Flood Risk Mapping with Remote Sensing and GEE

This lecture introduces the core concepts of remote sensing, including the physics of satellite sensors, types of data (optical, radar, thermal), and the importance of spatial, spectral, and temporal resolution. Students will learn how satellite imagery captures earth surface features and how this data supports environmental monitoring and disaster management. The session covers basic image processing techniques and introduces key satellite platforms relevant to flood risk assessment. By the end, learners will understand how remote sensing forms the backbone of modern geospatial analysis.

This lecture focuses on applying remote sensing techniques for risk mapping, with an emphasis on flood risk. Students will explore how elevation, precipitation, land cover, and other environmental factors are derived from satellite data to assess vulnerability. The session will cover normalization and index calculation, combining multiple data layers to generate comprehensive risk scores. Real-world examples will highlight flood-prone regions and how satellite data helps predict and manage flood hazards effectively.

This lecture offers a hands-on introduction to Google Earth Engine, a cloud-based geospatial processing platform. Students will learn how to access and manipulate large satellite datasets, apply filtering, map functions, and perform statistical analysis using GEE’s JavaScript API. Emphasis will be on practical skills such as loading datasets, clipping regions of interest, visualizing outputs, and exporting results. This foundational knowledge prepares learners for implementing complex geospatial workflows in flood risk and other environmental applications.

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 apply their remote sensing and GEE skills to create flood risk maps. They will work through a step-by-step case study using elevation (DEM) and precipitation data, learn how to normalize and weight layers, and classify risk zones. The session includes techniques for combining data sources, visualizing risk maps, and exporting geospatial products for further analysis. By the end, students will be equipped to build their own flood risk models and understand the practical challenges of geospatial disaster risk assessment.