(This module targets BSc students aiming for deeper knowledge of remote sensing and an entry into applied R programming. Students are expected to have successfully completed BSc modules 3 (statistics) and 6 (GIS) as well as module 7 "Introduction to remote sensing" or equivalent.)
The monitoring and mapping of vegetation and land cover is one of the key activities in Earth observation (EO). Advanced EO products are pivotal for many geographic and environmental studies. In this module students learn concepts and techniques for analyzing and mapping (vegetated) land cover and its characteristics at various spatial scales and with different sensor systems. Data analysis is fully done in R and students learn to create customized R-scripts along a series of processing tasks throughout the semester.
The advanced remote sensing topics module is designed for advanced BSc students who want to deepen and extend their remote sensing skills with regard to theory and application (e.g. to pursue a BSc thesis related to remote sensing or as preparation for MSc studies) as well as to gain problem-driven knowledge in R programming. Participants must have successfully completed Module 6 “Introduction to Geoinformation Science” and Module 7 “Introduction to Remote Sensing” or present equal experience.
The module is fully taught in English language and includes reading of English original articles. Student presentations and written reports may be held in English or German. International students with relevant experience are welcome.
Registering for the course
Students are asked to register online for the course and come to the first seminar session in week 1 of the summer term. Students who do not come to the first session must contact the lecturers prior to the session!
The module is organized in two parallel sections: in the first part students gain deeper knowledge on the theory of (vegetation) remote sensing, learn about in-situ techniques, common imaging sensors and advanced analysis methodology from original literature; theory is deepened and exemplified along small exercises. The second part introduces students to script programming in the R language and teaches students how to develop analysis frameworks for digital image analysis.
Four selected topics will be explored in detail by students. Each topic involves reading of original literature, new methodologies and data sets, as well as implementation of these methodologies in R. The topics will include:
1) Vegetation characteristics with field and laboratory measurements
2) Quantitative mapping of impervious urban land cover
3) Mapping land cover from multi-seasonal data
4) Mapping biomass from multispectral satellite data and lidar data
Each of the topics is covered in three seminar sessions and three related weekly assignments including i) literature work, ii) programming, iii) documentation.