The Extraction of Tidal Frontlines and Analysis of Their Instabilities Using the GIS Approach

A lot of short videos were taken in Connecticut River discharges, including tracking videos and static ones. It should be noted that the area depicted in the videos does not contain any land surface, it contains only the unstable water surface, so only the camera and GPS parameters have been used for the rectification.

In this research several static videos (30 frames per second) have been processed. The five second time period has been chosen as the most reasonable one. After georeferencing, Maximum Likelihood Classification has been applied to extract the frontline. The frontlines have been adjusted applying median filtering in Python and Smoothing tool (Polynomial Approximation with Exponential Kernel) of ArcGIS. Also, and the polynomial line has been created using lowpass filtering in Python.  The nearest distances between polynomial lines and frontlines have been used as amplitudes. Using the relationship between the amplitudes and the distance of the frontline, and choosing five second time period, the cross-correlation coefficient, lag, and front speed have been estimated for each image and for the series of images.

The novelty of this method is that it gives a very precise and visually elegant identification of the frontline. The GIS approach allows users to visually (using the map) estimate the process, it can also facilitate and speed up the mapping and analysis of unstable surfaces.

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