Teaching and Supervision
Thesis Topics
Here we present a curated list of potential thesis topics for both Bachelor's and Master's programs. These topics have been defined to reflect current trends and demands in geomatics, specifically remote sensing science. Additionaly, students are encouraged to propose a thesis topic of their own interest. For more information please send an email to rsrgedu@uni-bonn.de
Winter Semester 2025/2026
Optical Remote Sensing for Land Surface Mapping and Environmental Monitoring
Remote Sensing Methods for Wildfire Landcover Change Detection
Forests play a crucial role in preserving our planet, serving as carbon sinks that absorb CO2 and regulating the climate. Due to climate change, natural ecosystems on Earth were heavily impacted and forests are more and more threatened by severe wildfires. In 2016 and 2019, Australia, especially the Tasmanian island, was subjected to some of the most widespread and devastating wildfires in recent history. What made these fires so devastating is the particularly ”dense bush” type of Australian vegetation that they affected. In 2016, large parts of the Tasmania World Heritage Area and the Arthur-Pieman Conservation Area were destroyed. These vegetation communities are unique and take thousands of years to regrow. Many affected areas are remote and largely inaccessible, making a ground assessment of the true scale of the fire devastation difficult, in some locations even impossible. In this Geomatic Method seminar, you will use multispectral remote sensing images of Sentinel-2A and Landsat-8 satellites to identify the extent of the wildfires within the Arthur-Pieman Conservation area and estimate the types and amounts of vegetation that were affected by the 2016 burn.
Participants will individually execute 12 assignment tasks. The tasks will be split into two parts:
literature work (2 tasks), resulting in a short (400 words max.) essay describing theoretical bases of forest fire mapping from multispectral space-borne image data (written in English), and
computer work, consisting in 10 practical tasks and several questions about satellite image pre-processing methods (e.g., spatial and radiometric corrections), spectral analyses (e.g., automatic image classifications), and landcover change detection (e.g., basic statistics). The results will be submitted to the lecturer as a report, i.e., *.PDF or *.DOCX digital document written in English.
Each of the 2 x 45 min seminars will consist of:
15-minute theoretical introduction related to the assigned task and presented by the lecturer,
5-10-minutes of discussion about general issues and problems related to individual tasks, and
about 65 minutes of individual hands-on computer work under the supervision of the lecturer.
The final mark will reflect the quality of the literature work delivered in form of an essay (30% of the overall mark) and the correctness of the satellite image analyses’ outputs and answers to questions given in the computer work part (70% of the overall mark).
Upon successful completion of this module, you will know how to:
use scientific literature to search for and process relevant published information,
apply satellite image processing techniques in the context of forest fire detection and bunt area assessment, and
extract quantitative and thematic information from satellite multispectral imagery by means of image pre-processing, classification, and change detection techniques.
The professional remote sensing software ENVI will be used in this module for computer-based work. The basic functionality of this software, i.e., loading and displaying an image with different spectral band combinations, image composition contrast enhancement, plotting of spectral signatures, working with regions of interest (ROIs), application of required image analyses, and export of images and plots suitable for inclusion in your assignment report, will be explained during first seminar sessions. Previous knowledge about remote sensing image processing will be to your advantage.
If you have questions or need further information please contact rsrgedu@uni-bonn.de.
Introduction to Geomatics
This module introduces geomatics, including the theory and application of geo(informatic) methods. It combines the complementary and methodologically fundamental components that are central to geography education: working with Geographic Information Systems (GIS), Cartography (KART), and Remote Sensing (RS). The focus is on the acquisition, analysis, modeling, and visualization of spatial information. Remote Sensing deals with the collection and evaluation of information about spatiotemporal processes and structures using aircraft- and satellite-based sensor systems. GIS is used for the analysis and modeling of spatial structures, patterns, and processes.
Cartography is addressed in the lecture from two perspectives: first, as a spatial reference system for geodata, and second, as a methodological toolkit for the visualization of spatially referenced data. In this module, you will acquire the necessary theoretical, methodological, and practical foundations to work with geodata, interpret and analyze it professionally, and apply your knowledge practically using various software packages.
The lecture consists of two parallel parts with joint exercises. In the first part, knowledge of GIS and Cartography is taught; in the second, that of Remote Sensing. Participants form working groups that alternately complete exercises from the GIS/Cartography and Remote Sensing areas. They are supported by the tutors of the Geomatics Student Workshop.
