天峻县草地地上生物量遥感监测模型

Remote sensing monitoring model for above ground biomass of grassland in Tianjun County

【目的】快速、准确和大范围地对天峻县草地地上生物量(Above-Ground Biomass,AGB)进行监测。【方法】利用天峻县Landsat 8 OLI遥感图像数据和同期43处样点实测生物量数据,分别建立了归一化植被指数(Normalized Difference Vegetation Index,NDVI)、土壤调节植被指数(Soil-Adjusted Vegetation Index,SAVI)、修改型土壤调节植被指数(Modified Soil-Adjusted Vegetation-Index,MSAVI)、比值植被指数(Ratio Vegetation Index,RVI)与草地地上生物量的遥感统计模型,分析遥感植被指数与草地地上生物量之间的相关性。【结果】天峻县遥感植被指数与草地地上生物量之间存在较好的相关性,但不同的统计模型的拟合效果不同;由4个自变量建立的多元线性回归模型的比一元线性回归模型有更好的拟合效果;遥感植被指数与草地地上生物量建立的三次项回归模型在拟合精度上较一元线性和多元线性高,为y=116.12x3–898.48x2+1672.1x–1003.4。【结论】RVI与草地地上生物量三次项模型适用于监测天峻县地区的草地地上生物量。

【Objective】To monitor the above-ground biomass(AGB)of grassland in Tianjun County quickly,accurately and on a large scale.【Method】This research used the Landsat 8 OLI remote sensing image data of Tianjun County and the biomass data of 43 sample points in the same period to establish a Normalized Differential Vegetation Index(NDVI),Soil Adjusted Vegetation Index(SAVI),Modified Soil-Adjusted Vegetation Index(MSAVI),Ratio Vegetation Index(Ratio)Vegetation Index,(RVI)and a remote sensing statistical model of aboveground biomass in grassland by analyzing the correlation between the remote sensing vegetation index and the aboveground bio-mass of grassland.【Result】The research showed that there was a good correlation between the remote sensing vegetation index and the aboveground biomass of grassland in Tianjun County,but the fitting effects of different statistical models were different.The multiple linear regression model established by 4 independent variables had a better fitting effect than the univariate linear regression model.The cubic regression model established by the remote sensing vegetation index and grassland aboveground biomass had better fitting accuracy than the univariate linear and multivariate regression models.【Conclusion】The linear height was y=116.12x3+898.48x2+1672.1x-1003.4,which was suitable for monitoring the aboveground biomass of grassland in Tianjun County.This study provides model support and theoretical reference for the estimation of aboveground biomass of grassland in Tianjun County.