Observing river stages using unmanned aerial vehicles

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Abstract

We elaborated a new method for observing water surface areas and river stages using unmanned aerial vehicles (UAVs). It is based on processing multitemporal five orthophotomaps produced from the UAV-taken visible light images of nine sites of the river, acquired with a sufficient overlap in each part. Water surface areas are calculated in the first place, and subsequently expressed as fractions of total areas of water-covered terrain at a given site of the river recorded on five dates. The logarithms of the fractions are later calculated, producing five samples, each consisted of nine elements. In order to detect statistically significant increments of water surface areas between two orthophotomaps, we apply the asymptotic and bootstrapped versions of the Student's test, preceded by other tests that aim to check model assumptions. The procedure is applied to five orthophotomaps covering nine sites of the Åšcinawka river (south-western (SW) Poland). The data have been acquired during the experimental campaign, at which flight settings were kept unchanged over nearly 3 years (2012-2014). We have found that it is possible to detect transitions between water surface areas associated with all characteristic water levels (low, mean, intermediate and high stages). In addition, we infer that the identified transitions hold for characteristic river stages as well. In the experiment we detected all increments of water level: (1) from low stages to mean, intermediate and high stages; (2) from mean stages to intermediate and high stages; and (3) from intermediate stages to high stages. Potential applications of the elaborated method include verification of hydrodynamic models and the associated predictions of high flows as well as monitoring water levels of rivers in ungauged basins.

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CITATION STYLE

APA

Niedzielski, T., Witek, M., & Spallek, W. (2016). Observing river stages using unmanned aerial vehicles. Hydrology and Earth System Sciences, 20(8), 3193–3205. https://doi.org/10.5194/hess-20-3193-2016

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