This course offers a detailed guide to monitoring tree canopy cover using MODIS satellite data and the powerful Google Earth Engine (GEE) platform. You will learn how to analyze large-scale environmental data with practical coding skills, even if you’re new to remote sensing or GIS. The course covers foundational concepts in remote sensing, explains the ecological significance of tree canopy cover, and introduces the GEE environment for cloud-based geospatial analysis.
A key feature is hands-on experience working with real MODIS data. For example, you will learn how to use GEE code to access the MODIS Percent Tree Cover dataset for the year 2020, and visualize tree canopy density. The course teaches how to clip satellite data to a specific region, apply classification techniques such as identifying dense forest areas with greater than 60% canopy cover, and export processed images for further use.
By completing this course, participants gain the skills to write scripts that filter image collections, apply masks, style maps with color palettes, and export geospatial data as GeoTIFF files. These abilities are essential for environmental scientists, urban planners, conservationists, and anyone interested in tracking forest health and land cover changes. Whether your goal is research, policy-making, or natural resource management, this course equips you with practical tools to monitor tree canopy dynamics efficiently and accurately using satellite data.
- Students and researchers in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.
