Soil Erosion Mapping Using Remote Sensing and GEE

This lecture introduces the basic principles of remote sensing, covering the science behind capturing and interpreting data from satellite and aerial sensors. Students will learn about electromagnetic spectrum, sensor types, image acquisition, and preprocessing techniques. Understanding how remote sensing data is collected and processed lays the foundation for environmental monitoring and mapping. This lecture also discusses key applications of remote sensing in land use, agriculture, and natural resource management, providing essential knowledge to use remote sensing effectively in geospatial analysis.

In this lecture, students explore how remote sensing data can be applied for risk mapping of environmental hazards such as soil erosion, floods, and landslides. The focus is on identifying vulnerable areas by analyzing factors like terrain, vegetation, and rainfall. Methods to integrate various remote sensing datasets to assess and visualize risk levels are introduced. This lecture emphasizes practical approaches for disaster management and mitigation planning using satellite imagery and geospatial tools.

This session covers the basics of Geographic Information Systems (GIS), highlighting spatial data types, data models, and GIS software capabilities. Students learn how to collect, store, manage, and analyze spatial data for mapping and decision-making. The lecture introduces fundamental GIS operations like buffering, overlay analysis, and querying spatial databases. Practical applications in urban planning, environmental management, and infrastructure development are discussed, preparing students to use GIS alongside remote sensing for comprehensive spatial analysis.

Students will gain detailed knowledge of the Sentinel-2 satellite’s multispectral bands and their spatial, spectral, and temporal resolutions. This lecture explains the significance of each band for different environmental applications, such as vegetation monitoring, water quality assessment, and soil analysis. Understanding the characteristics of Sentinel-2 data enables students to select appropriate bands and resolutions for specific mapping tasks. The lecture also covers preprocessing techniques to enhance image quality and usability.

This lecture introduces Google Earth Engine as a powerful cloud-based platform for planetary-scale environmental data analysis. Students will learn to access vast remote sensing datasets, perform large-scale image processing, and run geospatial algorithms efficiently. The session covers basic GEE scripting concepts, including JavaScript coding, working with image collections, and visualization tools. Emphasis is placed on the advantages of GEE for rapid, scalable environmental analysis and monitoring.

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 the final lecture, students apply their knowledge to implement a practical soil erosion risk mapping project using Google Earth Engine. This session guides learners through processing DEM, rainfall, and vegetation indices to generate an erosion risk index. Techniques for normalizing data layers, weighting factors, and creating composite risk maps are demonstrated. Students also learn how to visualize results, interpret findings, and export maps for reporting. This hands-on experience solidifies skills in combining remote sensing, GIS, and GEE for environmental risk assessment.