Leaf Area Index and Vegetation Index Correlation of Cunninghamia Based on the WorldView 2 Images

doi:10.11924/j.issn.1000-6850.2012-2751

Abstract

Abstract: In order to research the correlation between leaf area index and vegetation index of Cunninghamia based on high-resolution remote sensing image data, regarding Huangfengqiao Forestry Farm of Youxian as the research object, the author combined the ground experiment with remote sensing technology, and extracted NDVI, SAVI, SARVI, RVI, MSAVI, ARVI 6 vegetation index from the remote sensing data of WorldView 2, and used leaf area index of Cunninghamia (LAI) that was measured by the LAI-2000 to establish the relation, research the estimation of leaf area index of Cunninghamia based on the remote sensing imaging of WorldView 2, and then analyzed the effects between vegetation index and Cunninghamia LAI. Different vegetation index were 2 curve models, linear model, exponential curve model and logarithmic curve model of LAI inversion. The results showed that: except for DVI and LAI correlation was slightly lower, other vegetation index and LAI had high correlation, and were higher than the low resolution image extraction of vegetation index, vegetation index (SAVI) and LAI correlation had unrelated to with the soil factor L. In the linear model, RVI and ARVI were more suitable for Cunninghamia LAI to establish a linear regression model, and the correlation coefficient R was 0.931, 0.895, respectively, the determination of coefficient R2 was 0.866, 0.800, respectively, which had a better fitting effect. In the nonlinear model, the inversion model of the best was 2 curve models, followed by an exponential model, and the worst was the logarithmic model. The model fitting result of NDVI, SAVI and RVI were better than others; the model fitting result of DVI was the worst; the best fitting model of the R2 was as high as 0.884. Cunninghamia LAI had better fitting effect of nonlinear model was NDVI and SAVI2 curve model.

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