The exams will most likely take place during the following periods:
1st date: 23–27 February 2026
2nd date: 23–27 March 2026
Please note that the exact dates will be announced during the lecture and will then be binding.
Geomatics-Coordination Study Workshop
The tutors support you in solving the exercise assignments. They also receive the solutions to the exercises and record your completion. Submitting the exercise assignments is a prerequisite for being admitted to the exam.
Within the tutorial sessions, please form fixed exercise groups of 2–3 people (only with participants from your own tutorial group). Through the email function of eCampus, you can contact all participants of your tutorial individually or together. You simplify communication by adding your name to your eCampus profile in addition to your university ID. You can access your eCampus profile by clicking the small arrow at the very top right.
You can reach the tutors by email. You can then arrange a Zoom meeting with your tutor or receive the answer by email. Or you can come to the times reserved for exercise groups on Monday (16:15–17:45), Tuesday (16:15–17:45), Thursday (10:15–11:45 or 16:15–17:45), in room Ü9 at Meckenheimer Allee 176. There you can use the computers. Two tutors are always present and available during these times.
The exercise assignments
A total of 11 exercise assignments will be given. You must submit the results of all 11 exercise assignments. The working periods for the exercises partially overlap because the assignments are often released on a weekly basis.
Introduction to Remote Sensing Time Series Analysis
This course introduces time series analysis for remote sensing with an emphasis on practical demonstrations and applications using Google Earth Engine (GEE). In the first section of the course, students familiarize themselves with GEE through its Code Editor, starting with core concepts such as data types, geometries, feature collections, image collections, filters, indices, and statistical reducers, paired with practical hands-on demonstrations sections using programming scripts. In the second section, the theoretical and practical scope of the course expands through the use of the Earth Engine's Python API, and use deploy this in Jupyter Notebooks to reproduce analyses and handle complex tasks while integrating Python packages into the different analysis pipelines. Topics include basics of remote sensing, time series concepts and analysis, using bitemporal change detection, Change Vector Analysis (CVA), Index-based time series, machine learning classification, Post-Classification Comparison (PCC), and dense time-series analysis using BFAST to detect seasonal trends and breakpoints and explore methods for smoothing and gap filling. Throughout the course, students visualise and quantify changes in satellite imagery, and finish by developing and delivering a final project based on Earth Observation analysis, applying the learned concepts and tools.Aufklapp-Text
Physische Geographie
Summer Semester 2025
Advanced Physical Geography
Course motivation and expected outcomes:
In this seminar, you will learn advanced knowledge and techniques used in the sub-disciplines of modern physical geography, specifically, geomorphology, hydrology, climatology, and vegetation geography but also remote sensing and geomatics. After finishing this course, you are expected to be capable of designing physical geography-related research experiments, operating instruments providing various physical measurements, mastering the state-of-the-art measurement processing methods, and interpreting the obtained information towards understanding spatiotemporal aspects of abiotic and biotic processes taking place on the Earth surface. Throughout the course, you will be trained in presenting correctly your research results as well as in adopting standards of different types of scientific communication.
Course content and organization:
The 4-hour bi-weekly sessions will consist of a student presentation with a subsequent discussion on a specific physical geography topic. It will be followed up by a lecture, in which the basics and theoretical backgrounds will be introduced, complemented by relevant practical exercises. Occasional short field trips will be organized to demonstrate the physical geography phenomena and measuring techniques in real-life conditions. In addition to the thematic content, methodological principles of scientific work will also be explained, principles of appropriate scientific writing will be taught, and presentation techniques will be deepened. The final grade will be deduced from your presentation performance and the results of a written examination.
Course conditions:
Since this course comprises advanced techniques, a prior prerequisite to its participation is successful completion of the BSc course B1: Introduction in Physical Geography (Einführung in die Physische Geographie).
The spoken and written language of this course is English.
Specific study literature will be recommended by the lecturer in the course of the seminar.
Introduction to Remote Sensing and GIS spatial analyses
Course motivation and expected outcomes:
In this seminar, you will learn how to use optical remote sensing (RS) for spatial analyses in geographic information systems (GIS) to tackle today’s societal challenges. Using practical examples, we will demonstrate how the raster data from space-borne observations combined with GIS vector layers can provide unique insights into various problems related to environmental protection, human health, food production and urban planning. In the final part of the course, we will also discuss a proper map design and various graphical presentations of results obtained from geospatial data analyses.
