Abstract
AbstractCloud‐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
Highlights
Remote sensing is transforming what we map, measure, and analyze in fluvial geomorphology (Marcus & Fonstad, 2010), helping transform the field from a data poor to a data-rich science (Church, 2010)
Drawing parallels with the perspective of Millington and Townshend (1987), who argued that early applications of satellite remote sensing in geomorphology lagged behind those of most other disciplines, we suggest a similar situation has arisen for applications of Google Earth Engine (GEE) in fluvial geomorphology
We describe the flows of big geospatial data that are openly accessible to fluvial geomorphologists, explore the opportunities to look beyond the water toward the wider dynamics of fluvial systems and critically examine the implications for geomorphic theory
Summary
Remote sensing is transforming what we map, measure, and analyze in fluvial geomorphology (Marcus & Fonstad, 2010), helping transform the field from a data poor to a data-rich science (Church, 2010). Satellite imagery analysis has often been complemented by analyses of other data sets, including historical mapping, aerial photography, topography, and field survey (e.g., Surian et al, 2016) Combined, these data have improved the understanding of river planform classification, planform evolution, bar morphodynamics, and planimetric form/process interactions over various spatiotemporal scales Technological advances in digital infrastructure, increased computing power, and data storage capabilities have given rise to cloud-based computing platforms, providing on-demand access to high-performance computing facilities without the need to own and maintain physical hardware (Sudmanns et al, 2020). This could potentially revolutionize remote sensing applications in geomorphology. 1972–2014 Interannual to decadal (e.g., 7 Large river confluence epochs in 40 years) dynamics and conceptual classification
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
