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Data from: Allometric models to estimate leaf area for tropical African broadleaved forests

Citation

Sirri, Nelly F. et al. (2019), Data from: Allometric models to estimate leaf area for tropical African broadleaved forests, Dryad, Dataset, https://doi.org/10.5061/dryad.63cj030

Abstract

Direct and semi-direct estimations of leaf area (LA) and leaf area index (LAI) are scarce in dense tropical forests despite the importance of such measurements to calibrate remote-sensing products, forest dynamics and biogeochemical models (e.g. Dynamic Global Vegetation Models). Extensive and destructive sampling of 61 trees belonging to 13 species spanning all diameter and wood density classes was performed in the semi-deciduous forest of southeastern Cameroon. For each tree, all leaves were weighed, counted for a subsample of branches and LA measured for 10-50 leaves. Allometric models were calibrated to allow semi-direct estimation at tree- and stand-levels, based on forest inventory data (R²=0.7, bias=21.2%, error=39.5%), also on novel tree metrics allowed by remote-sensing like airborne light detection and ranging (R²=0.63, bias=35.1%, error=58.73). Using twenty-one 1-ha forest inventory plots, stand-level estimations of LAI spanned from 4.42-13.99. Models produced at stand-level estimation may be considerably useful to climate-vegetation modelling and remote-sensing communities.

Usage Notes

Funding

National Science Foundation, Award: N° TF010038

Location

Cameroon
Semi-deciduous forest
Tropical African forests