Fire poses a significant threat to ecosystems, property, and human lives worldwide. Effective fire risk mapping is crucial for proactive management and mitigation efforts. This course provides a step-by-step approach to understanding and applying remote sensing data combined with cloud computing to assess fire risk accurately.
We begin with an introduction to the fundamentals of remote sensing, covering the basics of satellite imagery, spectral bands, and resolutions critical for environmental monitoring. The course then dives into the principles of risk mapping, where you learn to identify key variables influencing fire risk such as vegetation health (measured by NDVI), land surface temperature (LST), and wind speed.
Next, we explore Landsat satellite data, focusing on the spectral bands relevant for fire risk analysis, including how to process and interpret this data effectively. You will become proficient in using Google Earth Engine, a powerful platform that enables large-scale data processing without the need for extensive local computing resources.
Finally, the course culminates with a practical implementation module, guiding you through building a fire risk mapping model in GEE. You’ll learn to integrate multiple environmental datasets, normalize variables, calculate risk indices, and visualize results on an interactive map. This hands-on experience empowers students to create actionable fire risk assessments that aid environmental agencies, urban planners, and disaster management teams.
By the end of this course, you will possess the skills to harness remote sensing data and GEE technology to produce accurate, data-driven fire risk maps, enhancing your capacity for environmental analysis and decision-making.
- Students, researchers and professionals in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.
