Depth estimations in uncharted rivers would enable safe navigation and flood hazard predictions. Measurements of field-scale flows are required for accurately understanding, modeling and predicting the dynamics in riverine and marine environments. Techniques have been developed to accurately capture the field-scale flow measurements for both typical and hazardous flow conditions. For non-hazardous flows (e.g., non-debris flows or flows without large sediment loads), there are several standard techniques for measuring discharge such as turbine flowmeters and ultrasonic profilers. While ultrasonic meters have been developed to provide precise flow measurements, these meters require substantial installation infrastructure and cannot be rapidly placed for time-critical deployments. In addition, the meters cannot be used in hazardous flow conditions (e.g., violent mudslides, flash floods, or debris flows) due to the high risk of damage to any sensors placed in the flow. Also, the range of operation of such equipment (acoustic clarity in the case of ultrasonic profilers) is not designed for such hazardous events. For such extreme conditions, remote sensing flow measurement techniques are required. Particle Image Velocimetry (PIV) is a widely-use, non-contact, image processing laboratory technique which commonly utilizes cross-correlation of consecutive images via Finite Fourier Transform (FFT). Large Scale Particle Image Velocimetry (LSPIV) extends the laboratory technique using FFT to correlate image pairs in field measurements.
LSPIV typically consists of applying the traditional image correlation via Fast Fourier Transform algorithm used in PIV to field applications for measuring surface water flows. PIV itself is a laboratory technique and requires seeding material (e.g., olive oil mist for air flows and glass spheres for water flows) to create a random speckle pattern in the images so that image subregions can be tracked. The cross-correlation of images can be summarized as the process of taking fixed-sized subregions from an image and using it as a template to find the subregions which are most similar in another image.