This advanced geospatial analysis course explores how satellite imagery, aerial lidar, and environmental data are used to map, model, and predict patterns across landscapes. Students will use Google Earth Engine and R to develop skills in remote sensing, image analysis, lidar processing, and spatial modeling. Topics include satellite image processing, kriging, supervised classification, land cover mapping, and predictive modeling with statistical methods and machine learning. Students will process lidar point clouds to create terrain and canopy models, integrate environmental datasets, build reproducible workflows, evaluate model accuracy and uncertainty, and apply their results to ecological research, conservation, and natural resource management.

Anticipated Credit Equivalencies: 

4 - Environmental Studies: Advanced Remote Sensing, Lidar, and Spatial Modeling