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PhenoCam Lab for BIOL 368
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AMF_US-Ha1_BASE_HR_10-1.csv
BIOL 368_Week 11 Lab_ Phenocam and NEE.R
Lab-PhenoCam in R.pdf
README.md

README.md

PhenoCam Lab for BIOL 368 Plant Ecophysiology

Lab materials for an activity based around PhenoCams for BIOL_368 Plant Ecophysiology at Macalester College (Fall 2018)

The week prior to this lab activity, we discussed PhenoCams and the PhenoCam network (https://phenocam.sr.unh.edu/webcam/) and how data from them can be used as a proxy for tracking plant leaf development.

As a class, we read and discussed Wu et al. (2016) in Science (DOI: 10.1126/science.aad5068) and Richardson et al. (2018) in Nature (https://doi.org/10.1038/s41586-018-0399-1) and its accompanying blog write up (https://natureecoevocommunity.nature.com/users/82876-andrew-richardson/posts/37522-phenocams-have-an-eye-on-the-seasons-at-the-spruce-ecosystem-warming-experiment)

Included in this activity are: a pdf file of description of the lab, different elements to the lab, and prompts to guide student responses for hand-in; a csv file of Ameriflux data, and R script for lab activities for plotting with ggplot.

The lab is broken into 3 parts - Part I is an exploration of 'Phenocam Explorer' http://explore.phenocam.us/, starting with Harvard Forest. Student examine start of season and end of season dates, as well as overlay MODIS EVI and NDVI data (recently also discussed in class).

Part II is a practice in R/Rstudio using the script included here. Students practice using phenocamr to download data, again using the Harvard Forest data set. After producing plots in ggplot, they are challenged to pick a new phenocam location, and make the same plots, comparing how seasonality of greenness (here mean_gcc) changes with local climate and vegetation type. The Rcode for Parts II-III is posted here.

Part III continues with Harvard Forest data (I had to explain why it was all Harvard based so my students didn't think I was some elitist) downloaded from AmeriFlux (AMF) (http://ameriflux.lbl.gov/). I truncated the AMF data to start at 2008 to align with the early phenocam data. In Part III, we mainly focused on curating the data (removing -9999) and making a couple ggplots to look at overall trends. The truncated data is posted here in a csv file.

In summary, this lab allowed students to explore multiple datasets through different means, with the objective of practicing ggplot and being comfortable using new R packages with different functionalities (ie phenocamr).

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