Google Earth as a data source for investigating river forms and processes: Discriminating river types using form-based process indicators

Google Earth provides potential for exploiting an enormous reservoir of freely-available remotely sensed data to support river science and management. In this paper, we consider how the platform can support investigation of river physical forms and processes by developing an empirically-based reach-scale classification of semi-natural European single thread to transitional rivers. Using strict reach and image selection criteria, we identified 194 reaches of 68 rivers for analysis. Measurements of channel dimensions and counts of in-channel and floodplain features, standardised for reach length and channel width where necessary, were used to derive a series of geomorphologically-relevant process indicators. A suite of multivariate analyses were then applied to this data set, resulting in the discrimination of five river types: laterally stable, laterally active sinuous-meandering; transitional (near-braided); bedrock; and cascade/step dominated. The results of the classification were tested by examining the characteristics and distribution of the river classes in relation to known independent controls of river form including reach-scale energy and valley confinement conditions. Our results show that if methods of data extraction are carefully developed, physically meaningful river reach discrimination can be achieved using Google Earth. Although there are limits to the types of information that can be extracted such that field investigations cannot always be avoided, there is enormous potential to mine Google Earth across different space and time scales, supporting the assembly of large, reliable data sets relevant to river forms and processes in a very cost-effective way. © 2019 John Wiley & Sons, Ltd.