Measurements, Models and Maps: Large-area Forest Inventory from Airborne LiDAR Data
This project has organized and carried out initial investigations into the use of LiDAR remote sensing analysis to enhance the design and operation of inventory programs for Maine’s forest industry stakeholders. The research conducted here evaluates ground-based inventory plot designs in conjunction with existing, publicly available Airborne Laser Scanning (ALS) data sets, processed in a high-performance computing environment, for workflow efficacy in generating geospatial data products useful for forest management. For these initial investigations, we have partnered with the Seven Islands Land Company and Baskahegan Company to evaluate the impact of plot type, size, and location accuracy on model prediction of forest inventory attributes derived from relating field data sampling with wall-to-wall LiDAR measurements across the study areas.
NAIP EFI: Investigating the use of new 3-D canopy surface model data from the National Agricultural Imagery Program for developing Enhanced Forest Inventories in Maine
This project aims to incorporate 3D photo-based point cloud datasets from the National Agricultural Imagery Program (NAIP) into enhanced forest inventory (EFI) models. The project involves conducting a series of comparisons between NAIP and LiDAR models at several study areas in Maine where we have access to high-quality remote sensing data paired with field-based measurements for model calibration and validation. The results from this research will provide Maine’s forest industry stakeholders with information on how, and how well, the new and freely available statewide NAIP datasets can be used to update and enhance their forest inventory programs.