Avalanche Risk Mapping with Remote Sensing in GEE

This lecture provides a foundational understanding of remote sensing, focusing on how it is used to monitor and assess environmental conditions. Students will explore the core principles, including how satellites detect and measure energy across the electromagnetic spectrum, and how this information is processed into images. Key concepts such as spatial, spectral, radiometric, and temporal resolution will be introduced. The lecture also covers various types of satellite sensors and platforms (e.g., Landsat, MODIS, Sentinel), and their relevance for hazard and terrain analysis. This foundational knowledge is critical for anyone seeking to apply remote sensing in risk mapping and environmental monitoring.

In this lecture, students will learn how remote sensing data is applied in the detection, analysis, and mapping of natural hazards. The session explores techniques for extracting information related to terrain characteristics, snow and vegetation cover, and land surface dynamics. Real-world case studies are discussed to show how satellite imagery is used in avalanche, flood, landslide, and fire risk assessments. The lecture emphasizes how indicators like slope, land cover, and snow presence can be derived from remote sensing data to identify areas at risk. This practical understanding bridges theory with real applications, preparing students for data-driven hazard mapping.

This lecture introduces Google Earth Engine (GEE), a powerful cloud-based platform for geospatial analysis using satellite imagery and Earth observation data. Students will learn the basics of the GEE Code Editor, including the JavaScript API, interface layout, and methods to visualize and analyze raster and vector datasets. The session explains how to access public datasets such as MODIS, SRTM, and Landsat, and how to perform basic operations like image filtering, compositing, masking, and charting. The lecture is designed to help learners with little or no coding experience become confident in using GEE for environmental and hazard analysis tasks.

This lecture explores the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and its relevance for environmental monitoring, especially in snow and hazard studies. Students will learn about the different MODIS sensors aboard the Terra and Aqua satellites, the spectral bands they offer (36 in total), and how these bands are used to analyze snow cover, vegetation, water, and temperature. Special focus will be placed on bands used for snow detection (such as Band 4 and Band 6), temporal resolution (daily revisit time), and spatial resolution (250m, 500m, and 1km depending on the band). The session also includes guidance on accessing MODIS datasets in Google Earth Engine and interpreting the data's quality assurance layers. By the end of this lecture, students will have a clear understanding of how to leverage MODIS data effectively for remote sensing projects like avalanche risk mapping.

This section introduces the Google Earth Engine platform, guiding learners through accessing the Code Editor, understanding its interface, and exploring key panels like the script editor, map viewer, inspector, and data catalog. It helps beginners get comfortable navigating GEE before writing any code or performing analysis.

In this lecture, students will apply the concepts and tools learned in previous sessions to create an operational avalanche risk map using Google Earth Engine. The workflow includes defining the study region (e.g., the Alps), processing DEM data to derive slope, analyzing snow cover using MODIS imagery, and developing a multi-criteria risk classification. Learners will write and interpret GEE code to identify low, medium, and high-risk avalanche zones. The final outputs include risk maps, layered visualizations, and exportable data products. This hands-on project will empower students to independently conduct geospatial hazard assessments using GEE.