NR Forestry Administrative Supervisor Minnesota Department of Natural Resources Grand Rapids, Minnesota, United States
The Minnesota DNR needed to update and improve forest inventory data using less expensive and more robust methods and cutting-edge remote sensing technologies and monumented fixed radius field plots provides that need. Thus, forest inventory models have been developed for Minnesota’s diverse forested ecological landscape across multiple ownerships.
Learning Objectives:
Understand effectiveness of high density lidar in characterizing large-scale forest structural attributes
Realize the importance of fixed radius plots (base on a grid) and lidar to obtain (highly resolute and accurate) inventory estimates more efficiently compared to traditional stand-based designs that requires multiple variable radius plots per stand.
Derive small area estimation (SAE) with uncertainty estimates from high resolution (20-m) inventory predictions based on lidar.
Update inventory estimates using other geospatial datasets such as stereo NAIP combined with FIA ground sampling data.
Compare inventory models based on high density lidar against the old statewide lidar.
