Using remote sensing and climate model data in Earth Engine with Dr. Nicholas Kohler This workshop is an introduction to the ‘big data’ analysis capabilities of Google Earth Engine via the Python API, including accessing remote sensing and earth model data collections, calculating spectral indices, and sampling environmental variables for areas of interest. Two examples are used in the workshop. One examines the legacy of redlining in the United States - using the 'area descriptions' created by the US 'Home Owners' Loan Corporation' (HOLC) in the late 1930s (the origin of the term ‘redlining’). Based on measures such as land surface temperature, vegetation greenness, and seasonal temperature variation, modern day environmental conditions are compared across differently graded neighborhoods of Portland, Oregon. The second example uses Sentinel-2 data to estimate the impacts of the 2020 Holiday Farm Fire and post-fire response on the forest landscape of the McKenzie River, Oregon.
- Prerequisites: Students need an Earth engine account and provide their own laptop.
- Instructor Information: Dr. Kohler teaches courses in remote sensing, geospatial data science, and human-environment relations at the University of Oregon. His research concerns natural and cultural world heritage management and conservation, land use change related to economic development, environmental monitoring with remotely sensed data, crowdsourced database development and mapping, and the geography of outdoor adventure sports.
