Geog 9035: Remote Sensing with LiDAR

This course introduces students to working with LiDAR and derived data products including raw .las files, digital elevation models and digital surface models with a particular emphasis on vegetation classification. This course is designed for users interested in remote sensing that incorporates 3D data, and consists of lecture, examples, and labs. Hands-on lab exercises will focus on the combined use of LiDAR and high resolution satellite imagery for forest canopy classification using object-based image analysis. Topics include creating LAS datasets, vegetation canopy height models, and vegetation classification strategies using object based image analysis. Softwares include eCognition and ArcGIS Desktop. 

Event Date: Friday, March 24, 2017 - 08:30 to Saturday, March 25, 2017 - 17:30
Location: 
HSS 290
Semester: 
Class format: 
Approximately 20% lecture, 80% software application.
Instructor: 
Jeff Milliken
Prerequisites: 
Introduction to Geographic Information Systems (Geog 9003) or equivalent experience. Remote Sensing Part I (Geog 9011) is recommended, but not required.