TITLE

Sustainable Urban Forest using Multiple Regression Models

AUTHOR(S)
Abdullah, Noraini; Zainodin, H. J.; Ahmed, Amran
PUB. DATE
March 2012
SOURCE
Research Journal of Forestry;Mar2012, Vol. 6 Issue 1, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Global Warming and carbon sequestration have expedited the urgency of proper forest management and its practices. This sustainability comes with stringent environmental policies, planning and management. With increased pressure for better and quality urban living, the existence of well-managed urban forest is of prime importance where the need for human recreational activities, balanced natural ecosystem and habitat, as well as the oxygen and carbon cycle, have to be sustained. Since, the green canopy is directly related to the bole and tree volume, urban forests sustainability can be mathematically modeled using multiple regressions. The Multiple Regression (MR) models are based on the tree-stem mensuration data. Three volumetric formulas are used to calculate the tree stem volume, namely, the Newton, Huber and Smalian equations. Data are collected and categorized according to sizes: small (S), medium (M) and large (L). Six independent variables based on measurable variables and five categorical variables based on location samples have been taken. Data transformations are done for normality and Spearman correlation coefficient matrix is used to identify bivariate relationships between them. Removal of high multicollinearity and insignificant variables and applying parameter tests are done on the models. Three selected best volumetric models from the equations are chosen based on the eight selection criteria (8SC). Comparisons are made to have the best model equation. The Newton's MR models on all the three sizes are found to be the best to represent the mensuration growth factors which affect the sustainability of the urban forests.
ACCESSION #
76126531

 

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