Course content and organization:
The 2-hour sessions will consist of a theoretical part, in which the basics and theoretical backgrounds will be introduced, and a ‘hands-on’ computer exercise where you will practice the newly-gained theoretical knowledge. Throughout the course, you will gain skills in operating open-source RS and GIS software packages (e.g., SNAP & QGIS). Whenever appropriate, we will look at interacting with the Google Earth Engine and Python scripting language to automate some work steps. The required software will be installed and accessible during the whole course in C-Pool at GIUB. The course will be concluded by a computer-based examination test (multiple-choice). The final grade will be deduced from your test results and the results you generate during the practical computer exercise.
Course conditions:
IMPORTANT: Successful completion of the course without the basic knowledge of airborne and satellite remote sensing and quantitative statistical methods is hardly possible. Therefore the prerequisite for participating in this course is a prior successful completion of the BSc courses: Introduction to Geomatics (B7) and Statistics (B6).
The spoken and written language of this course is English.
Specific study literature will be recommended by the lecturer during the seminar lectures.
Links: QGIS
SNAP
Google Earth Engine
Biospheric Impacts of Global Change Assessed by Optical Remote Sensing
Course motivation:
The ongoing climate change (specifically the increase in air temperature caused by human activities) is impacting not only natural processes and biogeochemical cycles but also human societies and their economic activities on a global scale. Recent space-borne constellations of Earth-observing satellites together with advanced remote sensing methodologies are producing a high number of global measurements mapping the Earth's surface changes and biospheric processes. They are providing us with data on, for instance, unprecedently severe forest fires, extensive thawing of permafrost, frequent river floods, rapid melting of glaciers, and more. In this project seminar module, we will use global remote sensing optical image acquisitions (e.g., Copernicus/ESA’s Sentinel-2 and NASA’s Landsat-8) and/or thematic products (e.g., NASA’s MODIS FPAR/LAI product) to detect and map the actual impacts of some of the climate change events in space and time. This course is designed for MSc. students interested in carrying out their master thesis using global remote sensing datasets. Participants will learn how to search, select, and process global satellite datasets stored in open access ”cloud” storages, and how to interpret the content of this data to address recent global climatic societal challenges.
Course expected outcomes:
Upon successful completion of this module, you will know how to:
- find online available optical remote sensing satellite data and process it in the state-of-the-art remote sensing software,
- design and execute a research project, including a proper definition of scientific hypothesis/questions, selection of appropriate optical remote sensing input data and methods, and make synthesis of analytical results answering the research questions,
- write a report in accordance with the scientific standards, including proper presentation of graphical outputs (e.g., maps) and correct referencing of the relevant scientific literature (research papers, books, and conference proceedings), and
- work in a team, present your intentions and results in a concise and understandable manner, receive feedback from your peers and disseminate it to improve your work and conduct an evidence-based scientific discussion.
Participants will form small research teams (groups of approx. 3-4 students) and work together on one of the global climate change-related topics of their own selection or provided by the lecturer. Examples of project topics are (not exclusively):
- Effects of deforestation and removals of natural ecosystems on the decline of natural biodiversity and land degradation (whole World),
- Large-size river floods and their destructive impacts (Europe, Asia, Australia),
- Impact of severe droughts on large-scale forest diebacks and outbreaks of pests (Europe & Australia),
- Thawing Arctic permafrost and its impacts on land cover changes (N. America & Asia),
- Assessing natural and/or societal impacts of large wildfires (N. America, Asia, Australia),
- Spatiotemporal analyses of melting and retreating of large continental glaciers (Alaska & Greenland).
- 30-45-minute theoretical lecture introducing the state-of-the-art remote sensing techniques relevant to your projects,
- short presentation by research teams outlining the status and progress of the project work, and
- actual teamwork with hands-on data processing supported by the lecturer.
The remote sensing image analyses and interpretations will be done in professional remote sensing software ENVI, QGIS, SNAP, and/or Google Earth Engine. The required software will be installed and accessible during the whole course in C-Pool at GIUB.
Course participation conditions:
- IMPORTANT:Successful completion of the course without the basic knowledge on airborne and satellite remote sensing and quantitative statistical methods is hardly possible. Therefore, a prerequisite for participating in this course is previous successful completion of the BSc. courses: Introduction to Geomatics (B7) and Statistics (B6).
