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Wildfire Mapping with MODIS and Google Earth Engine
Rating: 4.1 out of 5(2 ratings)
151 students

Wildfire Mapping with MODIS and Google Earth Engine

Satellite Wildfire Mapping
Created byEarth's AI
Last updated 7/2025
English

What you'll learn

  • Understand the fundamentals of remote sensing and its role in environmental monitoring, with a focus on wildfire detection.
  • Explore MODIS satellite data, specifically the MOD14A1 Fire product, and interpret fire confidence values for accurate wildfire mapping.
  • Gain hands-on experience using Google Earth Engine (GEE) to process, visualize, and analyze wildfire events using real MODIS data.
  • Build a complete wildfire detection workflow, from importing satellite data to generating high-confidence fire maps over a chosen region

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Course content

1 section • 8 lectures • 1h 0m total length

  • Intro1:12

    This lecture series covers remote sensing fundamentals and the importance of wildfire mapping using satellite data. It introduces Google Earth Engine (GEE) and the MODIS MOD14A1 fire product. The series concludes with a practical wildfire detection project in California, providing essential skills for satellite-based fire monitoring.


  • Lecture 1: Fundamentals of Remote Sensing13:19

    Introduce students to the core principles of remote sensing — electromagnetic spectrum, spatial/spectral/temporal resolution, and how satellites detect changes on Earth's surface. Set the context for wildfire detection.

  • Lecture 2: Wildfire Mapping – Importance and Satellite Roles8:52

    This lecture explores the global significance of wildfire mapping and how satellite data supports rapid detection and monitoring. Using real-world examples from California and Australia, it highlights key satellite missions like MODIS, VIIRS, and Sentinel-3, and explains how fire confidence levels, such as MODIS FireMask values, inform decision-making and disaster response.


  • Lecture 3: Introduction to Google Earth Engine (GEE)9:25

    This lecture introduces Google Earth Engine (GEE), a powerful cloud-based platform for geospatial analysis. Students will explore its interface, including the Code Editor and Docs, and learn how to load, filter, and visualize satellite data. Basic JavaScript concepts for GEE scripting will also be covered to build foundational coding skills.


  • Lecture 4: Understanding MODIS & MOD14A1 Fire Product7:46

    This lecture explores the MODIS MOD14A1 daily fire product, focusing on its FireMask band and how to interpret fire confidence levels. Students will understand the dataset’s spatial and temporal resolution, the meaning of FireMask values, and why high-confidence detections are preferred for reliable wildfire mapping.


  • 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 5: Wildfire Detection in California – GEE Implementation13:35

    This lecture provides a practical guide to detecting wildfires in California using Google Earth Engine. Students will learn how to load and filter MODIS MOD14A1 data, mask high-confidence fires, clip the results to California, visualize fire occurrences, and create composite fire maps using the max function for effective wildfire monitoring.


  • Extra Lecture: Wildfire Mapping in Australia (2025) Using MODIS in GEE2:38

Requirements

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

Description

his course offers a comprehensive introduction to wildfire mapping using satellite remote sensing and Google Earth Engine (GEE). Starting with the fundamentals of remote sensing, students will learn how satellite imagery captures changes on Earth’s surface and why it is crucial for environmental monitoring. The course then delves into the global significance of wildfire mapping, highlighting real-world case studies and the satellite missions—such as MODIS and VIIRS—that enable timely detection and monitoring of fire events.

Students will gain hands-on experience with Google Earth Engine, a powerful cloud-based platform designed to process large geospatial datasets efficiently. Through practical examples, learners will become familiar with loading datasets, filtering images, and basic JavaScript coding within GEE. A detailed exploration of the MODIS MOD14A1 fire product follows, explaining how to interpret FireMask confidence values and why high-confidence data is essential for accurate wildfire identification.

Finally, the course culminates in a practical demonstration of wildfire detection over California using MODIS data in GEE. This includes filtering for high-confidence fires, masking non-fire areas, clipping the region of interest, and creating composite fire maps for visualization. By the end, students will be equipped with the knowledge and skills to perform wildfire monitoring using satellite data and cloud computing tools.

Who this course is for:

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

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