Mitigating systematic error in topographic models derived from UAV and ground‐based image networks

High resolution digital elevation models (DEMs) are increasingly produced from photographs acquired with consumer cameras, both from the ground and from unmanned aerial vehicles (UAVs). However, although such DEMs may achieve centimetric detail, they can also display systematic broad‐scale error that restricts their wider use. Such errors which, in typical UAV data are expressed as a vertical ‘doming’ of the surface, result from a combination of near‐parallel imaging directions and inaccurate correction of radial lens distortion. Using simulations of multi‐image networks with near‐parallel viewing directions, we show that enabling camera self‐calibration as part of the bundle adjustment process inherently leads to erroneous radial distortion estimates and associated DEM error. This effect is relevant whether a traditional photogrammetric or newer structure‐from‐motion (SfM) approach is used, but errors are expected to be more pronounced in SfM‐based DEMs, for which use of control and check point measurements are typically more limited. Systematic DEM error can be significantly reduced by the additional capture and inclusion of oblique images in the image network; we provide practical flight plan solutions for fixed wing or rotor‐based UAVs that, in the absence of control points, can reduce DEM error by up to two orders of magnitude. The magnitude of doming error shows a linear relationship with radial distortion and we show how characterization of this relationship allows an improved distortion estimate and, hence, existing datasets to be optimally reprocessed. Although focussed on UAV surveying, our results are also relevant to ground‐based image capture. © 2014 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Hydrological evaluation of the LPX dynamic global vegetation model for small river catchments in the UK

Assessments of water resources by using macro‐scale models tend to be conducted at the continental or large catchment scale. However, security of freshwater supplies is a local issue and thus necessitates study at such a scale. This research aims to evaluate the suitability of the Land Processes and eXchanges dynamic global vegetation model (LPX‐DGVM) for simulating runoff for small catchments in the UK. Simulated annual and monthly runoff is compared against the National River Flow Archive streamflow observations from 12 catchments of varying size (500–10 000 km²) and climate regimes. Results show that LPX reproduces observed inter‐annual and intra‐annual runoff variability successfully in terms of both flow timings and magnitudes. Inter‐annual variability in flow timings is simulated particularly well (as indicated by Willmott's index of agreement values of ≥0.7 for the majority of catchments), whereas runoff magnitudes are generally slightly overestimated. In the densely populated Thames catchment, these overestimations are partly accounted for by water consumption. Seasonal variability in runoff is also modelled well, as shown by Willmott's index of agreement values of ≥0.9 for all but one catchment. Absence of river routing and storage from the model, in addition to precipitation uncertainties, is also suggested as contributing to simulated runoff discrepancies. Overall, the results show that the LPX‐DGVM can successfully simulate runoff processes for small catchments in the UK. This study offers promising insights into the use of global‐scale models and datasets for local‐scale studies of water resources, with the eventual aim of providing local‐scale projections of future water distributions. Copyright © 2013 John Wiley & Sons, Ltd.