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This research aimed to quantify the leaf area index (LAI) of a forest of Pinus taeda L. and Cupressus lusitanica Mill. using optical data from the Landsat-8/OLI and Sentinel-2/MSI sensors. For that, 50 circular plots of 500m2 in the area were allocated, in which LAI readings were performed using the LAI-2200 equipment. The remotely located data comprised images of the Landsat-8/OLI and Sentinel-2/MSI orbital sensors. After digital processing, 15 vegetation indexes were calculated for each image used. These data were correlated with LAI by plot. With the best indexes correlated with the LAI variable, regression models were constructed using the Stepwise technique (Backward and Forward). The best model was determined based on the adjusted coefficient of determination (R2 adjusted), standard error of estimate (Syx%), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Root Mean Squared Error (RMSE). The results showed that there was a significant correlation among the average rates per plot and the LAI per plot. The fitted models developed with the indexes derived from Sentinel-2 had superior performance to the models built with the Landsat-8 data. The best estimate was the LAI per plot with 4.75% of error and adjusted R2 of 0.9134. There was no significant difference between the LAI obtained from the field campaign and the LAI estimated by the spectral data (Landsat-8/OLI and Sentinel-2/MSI). However, it is recommended to test this methodology using sensors of high spatial resolution and with other species of the genera Pinus and Cupressus.

Estimation of leaf area index of Pinus taeda L. and Cupressus lusitanica Mill. by vegetation indices