No abstract

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

Reliable pathological interpretation is vital to so many aspects of tissue-based research as well as being central to patient care. Understanding the complex processes involved in decision-making is the starting point to improve both diagnostic reproducibility and the definition of diagnostic groups that underpin our experiments. Unfortunately, there is a paucity of research in this field and it is encouraging to see The Journal of Pathology publishing work in this area. This review attempts to highlight the opportunities that exist in this field and the technologies that are now available to support this type of research. Key amongst these are the use of decision analysis tools such as inference networks, and virtual microscopy that allows us to simulate diagnostic decision-making. These tools have roles, not only in studying the subtleties of diagnostic decision-making, but also in delivering new methods of training and proficiency testing. Research which helps us to better understand what we see, why we see it, and standardizing interpretative reasoning in pathological classification is essential for improving the wide range of activities that pathologists support, including clinical diagnosis, teaching, training, and experimental research.

In an era of big-data acquisition and semiautomation of geomorphic river surveys, it is timely to consider how to better integrate this into existing and widely used conceptual frameworks and approaches to analysis. We demonstrate how Stage 1 of the River Styles Framework, which entails identification and interpretation of river character and behavior, patterns and controls, can be used as a "powerboard" into which available, developing and future semiautomated tools and workflows can be plugged (or unplugged). Prospectively, such approaches will increase the efficiency and scope of analyses, providing unprecedented insights into the diversity of rivers and their morphodynamics. We appraise the role of human decision-making in conducting expert-manual analyses and interpretations. Genuine integration of big-data analytics, remote-sensing based tools for semiautomated river analysis with expert-manual interpretations including field insights, will be an essential ingredient to fully exploit emerging computational and remote sensing technologies to advance our understanding of river systems, to translate information into knowledge, and raise the standards of practice in river science and management.

Quantifying sedimentary deposits is crucial to fully test generic trends cited within facies models. To date, few studies have quantified downstream trends alongside vertical and lateral variations within distributive fluvial systems (DFS), with most studies reporting qualitative trends. This study reports on the generation of a quantitative dataset on the Huesca DFS, Ebro Basin, Spain, in which downstream, vertical and lateral trends in channel characteristics are analyzed using a fusion of field data and virtual outcrop model derived data (VOM). Vertical trend analysis reveals that the exposed portion of the Huesca DFS does not show any systematic changes through time, which suggests autogenic-driven local variability. Proximal-to-distal trends from field data display a downstream decrease in average channel body thicknesses (13.1–0.7 m), channel deposit percentage (70–4%), and average storey thicknesses (5.2–0.7 m) and confirm trends observed on other DFS. The VOM dataset shows a similar downstream trend in all characteristics. The range in values are, however, larger due to the increase in amount of data that can be collected, and trends are thus less clear. This study therefore highlights that standard field techniques do not capture the variability that can be present in outcrops. Channel percentage was found to be most variable (37% variation) in the medial setting, whereas channel body thickness is most variable (∼15 m range) in the proximal setting. Storey thickness varied in both the proximal and medial settings (range of 9 and 11 m for field and VOM data respectively) becoming more consistent downstream. Downstream shifts in architecture are also noted from massive, highly amalgamated channel-body sandstones in proximal regions to isolated or offset-stacked channel-bodies dominating the distal region. Trends are explained by spatial variability in DFS processes and preservation potential. The overlap present indicates that no single value is representative of position within a DFS, which has important implications for interpreting the location that a data point sits within a DFS when using limited (i.e., single log) datasets. These comparative results contribute to improving the accuracy of system-scale downstream predictions for channel characteristic variability within subsurface deposits.