Advanced remote sensingLaajuus (6 cr)

Objective

The student will be able to • Understand how digital forest resource maps are constructed based on airborne laser scanning data and satellite images. • Evaluate the reliability of forest resource maps obtained with various remote sensing methods. • Understand the principles and applications of photogrammetry and radar remote sensing. • Understand how biophysical forest variables influence the reflectance of forests. The course develops the following generic skills: digitalization, critical thinking.

Content

The use of airborne laser scanning and satellite images in forest inventory, physically based remote sensing, photogrammetry, radar remote sensing.

Qualifications

Courses introduction to remote sensing or introduction to remote sensing and geoinformatics MOOC, R-language, mathematics for forestry students 1 and 2, or comparable knowledge: basics of remote sensing, R programming, and multivariate regression analysis.

Materials

Lecture and exercise materials. As a literature exam: 5. Lillesand, T., Kiefer, R. & Chipman, J., Remote sensing and image interpretation, 7th ed., 2015, 6. pages 147-217, 385-484 7. Jones, H.G. Vaughan, R.A., Remote sensing of vegetation: principles, techniques, and applications, 2010, pages 7-35, 163-225 8. A collection of research literature, to be announced separately.

Further information

Every other year (spring 2024, 4rd period). Language of instruction is English. Exam and exercise reports can also be returned in Finnish.

Execution methods

Multi-modal teaching.

Accomplishment methods

Taking an exam based on lectures and video lectures (24 h) and computer labs (24 h) is recommended. The computer labs are taught in a class, but it is possible to do them remotely. It is also possible to take the course as a literature exam. Grading 0-5. Electronic exam in Moodle or the Exam system. Exercises grant bonus points for the exam.