Evaluating LiDAR Tools for Large-area Enhanced Forest Inventory Applications in Maine
PI: Hayes, Daniel (Forest Resources, UMaine)
Sector: Forestry, Computer Science
Partners: UMaine Fort Kent
Abstract: Maine’s economy depends heavily on its forest resource base: it accounts for over 6% of the total GDP and has an estimated total annual economic impact of $8-10 billion. The sound, scientifically-based management of the forest resource requires a significant investment in inventory programs. While traditional, ground-based inventory is expensive and imprecise, recent advances in remote sensing technology are revolutionizing the way in which forests are measured and monitored. In particular, Light Detection and Ranging, or LiDAR, technology allows for the development of high quality, Enhanced Forest Inventory (EFI) information over large areas efficiently and at lower cost relative to field-based methods. There is a fast-growing need for leveraging the growing collection of LiDAR data across Maine for usable and reliable EFI data products to support management and decision-making in the state’s forest industry. A significant obstacle has been that basic, supporting research on the topic is lacking in three main areas, including remote sensing, forest mensuration and computer science disciplines. The goal of this project is to evaluate available LiDAR data sets and modeling techniques for their comparative efficacy in generating geospatial EFI information products useful for sustainable forest management in Maine.