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Remote Sensing Field & Analysis Experience - EAS 501

2 credits

Course number: EAS 501.034

Prerequisites: This is a graduate field extension of content covered in EAS541, "Remote Sensing." You should to have taken Remote Sensing or the equivalent by permission (e.g. EAS531, ENVIRON 403, EARTH 408, URP520). 

Dates: May 12-25, 2019, but you will enroll for fall term credit*.

Location: U-M Biological Station

Instructor: Kathleen Bergen

TO ENROLL: We will have an interest form here in 2019 that you can use to indicate interest, after which you will register through Wolverine Access in early April. Students not returning in Fall 2019 should plan to register for an independent study with the Prof. Bergen for Spring 2019.

*Note: This course runs for two weeks in May, 2019, but you will enroll for FALL term credit. You are financially responsible for these credits, but you may not have to pay additional tuition if your total fall credits are within 12-18 hours. Students taking fewer than 12 or more than 18 credit hours will be assessed fees on those credits.
You will live at the Biological Station for the duration of the course. Your room and board is covered by UMBS Transforming Learning scholarships.


Learn how to collect and process field "ground-truth" for remote sensing projects. You will use the remote sensing-derived information with other spatial data to address an analysis question. Plan to work with aerial photography, Landsat imagery, lidar data, and various other spatial data, as well as ERDAS IMAGINE, ArcGIS, GPS software, and data collection hardware and software.

Note: this two-week course does not substitute for the EAS541 remote sensing requirement for the SEAS Environmental Informatics track or co-track, nor for an EAS541 requirement in the Graduate Certificate in Spatial Analysis; this course can be counted as an elective in both.

A combination of lab and field time lets you see how remotely sensed data looks out in the real world and vice versa. Here a student is comparing pixel values in a Landsat 8 multispectral image to see if the pixel can be used to represent the phenomenon of interest in a classification.