Multi-Temporal Image Analysis for Fluvial Morphological Characterization with Application to Albanian Rivers

Spada, Daniele; Molinari, Paolo; Bertoldi, Walter; Vitti, Alfonso; Zolezzi, Guido

doi:10.3390/ijgi7080314

Cited by 36 publications

(31 citation statements)

References 45 publications

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“…Frequently used multispectral indices include the normalized difference vegetation index (NDVI; Rouse, Haas, Schell, & Deering, 1973), the enhanced vegetation index (EVI; Huete et al, 2002), the normalized difference water index (NDWI; McFeeters, 1996), and the modified normalized difference water index (MNDWI; Xu, 2006). Different multispectral indices can therefore support highly differentiated fluvial geomorphology applications (Spada, Molinari, Bertoldi, Vitti, & Zolezzi, 2018).…”

Section: Big Geospatial Data Flowsmentioning

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: Big Geospatial Data Flowsmentioning

confidence: 99%

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

et al. 2020

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“…The active channel is defined in literature as the low-flow channel, plus adjacent sediment bars at the edges of perennial, terrestrial vegetation, usually subjected to erosion and deposition processes and by vegetation encroachment [8][9][10]. Mapping riverscape units is important for the understanding of the macrodynamics of a river system and if repeated through time it can be a powerful tool for different purposes, such as the assessment of evolutionary trajectories of river reaches [11], stream fragmentation [12] or human pressures [13,14], just to mention a few.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, few researchers have attempted to map these key units at the continental scale in an automated/semi-automated way through the use of RS data. Spada et al [11] analyzed the main Albanian rivers over a 40-year period, extracting the river centerlines, as well as active and wet channel width using multisource satellite data, such as Landsat and Sentinel-2. Bertrand et al [15] mapped some riverscape units and channel types based on RGB orthophotos for the Drôme catchment (France), using a geographical object based image analysis (GEOBIA) technique.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

2020

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Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe.

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“…So-called photo-sieving methods that manually measure gravel sizes from ground level images (Adams, 1979;Ibbeken and Schleyer, 1986) were first proposed in the late 1970s. Much research tried to automatically estimate grain size distributions from ground level images (Butler et al, 2001;Rubin, 2004;Graham et al, 2005;Verdú et al, 2005;Detert and Weitbrecht, 2012;Buscombe, 2013;Black et al, 2014;Spada et al, 2018;Buscombe, 2019). On the contrary, relatively little research has addressed mapping of grain sizes from images at larger scale (Carbonneau et al, 2004(Carbonneau et al, , 2005(Carbonneau et al, , 2018Zettler-Mann and Fonstad, 2020), needed for practical impact.…”

Section: Introductionmentioning

confidence: 99%

GRAINet: Mapping grain size distributions in river beds from UAV images with convolutional neural networks

Lang¹,

Irniger²,

Rozniak³

et al. 2020

Preprint

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Abstract. Grain size analysis is the key to understand the sediment dynamics of river systems. We propose GRAINet, a data-driven approach to analyze grain size distributions of entire gravel bars based on georeferenced UAV images. A convolutional neural network is trained to regress grain size distributions as well as the characteristic mean diameter from raw images. GRAINet allows the holistic analysis of entire gravel bars, resulting in (i) high-resolution maps of the spatial grain size distribution at large scale, and (ii) robust grading curves for entire gravel bars. To collect a large training dataset of 1,491 samples, we introduce digital line sampling as a new annotation strategy, following the widely applied line sampling analysis of Fehr (1987). Our evaluation on 25 gravel bars along 6 different rivers in Switzerland yields high accuracy: The resulting maps of mean diameters have a mean absolute error of 1.1 cm, with no bias. Robust grading curves for entire gravel bars can be extracted if representative training data is available. At the gravel bar level the MAE error of the predicted mean diameter is even reduced to 0.3 cm. Extensive experiments were carried out to study the quality of the digital line samples, the generalization capability of GRAINet to new locations, the model performance w.r.t. human labeling noise, the limitations of the current model, and the potential of GRAINet to analyze images with low resolutions.

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Multi-Temporal Image Analysis for Fluvial Morphological Characterization with Application to Albanian Rivers

Cited by 36 publications

References 45 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

Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

GRAINet: Mapping grain size distributions in river beds from UAV images with convolutional neural networks

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