The course is designed to give an introduction on how to generate information from remote sensing data and how to analyse these data in a geographic information system, in order to map forest resources and monitor relevant changes in forest canopy cover.
The course examines the basics of theoretical issues and image classification to help students understand and choose remote sensing solutions for forest classification and forest monitoring problems. The main topics are covered with many practical exercises of forest classification and forest change detection.

Expected Learning outcomes:
1) Knowledge and understanding: comprehensive knowledge of the basics of theoretical issues behind optical remote sensing and image classification
2) Applied knowledge and understanding: ability to select, conceptualize, and implement image classification techniques of multispectral RS images in QGIS with respect to a given practical application in forest cover mapping and change detection
3) Making judgments: critical analysis and evaluation of the potentials and limitations of different image classification methods
4) Communication skills: Refined presentation skills of an own image classification project for forest applications
5) Learning skills: an own mental model for addressing simple tasks exercises of forest classification and forest change detection (competent practitioner of RS)