New to GEOG 883?
The schedule of course offerings can be found in the Penn State GIS program calendar. Class size will be limited to 25 students on a first-come, first-serve basis.
This GEOG 883 website provides the primary instructional guidance for the course. The Resources menu contains links to important supporting materials and external websites. The Orientation, Lesson 0, contains material that all students should read thoroughly, even if they have taken other online courses in the Penn State Geospatial curriculum. The first week of class will focus on the Orientation and will include assignments for credit based on this material. The Lessons menu contains links to lesson content specific to this course. Canvas, Penn State's course management system, is used to support the delivery of additional course materials, including e-mail, discussion forums, calendar, lab data, lab instructions, and assignment submission tools.
Browse the Course Content
Use the links under the Lessons menu to preview the online course content. All of the content on this website is freely available through the Open Educational Resources Initiative. You are welcome to use and re-use materials that appear in this site (other than those cited as being copyrighted by others) subject to the licensing agreement linked to the bottom of this and every page.
Quick Facts about GEOG 883
Online, 12-15 hours a week for 10 weeks
A graduate level course focusing on remotely sensed data for geospatial applications. This course assumes that students have prior knowledge in the basics of remote sensing, mapping, and GIS, and have experience with geospatial software, particularly ArcGIS. Students will develop a strong understanding of the tools and techniques used to display, process, and analyze remotely sensed data. Upon completion of GEOG 883 students will be able to develop analytical workflows to derive products and extract information from remotely sensed data for a broad range of applications. The culmination of this course is a independent final project in which students will demonstrate their ability to apply new skills to a real-world situation of personal or professional interest.
The course is specifically designed for adult professionals and is offered exclusively through the World Campus and the John A. Dutton e-Education Institute of the College of Earth and Mineral Sciences. Geog 883 is a required course in the Graduate Certificate in Remote Sensing and Earth Observation. Geography 883 also fufills a remote sensing requirement for the Graduate Certificate in GEOINT Analytics, and can be used as an elective in the Certificate of Geographic Information Systems, Master of Professional Studies in Homeland Security - Geospatial Intelligence Option, or the Master of Geographic Information Systems.
Topics of Study:
Lessons are to be completed in the order below over the first 8 weeks of the course. The final project will be developed over the entire ten-week session, with the final week of the course devoted to sharing the final projects among peers in the class.
- Lesson 0: Orientation
- Lesson 1: The Remote Sensing Analytical Process
- Lesson 2: Remote Sensing Data Types and Formats
- Lesson 3: Preprocessing of Remotely Sensed Data
- Lesson 4: Image Enhancement and Interpretation
- Lesson 5: Feature Extraction from Remotely Sensed Imagery
- Lesson 6: Classification Accuracy Assessment & Evaluation
- Lesson 7: Remote Sensing Applications
- Final Project
Upon completion of the course, students who excel are able to:
- process remotely sensed data to make it useful in geographic information systems;
- perform image enhancement on remotely sensed imagery;
- extract information from remotely sensed data using a variety of manual and automated techniques;
- critically assess the strengths and weaknesses of remote sensing instruments and platforms for a variety of application scenarios;
- develop multi-step remote sensing workflows to solve problems in a variety of application areas;
- apply acquired knowledge and critical thinking skills to solve a real-world problem with appropriate remote sensing data and processing methods.