Agricultural & Biological Engineering

ABE 590 Special Projects in Geographical Information Systems

Instructor: Dr. Bernie Engel
Offered during fall semester
Course Overview:
This course is targeted at graduate students seeking to develop their own GIS projects "from the ground up." Students define a project, assemble spatial data and perform spatial analysis. GIS-based programming and tools to extend popular GIS software are explored.

ABE 591E GIS Applications

Instructors: Dr. Bernie Engel and Larry Theller
Offered during spring semester
Course Overview: This is a senior and graduate level course that provides an introduction to GIS and its applications. A significant amount of the course is spent using GIS tools to address a range of problems.

ASM 322 Technology for Precision Agriculture

Instructor: Dr. Gaines E. Miles
Course Overview:
Topics include: personal computers, electronic sensors, yield monitors, crop loss monitors, machine controllers, global positioning systems (GPS), production management information systems, processing and marketing information systems, yield mapping, Geographical Information Systems (GIS), and computer software for precision agriculture.

 

 Agronomy

AGRY 545/ASM 591R, Remote Sensing of Land Resources

Instructor: Keith A. Cherkauer
Offered during fall semester
Course Overview:
Application of remote sensing and spatial databases for observing and managing land resources within the Earth System; analysis and interpretation of remotely sensed data in combination with field observations and other data sources; conceptualization and design of a global earth resources information system.

 

 Electrical & Computer Engineering

EE 577 Engineering Aspects of Remote Sensing

Instructor: Dr. Okan Ersog
Offered during spring semester
Course Overview:
Introduction to the concepts and methods of multispectral and hyperspectral image generation, processing, and analysis. Fundamentals of imaging sensor design and image analysis for complex scenes. Application of signal processing and signal design principles, and of pattern recognition to these problems. Spatial image processing and hyperspectral data analysis as appropriate to land scene data. Projects involving machine analysis of multispectral data sets. The course is suitable for graduate students both in engineering and non-engineering disciplines.

 

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