Using the Project EDDIE Remote Sensing of Plants and Topography in R module in BIOL 533 – GIS Applications in Landscape Ecology
About this Course
BIOL 533 – GIS Applications in Landscape Ecology
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HideEDDIE Module(s) Adopted and/or Adapted
Asynchronous Implementation of an Ecological Project Incorporating Remote Sensing GIS Analysis
Landscape ecology provides a perfect platform for how to use Geographic Information Systems (GIS) to better understand ecological topics. The implementation of the Project EDDIE module into this course provided students with an opportunity to gain practice with an accessible GIS analytical tool. Furthermore, it allowed students to explore ecological topics of vegetation patch metrics using real data, and utilize statistical methods to evaluate correlations between variables. Several students expressed their appreciation of exploring landscape ecology through use of remotely sensed data. Students selected two National Ecological Observatory Network (NEON) sites and predicted what they expected to see based on the characteristics of the two ecosystems. They performed their analysis with R using RStudio Cloud, which is highly recommended for use with asynchronous, remote classes due to the ability to minimize installation issues and review work and assist with errors easily. Students had minimal problems in navigating the materials, proving that the use of accessible programs such as R for GIS analysis are intuitive and well received.
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Relationship of EDDIE Module(s) to my Course
The EDDIE module fit well with my course as we were discussing patch metrics and use of remotely sensed data and its analysis in ArcGIS. Students were asked to prepare by going through the first two activities of the module, prior to a self-guided exploration of the data for a course project.
Teaching Details
I utilized the entire first two activities and adapted the activity sheet in order to help clarify questions and concepts. I took inspiration from the final module to develop a comparative landscape ecology project where they were asked to research and compare two NEON sites.
Adaption Materials
Project Description (Acrobat (PDF) 381kB May21 21)
Project_Description.docx (Microsoft Word 2007 (.docx) 317kB May28 21)
Project Rubric (Acrobat (PDF) 137kB May28 21)
Project_Rubric.docx (Microsoft Word 2007 (.docx) 30kB May28 21)
Modified Lab Activity questions (Microsoft Word 2007 (.docx) 198kB May21 21)
Project_Example.mp4 (MP4 Video 11.8MB May28 21)
How did the activity go?
I enjoyed the implementation of this module as it provided a streamlined method for engaging students in real data and exposing them to an accessible, analytical tool for assessing remotely sensed data. For this project, I decided to use the first two activities as is to introduce the techniques for project analysis, then took inspiration from the third activity to develop a comparative landscape ecology project that allowed students the autonomy to explore data. For the first two activities of the original model, I modified the activity sheet in order to clarify questions as they struggled with the text used in the questioning. This seemed to help them better understand correlation analysis as well as how to evaluate the data. A second week of activities involved their selection of a second NEON site to compare to the ones from the first activities. All elements of this project utilized the R script available from the original model. Students seemed to have limited issues with the R code and data for this project, likely due to the use of RStudio Cloud, which enabled easy of review and assistance. The goal in this was to have them compare the ecosystems and predict what types of correlations they might see from a second set of data. They presented their analysis and assessment through the creation of a short, narrated video. This aspect provided an active learning means of doing science and sharing their understanding of the materials. It worked really well based on their submissions (example provided) and adherence to the rubric!
Students mostly expressed interest in being able to work with data and have some autonomy to explore on their own. They appreciated the balance of guidance in this module as well as the accessible data to select from. They struggled most with understanding how to compare two regions (e.g. mountains vs. desert) in terms of what they expected to see in the correlation. I believe the correlation itself took a bit of time to understand as well. Additionally, while they struggled a bit with importing new data into the code, this was mitigated through the RStudio Cloud platform as an instructor can go into their file and view the issues in order to help resolve. As an asynchronous course that does not benefit from in person troubleshooting, this was a critical element in the success of this project. I additionally used a screen capture tool as a means of them communicating their problems with the data. Overall they reacted positively to the activity despite the fact that it involved introducing a new tool that was unfamiliar to most of them.
Future Use
