Remote Sensing of Permafrost Regions - Einzelansicht

In this module, we will focus on remote sensing of terrestrial regions of the Arctic that are not glaciated but affected by permafrost - about one quarter of the northern hemisphere landmass is part of the permafrost zone and thus a huge diversity of landforms, land cover, processes, and dynamics are encountered that are partially or fully driven by freezing and thawing processes on various spatial and temporal scales.

These regions are vast, far away, logistically challenging, and data-sparse - Remote sensing therefore often provides the tools of choice for many analyses needed to better understand how permafrost regions change in a rapdily warming Arctic and what the local to global feedbacks are.

Learn about Arctic Climate Change, Cryosphere, and Permafrost; Permafrost landscapes, disturbances, trends; Northern land cover and vegetation; Permafrost terrain and landforms; Thaw subsidence and frost heave; Permafrost coastal dynamics; and Thermokarst lake dynamics.

Learn to use cool data and techniques for characterizing and quantifying landscape dynamics in a rapidly changing Arctic: We will cover high resolution airborne data, high and medium resolution satellite imagery, LiDAR, Big Data approaches with Google Earth Engine, and an overview into several other methods.

The remote sensing methods will be easily applicable to other regions on Earth.

Occasionally there may be polar bears or other fluffy animals showing up in the lecture materials and many permafrost-related things you probably have never seen before...

Due to Corona restrictions, the module will consist of asynchronous video lectures and synchronous hands-on exercises through Big Blue Button video conferencing. You also will work on a specific semester project which you will select at the beginning of the module and present at the semester end.

The module will rely on latest research papers on remote sensing of permafrost regions. Detailed literature lists will be provided through moodle. Here some general literature examples on the topic:

Permafrost

• French, H. M. (2007) The Periglacial Environment, 3rd Edition, Wiley, ISBN: 978-0-470-86588-0, 478 pp.

Remote Sensing of Permafrost Regions

• Jorgenson MT, Grosse G (2016): Remote Sensing of Landscape Change in Permafrost Regions. Permafrost and Periglacial Processes, 27(4): 324-338. doi: 10.1002/ppp.1914.

• Westermann S, Duguay C, Grosse G, Kääb A (2015): Remote sensing of permafrost and frozen ground. In: Tedesco M (ed.): Remote sensing of the Cryosphere, pp. 307-344. Hoboken, NJ, Wiley Blackwell, 408 p., doi: 10.1002/9781118368909.ch13.

Seminars/Exercises will have a strong focus on Google Earth Engine and GIS Desktop systems.

- You will need to establish a free Google Earth Engine User Account

- You will need to install an ArcGIS (student) license on your computer

- For one of the seminars on remote sensing of permafrost coastal erosion you will need to install the Digital Shoreline Analysis System (DSAS) for ArcGIS tool

- Introduction to Climate Change, Arctic Cryosphere, and Permafrost

- Introduction to Remote Sensing of Permafrost Regions

- Permafrost Landscapes and Dynamics

- Permafrost Terrain and Landform Characterization 

- Permafrost Landscape Land Cover and Vegetation

- Time Series Analysis and Change Detaction

- Big Data and Machine Learning in Remote Sensing

- Big Data and Deep Learning in Remote Sensing

- Remote Sensing of High Latitude Lakes and Lake Change 

- Observing Permafrost Coastal Dynamics 

- Advanced remote sensing methods for permafrost

- Repetitorium / Q&A

- Presentation of remote sensing project results 

- Written Exam 

The module targets remote sensing enthusiasts with an interest in Polar Regions. Ideally, you are already aware of the importance of climate change in the Arctic and understand the importance of global-scale feedbacks in the Earth System, you like to play with different remote sensing approaches, and you enjoy working with images as well as bits and bytes. Overall, the remtoe sensing methods used are generally applicable also in other regions.