Remote Sensing · 2016 · 305 citations · 27 references
EngineeringEarth SciencesGeoinformaticsDigital Elevation ModelingRemote SensingSurveyingDigital Elevation ModelsDigital Surface ModelsDigital Terrain ModelingPhotogrammetric SurveyingPhotogrammetryTerrain MorphometryAerial PhotogrammetryAerial SurveysControl PointsDigital Elevation Model GenerationDigital Surface Model GenerationStereopsisUnmanned Aerial Vehicle PhotogrammetryUnmanned Aerial Vehicle Surveying
UAV‑based Structure‑from‑Motion photogrammetry with Multi‑View Stereopsis is widely used, yet key workflow components still require validation. The study seeks to guide researchers by evaluating how the number and placement of ground control points affect data quality for UAV surveys over undulating terrain. Sixteen digital surface models were generated from a UAV flight with 3–101 GCPs, compared to 530 dGPS spot heights for vertical error, and the effect of GCP distribution was examined by contrasting evenly and clustered networks. DSM quality improved from RMSE < 0.2 m to < 0.09 m with four or more GCPs, while vertical error increased with distance from GCP clusters following a strong polynomial trend (R² = 0.792), highlighting the need for evidence‑driven GCP placement to achieve reproducible, high‑quality topographic datasets.
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 (R2 = 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.
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