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Earth Engine & R for presentation-quality contextual maps (#maptimeDavis Workshop)

January 12 @ 10:00 am - 12:00 pm

#maptimeDavis Workshop: Earth Engine & R for presentation-quality contextual maps

Description: We will combine the power of Earth Engine with the user-friendliness of R to generate beautiful, presentation-quality contextual maps. The workshop will focus on Landsat imagery, but is generalizable to other remote sensing products. Creativity is encouraged! We will use Earth Engine to access, filter, and crop data for download, and then use R to wrangle our dataset and generate beautiful, high-resolution plots.

Location: Online via Zoom. Please register for the link to participate.

Registration: Register Here

Cost: Free of charge

Learning Objectives: Attendees will learn how to access, filter, and download data using Google Earth Engine, and to process and plot raster datasets using a tidyverse approach in R.

Prerequisites: No experience necessary, but Earth Engine and R exposure are a bonus!

Software: Attendees will need to have a Google Earth Engine account prior to attending the workshop (may take a few days to process a request for a new account), and have R installed. Attendees will also need to have the following R packages installed: tidyverse, raster, sf, rgdal, RStoolbox.

Presenter: Christian John
Presenter Bio: Christian is a 4th-year PhD student in the Graduate Group in Ecology using novel remote sensing data to explore how snowmelt and plant growth drive bighorn sheep migration.

Whenever possible, #maptimeDavis Workshops will be recorded and posted on the DataLab YouTube channel. #maptimeDavis Studio will not be recorded to encourage open discussion. Weekly reminders will be sent on the Geospatial Email List.

#maptimeDavis Workshops and Studio are a product of UC Davis DataLab’s Spatial Sciences Research Cluster and are supported by the contributions and participation of our community. Please send questions to Michele Tobias mmtobias@ucdavis.edu


January 12
10:00 am - 12:00 pm
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Spatial Sciences Research Cluster