystems have huge vegetation carbon reserves and play an important role in the carbon cycle in China.However,the ecological environment of the Muli mining area in Qinghai Province has been affected by mining for many years,and water,land and vegetation resources in this region have been damaged to varying degrees,resulting in a series of ecological and environmental problems such as degradation of grasslands and reduction of the water-supporting function of the ecosystem,which has caused great concern.Therefore,this study uses Landsat data from 2000 to 2021 to comprehensively evaluate and analysis the ecological changes of vegetation in the Muli mine area over a long period of time by inverting the information on vegetation cover,above-ground biomass and physical indicators around the Muli mine area.The SPRI and EVI indices generated from MODIS and other remote sensing data were used as the main model parameters to construct a model of gross primary productivity applicable to Qinghai grassland,and the model was applied to monitor the changes in the spatial and temporal patterns of vegetation carbon sinks in Qinghai Province,providing certain scientific reference values for the rational regulation of the carbon cycle of the vegetation ecosystem and the implementation of ecological protection projects in Qinghai.The main research results are as follows.(1)Based on the remote sensing images of Landsat5,7 and 8,the image dichotomy method was applied to generate the grassland fractional vegetation cover data of the Muli mining area from 2000 to 2021.Besides,the remote sensing estimations of grassland biomass and phenology from 2000 to 2021 were obtained by establishing a regression model and the curvature rate of change method.Based on the above data set,the annual average change of vegetation cover and biomass was obtained using slope analysis.As a result,the growth status and change trends of grassland around the Muli mine area and the possible influencing factors were analyzed.(2)Various statistical models and light use efficiency models were compared and analyzed.The sensitivities of various meteorological and remote sensing factors in the models were considered,and based on the results of the correlation analysis,two statistical models and a light use efficiency model based on SPRI were constructed using PAR,SPRI and EVI.The performances of the models were compared and analysed with ground measurements.The results show that the SPRI-based light use efficiency model is more accurate with fewer parameters,simple and easy to obtain,and thus can be used to estimate the daily or monthly GPP in Qinghai Province.(3)The annual GPP and monthly GPP data of Qinghai Province for two years,2009 and 2018,were calculated based on the SPRI light use efficiency model developed in this experiment.The patial distribution characteristics of annual GPP sin these two years were quantified.The results show that the total primary productivity of vegetation in Qinghai Province was low in 2009 and significantly higher in 2018 due to the disaster;the high value areas of annual total GPP were found in the southeastern and south-central parts of Qinghai Province,and the low value areas were found in a large area in the northwestern part.The pattern of changes in the maximum and mean values of monthly GPP and SPRI was calculated,and it can be seen that the monthly GPP mean values in 2018 were higher in February-August than in 2009,and slightly lower or basically the same in other months;the monthly GPP changes in 2018 were large,with the highest GPP values in four months from May-August,and significantly lower in other months than in these four months,and slightly higher in spring from February-April than in winter;monthly GPP changes were flatter in 2009 and overall less pronounced than in 2018.The correlation between precipitation and temperature data and monthly GPP values was calculated and the results showed that the highest correlation among the influencing factors of monthly GPP in 2018 was the maximum value of monthly temperature;the highest correlation among the influencing factors of monthly GPP in 2009 was the average value of precipitation.Based on the comprehensive analysis of the influencing factors and spatial and temporal distribution characteristics of GPP in Qinghai Province,it is called to improve the understanding of the carbon sequestration function of grassland ecosystems and to better deal with the relationship between economic development and grassland conservation. |
