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Promote Forest Health for a Stable Bio-Economy Understand and Separate Wood Components Create and Commercialize New Bioproducts

Faculty & Staff

Aaron Weiskittel

Dr. Aaron Weiskittel is an assistant professor of forest modeling and biometrics at the University of Maine, School of Forest Resources. He received his BS in Natural Resources from The Ohio State University. His MS and PhD are in forestry from Oregon State University. Before joining the faculty at Maine, he worked as a research fellow at the Research Institute of Baden-Württemberg in Freiburg, Germany as well as The Weyerhaeuser Company in Oregon. His research interests range from empirical modeling of individual tree growth to mapping regional potential productivity.

 

My Research

My research focuses on the development of empirical and process-based models to predict biomass growth and yield across a range of forest types. These models can be used to forecast changes in biomass due to varying silvicultural regimes, changes in annual weather patterns, and the interaction between these factors. In addition, I am also working on a high-resolution map of potential biomass density for the region and methodology for measuring stand biomass effectively. Finally, I will be exploring the potential of remote sensing technologies such as LiDAR and Landsat to predict forest biomass. Essentially, my research is intended to quantify, characterize, and model the region’s forest biomass resource.

 

In Other Words

Foresters need tools to help them to manage forests in a sound manner. Computer simulation models of forest growth assist in predicting changes in the forest and evaluating the timing and intensity of management activities. Foresters also need maps of forest potential productivity so that they can evaluate the effectiveness of their management and plan sustainable harvests. The development of these tools can be aided with technology such as satellites that continuously monitor the development and extent of the forest. Understanding how to use this data effectively will help to detect changes in the forest more quickly and at a significantly lower cost.

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Research Projects
  • Resource Modeling

 

NSF EPSCoR The University of Maine EPSCoR Department of Energy
This project is supported by the National Science Foundation under Grant No. EPS-0554545 This project is supported by the Department of Energy EPSCoR program under award number DE-FG02-07ER46373