Synthetic Aperture Radar: Hazards

Remote sensing observations from airborne and spaceborne platforms have become an essential tool in disaster management. They provide an immediate and large-area overview of evolving disaster situations, revealing important hazard information to disaster response personnel.

This course will introduce you to Synthetic Aperture Radar (SAR), a remote sensing technology that can see the earth surface even during darkness and through rain, clouds, or smoke. As a participant in this course, you will gain an intuitive understanding of the information contained in SAR earth observations and learn to use a range of analysis techniques and algorithms to apply SAR data to disaster mapping and management. Specific topics will include:

• The mathematical and physical principles of SAR remote sensing
• How to access and visualize SAR data
• Interpretation of SAR imagery in the context of disaster monitoring
• Interferometric SAR (InSAR) concepts
• Flood mapping and SAR change detection for hazard analysis
• InSAR-based analysis of volcanoes and landslides
• The learned concepts will be put into action in simulated disaster response exercises, in which class participants will analyze SAR data sets to create hazard information for several real-life disaster events from the recent past.

Learners registered for the verified track will additionally get the opportunity for hands-on lab exercises using Jupyter Notebooks. The verified track will also include peer discussions and a verified certificate upon successful completion.

This course is produced by the Alaska Satellite Facility at the University of Alaska Fairbanks, which has been selected as the NASA data hub for the upcoming NISAR mission.

Week 1: Course Introduction and Basic Capabilities of SAR & InSAR

Week 1 will introduce you to the main content and structure of this course. You will also learn about the lab environment that will be available for learners on the “verified track”. The following items will be addressed:

• Introduction to course
• Presentation of course syllabus
• Basic principles and capabilities of Synthetic Aperture Radar (SAR) imaging
• Concepts of Interferometric SAR processing and its applications to disaster monitoring
• Verified Track: Lab exercise on exploring SAR time series for flooding events
• Verified Track: Lab exercise analyzing and interpreting InSAR products

Week 2: InSAR Time Series Analysis Techniques

InSAR time series analysis techniques enable the tracking of cm-scale surface displacements over long times. These techniques are relevant for a number of hazard applications such as landslide and volcano monitoring. In Week 2 we will address:

• The Concept of InSAR Time Series Analysis
• Point Target-Based InSAR Time Series Analysis
• Distributed Scatterer InSAR Time Series Analysis
• Verified Track: Lab exercise exploring PSI time series data over Norway

Week 3: Disaster Monitoring using SAR Image Data

SAR sensors are highly stable imaging systems that are ideal for the identification of changes on the earth surface. Week 3 will introduce participants to a range of techniques to monitor hazard events and identify changes from multi-temporal SAR images.

• Surface water signatures in SAR
• A Threshold-based surface water mapping method
• Verified Track: Hands-on flood mapping using Jupyter Notebooks
• SAR change detection techniques
• Verified Track: Lab exercise on time series change detection to map deforestation

Week 4: Disaster Monitoring using InSAR Techniques

In this part of the course, we will apply the information learned in Week 4 to monitor surface displacements at volcanoes and in landslide-prone areas. Specifically, Week 6 will address the following items:

• Review of InSAR workflow for a geophysical application
• Estimating volcano source model parameters from InSAR
• Verified Track: Lab exercise estimating Mogi source parameters for Okmok volcano, Alaska
• Landslides monitoring using SBAS InSAR
• Verified Track: Lab exercise mapping landslide motion in California using InSAR time series data