Remote Sensing as a Tool for Analysing Channel Dynamics and Geomorphic Effects of Floods

Righini, Margherita; Surian, Nicola

doi:10.1007/978-3-319-63959-8_2

Cited by 10 publications

(10 citation statements)

References 79 publications

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“…This is exemplified where river widths <90 m (three Landsat pixels) were found to be less accurate and more incomplete in the GRWL database (Allen & Pavelsky, 2018). Data sources must therefore be appropriate for the purposes to which they are to be used (Fuller, Reid, & Brierley, 2013) and an awareness of limitations to applicability in certain river settings is needed (e.g., awareness of the limited application of optical satellite imagery approaches in high energy and small headwater streams; Righini & Surian, 2018). Recent and future improvements in the spatial resolution of satellite imagery will likely increase the applicability of approaches to smaller systems (Khorram, Van der Wiele, Koch, Nelson, & Potts, 2016).…”

Section: Future Challengesmentioning

confidence: 99%

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

et al. 2020

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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

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Section: Future Challengesmentioning

confidence: 99%

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

et al. 2020

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“…Geospatial techniques have become the most frequently utilized technique for the study of diverse types of alluvial river channel planform dynamics, and their geomorphology [1,33,54]. River Channel Shifting, the geomorphological process mainly driven by the combination of bank erosion and point bar deposition over time [15] associated with the natural process of meandering [21], where a river forms sinuous curves [56] or bends as the river ows tends to shift river laterally within its oodplain [40,72].…”

Section: Introductionmentioning

confidence: 99%

GIS and Remote Sensing Based Assessment of West Rapti River Channel Migration in Nepal

Gharti,

Silwal,

Poudel

et al. 2024

Preprint

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This study uses geospatial technique to analyze the spatio-temporal changes of West Rapti River Channel from 1990 to 2023, to assess their impact in Deukhuri Valley. Using freely available Landsat satellite imagery we extracted water surface features through the Normalized Difference Water Index (NDWI) and generated 27 cross-sections (CS) to track channel shift. The results indicated the significant shifts, notably 1426.97 m northeast shift in CS-23 and 718.55 m south shift in CS-25 attributed to sand deposition during flooding. Nearly no shifting impact was observed from CS-12 to CS-17 showing embankment and mitigation efforts being effective within this range. Study shows river is naturally multi-channeled and exhibits braided planform with sinuosity index values ranging from 1.02 to 1.38, indicating highly braided during 2010 and 2015. The river channel was found to be driven by a high sediment deposit of about 15.35 Sq. km across the 53 km range. These results are crucial for formulating integrated river basin management plans in the West Rapti River basin laying foundation for future research to explore sustainable management practices in the future.

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“…In this context, airborne and spaceborne remote sensing data has become a crucial tool (e.g., Bizzi et al., 2019; Righini & Surian, 2018; Tomsett & Leyland, 2019). Sub‐metric resolution images have often been analyzed to detect fluvial geomorphic features, arguing that high resolution images can better support the translation of river geomorphic changes into process‐understanding (Carbonneau et al., 2012; Rivas Casado et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Impact of Spatiotemporal Resolution on the Interpretation of Fluvial Geomorphic Feature Dynamics From Sentinel 2 Imagery: An Application on a Braided River Reach in Northern Italy

Bozzolan,

Brenna,

Surian

et al. 2023

Water Resources Research

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Machine learning algorithms applied on the publicly available Sentinel 2 images (S2) are opening the opportunity to automatically classify and monitor fluvial geomorphic feature (such as sediment bars or water channels) dynamics across scales. However, there are few analyses on the relative importance of S2 spatial versus temporal resolution in the context of geomorphic research. In a dynamic, braided reach of the Sesia River (Northern Italy), we thus analyzed how the inherent uncertainty associated with S2's spatial resolution (10 m pixel size) can impact the significance of the active channel (a combination of sediment and water) delineation, and how the S2's weekly temporal resolution can influence the interpretation of its evolutionary trajectory. A comparison with manually classified images at higher spatial resolutions (Planet: 3 m and orthophoto: 0.3 m) shows that the automatically classified water is ∼20% underestimated whereas sediments are ∼30% overestimated. These classification errors are smaller than the geomorphic changes detected in the 5 years analyzed, so the derived active channel trajectory can be considered robust. The comparison across resolutions also highlights that the yearly Planet‐ and S2‐derived active channel trajectory are analogous and they are both more effective in capturing the river geomorphic response after major flood events than the trajectory derived from sequential multiannual orthophotos. More analyses of this type, across different types of river could give insights on the transferability of the spatial uncertainty boundaries found as well as on the spatial and temporal resolution trade‐off needed for supporting different geomorphic analyses.

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Remote Sensing as a Tool for Analysing Channel Dynamics and Geomorphic Effects of Floods

Cited by 10 publications

References 79 publications

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change

GIS and Remote Sensing Based Assessment of West Rapti River Channel Migration in Nepal

Quantifying the Impact of Spatiotemporal Resolution on the Interpretation of Fluvial Geomorphic Feature Dynamics From Sentinel 2 Imagery: An Application on a Braided River Reach in Northern Italy

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