Influence of woody tissue and leaf clumping on vertically resolved leaf area index and angular gap probability estimates.

Abstract Leaf area index L is a key vegetation parameter that can be used in soil–vegetation–atmosphere exchange modeling. To represent the structure of ecosystems in vertically distributed modeling, vertically resolved L distributions as well as vertical and angular gap probability P gap distributions are needed, but they are rarely available. On the experimental side, studies often neglect woody plant components when using indirect methods for L or P gap observations. This can lead to significantly biased results, particularly in semi-arid savannah-type ecosystems with low L values. The objective of this study is to compare three non-destructive leaf area index measurement techniques in a sparse savannah-type cork oak canopy in central Portugal in order to derive vertically resolved L as well as vertically and angularly resolved P gap . We used the established LAI-2000 device as well as fast digital cover photography (DCP), which was vertically and angularly distributed. We applied object-based image analysis to DCP to exclude woody plant components. We compared the results with vertically distributed LAI-2000 measurements and with vertical estimates based on easily measurable crown parameters. Height and angularly distributed DCP was successfully applied here for the first time. It delivers gap probability P gap and effective leaf area index L e measurements that are comparable to the established LAI-2000. The height and angularly dependent leaf clumping index Ω could be determined with DCP, which led to a 30% higher total leaf area index L for DCP compared to LAI-2000. The exclusion of woody tissue from DCP yields on average a 6.9% lower leaf area index L. Including Ω and excluding woody tissue, the L of DCP matched precisely with direct measurements using litter traps. However, the set-up and site-specific adjustment of the image analysis algorithm remains challenging. We propose a special filter for LAI-2000 to enhance data quality when used in open canopies. Finally, if height-dependent observations are not feasible, ground-based observations of crown parameters can be used to derive very reasonable L height distributions from a single, ground-based L observation.

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