DATA USE IN AN UNDERGRADUATE REMOTE SENSING COURSE: AN INTEGRATED EXPLORATION OF GEOLOGY, HYDROLOGY AND VEGETATION IN THE MONO LAKE REGION OF CALIFORNIA

Data collected by satellite- and aircraft-mounted remote sensing instruments can be analyzed to gain useful insight into a wide variety of Earth processes. In the remote sensing course at Pomona College, which is offered every other year and requires as a prerequisite only that students have taken any two science courses of their choosing, students spend roughly ten weeks learning about the strengths and limitations of various common remote sensing datasets (e.g., multispectral, thermal, etc.) via lectures and hands on data analysis labs. Following this basic introduction, students spend five weeks analyzing the complex geology, hydrology and vegetation of the Mono Lake region of California.

The Mono Lake investigation is driven by a broad, overarching question: what controls the vegetation cycle(s) across this geographically diverse area? Several different forms of remote sensing data are provided (so far, multi-year/seasonal Landsat TM; AVIRIS; DEM; DOQ, TIMS; AirSAR; bathymetry) and students must decide how they want to explore the question, what data they wish to use, how they are going to process it to extract the necessary information, etc. To help guide their efforts, students working in small groups over a two week period are randomly assigned to perform an initial assessment of either the geology, hydrology or vegetation in the region, and each group generates six Powerpoint slides explaining what they have learned. Larger groups are then defined, each including a geology team, a hydrology team and a vegetation team. The members of each large group pool what they have learned, figure out what else they need or want to know, and continue their analysis, ultimately producing a set of twenty four annotated Powerpoint slides addressing the question and targeted to be suitable for use as a stand-alone product in a high school science course; often multiple large groups are exploring the same question in parallel, promoting synergistic exchanges of information as well as healthy competition to see who can perform the most in-depth analysis! In this presentation I provide an overview of the project organization and the data employed, and illustrate how the students’ independent exploration of a complex array of Earth processes is promoted by this open-ended, question-driven, collaborative data analysis approach.