Advanced Remote Sensing and Geospatial Data Science

3 credits · Nicholas School of Environment

Understanding ecological processing and environmental modeling using advanced remote sensing approaches. We will focus on case studies that apply advanced remote sensing techniques, including LiDAR, multispectral, hyperspectral, and thermal imagery, at different spatial and temporal scales. We will focus on data management and coding using R and Google Earth Engine (GEE). Prerequisites: ENVIRON 559 (Fundamentals of Geospatial Analysis), or ENVIRON 558L (Remote Sensing for Environmental Analysis), or undergraduate students with a background in GIS may request permission to enroll from the instructor. This course is open to doctoral students from all disciplines.