Thunder Bay • Orillia

Type C: Engineering, Mathematical and Natural Sciences - Remote Sensing Applications

Natural Resources Management 4217 Remote Sensing Applications
An in-depth lecture-seminar-laboratory course in digital remote sensing. The relationships between tree physiology, vigor, and reflectance are stressed, as well as generalized soil and water spectra. Acquisition technology (passive and active) is covered in detail. Enhancement and classification techniques are taught and evaluated. A thorough understanding of technology limitations to forestry is emphasized. Case studies include multi-temporal and analysis (depletion mapping), vigor assessment, as well as cover-type identification.
Credit Weight: 0.5
Prerequisite(s): Natural Resources Management 2350
Offering: 0-0; 2-3
Course Classifications: Type C: Engineering, Mathematical and Natural Sciences