Start Date: January 18th, 2022
Significant advances in access to geospatial datasets and cloud-based computing resources have ushered in a new era of user-friendly big data analysis, and satellite remote sensing has become a critical component of many environmental research and monitoring programs. However, effective use of satellite imagery requires a foundational understanding of sensor, image and surface characteristics as well as methods for translating analysis-ready data to decision-ready analysis.
This course will introduce students to fundamentals of remote sensing theory and image processing techniques using the Google Earth Engine Platform, which “combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities.” By integrating hands-on practical training with evidence-based experiential learning, students will establish a basic understanding of how to use remote sensing as a tool for environmental problem-solving.
By the end of the course, the student will be able to:
- Improve their understanding of the physical processes involved in the acquisition of remote sensing imagery, as well as the unique spectral, spatial, temporal and radiometric properties of different image sources
- Build a working knowledge of a wide array of geospatial datasets available in the Earth Engine Data Catalog, including optical, thermal and microwave imagery from the Landsat, MODIS, Sentinel-2, and Sentinel-1 satellites, and derived products such as the Hansen Global Forest Change and JRC Global Surface Water datasets
- Explore both static and interactive data visualization techniques including tables, maps, charts, GIFs and Earth Engine Apps
- Apply standard multi-spectral and multi-temporal image processing techniques, including basics of land cover classification and change detection
- Develop a portfolio of example scripts, data visualizations and analyses across a range of environmental application areas