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

Leaf Area Index (LAI) plays a prominent role as an indicator of forest ecos ystem condition in research on change detection. For this, rapid and reliab le 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 vario us 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 be ing 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 degrees viewcap. The effect of sampling scheme and data aggregation method on LAIe was quantified by means of a Monte Carlo simulation. The methodolog y presented is generalized and can be applied to forest stands with differe nt canopy architectures. Our results show that for our study area the LAIe populations are normally distributed. A power function relationship was sho wn to exist between the relative accuracy of the acquired LAIe value and th e sampling intensity. Based on this information, an appropriate sampling sc heme can be selected for a predetermined relative accuracy. The method allo wed us to quantitatively assess LAIe in small stands often occurring in ver y heterogeneous environments, which is typically the case for large parts o f Western Europe. (C) 2000 Published by Elsevier Science B.V. All rights re served.