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Applied Remote Sensing and GIS for Landscape Analysis

This course will provide you with an understanding of the tools and methods needed for processing geodata for geoscientific and environmental research applications.

The course covers theory and applications of spatial analysis, modeling and visualization in landscape analysis, using remote sensing and GIS. During the course students are trained in software familiarization and methods for processing and visualization in scientific work.

This knowledge is much sought after in the workplace with our alumni working in local government, agencies and consultancies.

The course is part of Master's Programme in Geomatics with Remote Sensing and GIS (period A-B) and Master's Programme in Landscape Ecology period C-D. The course can also be read as a separate course.

  • Course structure


    • Remote Sensing, 7.5 credits
    • GIS, 7.5 credits

    Teaching format

    Instruction consists of lectures, seminars, exercises and project work.

    Learning outcomes

    After the course, students are expected to be able:

    • to critically evaluate research findings by reproducing and analyzing selected portions of published research
    • to use remote sensing and GIS in physical geographical analyses
    • to formulate and carry out independent projects with the use of remote sensing and GIS
    • to extract and process quantitative geo- and biophysical measurements from geodata


    For details see syllabus.


    Ian Brown

  • Schedule

    The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
  • Course literature

    Note that the course literature can be changed up to two months before the start of the course.

  • Course reports

  • Contact

    Registration is normally done at the beginning of each semester. 

    Student office
    Kerstin Hörnby

    Study counsellor
    Maria Damberg

    Course responsible
    Ian Brown