Abstract. This study presents the application of a costeffective, unmanned aerial vehicle (UAV) to investigate calving dynamics at a major marine-terminating outlet glacier draining the western sector of the Greenland ice sheet. The UAV was flown over Store Glacier on three sorties during summer 2013 and acquired over 2000 overlapping, geotagged images of the calving front at an ∼ 40 cm ground sampling distance. Stereo-photogrammetry applied to these images enabled the extraction of high-resolution digital elevation models (DEMs) with vertical accuracies of ± 1.9 m which were used to quantify glaciological processes from early July to late August 2013. The central zone of the calving front advanced by ∼ 500 m, whilst the lateral margins remained stable. The orientation of crevasses and the surface velocity field derived from feature tracking indicates that lateral drag is the primary resistive force and that ice flow varies across the calving front from 2.5 m d −1 at the margins to in excess of 16 m d −1 at the centreline. Ice flux through the calving front is 3.8 × 10 7 m 3 d −1 , equivalent to 13.9 Gt a −1 and comparable to flux-gate estimates of Store Glacier's annual discharge. Water-filled crevasses were present throughout the observation period but covered a limited area of between 0.025 and 0.24 % of the terminus and did not appear to exert any significant control over fracture or calving. We conclude that the use of repeat UAV surveys coupled with the processing techniques outlined in this paper have great potential for elucidating the complex frontal dynamics that characterise large calving outlet glaciers.

Abstract. Unmanned aerial vehicles (UAVs) and structure from motion with multi-view stereo (SfM–MVS) photogrammetry are increasingly common tools for geoscience applications, but final product accuracy can be significantly diminished in the absence of a dense and well-distributed network of ground control points (GCPs). This is problematic in inaccessible or hazardous field environments, including highly crevassed glaciers, where implementing suitable GCP networks would be logistically difficult if not impossible. To overcome this challenge, we present an alternative geolocation approach known as GNSS-supported aerial triangulation (GNSS-AT). Here, an on-board carrier-phase GNSS receiver is used to determine the location of photo acquisitions using kinematic differential carrier-phase positioning. The camera positions can be used as the geospatial input to the photogrammetry process. We describe the implementation of this method in a low-cost, custom-built UAV and apply the method in a glaciological setting at Store Glacier in western Greenland. We validate the technique at the calving front, achieving topographic uncertainties of ±0.12 m horizontally (∼1.1× the ground sampling distance) and ±0.14 m vertically (∼1.3× the ground sampling distance), when flying at an altitude of ∼ 450 m above ground level. This compares favourably with previous GCP-derived uncertainties in glacial environments and allows us to apply the SfM–MVS photogrammetry at an inland study site where ice flows at 2 m day−1 and stable ground control is not available. Here, we were able to produce, without the use of GCPs, the first UAV-derived velocity fields of an ice sheet interior. Given the growing use of UAVs and SfM–MVS in glaciology and the geosciences, GNSS-AT will be of interest to those wishing to use UAV photogrammetry to obtain high-precision measurements of topographic change in contexts where GCP collection is logistically constrained.

Measurements of albedo are a prerequisite for modeling surface melt across the Earth's cryosphere, yet available satellite products are limited in spatial and/or temporal resolution. Here, we present a practical methodology to obtain centimeter resolution albedo products with accuracies of ±5% using consumer-grade digital camera and unmanned aerial vehicle (UAV) technologies. Our method comprises a workflow for processing, correcting and calibrating raw digital images using a white reference target, and upward and downward shortwave radiation measurements from broadband silicon pyranometers. We demonstrate the method with a set of UAV sorties over the western, K-sector of the Greenland Ice Sheet. The resulting albedo product, UAV10A1, covers 280 km2, at a resolution of 20 cm per pixel and has a root-mean-square difference of 3.7% compared to MOD10A1 and 4.9% compared to ground-based broadband pyranometer measurements. By continuously measuring downward solar irradiance, the technique overcomes previous limitations due to variable illumination conditions during and between surveys over glaciated terrain. The current miniaturization of multispectral sensors and incorporation of upward facing radiation sensors on UAV packages means that this technique could become increasingly common in field studies and used for a wide range of applications. These include the mapping of debris, dust, cryoconite and bioalbedo, and directly constraining surface energy balance models

Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets.

Functional modelling has been in use for a number of years for the interpretation of the results of model based simulation of engineered systems. Its use enables the automatic generation of a textual design analysis report that interprets the results of qualitative (or numerical) simulation in terms of the system's purpose. We present a novel functional description language that increases the expressiveness of this approach, increasing the range both of systems and design analysis tasks for which the approach can be used. The language also allows closer integration of functional modelling into the design process. The language allows a device function to be decomposed either in terms of subsidiary functions or required effects. We discuss the use of such alternative decompositions and propose a logic of functional description that is used to underpin the proposed language. The language has been used in the interpretation of electro-mechanical, hydraulic and fluid transfer systems in the automotive and aerospace industries to support tasks Failure Modes and Effects Analysis, Sneak Circuit Analysis, and Diagnosis. The language is not inherently restricted to these applications and the paper makes use of indicative examples from other domains.