REM 440 - Applied Remote Sensing of Dryland & Agricultural Resources

(4 units) CO14

Undergraduate and graduate students collaboratively investigate the sustainability of agricultural systems through the development of a remote sensing-based (RS) research portfolio. The portfolio is a demonstration of the collaborative’s expertise in RS problem identification, experimental design, data gathering, processing, analyses, and interpretations, and the dissemination skills necessary to publish their research findings.

Maximum units a student may earn: 4

Prequisite(s): REM 241 or NRES 217

Grading Basis: Graded
Units of Lecture: 3
Units of Laboratory/Studio: 1
Offered: Every Spring

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. write a research proposal abstract, annotated bibliography, a progress report, and a basic research paper.
2. conduct RS research by developing familiarity with public databases and remote sensing data acquisition, pre- and post image processing, and the use of analytical techniques including statistical analyses with various types of RS data.
3. demonstrate basic knowledge of experimental design and the planning, operation, and maintenance of selected field equipment including stand alone & drone PPK and RTK GPS, radiometers, GPR, and TLS.
4. become familiar with and use both commercial and opensource software available for RS image processing including Pix4D, ENVI, CloudCompare, Quick Terrain Modeler, QGIS, R, Python and Google Earth Engine (GEE, Python and/or Java Script).
5. prepare an annotated bibliography/literature review in keyhole macro language (kml) to become conversant in the language/jargon of remote sensing.
6. write kml script for database storage and map display of their annotated bibliography in Google Earth.
7. design and present a poster and/or oral presentation.
8. work collaboratively in teams of diverse individuals and exercise leadership skills on projects.
9. develop a remote sensing portfolio that is a synthesis and integration of remote sensing theory towards practical application to agriculture and rangeland problems of interest to the students.

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