基于Hyperion影像的叶面积指数反演

摘要

Leaf area index is to describe one of the important indexes of plant growth condition,Hyperion data is used to calculate the NDVI SRI EVI RENDVI WBI.Vegetation surrounding the meijiang river basin as study area,combined with 30 sample points in the region of the measured LAI data,set up five vegetation index and LAI are multiple regression model and test the accuracy of the model.The experimental results show that the use of the above five vegetation index and leaf area index are established by the linear and nonlinear model,a linear quadratic polynomial and cubic polynomial regression model RENDVI best fitting effect,with the increase of variable frequency model fitting effect also showed a trend of increasing.Index in the model best fitting effect for EVI,WBI in logarithm model has a better fitting effect is 5 vegetation index as the independent variables are the multivariate linear regression model fitting effect is superior to all other models.%叶面积指数是描述植物生长状况的重要指标之一,利用Hyperion数据计算NDVI,SRI,EVI,RENDVI,WBI 5种植被指数.以梅江流域周边植被为研究区域,结合该地区30个样本点的实测LAI数据,建立5种植被指数与LAI的多种回归模型并检验各模型的精度.实验结果表明,利用上述5种植被指数与叶面积指数所建立的线性和非线性模型中,一元线性、二次多项式和三次多项式回归模型中RENDVI的拟合效果最佳,并且随着模型变量次数的增加拟合效果也呈递增趋势.指数模型中拟合效果最佳的为EVI,WBI在对数模型中有更好的拟合效果.将5种植被指数作为自变量的多元线性回归模型拟合效果明显优于其它所有模型.