Allometric Models to Estimate Leaf Area for Tropical African Broadleaved Forests. Issue 15 (5th August 2019)

• Record Type: Journal Article
• Authors: Sirri, N. F.; Libalah, M. B.; Momo Takoudjou, S.; Ploton, P.; Medjibe, V.; Kamdem, N. G.; Mofack, G.; Sonké, B.; Barbier, N.
• Is Part Of: Geophysical research letters. Volume 46:Issue 15(2019)
• Page Start: 8985
• Page End: 8994
• Publication Date: 2019-08-05
• Subjects: leaf area -- allometric models -- tropical African broadleaved forests; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2019GL083514
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: 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. Plain Language Summary: Leaf area (LA) and leaf area index (LAI) are useful parameters characterizing the plant‐atmosphere interface where matter and energy are exchanged. However, direct or semidirect estimations are not common in dense tropical forests. In this study, we used a destructive data set of trees of varied species and sizes from the semideciduous forest of southeastern Cameroon to predict total tree LA. Based on this data, we developed operational allometric models to allow for semidirect estimation of LA and LAI at tree and stand levels. These models would be of considerable use for climate‐vegetation modeling and remote sensing communities. Key Points: A destructive data set of leaf area was collected for 13 tree species representative of African semideciduous forests Operational allometric models are provided to estimate leaf area and leaf area index, which could apply to forests in this ecosystem Model exponents agree with theoretical expectations, and leaf area index in dense tropical forests seems higher than previously assessed