Qualitative remote sensing for environment monitoring
The ongoing global climate change, mainly the increase in air temperature caused by human activities since the industrial revolution, is impacting not only natural processes and cycles but also human societies and their economic activities on a global scale. We are witnessing, for instance, unprecedently severe forest fires in the northern America, la Niña induced floods in eastern Australia, extensive thawing of permafrost in Siberia and Alaska, unexpected drying of large water bodies due to the freshwater shortage, and rapid melting of glaciers in the Arctic regions and high mountains. In this project seminar module, we will use optical remote sensing image data acquired from satellites orbiting the Earth (e.g., Copernicus/ESA’s Sentinel-2 and NASA’s Landsat-8 & 9) or from hyperspectral visible, near-infrared, and thermal images from aircrafts (e.g., airplanes) to detect, map, and potentially monitor in space and time the actual impacts of climate change specific, but not exclusive, events on the Earth surface.
Course structure
Participants will form small research teams (groups of approx. 3 students) and work together on one of the climate change-related topics of their own liking or provided by the lecturer. Examples of project topics are:
- Drying of the Aral Sea and its consequent impact on local activities/land use,
- Outbreaks of insect parasites, e.g., bark beetle, in mid European temperate forests,
- Contribution of thawing Arctic permafrost to regional land cover changes,
- Mapping impacts of Monsoon-induced floods at Asian and Australian continents,
- Assessing natural and economic impacts of big forest fires in California in 2020,
- Cities as summer heat islands – existing causes and potential remediations.
- 30-45-minute theoretical lecture introducing the state-of-the-art remote sensing techniques relevant to your projects,
- short presentation by a member of each research team (each week different team member) outlining the status and progress of the project work, and
- actual teamwork with hands-on project work supported by the lecturer.
Upon successful completion of this module, you will know how to:
- find online available optical remote sensing satellite data and process it in the state-of-the-art remote sensing software,
- design and execute a research project, including a proper definition of scientific hypothesis/questions, selection of appropriate optical remote sensing input data and methods, and make synthesis of analytical results answering the research questions,
- write a report in accordance with the scientific standards, including proper presentation of graphical outputs (e.g., maps) and correct referencing of the relevant scientific literature (research papers, books, and conference proceedings), and
- work in a team, present your intentions and results in a concise and understandable manner, receive feedback from your peers and disseminate it to improve your work and conduct an evidence-based scientific discussion.
Links:
ENVI
QGIS
Google Earth Engine
Certificates:
The last seminar will be devoted to final presentations of teamwork achievements in form of the scientific poster, followed by scientific discussions. Each team is further expected to produce a short final report (maximum of 20 standard A4 pages without the list of references), written in English according to scientific communication standards. The final mark will reflect the quality of the final poster presentation and report but also the performance of individual team members during the course.
Physical modelling and inversion methods in optical remote sensing of terrestrial vegetation
Seminar motivation:
The state-of-the-art remote sensing of vegetation uses data collected with various optical sensors on towers, drones, aircraft, and satellite platforms. The recent boom of data-driven machine learning algorithms (e.g., deep-learning neural networks, random forests, or Gaussian processes) allows for an efficient interpretation of such multi-scale and multi-resolution data, transforming the optical signals into specific information about vegetation functional traits. Yet, this approach requires a robust and comprehensive knowledge base (i.e., training data) linking the vegetation canopy traits to the optical remote sensing observations to achieve a proper training of interpretational algorithms. Coupled leaf and canopy physical radiative transfer models (RTMs), simulating interactions of electromagnetic radiation within plant canopies, provide virtual environments suitable to generate such required knowledge base.
Course content:
In this course, you will learn how to map quantitative plant functional traits, such as leaf chlorophyll content, leaf water content, leaf area index, etc., from optical multi/hyper-spectral remote sensing data (e.g., Sentinel-2 satellite) using a combination of the physical RTMs and modern machine-learning methods. Upon successful completion of this module, you will know how to:
work and pre-process the multi/hyper-spectral remote sensing images of terrestrial vegetation formations,
parameterize and run in a forward mode leaf and canopy RTMs in order to simulate virtual spectral satellite observations,
train machine-learning models properly using RTMs simulated remote sensing data of the specific vegetation types (e.g., crops)
apply the machine-learning methods to quantify plant functional traits from the satellite observations, and
interpret the obtained maps, including the related uncertainty estimates
Important information:
The seminar will start with a 20-30 min long theoretical lecture related to the topic of this course. It will be followed up by a practical 'hands-on' part, where you will use leaf and canopy RTMs (e.g., PROSPECT, PROSAIL, DART) and the ARTMO toolbox to conduct sequentially retrieval of specific plant traits from satellite optical imagery.
