Published 7/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 3m | Size: 510 MB
Deforestation Monitoring with Landsat Imagery and Google Earth Engine
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 3m | Size: 510 MB
Deforestation Monitoring with Landsat Imagery and Google Earth Engine
What you'll learn
Gain foundational knowledge of remote sensing principles and their role in detecting forest change, enabling students to understand how satellite data is used i
Learn how to access, process, and interpret Landsat imagery to detect patterns and extents of deforestation across different time periods.
Apply vegetation indices such as NDVI to quantify forest health and identify significant vegetation loss due to human or natural factors.
Build complete deforestation analysis workflows in Google Earth Engine-from image preprocessing to visualization and GeoTIFF export-without prior coding experie
Requirements
No prior experience with Google Earth Engine is required - the course will guide you step-by-step.
Description
This course offers a technical deep dive into using remote sensing technology and cloud-based platforms for effective deforestation monitoring. It begins by introducing fundamental principles of remote sensing, emphasizing the interaction between electromagnetic radiation and Earth's surface features, particularly vegetation. Learners explore the spectral bands of the Landsat satellite series, focusing on how different wavelengths reveal specific information about vegetation health and land cover.A key component is understanding the Normalized Difference Vegetation Index (NDVI), a widely used vegetation index calculated using near-infrared and red spectral bands. The course explains the NDVI formula and its interpretation in detecting vegetation vigor and degradation. Students learn how spectral signatures vary for healthy vegetation, bare soil, and deforested areas.The course also covers the role of Google Earth Engine (GEE), a powerful cloud platform for processing large-scale satellite data efficiently. Learners are guided through the practical implementation of deforestation detection workflows in GEE, including image filtering, cloud masking, NDVI calculation, and change detection techniques.By integrating theory with hands-on application, the course prepares students for real-world environmental monitoring challenges. It is ideal for researchers, environmental scientists, policymakers, and GIS professionals seeking to leverage satellite data for sustainable forest management and conservation efforts. Upon completion, learners will be equipped with the technical expertise to analyze Landsat data using GEE and contribute to informed decision-making in forest monitoring.
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.
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