Regression Models for Tree Volume Prediction in Pinus Wallichiana Stands of South Western Himalayan Region of Kashmir

Abstract

 This study focuses on assessing tree species diversity and evaluating the efficacy of both linear and non-linear regression models for predicting tree volume. The research was conducted in the slopes of the Pir Panjal range within the Shopian Forest Division, situated in the South-Western Himalayan Region of Kashmir. Data on Pinus wallichiana stands, including diameter at breast height (D) and tree height (H), were meticulously collected using appropriate measurement instruments. Employing a multi-stage sampling technique, ten plots of uniform size (10m x 10m) were selected across 20 blocks, and subsequently, 25 trees were randomly chosen from each plot. Six different linear and non-linear regression equations were fitted to the data, and the most suitable equation was identified for volume estimation. To evaluate the performance of the fitted regression models, metrics such as R-squared (R²), adjusted R², root-mean-square error (RMSE), and Theil’s U statistic were employed. Additionally, validation procedures involved using the half-split approach and the Chow test. Upon analysis, it was determined that when employing diameter (D) and considering the joint effect 
of diameter and height (D2H (I)) as independent variables, the linear model (V=-1.41+15.12D) and power model (V=2.301 I0.502), quadratic model (V=1.8726+0.663 I- 0.011 I²) with highest R², lowest RMSE and Theil’s U statistic respectively, emerged as the most suitable for volume estimation, demonstrating superior accuracy compared to alternative models. Consequently, our findings extend beyond academic inquiry, offering practical implications for sustainable forest management and planning in the Western Himalayan Regions of Kashmir. By providing robust volume estimation models tailored to Pinus wallichiana stands, our study equips forest managers and policymakers with essential tools for informed decision-making.