Master level course in Physical Geography at Marburg University

Understanding complex forest structures through field data collection have been increasingly being complemented with remote sensing methods. However, many remote sensing methods work best at a particular scale. For example, Sentinel data can capture information to a very high resolution of 10m in 2D while LiDAR (Light Detection and Ranging) can most accurately explain forest structure in 3D. Joint use of remote sensing data from multiple sensors can help us capture the complex vegetation structure at both local and regional levels. Such an understanding can be helpful for forest conservation and biodiversity management. This course will use pre-collected data on species richness and many environmental parameters like temperature, soil, etc. collected at plot level to upscale biodiversity to a regional scale.

What will be done in the course

In this course, we will use airborne LiDAR and hyperspectral remote sensing data along with Sentinel data to upscale biodiversity measured at plot scales. The data used in this project is provided by the Kilimanjaro-SES project.

Intended learning outcomes

At the end of this course you should be able to

  • Feel comfortable with the usage of R,
  • Know the basics of remote sensing theory and machine learning,
  • Handle LiDAR, hyperspectral and Sentinel data,
  • Use upscaling methods,
  • Include and process different types of remote sensing data in a single workflow, and
  • Apply all of the above to a real-world example.


This course will take place in a hybrid setting (exept for the first session) with a digital classroom and additional students being present in person in the physical classroom (F 14 | 00A19). Details on this synchronous hybrid classroom format will be provided in the first session, which will take place in presence only on Wednesday 12.04.2023 at 09:15 am. The link to the digital classroom of the first session is provided in the Ilias course environment (only accessible for members of the course who are logged-in into Ilias). Please also seriously check and follow the Information on the Coronavirus of the University of Marburg.


The course encompassed 12 sessions from 12.04.2023 to 12.07.2023.

Session Date Topic Content
    Getting started  
01 12.04.2023 First things first Course introduction, R Basics, GitHub, GitHub classroom
    Remote sensing basics and data  
02 19.04.2023 Remote sensing basics and Sentinel-2 data Introduction to Kili-SES project, Remote sensing basics, Sentinel 2 data preparation and application
03 26.04.2023 LiDAR remote sensing Introduction to LiDAR, working with point clouds and deriving LiDAR products
04 03.05.2023 Hyperspectral remote sensing Introduction to hyperspectral data, speclibs and deriving vegetation indices
    Start research project  
05 10.05.2023 Set up your research project Defining project work, research questions and discussing deadlines
    Upscaling Methodology  
06 17.05.2023 Upscaling workflow 1 What is upscaling, understanding the workflow, data preparation, basics of machine learning models
  24.05.2023 Sport Dies Session canceled
07 31.05.2023 Upscaling workflow 2 Designing a machine learning model, testing and fine tuning the model
  07.06.2023 Project week of the department Session canceled
08 14.06.2023 Upscaling workflow 3 Upcaling : doing predictions
    Assisted working phase  
09 21.06.2023 Feedback on project outline and Work on projects Discussion on project outlines and time for working on projects
10 28.06.2023 Work on projects Time for working on projects
11 05.07.2023 Work on projects Time for working on projects
    Wrap up  
12 12.07.2023 Wrap up, Evaluation, and Feedback Time for questions and feedback, goodbye


The graded course certificate will be based on an individual portfolio hosted as a personal repository on GitHub. The individual portfolio items are defined in the respective course assignments. At the end of the course, a project work based on the individual assignments must be submitted, which will be marked.

Preparation and prerequisites

The course assumes basic knowledge and skills in R and geo-information science.

This course uses additionally provided material for teaching basic R skills, which can be found here.


Netra Bhandari

Philipps-Universität Marburg

Dirk Zeuss

Philipps-Universität Marburg