engleski

Transformation and Validation Methods in Allometric Remote Sensing Based Equations

In addition to classical (time-consuming and labor-intensive) field measurements, remote sensing methods can be used to estimate various forest attributes at individual tree, plot or stand level. Remote sensing methods can reduce field work and improve the efficiency, but the accuracy of obtained results have to be carefully tested and evaluated. In this study, plot level volume of pedunculate oak forest was estimated using metrics extracted from digital surface model derived from aerial images and normalized using digital terrain model derived from airborne laser scanning. Traditional allometric equations for volume estimation are developed using linear regression on log- transformed data, which often causes bias in the estimation when errors are not additive. Several correction methods have been proposed to varying degrees of success, depending on the quantity of interest. Additionally, smaller sample sizes can cause poor goodness of fit, and greater variance in model error estimation. In this paper, the authors study bias in volume estimation, using multiple assessment and validation methods, to propose the optimal model for this type of data.

allometry ; nonlinear regression ; log-linear regression ; validation

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