M.Forst.1609: Remote sensing image processing with open source software (6 C / 4 SWS)

Learning outcome:
This combined lecture and lab makes the student familiar with principles of digital image processing and GIS integration, with a focus on applications in forestry and ecology. The software GRASS is used which is freely available as open source software. Students are encouraged to bring their own notebook computers, if available.

Courses and examinations
Remote sensing image processing with open source software (Exercise, Lecture, 4 SWS)

Notions of remote sensing and digital imagery are briefly addressed. General characteristics of open source software are presented. The software GRASS is introduced and being used for typical tasks of digital image processing of remote sensing imagery, such as image enhancement, geometric corrections, cloud masking, 3D visualization, vector to raster transformation, and eventually image classification. If teaching progress allows, case studies and the integration of sampling and image interpretation are presented and discussed.

Oral exam (ca. 15 minutes) and practical exam (ca. 15 minutes)

Prerequisite for examination:
The students should give evidence that they know the application-oriented technical bases of remote sensing and the possibilities and limitations of remote sensing when applied to problems of forest management and conservation. They shall also prove that they have acquired sufficient insight and skills in using the software of the lecture so that they are able to solve basic image processing problems and they should give evidence that they can systematically approach larger problems.

Workload: 180 h (56/124 h, Attendance time/Self-study time)

Admission requirements:

Recommended previous knowledge:


Person responsible for module:
Prof. Dr. Christoph Kleinn

Course frequency:
Academic Term each winter semester

One semester

Number of repeat examinations permitted:
According to regulations

Recommended Semester:
First Semester

Maximum number of students: