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Remote Sensing of Plants and Topography in R (Project EDDIE) for Advanced GIS class

Author(s): Kristen Brubaker

Hobart and William Smith Colleges

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Students will explore different possible abiotic drivers of plant growth, defined as greenness and height. In the final step, students will analyze data from around the United States and consider macroscale patterns of vegetation controls.

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 20 May 2021 doi:10.25334/GP5E-SZ60 - cite this

Adapted from: Remote Sensing of Plants and Topography in R (Project EDDIE) v 1.0


Project EDDIE Environmental Data-Driven Inquiry & Exploration) is a community effort aimed at developing teaching resources and instructors that address quantitative reasoning and scientific concepts using open inquiry of publicly available data. Project EDDIE modules are designed with an A-B-C structure to make them flexible and adaptable to a range of student levels and course structures.

Successful students will produce a series of figures in R representing different vegetation and landscape variables, then compare these via scatterplots and regression. They will do this first with a single data set, then on a different data set of their choosing. Students will understand how to quantify the influence of topography on vegetation across multiple biomes. Students will practice statistical methods (regression, graphing) and develop higher-order thinking skills including hypothesis generation and synthesis. The final goal is for students to interpret the large scale spatial patterns of correlations, attributing their variation to geographical drivers like latitude, biome, or geologic history.

Overall Learning Goals are for students to:

  • I want students to gain experience and confidence in using large quantitative datasets in their analysis. I also want students to experience using data to answer a real-world scientific question, and to work through an analysis from start to finish. I would also like them to be able to use their data to design questions, and then answer those questions with data.  

    Quantitative learning objective 

    Students should gain confidence with data and use data for basic quantitative reasoning skills. Students should understand the basics of what a regression is.  

    Working with data learning objective 

    Students should learn that R is a basic analysis tool that can be used with spatial data. I also want them to learn about NEON as a data source.  

Full module details

Instructor notes are contained in a separate document. 

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