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

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.

Course content and structure:

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).

The course session will include:

• 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 last seminar session will be devoted to final presentations of teamwork achievements, followed by scientific discussions, and offers the possibility to provide feedback regarding the course structure and content. Each team is expected to produce a final report written according to scientific communication standards. The final grade of each student will reflect the progress and final project presentations as well as the quality of the final report.

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).

The communication language of this course is English (including the final written reports). Although English grammar and style are not part of the report assessment, it must be written in full and understandable sentences, using correct English (British or American) spelling and grammar.

Course motivation

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.

Weekly 4-hour courses will include:

• 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.

Course 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.

If you have questions or need further information please contact rsrgedu@uni-bonn.de.
 
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.

Course motivation and outcomes:

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.

Software required to perform the assignment tasks (e.g., QGIS, ENVI, SNAP, etc.) will be available during the course in ‘Geozentrum’ computer room Ü9.

Remarks

Attention, the module B7 (Introduction to Geomatics) must be successfully passed

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

 

Dieses Modul führt in die Geomatik, in Theorie und Anwendung von geo(infor)matischen Methoden ein. Es fasst die komplementä-
ren und für die Geographenausbildung zentralen methodischen Grundlagen, der Arbeit mit geographischen Informationssystemen
(GIS), Kartographie (KART) und der Fernerkundung (FE) zusammen. Es geht dabei um die Erfassung, Analyse, Modellierung und
Visualisierung raumbezogener Informationen. Dabei beschäftigt sich die FE mit der Erfassung und Auswertung von Informationen
über raumzeitliche Prozesse und Strukturen mit flugzeug- und satellitengestützten Sensorssystemen. GIS dient der Analyse und
Modellierung räumlicher Strukturen, Muster und Prozesse. Die Kartographie wird in der Vorlesung unter zwei Aspekten angespro-
chen: einmal als räumliches Bezugssystem von Geodaten und zweitens als ein Methodenbaukasten zur Visualisierung von Daten
mit Raumbezug. In diesem Modul erwerben Sie die erforderlichen theoretischen, methodischen und praktischen Grundlagen, um
mit Geodaten zu arbeiten, diese fachlich kompetent zu interpretieren, zu analysieren und mittels verschiedener Softwarepaketen
die Kenntnisse praktisch anzuwenden.
Die Vorlesung besteht aus zwei parallelen Teilen mit gemeinsamen Übungen. Im ersten Teil werden die Kenntnisse zu GIS und
Kartographie vermittelt, im zweiten die zur Fernerkundung. Die Teilnehmenden bilden Arbeitsgruppen, die Übungsaufgaben ab-
wechselnd aus den Bereichen GIS/Kartographie und Fernerkundung bearbeiten. Dabei werden sie durch die Tutor*innen der Stu-
dierendenwerkstatt Geomatik unterstützt.

 

Die Klausuren werden voraussichtlich im folgenden Zeitraum geschrieben werden:

1. Termin: 17.-21.02.2025

2. Termin: 17.-21.03.2025

 

Die Einsicht zu den Klausuren wird am 3. März 2025 und am 31. März 2025 stattfinden.

Bitte beachten Sie, dass die genauen Daten innerhalb der Vorlesung bekannt gegeben werden und diese dann verbindlich sind. 

Course motivation and content:

In the Anthropocene era, impacted by a man-caused global warming due to excessive CO2 emissions, computer models simulating and forecasting temporally dynamic eco-physiological processes of terrestrial vegetation, such as carbon uptake or evapotranspiration, are essential for a ‘carbon neutral’ economy and a ‘carbon reducing’ management of natural ecosystems. Optical remote sensing provides multitude of input data for dynamic land surface vegetation modelling, spanning from categorical maps of land cover classes to spatially explicit quantitative estimates of vegetation biochemical and physical traits such as: fraction of absorbed photosynthetic radiation, leaf area index, or contents of water, photosynthetic foliar pigments, and nitrogen. This remotely sensed information, retrieved from air-/space-borne spectral and thermal sensors, is getting more accurate and more frequent thanks to the improving sensing technology (e.g., hyperspectral imaging spectroscopy), a higher number of satellite constellations (e.g., new Sentinel and future Chime ESA satellites), and newly available optical signals like, for instance, sun-induced chlorophyll fluorescence (retrievable from NASA’s OCO-2/3 and ESA’s TROPOMI and future FLEX missions). This course is designed to enlarge knowledge and deepen understanding of M.Sc. students about actual and near future optical vegetation remote sensing products that can be assimilated by existing eco-physiologically based land surface vegetation models, such as ORCHIDEE, JULES or LPJ-GUESS. The knowledge gained in this course is essential for future study climate change related topics and useful for work in fields of precision agriculture, forest monitoring and natural ecosystems’ management.

Course structure:

To comprehend advanced approaches for quantitative estimations of vegetation traits from Earth observations, the course participants will learn how the solar electromagnetic radiation interacts with individual plant leaves and canopies, what are the main pre-processing and remote sensing image interpretation methods, and how are the obtain maps of vegetation traits assimilated by the land surface vegetation models. 

The seminar will be split into three thematic parts:

1. Remote sensing observations: Acquisition, calibration, and pre-processing of leaf and canopy spectral measurements data.
2. Interpretation methods: Methods for retrieval of vegetation traits from satellite spectral measurements.
3. Dynamic vegetation models: Assimilation of remote sensing products in selected land surface vegetation models.

