Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry

Abstract

Abstract:The use of small UAV (Unmanned Aerial Vehicle) and Structure-from-Motion (SfM) with Multi-View Stereopsis (MVS) for acquiring survey datasets is now commonplace, however, aspects of the SfM-MVS workflow require further validation. This work aims to provide guidance for scientists seeking to adopt this aerial survey method by investigating aerial survey data quality in relation to the application of ground control points (GCPs) at a site of undulating topography (Ennerdale, Lake District, UK). Sixteen digital surface models (DSMs) were produced from a UAV survey using a varying number of GCPs (3-101). These DSMs were compared to 530 dGPS spot heights to calculate vertical error. All DSMs produced reasonable surface reconstructions (vertical root-mean-square-error (RMSE) of <0.2 m), however, an improvement in DSM quality was found where four or more GCPs (up to 101 GCPs) were applied, with errors falling to within the suggested point quality range of the survey equipment used for GCP acquisition (e.g., vertical RMSE of <0.09 m). The influence of a poor GCP distribution was also investigated by producing a DSM using an evenly distributed network of GCPs, and comparing it to a DSM produced using a clustered network of GCPs. The results accord with existing findings, where vertical error was found to increase with distance from the GCP cluster. Specifically vertical error and distance to the nearest GCP followed a strong polynomial trend (R 2 = 0.792). These findings contribute to our understanding of the sources of error when conducting a UAV-SfM survey and provide guidance on the collection of GCPs. Evidence-driven UAV-SfM survey designs are essential for practitioners seeking reproducible, high quality topographic datasets for detecting surface change.

Three Decades of Volume Change of a Small Greenlandic Glacier Using Ground Penetrating Radar, Structure from Motion, and Aerial Photogrammetry

“…The results agree with previous findings based on UAV surveys (Tonkin and Midgley, 2016) and show a dependence of the validation error on the spatial extent covered by the GCPs. Validation and referencing errors become comparable in mean and variance when the reference GCPs cover the maximum area coverable by the GCPs, that is, when they are evenly distributed over the entire glacier area.…”

“…The accuracy of the DEM was assessed by investigating the dependency of model residuals in X, Y, and Z direction on the number of GCPs used for the georeferencing and the spatial distribution of GCPs over the DEM area, following an approach similar to Tonkin and Midgley (2016) for UAV surveys. The GCP data set was divided in referencing and validation points.…”

Assessment of UAV and Ground-Based Structure from Motion with Multi-View Stereo Photogrammetry in a Gullied Savanna Catchment

“…The number and distribution of GCPs has a strong influence on survey time and the quality of surface reconstruction [13,19,44,102,114]. In our UAV datasets, despite very low georeferencing error of GCPs, much greater elevation errors were observed in the final topographic models.…”

Structure-from-Motion Using Historical Aerial Images to Analyse Changes in Glacier Surface Elevation

“…Several studies have investigated the effect of a small number of unevenly distributed GCPs on DTM accuracy (e.g., [38]) and found a substantial decrease of accuracy, but still in a range of~1-3 m absolute accuracy. Gindraux et al (2017) [38] and Tonkin and Midgley (2016) [40] found a decrease in accuracy of 0.09 and 0.1 m per 100 m distance increase from the next GCP, respectively. In our case, no reference information from the glacier surface was available.…”