Amy Woodget

Subaerial Gravel Size Measurement Using Topographic Data Derived From a UAV-SfM Approach

Abstract Accurate and reliable methods for quantifying grain size are important for river science, management and in various other sedimentological settings. Remote sensing offers methods of quantifying grain size, typically providing; (a) coarse outputs (c. 1 m) at the catchment scale where individual grains are at subpixel level, or; (b) fine resolution outputs (c. 1 mm) at the patch scale. Recently, approaches using unmanned aerial vehicles (UAVs) have started to fill the gap between these scales, providing hyperspatial resolution data (< 10 cm) over reaches a few hundred metres in length, where individual grains are at suprapixel level. This ‘mesoscale’ is critical to habitat assessments. Most existing UAV‐based approaches use two‐dimensional (2D) textural variables to predict grain size. Validation of results is largely absent however, despite significant differences in platform stability and image quality obtained by manned aircraft versus UAVs. Here, we provide the first quantitative assessment of the accuracy and precision of grain size estimates produced from a 2D image texture approach. Furthermore, we present a new method which predicts subaerial gravel size using three‐dimensional (3D) topographic data derived from UAV imagery. Data is collected from a small gravel‐bed river in Cumbria, UK. Results indicate that our new topographic method gives more accurate measures of grain size (mean residual error ‐0.0001 m). Better results for the image texture method may be precluded by our choice of texture measure, the scale of analysis or the effects of image blur resulting from an inadequate camera gimbal. We suggest that at our scale of assessment, grain size is more strongly related to 3D variation in elevation than to the 2D textural patterns expressed within the imagery. With on‐going improvements, our novel method has potential as the first grain size quantification approach where a trade‐off between coverage and resolution is not necessary or inherent. Copyright

From manned to unmanned aircraft : adapting airborne particle size mapping methodologies to the characteristics of sUAS and SfM.

Subaerial particle size data holds a wealth of valuable information for fluvial, coastal, glacial and other sedimentological applications. Recently, we have gained the opportunity to map and quantify surface particle sizes at the mesoscale using data derived from small unmanned aerial system (sUAS) imagery processed using structure from motion (SfM) photogrammetry. Typically, these sUAS-SfM approaches have been based on calibrating orthoimage texture or point cloud roughness with particle size. Variable levels of success are reported and a single, robust method capable of producing consistently accurate and precise results in a range of settings has remained elusive. In this paper, we develop an original method for mapping surface particle size with the specific constraints of sUAS and SfM in mind. This method uses the texture of single sUAS images, rather than orthoimages, calibrated with particle sizes normalised by individual image scale. We compare results against existing orthoimage texture and roughness approaches, and provide a quantitative investigation into the implications of the use of sUAS camera gimbals. Our results indicate that our novel single image method delivers an optimised particle size mapping performance for our study site, outperforming both other methods and delivering residual mean errors of 0.02mm (accuracy), standard deviation of residual errors of 6.90mm (precision) and maximum residual errors of 16.50mm. Accuracy values are more than two orders of magnitude worse when imagery is collected by a similar drone which is not equipped with a camera gimbal, demonstrating the importance of mechanical image stabilisation for particle size mapping using measures of image texture.

Unmanned Aerial Systems (UAS) for environmental applications special issue preface

Drones are indeed a key new technology for remote sensing, and one that has grown rapidly in recent years. According to the database, Web of Science (published by Clarivate Analytics), the first significant use in IJRS of any of the terms UAS, unmanned aerial vehicle (UAV) or drones, for example, in the title, abstract, or keyword of an article, was in 2009, in a paper by Dunford et al. (2009). It was not until 2012 that another paper used one of those terms. After that, the numbers increased very quickly, and by 2017, IJRS published 68 papers that referenced these terms. Most notably, 2017 also saw the first IJRS special issue on UAS, titled ‘Unmanned aerial vehicles for environmental applications’ (The Editors 2017). This current special issue is a direct follow-on from that major collection of papers, and the fact that we are able to have two major special issues on this topic in the space of just over one year, is further evidence of its importance