Communication language (spoken as well as written): English
Modern methods in radar and spectral remote sensing
Remote sensing technologies and methodologies are advancing at a fast pace, getting more physically based, data-driven, and sophisticated. This course is designed as a continuation of Introduction to Remote Sensing Mapping and Environmental monitoring (B7). Participants will learn how to apply some of the concepts they have already studied and incorporate the actual remote sensing spectral and radar techniques to solve societal challenges. They will apply advanced classification and regression methods (e.g., machine learning), and various quantitative retrievals using hyperspectral image data, to address some of the current environmental issues.
Course structure:
The communication language will be English (including the final written assignment reports).
An overview of the sensors and methods to use will be given during the first lecture, when students will also choose a topic for the 1st assignment, i.e., a literature review. The outcomes of the 1st assignment will be a 3 pages essay and a short presentation. The typical seminar lecture will have two parts: 1/ a 20-30 min long theoretical lecture related to the topic of the course, and 2/ a practical session (60-min) with hands on tasks performed on computers. Exemplary topics covered by this seminar are:
- Qualitative remote sensing and emergency mapping with SAR sensors,
- Time series analysis with SAR and optical sensors,
- Classification with machine learning and time series,
- Acquisition, calibration, and pre-processing of imaging spectroscopy data,
- Quantitative remote sensing and regressions with machine learning.
Remarks
Attention, the module B7 (Introduction to Geomatics) must be successfully passed
Advanced Physical Geography
Im Aufbauseminar Physische Geographie werden vertiefende Einsichten in physisch-geographischen Themen und Fragestellungen sowie deren inhaltliche Verknüpfung und Ansätze zur regionalen Differenzierung vermittelt. Dies geschieht durch Seminarvorträge der TeilnehmerInnen, durch seminarbegleitende Lektüre und durch Bearbeitung von Übungsaufgaben in Einzel- und/oder Gruppenarbeit. Die Inhalte des Moduls B1 "Einführung in die Physische Geographie" werden als bekannt (und präsent) vorausgesetzt.
Neben diesen inhaltlichen Aspekten werden auch Präsentationstechniken sowie formale Gesichtspunkte bei der Anfertigung von Hausarbeiten im Seminar diskutiert. Da die erfolgreiche Teilnahme des Moduls B1 Eingangsvoraussetzung zur Seminarteilnahme ist, findet eine Vorbesprechung mit Themenvergabe am ersten Seminartermin der Vorlesungszeit im Sommersemester am 09.04.2025 statt.
Veranstaltung auf Deutsch, Teilnehmer können wahlweise ihre Prüfungsleistung auf Englisch erbringen.
Es findet KEINE Veranstlung zwischen 14.04. - 25.04. statt (Osterferien) statt. Anstelle dessen werden die Sitzungen zu wissenschaftlichem Arbeiten, wissenschaftlichem Schreiben und Präsentieren als Block am Freitag den 11.04.2025 von 9.00h - 15.00h durchgeführt.
Advanced Physical Geography: Field Methods
Im Rahmen des Geländepraktikums werden grundlegende Kenntnisse physisch-geographischer Erhebungs- und Auswertemethoden vermittelt. Die TeilnehmerInnen führen im Gelände in Kleingruppen klimatologische, hydrologische, sowie boden- und vegetationsgeographische Erhebungen durch. Das Geländepraktikum findet im auf dem Versuchsgut Frankenforst bei Bonn statt. Neben der aktiven Mitarbeit bei den Geländearbeiten wird die Ausarbeitung eines Gruppenprotokolls erwartet. Die Anreise muss selbst organisiert werden. Eine ÖPNV-Anbindung ist gegeben.
Es entstehen keine weiteren Kosten. Anforderungen: Regelmäßige Teilnahme und Mitarbeit, Gruppenprotokoll
Ort: Gut Frankenforst
Veranstaltung auf Deutsch; Teilnehmer können das Protokoll wahlweise auf Deutsch oder Englisch verfassen.
Zeitraum: 01.09-05.09.2025
Writing BSc./ MSc. Thesis at RSRG
If you are interested in writing a bachelor or master thesis in the field of remote sensing, please contact Vanessa Spitzer to arrange an appointment with Prof. Z. Malenovský.
Feel free to bring your own topic suggestions or find a suitable topic by talking to Prof. Malenovský.
Contact
For enquiries regarding courses, thesis or other educational matters, please contact:
For enquiries regarding research or projects, please contact:
Opening Hours Secretary
- Monday - Wednesday, Friday: Department of Geography
09:00 am - 03:00 pm - Thursday: ZFL
09:00 am - 03:00 pm