Level: Master

Course type: M4 Project Seminar

Credit Points: 18CP - 6h/week

Description:                                                 The ongoing climate change (specifically the increase in air temperature caused by human activities) is impacting not only natural processes and 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 surface changes and biospheric processes. They are providing us with data on, for instance, unprecedently severe forest fires, extensive thawing of permafrost, unexpected drying of large water bodies, rapid melting of glaciers, and more. In this project seminar module, we will use global optical remote sensing 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 to carry 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.

Level: Master

Course type: M2 Scientific Methods

Credit Points: 6CP - 2h/week

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 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 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.

Level: Bachelor

Course type: B8 I/II Scientific Methods

Credit Points: 6CP - 2h/week

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

Course type: B11 Project Seminar

Credit Points: 12CP - 4h/week

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.

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 am and 5:00 pm) 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.

Key areas in the remote sensing subfield:

• Theoretical and physical principles of FE: Acquisition systems: sensors, platforms, image formats
• Preparation and analysis of satellite data
• Case studies of geographic applications (e.g., land use change, urban growth, land degradation, glacier melt)

Qulaification goals in the subfield of remote sensing:

• Knowledge in the physical principles of remote sensing
• Knowledge of image interpretration and sattelite data processing
• Knowledge of RS data analysis (e.g., image classification)