Syllabus: Environmental Monitoring | Jonathan Perry-Houts
Jonathan Perry-Houts

Syllabus: Environmental Monitoring

I wrote this syllabus for a hypothetical undergraduate class on digital data collection via autonomous remote sensors. I would like to eventually expand this into a real course, but for now it's untested. The topics mentioned here would be really valuable skills to take away from an undergraduate education, but very few colleges seem to offer anything like this.

If you use any of this in your own course design, I'd be interested to hear feedback on it!

Environmental Monitoring

4 Credits. Meets 2x/week
Instructor: Jonathan Perry-Houts
Office Hours: TBA

Course Description
In this course we will explore techniques for collecting and analyzing data from remote environmental sensors. We will cover the technical skills needed to collect, retrieve, store, and analyze geospatial data. We will apply the methods discussed in class to collect and analyze a few months of environmental data from a river tributary near campus.

Course Objectives
This course is intended to familiarize students with techniques for collecting digital field data. By the end of the term, students will be able to:

Students should have completed the introductory sequence in any one of the physical sciences.

This course covers topics from many physical sciences, and therefore students will benefit from background knowledge in many different fields of study. Some background in Geology or Geography is highly recommended, but not required. Contact me prior to registering if you're concerned about prerequisites.

Course Materials
All students must have a waterproof notebook, usually available through the book store, or online. There is no required text book, but students will need to access academic articles via library resources during the term. Readings and homework will require access to a computer with an internet connection. There are two one-day field trips, which will require seasonally appropriate clothing, and sturdy closed-toe shoes or boots.

I recommend the following texts as further reading, but they are not required for successful completion of this course:

Feel free to contact me with concerns about access to any of these resources. We can make accommodations as necessary.

Term projects
There are two projects to be completed over the course of the term, in addition to the weekly homework assignments.

Project 1: Individually, read and report on a peer-reviewed paper that discusses field-based digital data collection. Write a short (~one page) summary of the methods. Discuss the expected accuracy and potential pitfalls of the particular data collection techniques. Prepare a ~10 minute presentation, to be given in class during week 5.

Project 2: In groups, identify and test a hypothesis by collecting and analyzing digital field data. This may build on our class field data collection, or require independent measurements. I will provide suggestions for possible project topics in class, but I encourage as much creativity as possible. Each individual will write a project report. Project reports have no length requirement, but aim for around five pages, double-spaced. Groups will each present a ~20 minute slideshow on their project at the end of the term in lieu of a final exam.

Expectations and Grading
Students are expected to attend every lecture period. There will be in-class activities almost every day, for which you will forfeit points if not in attendance. I will do my best to make rubrics available ahead of time for all assignments to make my grading methods as transparent as possible. Grades will be divided as follows:

50% In-class participation, including 10% for each of the two in-class presentations.
20% Weekly homework assignments.
5% Paper summary.
25% Term project report.

Open inquiry and freedom of expression are fundamental to higher education. As an institution, we are committed to encouraging exploration of divergent perspectives and diverse identities. For that reason, and many others, all students are expected to respect one another. Harassment or bullying will not be tolerated, and will lead to disciplinary action to the extent permitted by institutional policies.

Academic Integrity
All students are expected to complete assignments in a manner consistent with the spirit in which they were assigned. Group work is encouraged on most assignments, and I will make it clear when it is not allowed on a per-assignment basis. Individual submissions based on group assignments are expected to be each individual's own work. Sources for quotations, paraphrases, and very specific ideas are to be acknowledged (style of attribution doesn't matter to me — just be consistent). Academic dishonesty will be dealt with following all applicable institutional policies.

Students with Disabilities
I strive wherever possible to make my courses inclusive of all students. If there are aspects of the instruction or design of this course that create barriers to your participation, please notify me as soon as possible. I always encourage you to discuss concerns with me during office hours, so that we can strategize ways for you to get the most out of the course.

Additionally, I encourage you to take advantage of the resources on campus for accessible education. They can provide services beyond this particular course to help you get the most of your college experience.

Tentative Course Outline (subject to change, depending on student interests)

Week Topics
1 Intro, course overview, field trip prep  Field trip: Install sensor(s) in the field
2 Collection methods: stationed, vehicle mounted, aerial, drone, satellite Data recovery approaches: campaign vs real-time telemetry Discuss term projects. Form groups, and brainstorm project ideas
3 Types of available sensors Types of data loggers Field data storage and power sources Principles of circuits, electronics, and sensors Analog to digital conversion methods
4 Wireless communication Mesh networking Network security
5 Report presentations
6 Processing tools (Python, GIS, Paraview, etc) Working with data formats (ASCII, numpy, SQL, etc) Metadata Archival
7 Assessing data quality Filters Aliasing Artifacts Error propagation
8 Public archives (NSF, etc) Citizen science initiatives Data access with GeoMapApp,Google Earth,etc. Lab: Practice accessing archives.
9 Field trip: collect data from the deployed sensor(s) Lab: Data import and manipulation with Python
10 Group presentations
Written on September 9th, 2018 by JPH