Sampling methodology for LAI measurements with LAI-2000 in small forest stands

Abstract

Leaf Area Index (LAI) plays a prominent role as an indicator of forest ecosystem condition in research on change detection. For this, rapid and reliable estimation of the effective LAI (LAIe) — this is the ratio of the total one-sided area of vegetation elements over the unit ground area) — at various scales is of utmost importance. We used the Licor LAI-2000 Plant Canopy Analyzer (PCA) for the acquisition of point LAI values within small (about 1 ha) stands. Canopy influences, external to the stand for which LAI was being assessed, and direct sunlight were excluded from respectively the LAIe computations and the fields of view of the PCA sensors by the use of a 270° viewcap. The effect of sampling scheme and data aggregation method on LAIe was quantified by means of a Monte Carlo simulation. The methodology presented is generalized and can be applied to forest stands with different canopy architectures. Our results show that for our study area the LAIe populations are normally distributed. A power function relationship was shown to exist between the relative accuracy of the acquired LAIe value and the sampling intensity. Based on this information, an appropriate sampling scheme can be selected for a predetermined relative accuracy. The method allowed us to quantitatively assess LAIe in small stands often occurring in very heterogeneous environments, which is typically the case for large parts of Western Europe.