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.

These data and codes are used to reproduce the analysis and figures from the associated manuscript. 

Imagery data was collected from NASA Landsat via Google Earth Engine, using Python codes previously published on Zenodo (https://doi.org/10.5281/zenodo.7747389). In these codes, images for a given year were combined into median annual composites, and these composites were analyzed to produce binary images (masks) representing the river channel. For a given image, there are sixteen binary images representing distinct but defensible realizations of the river channel network, which were used to quantify uncertainty in riverbank position and propagate this uncertainty into river mobility. See the associated manuscript for details and references. 

River mobility data were collected using a method based on particle image velocimetry (PIV), developed in the main manuscript. Originally designed to track particles in a moving fluid, the PIV algorithm was here used to quantify riverbank migration by correlating numerous sub-images nested within each binary image pair. PIV-derived vector fields of riverbank migration were produced for all sixteen realizations of each studied river reach. Then, these data were post-processed using circular statistics to generate a mean vector field and associated uncertainty across realizations. See the associated manuscript for details and references.

Finally, we include codes used to plot this data for figures in the associated manuscript. 

Refer to the README.md and the DataLog_031623_1.xlsx files for usage notes and details.