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为探讨亚高山草甸地上生物量和植被指数的关系,更好服务于草地生态建设,论文利用2008年7月覆盖若尔盖地区的TM影像,分别建立了7种植被指数(NDVI、RVI、DVI、SAVI、MSAVI、PVI、GVI)与地上生物量的线性和4种非线性(二次多项式、三次多项式、对数、幂函数)回归模型。研究结果表明植被指数(NDVI、DVI、SAVI、MSAVI、PVI、GVI)与地上生物量模型表现出三次多项式回归模型最优,再次是二次多项式模型、线性模型,相对较差的是指数模型;而基于RVI的地上生物量模型表现为指数模型最优,其次为三次多项式模型、二次多项式模型、线性模型。分析表明,基于RVI的地上生物量幂函数模型的模拟效果最好,复相关系数R2=0.817 7,精度检验结果表明该模型的平均误差为6.80%,拟合精度达93.20%,根据此模型模拟出若尔盖县草地地上生物量分布图,表明该县草地生物量东南部较高而西北部较低。
In order to explore the relationship between aboveground biomass and vegetation index in subalpine meadow and better serve the ecological construction of grassland, seven vegetation indices (NDVI, RVI, DVI, SAVI, MSAVI, PVI and GVI) and four above-ground biomass (quadratic polynomial, cubic polynomial, logarithmic, power function) regression model. The results showed that the vegetation index (NDVI, DVI, SAVI, MSAVI, PVI, GVI) and aboveground biomass model showed the cubic polynomial regression model is the best, again quadratic polynomial model, linear model, the relatively poor is the exponential model; The aboveground biomass model based on RVI showed that the exponential model was the best, followed by cubic polynomial model, quadratic polynomial model and linear model. The analysis shows that the RVI simulation model is the best, the correlation coefficient R2 = 0.8177, the accuracy test results show that the average error of the model is 6.80% and the fitting accuracy is 93.20%. According to this model simulation The aboveground biomass distribution of grassland in Ruoergai County shows that the grassland biomass in the county is higher in the southeast and lower in the northwest.