A simplified methodology for the correction of Leaf Area Index (LAI) measurements obtained by ceptometer with reference to Pinus Portuguese forests

Forest leaf area index (LAI) is an important structural parameter controlling many biological and physiological processes associated with vegetation. A wide array of methods for its estimation has been proposed, including those based on the sunfleck ceptometer, a ground-based easy-to-use device taking non-deForest leaf area index (LAI) is an important structural parameter controlling many biological and physiological processes associated with vegetation. A wide array of methods for its estimation has been proposed, including those based on the sunfleck ceptometer, a ground-based easy-to-use device taking non-deForest leaf area index (LAI) is an important structural parameter controlling many biological and physiological processes associated with vegetation. A wide array of methods for its estimation has been proposed, including those based on the sunfleck ceptometer, a ground-based easy-to-use device taking non-destructive LAI measures. However, use of ceptometer in pine stands leads to the underestimation of LAI due to foliage clumping of this species. Previous studies have proposed a correction of biased LAI estimates based on the multiplication by a constant factor. In this study, a new method for obtaining a correction factor is proposed by considering the bias (the difference between the ceptometer measure and the reference LAI) as a function of the stand structural variables, namely the basal area. LAI data were collected from 102 sampling plots (age range: 14-74) established in Pinus pinaster forests all across northern Portugal. Data from 82 sampling plots were used for the adjustment of the LAI ceptometer correction model, while the remaining 20 plots were used for the model validation. The observed LAI ranged from 0.34 to 6.4 as expected from the large heterogeneity of the sampled pine stands. Significant differences were detected between LAI values estimated by ceptometers and LAI reference values. Different correction methods have been compared for their accuracy in predicting LAI reference values. Based on the results of the statistical analysis carried out, the new proposed LAI correction outperformed all the other methods proposed so far. The new approach for bias reduction proposed here has the advantage of being easily applied since the basal area is almost always available from forest inventory or can be inferred from remote sensing surveys. However, the bias correction model obtained is site-specific, being dependent on stand species composition, soil fertility, site aspect, etc. and should therefore be applied only in the study area. Nonetheless, the development of a correction methodology based on an allometric approach has proved to greatly improve LAI ceptometer estimations.