With increasing and conflicting demands on forests there is a growing need for information for decision making. Forest biometry may provide this information.

Forest biometry applies the principles and practice of statistics to forestry. Sampling for stand and tree attributes, modeling individual tree growth and yield, and using multivariate methods for ordination, analysis, and interpretation are all part of biometry.

At MSU, graduate students and faculty in forest biometry concentrate on research problems in modeling growth and yield, sampling issues, and multivariate methods. Recent projects have included developing and testing individual tree growth models, using multivariate methods to select homogeneous sites for gradient analysis, modeling the effect of cutting methods on stumpage prices for national forests, and comparing multistage to multiphase sampling using LANDSAT imagery.