Assessment of allometric models for leaf area index estimation of Tectona grandis

Abstract

Abstract:We developed allometric models for accurate estimation of Tectona grandis (teak) leaf area index (LAI), for different stem diameter-classes (D-classes). In this study, we harvested teak trees in the tropical dry region of India in the ten stem diameter classes and measured LAI, and developed regression models for the non-destructive estimation of LAI with the help of wood density (ρ), diameter at breast height (D) and plant height (H). Models used for the prediction of biomass of tree components were of the form, linear, logistic, Gompertz and Chapman. Among the four models, non-linear models were more efficient compared to the linear model. We observed more than 60% variability in the LAI, explained by non-linear regression models. The models including ρ and D had greater R 2 and lower standard error of estimate. Our study detected logistic models more appropriate for broad diameter range and Gompertz models for small D-classes. The regression models developed in our study can be applied separately for the ten D-classes, and this could minimize the error occurring during indirect estimation of teak LAI.

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

“…However, it will be helpful to study similar deciduous patches in foothills of Himalaya as well as tropical deciduous patches of central India (e.g. Vindhyan forest ranges) with low LAI value ( Chaturvedi et al, 2017 ).…”

Carbon Storage of Some Rubber Trees (Hevea brasiliensis) Clones in HEVECAM’s Plantations in South Cameroon

“…LAI was determined using the logistic regression model proposed by [18], express by the following formula:…”