Semantic analysis of gradual tree vitality loss using Sen2Cube.at

 Suggested by: Martin Sudmanns, Dirk Tiede

Abrupt changes in vegetation are easy to spot, gradual changes; however, such as those resulting from disease are difficult to identify.

Short description

Understanding the gradual loss of tree vitality is essential for sustainable forest management, particularly in the face of climate change and other environmental stressors. Long-term trends in forest health can be analysed using Earth observation data. Sen2Cube.at is a semantic EO data cube, that enables the analysis of large volumes of Sentinel-2 data semantically enriched over extended periods to monitor changes in forest vitality.

The goal of this master thesis is to develop a workflow for detecting and analysing temporal changes in forest vitality using the Sen2Cube.at . This study, to be conducted in collaboration with the “Bundesforschungszentrum für Wald” will focus on making use of spectral indices and semantic layers to assess vitality loss in forests, with a specific focus on gradual changes.

A specific forest area will be selected as a case study, with results validated using in-situ measurement. The outcomes of this thesis are expected to enhance the understanding of long-term forest health trends and support evidence-based forest management strategies.

Suggested reading

Sudmanns, M., Augustin, H., van der Meer, L., Baraldi, A., & Tiede, D. (2021). The Austrian semantic EO data cube infrastructure. Remote Sensing, 13(23), 4807. https://www.mdpi.com/2072-4292/13/23/4807

Start

As soon as possible

Prerequisites/qualification

• Remote Sensing & GIS
• Basic Scripting/Programming (e.g., Python)
• Interest in the topic 

Multiscale analysis of thermal data for urban sustainability

 Suggested by:  Dirk Tiede, Martin Sudmanns

Example data for a thermal acquisition during a hot summer day in a city

Short description

Understanding urban heat dynamics is critical for addressing challenges related to climate change, urban planning, and sustainability. Thermal data from various sources, including satellite imagery and aerial photographs, provide valuable insights into phenomena such as heat islands and green space changes. By analysing these data at multiple scales and times, it is possible to assess and monitor urban thermal behaviour more effectively.

The goal of this master thesis is to perform a multiscale analysis of thermal data as part of the funded project “Prometheus” – Progressive Methods of Thermal High-Resolution Earth Surveillance for Urban Sustainability. The study may focus on one or many of the following topics (tbd):

• Comparing day and night thermal data to analyze urban heat distribution patterns.
• Delineating urban heat islands based on thermal data pattern (spatial/temporal), urban structure and additional data integration
• Investigating the relationship between heat islands and changes in urban green spaces over time.
• Combining thermal data from different scales, such as high-resolution aerial imagery and satellite data, to enhance spatial and temporal understanding.

The outcomes are expected to contribute to sustainable urban planning strategies by providing actionable insights into heat management and green infrastructure optimization. The results will be documented with clear methodologies to support further applications in urban sustainability projects.

 Start

As soon as possible

Prerequisites/qualification

++ interest in the topic
+ programming skills
+ Earth observation

Change analyses: Soil sealing

 Suggested by: Martin Sudmanns, Dirk Tiede

Example of an analysis result that indicates vegetation loss or soil sealing.

Short description

The process of covering natural soil with impervious materials such as concrete or asphalt, in other words soil sealing, is a critical issue in urban planning and environmental management. Monitoring and analysing changes related to it over time is essential for sustainable land-use planning and for supporting authorities in decision-making processes.

This master thesis aims on analysing changes in soil sealing using Earth observation data and geospatial techniques. By employing advanced (geo)visualization methods, the study has the objective to provide intuitive and actionable insights into temporal patterns of soil sealing. The work will explore how these analyses can be effectively integrated into planning workflows and applied to support authorities in managing land-use changes. The results are expected to enhance urban planning strategies by offering tools for better understanding and mitigating the impacts of soil sealing on ecosystems and urban environments.

Suggested reading

Sudmanns, M., Augustin, H., van der Meer, L., Baraldi, A., & Tiede, D. (2021). The Austrian semantic EO data cube infrastructure. Remote Sensing, 13(23), 4807. https://www.mdpi.com/2072-4292/13/23/4807

Start

As soon as possible

Prerequisites/qualification

++ interest in the topic
++ programming skills
+ Earth observation

Time Series Analysis for Natural Hazard Monitoring

 Suggested by: Karima Hadj-Rabah

These images are displacement mean velocity maps of “Mitidja” Bassin (Algeria), using two different time series analysis techniques Permanent Scatterers (PS) on the left and Small Baseline Subset (SBAS) on the right. (c) LTIR/USTHB

Short description

Understanding long-term ground displacement is essential for assessing the impacts of environmental changes and human activities on land stability. Differential Interferometric Synthetic Aperture Radar (DInSAR) time series analysis provides a powerful method for detecting and quantifying such displacements over extended periods. This approach is particularly relevant for studying phenomena like subsidence caused by groundwater extraction and reduced precipitation, as seen in regions affected by similar dynamics to the Mitidja Basin in Algeria.

This master thesis focuses on using DInSAR time series analysis to monitor long-term displacement associated with natural hazards and environmental changes. By leveraging Sentinel-1 SAR data and open-source tools such as GMTSAR and LicSAR, the research aims to develop a workflow for detecting and analyzing ground displacement trends. The outcomes will provide valuable insights into the spatial and temporal dynamics of land deformation, contributing to sustainable land management and hazard mitigation strategies.

