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.