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Tree Biomass and Phenology

Author(s): Noam Tomoya Altman-Kurosaki1, Emily Rose Brown2, Sarah Roney1, Emily Weigel1

1. Georgia Institute of Technology 2. Florida Gulf Coast University

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Summary:
This lesson centers around understanding the connection of tree biomass and phenology with climate change. It uses both swirl and LearnR lessons in the R programming language to help students make sense of a larger dataset.

Description

This lesson centers around understanding the connection of tree biomass and phenology with climate change, visualizing data to find patterns in the data, and understanding how a priori knowledge about where data comes from can assist when fitting the data to a curve. The prelab swirl lesson goes step-by-step to show students how to do basic data and data frame manipulations as they work an example dataset of tree size data based on the measurements they will take during the class time. In class, the first LearnR lesson (Data Visualization) guides students to build on their knowledge to visualize the phenology data to look for patterns. In the second LearnR lesson (Time Series), the students practice fitting different curves to the tree phenology dataset.

Although this lesson could be adapted for any set of tree biomass and phenology information, this lesson uses data collected by students from trees on the campus of Georgia Tech, a Level II Arboretum (http://carto.gis.gatech.edu/gttree-arboretum/). The csv file provided for the prelab swirl lesson includes data from August 2012 through March 2016 and the data included in the LearnR lessons spans August 2017 to March 2020.

Learning objectives:

  1. Define basic tree measurements and changes expected in tree phenology with season
  1. Practice basic data manipulation in R
  1. Practice tree measurements and biomass calculations and connect these measurements to climate change
  1. Practice coding elements necessary to visualize datasets; generate hypothesizes based on patterns found in the data
  1. Practice coding elements necessary to fit different curves to data; use and apply a priori knowledge to choose the best method for fitting a curve

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