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Remote Sensing of Plants and Topography in R (Project EDDIE)

Author(s): Kyla M. Dahlin

Michigan State University

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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.

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Version 1.0 - published on 09 Apr 2021 doi:10.25334/9MCH-JE87 - cite this


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:

  • Test whether plant growth (greenness and height) is driven more by elevation, slope, or aspect.
  • Investigate an ecological question at both local and continental scales.
  • Analyze spatial raster data in R, moving between making maps and doing non-spatial statistical tests.
  • Consider macroscale (continental scale) patterns of relationships between topography and vegetation.

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