Date of Award

Spring 5-14-2016

Level of Access Assigned by Author

Campus-Only Thesis

Degree Name

Master of Arts (MA)




Thomas Bellsky

Second Committee Member

Ramesh Gupta

Third Committee Member

Billy Jackson


This thesis describes an iterative data assimilation strategy, both Bayesian and Monte Carlo in nature, that combines an estimate from a model forecast with observational data to form a better realization of a true state. In particular, we outline the development of the Kalman filter and ensemble Kalman filter methods, which have been used in many applications including climate and weather forecasting. These methods are novelly applied to predict forest stand characteristics. In our first application a Chapman--Richards forecast yield model and observed field data from a collection of fully stocked eastern white pine stands is utilized. The results from this application demonstrate the ease of implementation of the ensemble Kalman filter data assimilation methods, and show the capacity of these methods to accurately forecast future forest stand basal area when compared to other methods. This application has been developed into a web app and is available for further study and use online. Our second application utilized the United States Department of Agriculture's Forest Inventory Analysis data set for the state of Maine and a spatially coupled variation on the Chapman--Richards Yield Model. The results of this application shows the robust capacity of the described method when dealing with large, spatially explicit data sets. As a byproduct of this project, code for the statistical software \textsf{R} has been developed to carry out the ensemble Kalman filter data assimilation as well as a comprehensive comma separated value file for matching tree species codes with their scientific and common names. Both of these files are available on the internet.