Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21948
Title: Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change
Authors: Boothroyd, RJ
Williams, RD
Hoey, TB
Barrett, B
Prasojo, OA
Keywords: cloud-based computing;multitemporal;planform analysis;remote sensing;river science
Issue Date: 1-Dec-2020
Publisher: Wiley
Citation: Boothroyd, RJ, Williams, RD, Hoey, TB, Barrett, B, Prasojo, OA. Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change. WIREs Water. 2020;e21496 (27 pp.). doi: 10.1002/wat2.1496.
Abstract: © 2020 The Authors. Cloud-based computing, access to big geospatial data, and virtualization, whereby users are freed from computational hardware and data management logistics, could revolutionize remote sensing applications in fluvial geomorphology. Analysis of multitemporal, multispectral satellite imagery has provided fundamental geomorphic insight into the planimetric form and dynamics of large river systems, but information derived from these applications has largely been used to test existing concepts in fluvial geomorphology, rather than for generating new concepts or theories. Traditional approaches (i.e., desktop computing) have restricted the spatial scales and temporal resolutions of planimetric river channel change analyses. Google Earth Engine (GEE), a cloud-based computing platform for planetary-scale geospatial analyses, offers the opportunity to relieve these spatiotemporal restrictions. We summarize the big geospatial data flows available to fluvial geomorphologists within the GEE data catalog, focus on approaches to look beyond mapping wet channel extents and instead map the wider riverscape (i.e., water, sediment, vegetation) and its dynamics, and explore the unprecedented spatiotemporal scales over which GEE analyses can be applied. We share a demonstration workflow to extract active river channel masks from a section of the Cagayan River (Luzon, Philippines) then quantify centerline migration rates from multitemporal data. By enabling fluvial geomorphologists to take their algorithms to petabytes worth of data, GEE is transformative in enabling deterministic science at scales defined by the user and determined by the phenomena of interest. Equally as important, GEE offers a mechanism for promoting a cultural shift toward open science, through the democratization of access and sharing of reproducible code.
URI: https://bura.brunel.ac.uk/handle/2438/21948
DOI: https://doi.org/10.1002/wat2.1496
ISSN: 2049-1948
Appears in Collections:Dept of Mechanical Aerospace and Civil Engineering Research Papers

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