Potential of time‐lapse photography for identifying saturation area dynamics on agricultural hillslopes

Mapping saturation areas during rainfall events is important for understanding the dynamics of overland flow. In this study, we evaluate the potential of high temporal resolution time-lapse photography for mapping the dynamics of saturation areas (i.e., areas where water is visually ponding on the surface) on the hillslope scale during natural rainfall. We take 1 image per minute over a 100 × 15 m2 depression area on an agricultural field in the Hydrological Open Air Laboratory, Austria. The images are georectified and classified by an automated procedure, using grey intensity as a threshold to identify saturation area. The optimum threshold T is obtained by comparing saturation areas from the automated analysis with the manual analysis of 149 images. T is found to be highly correlated with an image brightness characteristic defined as the greyscale image histogram mode M (Pearson correlation r = 0.91). We estimate T as T = M + C where C is a calibration parameter assumed to be constant during each event. The automated procedure estimates the total saturation area close to the manual analysis with mean normalized root mean square error of 9% and 21% if C is calibrated for each event and taken constant for all events, respectively. The spatial patterns of saturation are estimated with a geometric mean accuracy index of 94% as compared to the manual analysis of the same photos. The patterns are tested against field observations for one date as a preliminary demonstration, which yields a root mean square error of the shortest distance between the measured boundary points and the automatically classified boundary as 23 cm. The usefulness of the patterns is illustrated by exploring run-off generation processes of an example event. Overall, the proposed classification method based on grey intensity is found to process images with highly varying brightnesses well. It is more efficient than the manual tracing for a large number of images, which allows the exploration of surface flow processes at high temporal resolution.

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