AbstractAn imaging‐based automated large‐scale particle image velocimetry (LSPIV) system for flash flood monitoring is developed and deployed in a mountainous stream in the Longchi Catchment, Chengdu, China. This system is built from a low‐cost Raspberry Pi board‐level computer with a camera module, which can acquire continuous images/videos automatically at programmed intervals. The minimum quadratic difference algorithm tracks surface patterns as flow tracers to estimate the distribution of surface velocities. Meanwhile, a stereo imaging‐based ‘virtual pole’ method has been developed to reconstruct the three‐dimensional topography with a stereo digital camera, and a cross‐sectional bathymetry has been generated without manual surveying. The varying water stage and water surface gradient, which are critical parameters that affect image rectification and surface velocity measurements, can also be directly resolved by applying the two imaging modules together. Discharge can then be estimated with the velocity–area method through selected cross sections.A flash flood that occurred between 24 July 2014 and 25 July 2014 is selected for analysis. The water surface level reconstructed from image processing was validated with marked water levels, and a good agreement was found with a root mean square error of 3.7 cm. The discharge recorded during the flood recession process ranged from approximately 3.5 to 27 m3/s. The rating curve obtained can be well described by a power function, and the linear regression suggested a Manning's n roughness coefficient of 0.18 of one specific cross section. Some limitations of the presented large‐scale particle image velocimetry system are also put forward, and possible solutions are provided for future improvements. With these proposed upgrades, the system can provide valuable datasets of flash floods in steep mountainous streams, which are critically needed for improving our understanding and modelling of many hydrological processes associated with flood generation, propagation and erosion, as well as for real‐time forecasting. Copyright © 2016 John Wiley & Sons, Ltd.