Leaf Area Index Monitoring Using MODIS in Google Earth Engin

Satellite Monitoring of Leaf Area Index in Google Earth Engine

What you’ll learn
Understand the concept and importance of Leaf Area Index (LAI) for vegetation monitoring and environmental analysis.
Access, process, and analyze MODIS satellite data using Google Earth Engine to derive meaningful LAI metrics.
Create visualizations and time series charts to monitor LAI trends over multiple years for specific regions.
Export and share processed satellite data products, such as LAI maps and composites, in standard geospatial formats like GeoTIFF.

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

Description
This course provides a comprehensive introduction to monitoring Leaf Area Index (LAI) using MODIS satellite data within the Google Earth Engine (GEE) platform. LAI is a critical biophysical parameter representing the total leaf area per unit ground surface area, essential for understanding vegetation health, growth, and ecosystem productivity. Through this course, learners will gain expertise in remote sensing fundamentals and how to leverage satellite-derived LAI data to analyze vegetation dynamics across spatial and temporal scales.Focusing on the MODIS MOD15A2H dataset, which provides 8-day composites of LAI at 500-meter resolution, students will learn how to filter, process, and analyze time series data in GEE. The course guides learners in defining Areas of Interest (AOIs), extracting relevant data, computing composites such as mean LAI over specific periods, and visualizing results through thematic maps. Additionally, the integration of true color imagery (MODIS MOD09A1) enables users to validate and contextualize vegetation patterns visually.The practical component includes exporting processed LAI and true color imagery as GeoTIFF files for further analysis in GIS software, making it valuable for both academic research and industrial applications such as precision agriculture, forestry management, and environmental monitoring. Learners will also build time series charts within GEE to track seasonal and annual LAI variations, fostering an understanding of phenological trends and environmental impacts.By the end of the course, participants will be equipped to implement advanced remote sensing techniques, interpret satellite data for vegetation analysis, and apply these skills to real-world scenarios using the powerful cloud-based GEE platform.

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.

https://www.udemy.com/course/leaf-area-index-modis-google-earth-engine/