Abstract

There is a huge demand for the broad range information and status on the forest, which can be useful for monitoring the progress towards the sustainable forest management. Allometry is the statistical relationship between the dimensions of trees and their other properties and is a useful tool for obtaining information of the species with respect to forest. Generally, it deals with the parts of the organisms and data analysis of forest. In this paper we assessed suitability of allometric models for analyzing morphology traits and predicting volume of an individual tree. There are mainly four forest species found in the Sariska Tiger Reserve. Out of these species, we selected Anogeissus pendula for developing the appropriate model and estimating its volume. Thirty representative tree groups were chosen randomly from the study area for allometric analysis. We first determined D (cm), D0.1 (cm), H (m), V (cm3) morphology traits and normalized difference vegetation index (NDVI) of thirty samples of Anogeissus pendula Edgew. The allometric relationship of volume and various degrees of stem’s diameter and height was established using linear, logarithmic, quadratic and cubic equations. The regression analysis revealed that the product of the diameter and height (D2H) showed the strong relationship with tree volume in linear regression equation (R2= 0.9996). The linear regression equation model was validated with NDVI. The product of diameter and height also showed strong relationship with NDVI (R2= 0.9036) in linear equation model. Thus, the volume of the tree showed more relation with the basal area as compared to the DBH. For the practical application of stand volume investigation, D2 should be preferred as an independent variable in linear equation model. Linear regression equation model proved to be useful model to analyze morphology traits and predicting stem volume while using the product of the diameter and height (D2H). The results obtained through allometric model may help in sustainable development and management of forest ecosystem.

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