Digital Surveys Reveal the Complexity of Rivers and Coastlines

by | Dec 31, 2018

Drones and three-dimensional modelling methods provide us with the most detailed picture yet of some of the most dynamic environments on the planet.

Rivers and coastlines are constantly changing in response to storms and floods. With large population centers often built in vulnerable locations close to these natural features, understanding and predicting the changing character of rivers and coasts has never been more important.

Fortunately, help is at hand. Recent advances in digital survey technologies have revolutionized our ability to map and model the environment. A new WIREs Water article by Jonathan Carrivick and Mark Smith of the University of Leeds, UK, shows clearly that river and coastal scientists have been at the forefront of these advances.

Images from drones can be coupled with Structure from Motion workflows to yield rich datasets and improve our process understanding of these dynamic environments. For example, three-dimensional modelling can now be performed with relative ease, and even used to map sediment movement, quantify river channel or coastline changes and map ecological suitability. We can now evaluate the impact of management decisions with much greater detail, as seen by the use of drones and Structure from Motion, to evaluate the reintroduction of Eurasian beavers and install large woody dams into river systems.

Advances in computing power mean that we can now observe and model rivers and coastlines over large scales, with some surveys covering tens of kilometers. Seeing through water remains a challenge; yet some scientists are developing new ways of doing exactly that, while others head underwater to obtain three dimension models of coral reefs, shipwrecks, and other underwater archaeological structures.

Overall, this body of work highlights the complexity of rapidly changing rivers and coastlines, and enables a more detailed understanding of the processes driving those changes, far beyond that expected from classic conceptual models.


Kindly contributed by the authors.

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