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Allometric Models to Estimate Leaf Area for Tropical African Broadleaved Forests
Author(s) -
Sirri N. F.,
Libalah M. B.,
Momo Takoudjou S.,
Ploton P.,
Medjibe V.,
Kamdem N. G.,
Mofack G.,
Sonké B.,
Barbier N.
Publication year - 2019
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2019gl083514
Subject(s) - allometry , leaf area index , tree allometry , tropical forest , environmental science , tropical and subtropical moist broadleaf forests , biogeochemical cycle , forest ecology , tropics , forestry , ecosystem , basal area , geography , ecology , agroforestry , subtropics , biology , biomass (ecology) , biomass partitioning
Direct and semidirect estimations of leaf area (LA) and leaf area index (LAI) are scarce in dense tropical forests despite their importance in calibrating remote sensing products, forest dynamics, and biogeochemical models. We destructively sampled 61 trees belonging to 13 most abundant species in a semideciduous forest in southeastern Cameroon. For each tree, all leaves were weighed, and for a subsample of branches, leaves were counted and the LA measured. Allometric models were calibrated to allow semidirect estimation of LAI at tree and stand levels based on forest inventory data ( R 2 = 0.7, bias = 21.2%, error = 39.5%) and on predictors that could be extracted from very high resolution remote sensing data ( R 2 = 0.63, bias = 35.1%, error = 58.73). Using twenty‐one 1‐ha forest plots, stand level estimations of LAI ranged from 4.42–13.99. These values are higher than previous estimates generally obtained using indirect methods. These results may have important consequences on ecosystem exchanges and the role of tropical forest in global cycles.