Increasing LSPIV performances by exploiting the seeding distribution index at different spatial scales

Image-based approaches for surface velocity estimations are becoming increasingly popular because of the increasing need for low-cost river flow monitoring methods. In this context, seeding characteristics and dynamics along the video footage represent one of the key variables influencing image velocimetry results. Recent studies highlight the need to identify parameter settings based on local flow conditions and environmental factors apriori, making the use of image velocimetry approaches hard to automatise for continuous monitoring. The seeding distribution index (SDI) – recently introduced by the authors – identifies the best frame window length of a video to analyse, reducing the computational loads and improving image velocimetry performance. In this work, we propose a method based on an average SDI time series threshold with noise filtering. This method was tested on three case studies in Italy and validated on one in UK, where a relatively high number of measurements is available. Following this method, we observed an error reduction of 20-39% with respect to the analysis of the full video. This beneficial effect appears even more evident when the optimisation is applied at sub-sector scales, in cases where SDI shows a marked variability along the cross-section. Finally, an empirical parameter was proposed, calibrated, and validated for practical uses to define the SDI threshold. This parameter showed relatively stable values in the different contexts where it has been applied. Application of the seeding index to image-based velocimetry for surface flow velocity estimates is likely to enhance measurement accuracy in future studies.

基于图像的表面流速估计方法因低成本河流流量监测需求的日益增长而日益受到青睐。在此背景下，视频素材中的播种特性和动力学成为影响图像速度测量结果的关键变量之一。近期研究凸显了基于当地流量条件和环境因素事先确定参数设置的重要性，这使得图像速度测量方法在连续监测中难以实现自动化。播种分布指数（SDI），由作者近期提出，可识别分析视频的最佳帧窗口长度，从而降低计算负荷并提升图像速度测量的性能。在本研究中，我们提出了一种基于平均SDI时间序列阈值的噪声滤波方法。该方法已在意大利的三项案例研究中进行测试，并在英国的一项案例研究中得到验证，其中可获得相对较多的测量数据。遵循此方法，我们观察到与分析完整视频相比，误差降低了20-39%。当优化应用于子区域尺度时，即SDI在横截面上表现出显著变异性时，这种有益效果更为明显。最后，我们提出了一种经验参数，对其进行了校准和验证，以适用于实际应用中定义SDI阈值。该参数在不同应用环境中表现出相对稳定的数值。将播种指数应用于基于图像的流速测量，有望在未来研究中提升测量精度。