Data and code for: Remote sensing of riverbank migration using particle image velocimetry

Roughly three billion people worldwide live along large rivers and rely upon them for food, water, transport, and energy. To ensure the safety and sustainability of these riverside communities, it is important that we understand how rivers migrate over time. Satellite missions like NASA Landsat have captured millions of images of migrating rivers worldwide for more than thirty years—more images than can be feasibly mapped manually. These data and codes accompany the manuscript "Remote Sensing of Riverbank Migration using Particle Image Velocimetry" by Austin J. Chadwick, Evan Greenberg, and Vamsi Ganti. In this manuscript, we build on previous work and present a method to automatically map riverbank migration from satellite images using a technique called particle image velocimetry (PIV). We apply PIV to Landsat-image time series for 21 example rivers and show PIV results are efficient, reproducible, and accurate compared with existing automatic techniques. Importantly, unlike existing techniques, the PIV method directly accounts for the inherent uncertainty in migration-rate measurements that arises when identifying riverbanks from satellite imagery. Furthermore, PIV is equally applicable to all kinds of rivers (e.g., meandering, braided), opening up new opportunities to investigate the diversity of rivers and their responses to climate change and human activities in our rapidly changing world.