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Field Remote Sensing - EAS 501/ENVIRON 463

2 credits

Course number: EAS 501/ENVIRON 463

Prerequisites: This is a graduate field extension of content covered in EAS541, "Remote Sensing." You should to have taken EAS 541 / ENVIRON 441 Remote Sensing or equivalent by permission of instructor.

Dates: May 9-23, 2020, but you will enroll for fall term credit*.

Location: U-M Biological Station

Instructor: Kathleen Bergen

TO ENROLL: Indicate your interest in the class using this Google form. When it's time to register for fall classes, you will receive permission to enrolll and will have to register yourself on Wolverine Access.

*Note: This course runs for two weeks in May, 2020, 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.

COURSE DESCRIPTION

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 complete labs, exercises, and a team final project. Plan to work with aerial photography, Landsat imagery, drone data, lidar data, and various other spatial data, as well as ERDAS IMAGINE, ArcGIS, Collector, GPS and other technologies.

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.