Modeling leaf area index from litter collection and tree data in a deciduous broadleaf forest
作者： Masae Iwamoto Ishihara , Tsutom Hiura
DOI: 10.1016/J.AGRFORMET.2011.02.007
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摘要: Abstract Leaf area index (LAI) is an important in ecological and meteorological studies. The litter trap method commonly used to measure LAI deciduous forests. To reduce the time consumed sorting leaf litterfall by species method, we developed four models predict using traps tree census data. local dominance model, which estimates amount of each their dominance, predicted mean spatial variability most accurately compared 2 that did not take into account heterogeneity distribution within a forest or model estimated from dispersal function. Therefore, this can be employed instead species. Furthermore, found mass per (LMA) at least 10 dominant are essential for accurate estimation LAI. Present results suggest mainly due variance followed forest, difference LMA among
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