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This NASA funded project integrates key remote sensing variables with continental scale ecological data to provide broadly accessible ecological forecasts to determine which species and communities are most vulnerable to climate change and to forecast responses of entire communities.

This github site includes tutorials for two packages GJAM and geedataextract

  • GJAM is an R package that analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. The tutorial in the folder tutorialsGJAM describes how to fit a GJAM and interpret results in R. For even more information on how to use GJAM, see the full GJAM vignette

  • geedataextract is a python package for processing and downloading environmental and remote sensing data from Google Earth Engine, using the GEE python API. A tutorial in jupyter notebooks is in the folder tutorialsGEEDATAEXTRACT.

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