In shallow flow conditions, turbulence effects appear on a water surface as a form of irregularity of surface shape composed of a large number of fluctuating ripples. The intensity of such a fluctuation increases with the Froude number and also with the Reynolds number as can be observed in flooding river flow. In such a flow condition, surface irregularities are viewed as surface features or textures moving with the flow. Although there has been a discussion in terms of the traceability of surface features, the advection speed of surface features agrees well with the surface velocity from a practical point of view. Based on the assumption about the traceability of surface features, image-based techniques have been developed in the past decades. The space-time image velocimetry (STIV) is one of those techniques developed by Fujita et al. (Int J River Basin Man 5(2):105-114, 2007), with success of measuring river surface velocity distributions without seeding the flow. However, there is still some room for improvement in determining accurate surface velocity from a space-time image (STI) used in STIV. For that purpose, a novel technique was developed that utilizes the two dimensional auto-correlation function of the image intensity in an STI together with quality indices of STI. The performance of the new technique was verified using synthetic images as well as its application to the measurement of snowmelt flood.

Due to the remarkable development of unmanned aerial vehicle (UAV) in recent years, its application in river engineering increases widely mainly for the measurement of ground topography such as by the technique Structure from Motion (SfM) using a series of high-resolution static images. However, although UAV usually installed a high density video camera, the use of the movie is limited just for watching and observing the geometrical feature of the ground. In the light of such a present status, the authors have developed an aerial space-time image velocimetry (STIV) technique to measure streamwise river surface velocity distributions. However, as STIV is insensitive to the change of flow direction, the aerial space-time volume velocimetry (STVV) technique, which is an extension of STIV, was developed in this research. STVV examines the change of volumetric texture within a space-time volume (STV) instead of examining the change of image intensity on a line segment as in STIV. The performance of STVV was investigated during the measurement of snowmelt flood of the Shinano River by comparing it with those obtained by the other techniques such as STIV, LSPIV and ADCP. It was made clear the aerial STVV has a great advantage over the existing image-based techniques.

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In recent years, the so-called unmanned air vehicles (UAV), remotely controlled airplanes or multicopters, have become available for various civil engineering purposes. In the field of river engineering, although they have been used to investigate the area of vegetation zone or other objectives, measurement of river flow has not been conducted by using an UAV, probably due to the difficulty of image stabilization. In this research, we developed a method to measure river surface velocity distributions by using videotaped images. The specific feature of the method is the introduction of a high-accurate and efficient image stabilization in order to apply the space-time image velocimetry (STIV) to airborne images. The developed method was applied to investigate snowmelt flood of the Uono River, with success of measuring twodimensional velocity distributions for a river reach of about four hundred meters. The obtained results were used to compare with those measured by ADCP or STIV using oblique images from a riverbank.

The heavy rain disaster in the Kinugawa River basin that occurred along with the passage of the Typhoon 18 caused the embankment destruction in the middle reach of the river on September 10, 2015. Due to the overflow, the houses in the vicinity of the embankment collapsed, causing a flood inundation spreading over a wide area. Because the embankment breakwater occurred during the daytime, the state of the inundating flow was recorded from various angles by media helicopters or drones. In this study, we developed a method to extract quantitative flow information from a helicopter video image in which the shooting position and angles are changed one after another, because it was taken in emergency. In the analysis, the images were orthorectified after stabilizing the images, from which surface velocity distributions were measured by image-based technique such as the large-scale particle image velocimetry (LSPIV) or the space-time image velocimetry (STIV). As a result, the time change of water entering from the broken embankment and the total inundated water volume during the disaster were estimated. In addition, two-dimensional surface velocity distributions were analysed to show the spreading of inundated flow.