A parametric method for estimation of leaf area index using landsat ETM+ data

Abstract

Leaf Area Index (LAI) is a key variable for monitoring biophysical and biochemical characteristic of vegetation. So far, various remote-sensing methods are proposed to assess this index; each has its own advantages and limitations. In this study, the Scatterplot of NIR and Red bands (SNIR-R) of ETM+ images was used for LAI estimation. For this, nine different parameters consisting of five distances and four angles were extracted from SNIR-R. All possible combinations of these nine parameters were taken into account and as a result, 511 different regression equations were developed for estimation of LAI. The best regression equation (5P-LAI3) was made of two angles and three distances had the highest correlation coefficient (R) of 0.94 and root mean square (RMSE) of 0.75. On another approach, the triangle of scattered data in the SNIR-R was divided into three separate regions based on PVI (Perpendicular Vegetation Index) values. Three different regression equations were fitted to each region. Use of this Triangle Segmentation Model (TSM) improved the results slightly; that is, comparing with the results of general model 5P-LAI3, RMSE reduced to 0.66 and R increased to 0.96. The data collected throughout BigFoot project was used in this study. Comparing with other models in which BigFoot data were used, it was concluded that despite the simplicity of 5P-LAI3 model, it has an acceptable accuracy and TSM showed the highest accuracy, after all.