Start

As soon as possible

Prerequisites/qualification

• Remote Sensing & GIS
• Interest in the topic
• Basic Scripting/Programming (e.g., Python) 

Generating displacement maps for mapping geohazards using Sentinel-1 images

Suggested by: Karima Hadj-Rabah

The two images are displacement maps of “Thessaly” earthquake (Mar.2021), the left one is according to the radar line of sight while the right one is according to the radar platform movement direction. (c) LTIR/USTHB

Short description

Ground displacement caused by geohazards such as earthquakes, volcanic eruptions, and landslides is very challenging to monitor using traditional methods (GNSS, geotechnical instruments,...). Satellite radar images, such as those provided by Sentinel-1, offer a valuable way to detect and measure ground movements over large areas. Two main techniques can be used for that purpose: Differential Interferometric Synthetic Aperture Radar (DInSAR) and Multiple Aperture Interferometry (MAI) to map displacement with high accuracy.

 This master thesis objective is to create a workflow for generating ground displacement maps using Sentinel-1 images in order to support geohazard monitoring and management. The tasks to be accomplished are:

• Reviewing the state-of-the-art in geohazard displacement mapping using SAR data, with a focus on DInSAR and MAI techniques.
• Developing a workflow for processing Sentinel-1 images to generate displacement maps, using free tools such as ESA SNAP software.
• Applying the workflow to a specific case study such as an earthquake in California, volcanic activity of Etna, or landslide event in Salzburg.
• Evaluating the results and discussing how the displacement maps could help in disaster response or risk management.

The purpose of this master thesis is to conduct a case study-based analysis of displacement mapping techniques, with detailed documentation in English. The thesis is ideal for students with a background in remote sensing, and an interest in geohazard applications.

Suggested reading

Teodosio, B., Wasantha, P. L. P., Yaghoubi, E., Guerrieri, M., Fragomeni, S., & van Staden, R. C. (2022). Monitoring of geohazards using differential interferometric satellite aperture radar in Australia. International Journal of Remote Sensing, 43(10), 3769–3802.

Start

As soon as possible

Prerequisites/qualification

Remote Sensing & GIS
Interest in the topic 

Live queries in the field: Bridging Earth Observation data and in-Situ Measurements

 Suggested by: Martin Sudmanns, Dirk Tiede

From big data to smartphone apps: Examples of Earth observation queries in the field.

Short description

Integrating Earth observation data with in-situ measurements for live analysis can be a powerful approach to enhance decision-making in agriculture, particularly within the framework of the Common Agricultural Policy (CAP). By enabling live queries in the field, accessing and analyzing EO data directly in real time become possible, supporting more accurate assessments of crop conditions, land use, and compliance with agricultural regulations.

This master thesis topic aims to explore the development of a seamless interface between EO data and in-situ measurements for agricultural applications. The objective is to create workflows that enable live queries in the field, utilizing EO datasets to provide actionable insights to stakeholders. Using a valuable tool such as Sen2Cube which is a semantically enriched data cube, the thesis will investigate how a mobile smartphone or tablet application can be used for live-interpretation of satellite imagery. The research outcomes are expected to improve the integration of EO data into agricultural monitoring systems, offering practical solutions for CAP-related assessments and supporting sustainable agricultural practices.

Suggested reading

Sudmanns, M., Augustin, H., van der Meer, L., Baraldi, A., & Tiede, D. (2021). The Austrian semantic EO data cube infrastructure. Remote Sensing, 13(23), 4807. https://www.mdpi.com/2072-4292/13/23/4807

Start

As soon as possible

Prerequisites/qualification

++ interest in the topic
++ programming skills
+ Earth observation

Evaluation and visualization of long earth observation time series

 Suggested by: Martin Sudmanns, Dirk Tiede

Examples of long Earth observation time series. Visualization capabilities are usually on a lower level than the analytical capabilities. 

Short description

Long-term Earth observation data provide valuable insights into environmental changes, particularly in sensitive regions like the Alps. Visualizing and analysing these data dynamically can help better understand temporal trends and support environmental monitoring. For example, Essential Climate Variables (ECVs), such as vegetation indices or snow cover, are key indicators for assessing the impacts of climate change in mountainous regions and require data for multiple decades.

 The objective of this thesis is to evaluate and visualize long EO time series using the Sen2Cube.at which is a semantic data cube that enables the analysis of large volumes of EO. The work can be summarized into the following key points:

• Developing dynamic visualizations of long EO time series to highlight temporal trends in selected ECVs for the Alpine region.
• Using Sen2Cube.at queries to efficiently extract and analyze semantic data related to ECVs, such as vegetation health or snow cover dynamics.
• Demonstrating the applicability of the approach through case studies of key climate variables in specific Alpine areas.

This master thesis workflow will provide innovative tools for environmental monitoring, offering dynamic and accessible visualizations of climate-related changes that can be used to support decision-making in the Alpine region.

Suggested reading

Sudmanns, M., Augustin, H., van der Meer, L., Baraldi, A., & Tiede, D. (2021). The Austrian semantic EO data cube infrastructure. Remote Sensing, 13(23), 4807. https://www.mdpi.com/2072-4292/13/23/4807

Start

As soon as possible

Prerequisites/qualification

++ interest in the topic
+ programming skills
+ Earth observation
+ Experience or willingness to get familiar with (3D) visualisation engines (Blender, unity, …)