Landslide Risk Mapping Using Remote Sensing and GEE

Name: Landslide Risk Mapping Using Remote Sensing and GEE
Rating: 5.0 (2 reviews)

Landslide Risk Mapping Using Remote Sensing and Google Earth Engine

Created byEarth's AI

Last updated 8/2025

English

What you'll learn

Understand the fundamentals of remote sensing and its applications in natural hazard assessment.
Learn how to process and analyze DEM, slope, aspect, and rainfall data using Google Earth Engine.
Develop skills to create landslide risk maps by integrating multiple environmental factors.
Gain hands-on experience in exporting and visualizing geospatial risk data for decision-making and disaster management.

Course content

1 section • 5 lectures • 56m total length

Lecture 1: Fundamentals of Remote Sensing13:19
This lecture covers the basics of remote sensing technology, including how satellite and aerial sensors capture data about Earth’s surface. Students will learn about different types of sensors, image resolution, and spectral bands. The session introduces how remote sensing is used to monitor environmental variables such as land cover, elevation, and vegetation. This foundational knowledge is crucial for understanding how remote sensing supports various applications like disaster management and natural resource monitoring.
Lecture 2: Remote Sensing Risk Mapping10:54
This session explores the use of remote sensing data for identifying and assessing risks in the environment. Students will learn how to analyze satellite-derived information to map hazard-prone areas. Topics include integrating multiple environmental factors such as terrain, vegetation, and climate data to create risk models. The lecture highlights practical applications in disaster risk assessment, including floods, landslides, and fire hazards.
Lecture 3: Introduction to Google Earth Engine (GEE)9:25
In this lecture, students are introduced to Google Earth Engine, a cloud-based platform for processing and analyzing geospatial data. The focus is on navigating the GEE interface, working with large datasets, and using basic programming to perform spatial analyses. Students will understand how GEE enables efficient, large-scale environmental monitoring and supports various remote sensing applications.
Getting Started with the Google Earth Engine Interface3:38
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.
Lecture 4: Implementation of Landslide Risk Mapping in GEE18:58
In this practical lecture, students will apply their remote sensing and GEE knowledge to develop a landslide risk map. Using real-world datasets like DEM, slope, aspect, and CHIRPS rainfall data, learners will implement risk modeling by combining environmental factors. The session includes scripting techniques to classify risk levels and visualize susceptible zones. Students will also learn to export their risk maps for further use. This hands-on approach equips learners with skills to conduct hazard mapping and support decision-making for disaster risk reduction.

Requirements

No prior experience with Google Earth Engine is required — the course will guide you step-by-step.

Description

Landslides pose significant threats to communities, infrastructure, and ecosystems, making effective risk mapping essential for mitigation and planning. This course offers a comprehensive introduction to remote sensing technologies and their practical application in landslide risk assessment. Students will begin by understanding the fundamentals of remote sensing, learning how satellite imagery and digital elevation models (DEMs) provide vital terrain information. The course covers risk mapping concepts and demonstrates how environmental factors such as elevation, slope, aspect, and rainfall contribute to landslide susceptibility.

Using Google Earth Engine, a powerful cloud-based geospatial processing platform, learners will gain hands-on experience in analyzing vast datasets efficiently. They will learn how to integrate terrain data (such as SRTM DEM), rainfall data (e.g., CHIRPS), and land cover information to build multi-factor landslide risk models. This includes normalizing data layers, applying threshold values, and classifying areas into low, medium, and high-risk zones.

The course emphasizes practical implementation with step-by-step guidance on coding workflows in GEE, allowing students to generate maps, visualize risk areas, and export their results for further use. Whether for academic research, disaster management, or environmental consultancy, participants will acquire valuable skills to perform spatial risk analysis.

By the end of this course, students will be confident in applying remote sensing data and GEE tools to real-world landslide risk mapping challenges, supporting more informed decision-making and contributing to community safety and resilience.

Who this course is for:

Students, researchers and professionals in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.