GIS with Remote Sensing Imagery

Millions of overhead images are collected every day around the world, from satellites, manned aircraft, and drones.  These imagery sources capture the current status of the landscape, as well as provide a historical archive of imagery collected in the past.  The term "remote sensing" refers to the process for collecting and processing overhead imagery so that the imagery can be integrated into a GIS.

In this course students will learn methods to interpret remote sensing imagery for monitoring the earth's environment and human infrastructure, and for detecting change.  The course will cover essential knowledge about the physics of light, and how electromagnetic spectra can help to detect vegetation vigor, measure impervious surfaces, and characterize forest types.

During the course we will examine several types of widely available imagery - from Landsat to Lidar, aerial photography to drones.  Students will experience some commonly used workflows with hands-on guided exercises that teach methods to process image sources for insertion into GIS workflows.  Other modules will help students explore elevation data (Digital Elevation Models) and create immersive “fly through” views of 3D scenes.

This course will help students seeking to perform research or work in the fields of environmental study, climate change, and habitat conservation and awareness for plants and animals.

This course will be offered in hybrid mode: students can take the course in the TESC Computer Applications Lab (CAL), or students can take the course from a home computer over Zoom simulcast.  To take this course from home, students must have reliable broadband internet access and dual computer monitors.

Owing to the large file volumes inherent in imagery datasets, students must have a sufficiently powerful computer – with Windows operating system - to take this course from home.  The CAL has sufficiently configured machines to support students who take this course in-person.