Friday, October 3, 2014

Photo Interpretation and Remote Sensing student spotlight!

GIS4035 Photo Interpretation and Remote Sensing, Dr. Brian Fulfrost

Ground Truthing and Accuracy Assessment


Lab description - Last week, students honed their skills of recognizing features on the ground using a natural color aerial photograph. They digitized an area of Pascagoula, MS, creating a land use/land cover map. Students identified features on the ground based on size, shape, color, pattern, shadows, associations, etc. This week In this lab, students investigated areas of their LULC maps to verify their classification schemes from last week’s lab. This is a way for them to gauge their skills of aerial photo interpretation that they have been learning. 

Student Learning Outcomes:

  • Construct an unbiased sampling system
  • Locate and identify features using Google Maps street view
  • Calculate the accuracy of a Land Use / Land Cover classification map

STUDENT SPOTLIGHT AWARDS

The following student was chosen for their exceptional work on the Ground Truthing and Accuracy Assessment assignment:

Shana Dooley


About Shana: Shana lives on a small sand spit in the Republic of the Marshall Islands; if you don’t know what that is, just picture sand, sand, and well more sand!  This dot of land is located west of Hawaii in the middle of a vast blue ocean.  Residing on an Army base, she works as an archaeologist for 2 of the islands which are both WWII National Battlefields.  Originally from the desert, Shana has made the best of island life taking up snorkeling, SCUBA diving, and sailing; all of the hobbies helped her expand the size of this dot of land considerably.  Shana is a member of the Archaeology tract and is interested in 3D mapping as she expands the skillset!  Welcome to the spotlight Shana!

What we like: We like that Shana updated her original land use map based on feedback from the previous week. She was able to choose an unbiased sampling method and fully grasped the idea of ground truthing her points using Google Maps. Her sampling points gave her a 90% overall accuracy of her map, and in her discussion she was able to justify why she marked a point as correct or incorrect. Way to go, Shana!



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