New analytical models are introduced to describe the motion of a Herschel–Bulkley fluid slumping under gravity in a narrow fracture and in a porous medium. A useful self-similar solution can be derived for a fluid injection rate that scales as time $t$ ; an expansion technique is adopted for a generic injection rate that is power law in time. Experiments in a Hele-Shaw cell and in a narrow channel filled with glass ballotini confirm the theoretical model within the experimental uncertainty.

Synthetic Aperture Radar has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths, such as L-band, are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is much interest due to its high temporal resolution (five days) and “free, full, and open” data policy. We tested its ability to distinguish forest from non-forest in six study sites, located in Alaska, Colombia, Finland, Florida, Indonesia, and the UK. Using the time series for a full year significantly increases the classification accuracy compared to a single scene (a mean of $85\%$ compared to $77\%$ across the study sites for the best classifier). Our results show that we can further improve the mean accuracy to $87\%$ when only considering the annual mean and standard deviation of co-polarized (VV) and cross-polarized (VH) backscatter. In this case, separation accuracies of up to $93\%$ (in Finland) are possible, though in the worst case (Alaska), the highest possible accuracy using these variables was $80\%$ . The best overall performance was observed when using a Support Vector Machine classifier, outperforming random forest, k-Nearest-Neighbors, and Quadratic Discriminant Analysis. We further show that the small information content we found in the phase data is an artifact of terrain slope orientation and has a negligible impact on classifier performance. We conclude that for the purposes of forest mapping the smaller file size and easier to process GRD products are sufficient, unless the SLC products are used to compute the temporal coherence which was not tested in this study.

A numerical method that employs a combination of contour advection and pseudo-spectral techniques is used to simulate shear-induced instabilities in an internal solitary wave (ISW). A three-layer configuration for the background stratification, in which a linearly stratified intermediate layer is sandwiched between two homogeneous ones, is considered throughout. The flow is assumed to satisfy the inviscid, incompressible, Oberbeck–Boussinesq equations in two dimensions. Simulations are initialized by fully nonlinear, steady-state, ISWs. The results of the simulations show that the instability takes place in the pycnocline and manifests itself as Kelvin–Helmholtz billows. The billows form near the trough of the wave, subsequently grow and disturb the tail. Both the critical Richardson number ( ${\mathit{Ri}}_{c} $ ) and the critical amplitude required for instability are found to be functions of the ratio of the undisturbed layer thicknesses. It is shown, therefore, that the constant, critical bound for instability in ISWs given in Barad & Fringer (J. Fluid Mech., vol. 644, 2010, pp. 61–95), namely ${\mathit{Ri}}_{c} = 0. 1\pm 0. 01$ , is not a sufficient condition for instability. It is also shown that the critical value of ${L}_{x} / \lambda $ required for instability, where ${L}_{x} $ is the length of the region in a wave in which $\mathit{Ri}\lt 1/ 4$ and $\lambda $ is the half-width of the wave, is sensitive to the ratio of the layer thicknesses. Similarly, a linear stability analysis reveals that ${\bar {\sigma } }_{i} {T}_{w} $ (where ${\bar {\sigma } }_{i} $ is the growth rate of the instability averaged over ${T}_{w} $ , the period in which parcels of fluid are subjected to $\mathit{Ri}\lt 1/ 4$ ) is very sensitive to the transition between the undisturbed pycnocline and the homogeneous layers, and the amplitude of the wave. Therefore, the alternative tests for instability presented in Fructuset al. (J. Fluid Mech., vol. 620, 2009, pp. 1–29) and Barad & Fringer (J. Fluid Mech., vol. 644, 2010, pp. 61–95), respectively, namely ${L}_{x} / \lambda \geq 0. 86$ and ${\bar {\sigma } }_{i} {T}_{w} \gt 5$ , are shown to be valid only for a limited parameter range.

We introduce a novel edge tracing algorithm using Gaussian process regression. Our edge-based segmentation algorithm models an edge of interest using Gaussian process regression and iteratively searches the image for edge pixels in a recursive Bayesian scheme. This procedure combines local edge information from the image gradient and global structural information from posterior curves, sampled from the model’s posterior predictive distribution, to sequentially build and refine an observation set of edge pixels. This accumulation of pixels converges the distribution to the edge of interest. Hyperparameters can be tuned by the user at initialisation and optimised given the refined observation set. This tunable approach does not require any prior training and is not restricted to any particular type of imaging domain. Due to the model’s uncertainty quantification, the algorithm is robust to artefacts and occlusions which degrade the quality and continuity of edges in images. Our approach also has the ability to efficiently trace edges in image sequences by using previous-image edge traces as a priori information for consecutive images. Various applications to medical imaging and satellite imaging are used to validate the technique and comparisons are made with two commonly used edge tracing algorithms.

This case study explores a novel approach to data visualization that integrates statistical data with qualitative narratives, guided by the principles of data feminism and data humanism. The narrative, titled “Paths in Education”, focuses on secondary education in Uruguay, a country where almost half of young adults have not completed mandatory education. Through a combination of quantitative analysis and interviews, the narrative is presented on an interactive scrollytelling website. The paper discusses the critical visualization theories that inspired the chosen approach, the methodologies used for data collection and analysis, and the design process. It also discusses how the project relates to data feminism and humanism, as well as some lessons learned.

Obtaining abundance and density estimates is a particularly important aspect within wildlife conservation and management. To monitor wildlife populations, the use of motion-sensor camera traps is becoming increasing popular due to its non-invasive nature. However, animal identification is not always feasible in practice due to poor quality images and/or individuals not having uniquely identifiable physical characteristics. Spatially explicit models for unmarked individuals permit the estimation of animal density when individuals cannot be uniquely identified. Due to the structure of these models, a Bayesian super-population (data augmentation) approach is often used to fit the models to data, which involves specifying some reasonably large upper limit for the population. However, this approach presents substantial computational challenges for larger populations, as demonstrated by the motivating dataset relating to barking deer (Muntiacus muntjak) collected in Ujung Kulon National Park, Indonesia (with a population size in the low thousands). We develop a new and computationally efficient Bayesian algorithm for fitting the models to data that does not require specifying an upper population limit a priori. We apply the new algorithm to the large barking deer dataset, where the standard super-population approach is computationally expensive, and demonstrate a substantial improvement in computational efficiency.Supplementary material to this paper is provided online.