Data Use in an Undergraduate Remote Sensing Course: An Integrated Exploration of Geology, Hydrology and Vegetation in the Mono Lake Region of California
Eric B. Grosfils Pomona College
This is a partially developed activity description. It is included in the collection
because it contains ideas useful for teaching even though it is incomplete.
This activity requires students to process, interpret and integrate a wide variety of remote sensing and other data as they investigate a complex, open-ended research question. It is hands-on, collaborative, and manageable for a variety of class sizes. The problem addressed has geological, hydrological, biological and political implications, and is thus of interest to a wide array of undergraduates.
GSA Poster (Acrobat (PDF) 25.9MB Oct1 03)
Higher Order Thinking Skills:
managing a complex array of data; framing and testing hypotheses given limited time and resources; identifying core questions; drawing conclusions from data; focus on communicating complex results to trained peers and to a lay audience
gaining confidence working independently with sophisticated software; presenting results verbally and in writing in a fashion suitable for a general audience; intelligent design of slides
undergraduate, works at any level, most useful for students in physical and social sciences
The exercise comes at the end of a remote sensing course, so students are familiar with some basic remote sensing techniques and how to answer questions they encounter about software strengths *beyond* what has been employed in labs, etc. The remote sensing course itself has no prerequisites.
Role of Activity in a Course:
It replaces the lab/HW component of the class for the last five weeks of the semester.
Data, Tools and Logistics
The better your remote sensing software, the easier the project will be!
Sufficient computers and software licenses for students. Sufficient data storage space and student access.
I want to know if students can select intelligent ways to use remote sensing data to address an interesting, socially important problem. They need to know how to select appropriate data types for the questions they pursue and learn effective techniques for processing and analyzing them. They need to be able to communicate their results to a lay audience (target audience is a high school science class)
Final slides generated over many iterations, along with their use in a local high school science class, illustrate qualitatively that the learning objectives are being met.
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.