Lehrveranstaltungen

Level: Master

Kurs: M4 Projektseminar

Credits: 18LP - 6SWS

The ongoing climate change, namely 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 economics on a global scale. We are witnessing, for instance, unprecedently severe forest fires in the northern hemisphere and Australia, extensive thawing of permafrost, unexpected drying of large water bodies due to the freshwater shortage, 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 (e.g., Copernicus/ESA’s Sentinel-2 and NASA’s Landsat-8) or aircraft (hyperspectral visible, near-infrared, and thermal images) to detect and map the actual impacts of some of the climate change events in space and time.

Upon successful completion of this module, you will know how to:

• design and execute a research prokject, including a proper definition of scientific hypothesis/questions, selection of appropriate optical remote sensing input data and processing methods, and make a synthesis of analytical results answering the research questions 
• work efficiently with optical remote sensing image data and state-of-the-art image processing software,
• write a scientific report in accordance with current standarts, inclduing proper presentation of graphical outputs (e.g. maps) and correct referencing of the relevant scientific communications, and
• work in a team, present your intentions and results in a concise and understandable manner, recieve feedback from your peers and disseminate it to improve your work, and conduct an evidence-based scienitifc discussion.

The remote sensing image analyses and interpretaions will be done in a professional remote sensing software ENVI (QGIS  might be considered). A basic understanding of remote sensing image processing is required.

Level: Master

Kurs: M2 Forschungsmethoden

Credits: 6LP - 2SWS

In 2016 and 2019, Australia, especially Tasmania, was subjected to some of the most widespread and devastating forest fires in recent history. What made these fires so devastating is the particularly “dense bush” type of Australian vegetation they affected. In 2016, large areas within 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 of the affected areas are remote and largely inaccessible, making proper assessment of the true scale of the fire devastation difficult. For this Geomatic Method seminar, you will use multispectral remote sensing images of Sentinel-2A and Landsat-8 satellites to identify the extent of the blaze within the Arthur-Pieman Conservation area and estimate the types and amounts of vegetation affected by the 2016 burn.

On successful completion of this module, you will know how to:

• search for publicly available satellite imagery in the online repositories,
• 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.

In this module, the professional remote sensing software ENVI will be used 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, work 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 (if needed) during this module. Previous knowledge about remote sensing image processing will be to your advantage.

Level: Bachelor

Kurs: B8 I/II Forschungsmethoden

Credits: 6LP - 2SWS

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, such as machine learning, and various quantitative retrievals from hyperspectral image data, to solve some of the current environmental and scientific problems.

Level: Bachelor

Kurs: B11 Projektseminar

Credits: 12LP - 4SWS

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.

Schwerpunkte im Teilbereich Fernerkundung:

• Theoretische und physikalische Grundlagen der FE
  Aufnahmesysteme: Sensoren, Plattformen, Bildformate
• Aufbereitung und inhaltliche
  Auswertung von Satellitendaten
• Fallbeispiele für geographische Anwendungen (z.B. Landnutzungsänderungen,
  Städtewachstum, Landdegradation, Gletscherschmelze)

Qualifikationsziele im Teilbereich Fernerkundung:

• Kenntnisse in die physikalischen Grundlagen der Fernerkundung (FE)
• Kenntnisse in der visuellen Bildinterpretation und in der
• Aufbereitung von
  digitalen Satellitendaten
• Kenntnisse in der inhaltlichen Auswertung von FE-daten (z.B. Bildklassifikation,
  Veränderungsdetektion, Zeitreihenanalyse)

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 spectral satellite data (e.g., Sentinel-2) 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 satellite spectral 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.

Growing human population density combined with the ongoing climate change, both taking place on a global scale, are increasing pressure on the Earth's resources and services provided by ecosystems all over the World. Appropriate management and adequate solutions to environmental problems, such as deforestation, overgrazing, soil contamination and depletion, and water shortage, are heavily dependent on accurate and timely knowledge. To work collaboratively with experts from multiple disciplines, planners, managers, policymakers, and researchers require a comprehensive understanding of the complex factors involved in processes driving the environmental problems and challenges. Here, remote sensing data and their interpretation play a central role in the quest for required knowledge.

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. The module will be split into two parts: I) a block of morning and afternoon intensive sessions (4 + 4 SWS between 9.00 and 17.00 hrs) from 10 until 14 October 2022, and II) a morning session each second week from 20 October 2022 till 02 February 2023. 

Upon successful completion of this module, you will know how to:

• design and execute a research project, including a proper definition of scientific hypothesis/questions, selection of appropriate optical remote sensing input data and processing methods, and make a synthesis of analytical results answering the research questions,
• work efficiently with optical remote sensing image data and the state-of-the-art image processing software,
• write a scientific report in accordance with the current standards, including proper presentation of graphical outputs (e.g., maps) and correct referencing of the relevant scientific communications (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.