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Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change
Author(s) -
Boothroyd Richard J.,
Williams Richard D.,
Hoey Trevor B.,
Barrett Brian,
Prasojo Octria A.
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.413
H-Index - 24
ISSN - 2049-1948
DOI - 10.1002/wat2.1496
Subject(s) - fluvial , geospatial analysis , cloud computing , channel (broadcasting) , downscaling , earth observation , workflow , remote sensing , earth science , multispectral image , computer science , big data , geology , geomorphology , climate change , data mining , database , satellite , engineering , oceanography , structural basin , aerospace engineering , operating system , computer network
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. This article is categorized under: Science of Water