Abstract
Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV) have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud ( < 1–3 cm point spacing) is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS) are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25–40 mm can be obtained from imagery acquired from ~50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion) can be monitored.
Highlights
The data collection and processing methods described are the proposed technique for future change monitoring studies, there is a need for a clear understanding of the geometric accuracy of the Unmanned Aerial Vehicles (UAV)-multi-view stereopsis (MVS) point clouds
This study presented an assessment of the accuracy and applicability of point clouds derived by multi-view stereopsis (MVS) based on Unmanned Aerial Vehicle (UAV) photography for natural landscape mapping and monitoring
The UAV-MVS technique generates dense point clouds (1–3 cm point spacing) of natural environments using Structure from Motion (SfM) techniques to process imagery captured from a micro-UAV and georeferences the derived point cloud using Differential Global
Summary
Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. The miniaturisation and commercialisation of sensors, positioning systems, and UAV hardware provide scientists with a means to overcome some of the limitations of satellite imagery and aerial photography, namely spatial and temporal resolution. The datasets produced by UAV remote sensing are at such high detail that characteristics of the landscape can be mapped that are not distinguishable at the lower resolutions generally obtainable via manned aircraft (∼10–100 cm) and satellite systems (>50 cm). Recent studies [5,6,7,8,9] have successfully adopted MVS to derive dense point clouds from UAV photography.
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