Idaho LiDAR Consortium (ILC): St. Joe National Forest
DOI: https://doi.org/10.5069/G9KS6PHD OT Collection ID: OT.042012.26911.1
OT Collection Name: Idaho LiDAR Consortium (ILC): St. Joe National Forest
Short Name: ILC03_StJoe
Collection Platform: Airborne Lidar
The lidar survey was conducted by vendor Horizon's, 3600 Jet Drive, Rapid City, South Dakota. Lidar instrument was flown in a Leica ALS40 on July 23, August 11 or September 22, 2003. The data were delivered in ascii format with information on return number, easting, northing, elevation and intensity for each return. The ascii files were converted to las format and classified using the Multiscale Curvature Classification (MCC) method of Evans and Hudak (2007). This project is the data acquisition phase of an administrative study being done by Rocky Mountain Research Station - Forest Sciences Lab, Moscow, ID. The primary goal of the study is to provide operational implementation of Lidar technology in support of project level planning. The proposed applications of Lidar in support of planning are: vegetation structural modeling, erosion modeling, fuels, transportation planning, timber system planning, wildlife habitat modeling, and stream quality. The Rocky Mountain Research Station will provide the development of peer-reviewed forest structural metrics and technical support in implementation of Lidar technology. The technical specifications have been defined to specifically support vegetation modeling using Lidar data. The St. Joe National Forest area consists of one contiguous block totaling ~ 55684 hectares in north central Idaho, between Deary and Clarkia. The project area consists of moderately variable topographic configurations with diverse vegetation components.
Dataset Acknowledgement: Proper credit to Idaho Lidar Consortium.
The ascii files were converted to las format and classified using the Multiscale Curvature Classification (MCC) method of Evans and Hudak (2007): Evans, J.S., and A.T. Hudak. (2007) A multiscale curvature algorithm for classifying discrete return lidar in forested environments. IEEE Transactions on Geoscience and Remote Sensing 45(4): 1029-1038.