Methods in spatial and environmental biodiversity data analysis (EEB 713)
Taught by Walter Jetz, Giuseppe Amatulli, Petr Keil, Adam M. Wilson
Over the past few decades there has been an explosion of information for environmental and spatial biodiversity research. This “big data” now allows us to address a number of old and new important questions with unprecedented rigor and generality. Leveraging these new data streams requires new tools and increasingly complex workflows. This course provides an introduction to command line and scripting routines using open-source software and the Linux environment. Students also learn about multicore computation on a local computer, cluster computation via remote servers, and environmental analyses in the Google cloud via Google Earth Engine (GEE). Scripting knowledge is developed step-by-step during the course, emphasizing the concatenation process of the different software. The course has three sections: (1) methods and tools (Linux, command line scripting, GRASS, GEE, advanced R for spatial/environmental data); (2) example “big” datasets (environmental, remote sensing, biodiversity) and their analysis; and (3) example questions, data integration, and modeling (data-model dichotomy, Bayesian approaches, modeling uncertainty). Working through case studies from forestry, species distribution modeling, biodiversity, conservation, and remote-sensing image processing, participants gain a basic knowledge of the variety of open-source tools that are available for geographic information system (GIS), remote sensing (RS), and modeling. The target audience is students and postdocs with an interest in advancing their data analysis and modeling skill set.
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