The seminar will contain theoretical lectures (45-min per week) delivered by the lecturer and computer-based practical exercises (for instance, spectroscopy of vegetation leaves and canopies) performed by the course participants. A multiple-choice test will be carried out upon the completion of each thematic part. The final grade of each seminar participant will be deduced from correct answers of tests and computer exercises.

Course outcomes:

 On successful completion of this module, students will be able to:

• Understand how optical remote sensing can inform models predicting vegetation functioning within the carbon and water cycles.
• Find and assess quality of remotely sensed maps of vegetation traits relevant for eco-physiological modelling.
• Use/assimilate vegetation remote sensing product in to the dynamic land surface vegetation models in order to predict, for instance, vegetation carbon assimilation capacity or water use efficiency.  
   

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:

1. 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 preferably in English), and
2. computer work, comprised of 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).

On 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.

Links:

Sentinel-2 ESA missions
Landsat-8 NASA mission
ENVI software

If you have questions or need further information please contact rsrgedu@uni-bonn.de.

Im Kolloquium wird das wissenschaftliche Arbeiten reflektiert und das Management des Projektes Bachelorarbeit diskutiert. Dies beinhaltet die begründete Auswahl eines Arbeitsthemas mit der Einbindung in eine gesellschaftsrelevante Problemstellung und einen theoretischen Kontext. Dazu gehört die Entwicklung konkreter Zielsetzungen und wissenschaftlicher Fragestellungen sowie die Bildung von Hypothesen. Hierauf basierend sind das methodische Vorgehen und ein Arbeitsplan auszuarbeiten und zu begründen. Abschließend sind die Möglichkeiten der Ergebnisinterpretation und ihr Rückbezug zu Fragestellungen, Zielsetzungen und Hypothesen zu diskutieren.

Der Betreuer bzw. die Betreuerin der Bachelorarbeit ist frei wählbar, die Betreuung der Arbeit ist nicht an den Seminarleiter gebunden. Es sollen möglichst konkrete Bachelorarbeiten vorgestellt und besprochen werden. Notfalls kann auch ein fiktives Thema gewählt werden.

Der Hintergund des Seminarleiters liegt in der physischen Geographie, insbesondere in der Fernerkundung und GIS.

In Zeiten rasanter Umweltveränderungen und globaler Herausforderungen gewinnt die Fernerkundung zunehmend an Bedeutung und bietet innovative Möglichkeiten bestimmte Aspekte des globalen Wandels zu analysieren, zu verstehen und effektive Maßnahmen zu entwickeln. Das Seminar „Umweltsysteme im Wandel und Wasser im Globalen Wandel“ richtet sich an Studierende, die die aktuellen Methoden und Anwendungen der Fernerkundung kennenlernen und vertiefen möchten.

Der Klimawandel und menschliche Aktivitäten beeinflussen natürliche Prozesse so dass sich diese zum Teil erhebliche Veränderungen auf Umwelt, Gesellschaft und Wirtschaft auswirken. Fernerkundungsmethoden ermöglichen es, diese Veränderungen flächendeckend zu erfassen und zu analysieren. Sie bieten wertvolle Daten beispielsweise für die Erfassung von Wassermengen, Wasserqualität oder andere Aspekte des hydrologischen Kreislaufes und des Vegetationszustandes. Darüber hinaus unterstützen die Fernerkundung das Management und die nachhaltige Nutzung von natürlichen Ressourcen, indem sie Entscheidungsträgern zeitnahe und verlässliche Informationen liefern. In diesem Seminar werden anhand von Fallstudien Anwendung von Fernerkundung in der Analyse von Umweltsystemen beleuchtet, diskutiert und die die technischen und methodischen Fortschritte, wie auch Limitationen aufgezeigt.

Im Seminar werden die Fallstudien durch praktische Anwendungen und Umsetzung mit Softwartetools wie QGIS, Python oder der Google Earth Engine unterstützt.

Das Seminar soll jeweils 16.00h s.t. beginnen. 

Course motivation

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.

Weekly 4-hour courses will include:

• 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.

Course 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.

If you have questions or need further information please contact rsrgedu@uni-bonn.de.

Course Motivation and Content:

The Earth Engine public data catalog contains a variety of standard geoscience raster datasets (https://developers.google.com/earth-engine/datasets/catalog). You can import these datasets into QGIS using the Google Earth Engine plugin. After that, you can also process your raster or vector data by programming with the Python language to automate work steps, as well as visualize spatial data in QGIS.

Using practical examples, we will discuss how the combination of vector data and remotely sensed data opens up a wide range of applications in fields as diverse as environmental science, agriculture, and urban planning.

The seminar will focus on data collection, analysis and the QGIS plugin (Google Earth Engine) by combining theoretical lectures with hands-on programming exercises.

Course structure:

Participants will work individually or in pairs on weekly topics related to their specific areas of interest. These topics will focus on analyzing vector or raster data to monitor, detect, or provide insight into the following topics:

Weekly 2-hour classes will include:

• A 30-45 minute theoretical lecture introducing the state of the art in vector and raster data and analysis methods.
• After the lecture, students will receive a step-by-step (function-by-function) guide to programming in QGIS using two programming languages, Python and JavaScript.

Course Outcomes:

Upon successful completion of this module, students will be able to:

• Select appropriate vector and raster data, pre-processing, and analysis methods.
• Program Python and JavaScript in QGIS.

The language of communication (both oral and written) will be English.

Software used in the course: QGIS plus some plug-ins
Specific study literature will be recommended by the instructor during the seminar lectures.

If you have any questions or require further information, please contact rsrgedu@uni-bonn.de