Fractal dimension as correction factor for stand-level indirect leaf area index measurements

Rapid, reliable and objective estimation of Leaf Area Index (LAI) at various scales is of utmost importance in numerous studies on the Earth's ecosystem. The Licor LAI-2000 Plant Canopy Analyzer (PCA) correlates measured gap fractions to overall LAI by means of the inversion of a radiative transfer model. The PCA's model assumes a random distribution of foliage elements in the stand canopy. However, clumping is observed at different scales in nature. The objectives of this study were, first, the quantification of the LAI measurement error of the PCA due to foliage clumping at stand-level, and second, the derivation of an easily measurable correction factor. For this, foliage elements were simulated in a virtual 3D-space. PCA LAI measurements were simulated by applying the same PCA inversion model onto virtually taken hemispherical photographs resulting in both exact reference LAI values and corresponding PCA measurements. Fractal dimension, quantifying the deviation from a complete random foliage distribution, was tested as a correction factor for PCA measurements. Correction models for PCA measurements were build, based on the measured fractal dimension. A post validation as performed on field data obtained by means of littertraps (reference). A clear relation between fractal dimension and the proportion of underestimation of LAI by the PCA with increasing clumping of foliage was found. Implementation of the regression model resulted in significantly improved LAI measurements.