NDVI Monitoring and Analysis with MODIS Satellite in GEE

This lecture introduces the Normalized Difference Vegetation Index (NDVI), a key metric used to measure vegetation health from satellite imagery. You will learn how NDVI is calculated using near-infrared and red light reflectance, what its values indicate about vegetation density and condition, and why it’s essential for monitoring crops, forests, and ecosystems. We’ll also discuss NDVI’s limitations and practical applications in agriculture, environmental science, and land management, providing a foundational understanding to build upon in later lectures.

In this lecture, we explore the MODIS instrument aboard NASA’s Terra and Aqua satellites. You will understand its spectral capabilities, spatial resolution, and temporal frequency. The lecture covers MODIS’s role in Earth observation, particularly its NDVI products like MOD13A2, which provide frequent and consistent vegetation monitoring. We discuss why MODIS data is valuable for large-scale environmental studies and how it complements other satellite data sources, setting the stage for practical use in Google Earth Engine.

This lecture introduces Google Earth Engine, a cloud-based platform for analyzing geospatial data. You’ll learn how GEE provides access to petabytes of satellite imagery and powerful tools for data processing and visualization. We cover the JavaScript code editor interface, the benefits of cloud computing for remote sensing, and key concepts such as image collections and filtering by date and location. This session prepares you to work hands-on with MODIS NDVI data and other geospatial datasets in the upcoming lectures.

Here, you’ll learn to specify a geographic area and time period for analysis in Google Earth Engine. Using Paris as an example, we demonstrate how to define a point geometry, filter the MODIS MOD13A2 NDVI collection by date, select the NDVI band, and compute average NDVI over the specified period. This practical session teaches the core steps of remote sensing data preparation, enabling you to tailor analyses to your region and timeframe of interest.

In the final lecture, we focus on visualizing and interpreting NDVI data in Google Earth Engine. You will learn to set visualization parameters, including color palettes that represent vegetation health, and how to center the map on your area of interest. We cover best practices for displaying NDVI layers, interpreting spatial patterns, and exporting results for reporting. This lecture solidifies your ability to create meaningful vegetation maps, making your remote sensing analyses actionable and insightful.