Master level course in Physical Geography at Marburg University

One could claim that the fact living on the surface of the earth and only get to know a small space through direct personal experience is the most important motivation for most of the geographic work. Compensation for this lack of direct experience has been and is being made, especially in scientific geography, with the help of efficient spatio-temporal techniques of abstraction.

Knowledge of spatial and/or temporal aspects of our environment is increasingly in demand for action-relevant relationships. Whether we ask as tourists, consumers, producers or planners spatial information, or even knowledge.

Geographic Information Science (GIS) is based on versatile and powerful software tools that are used in modeling, analysis, data mining merging and numerous other spatio-temporal applications. Nevertheless the most powerful tool is our mind developing the concepts and developing the necessary algorithms.

Intended learning outcomes

At the end of this course you should be able

  • to understand, adapt and develop geographic information science methods
  • to design workflows suitable to solve common spatio temporal data-related issues
  • to deploy your workflows using geo-information science tools, R scripts and collaborative code management platforms for task management and issue tracking
  • to critically evaluate your spatiao-temporal analysis
  • to communicate your workflow and analysis results,

Course features

The course is linked to the research of the Nature 4.0 project, heavily integrated into the parallel Geo Information Systems and build upon the parallel Data Analysis courses. It is intended as a blended learning module in our study program although the provided introductions, explanations and examples might be useful for self-study, too.


Session Topic Content
  Geographic Information Science basics  
1 Geographic Information Science Get to know basic GIS principles the open software approach and the R-spatial-biotop
2 Understanding the working environment the concept of remote sensing GIS and data anaysis and how to deal with it Check out various methods for handling raster datasets and raster information retrieval in R and GIS
  Project 1: You cannot see the wood for the trees Baseline data analysis
3 Problem: Comprehensive discussion of whatever concepts for tree identification Deconstruct the problem, identify research tasks and sketch a project workflow
4 Spotlight: Setting up your working environment Getting your PC to work no matter what kind of operating system or hardware
5 Spotlight: Lidar CHMs and more Getting in touch with the technique and underlying concepts
6 Spotlight: Programming Improve your workflow and scripting skills
7 Spotlight: Segmentation of trees 20 Lines of Code for reaching the goal of project 1
8 Project 1 discussion Present your prelimary paper to your peers
9 Peer feedback Evaluate the work of your peers, reflect your own workflow and discuss potential improvements
  Project 2: Trees in a forest - competetion densities and more Index-based anaysis of tree patterns
10 Problem: Relationship of tree (species) in space Decompose the problem, identify research tasks and sketch a project workflow
11 Spotlight: Densities and Competition Identify and implement meaningful indices
12 Spotlight: Paper works How can I tie it together
13 Project 2 discussion Present your paper to your peers
  Wrap up  
14 Feedback and goodbye Get feedback from your peers and instructors, tell us how you self-assess your skills and happy holidays


The graded course certificate will be based on a team portfolio hosted as a team repository on GitHub. The individual portfolio items are defined in the respective course assignments along with the information if they will be marked or not. Marked portfolio items encompass the presentation and peer-review the paper which inform about the results of two problem solving assignments related to the computation and analysis of the geographic information systems products.

Preparation and prerequisites

The courses assumes basic knowledge and skills in remote sensing and GIS. Required R and GIS skills can be developed by the parallel courses Data Analysis and Geo Information Systems.


Christoph Reudenbach

Marburg University