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Large-area Enhanced Forest Inventory modeled on 3-D Airborne Laser Scanning data

Project Information

parallelization, data handling, performance, programming,
Project Status: Complete
Submitted By: Northeast Cyberteam
Project Email:
Project Institution: University of Maine
Anchor Institution: University of Maine
Project Address: 5708 Barrows
Orono, Maine. 04469-5708

Project Description

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. Current methods for monitoring the forest are limited as using traditional ground-based sampling techniques are expensive ($3 million annually spent in Maine alone), imprecise, not real-time, difficult, and spatially coarse. New capabilities for the aerial collection of high quality, detailed remotely sensed information on 3-D forest structure over large areas are providing inventory information more accurately, efficiently and at lower cost relative to traditional methods. Working with the large volumes of data and increasingly complex analytics requires computer science research and development designed to make progress on processing workflow solutions.