GIS: Remote Sensing

Millions of overhead images are collected every day around the world, from satellites, 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 of deriving information about something without physically touching it. In the context of satellite and aerial imagery, remote sensing allows for the processing of imagery so that data can be integrated into geographic information systems. Remote sensing is the science that produces much of the data that is used in GIS analysis, from land cover classification to quantification of change across an area.  

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. Remote sensing is the key to understanding landscape level dynamics in both natural and urban environments.

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 with supervised machine learning algorithms. Students will be required to source satellite data from online repositories and work at multiple temporal, spectral, and spatial scales.

This is the third of three core courses that comprise the GIS Certificate. Core courses must be taken in sequential order.