Abstract
Unmanned Aerial Vehicle (UAV) photogrammetry has recently become a powerful tool that offers a viable alternative to traditional remote sensing systems, particularly for applications covering relatively small spatial extents. This paper presents results of a study that aimed at investigating the use of UAV photogrammetry as a tool for the mapping of wetlands. A multi-rotor UAV and a digital camera on a motion compensated gimbal mount were utilised for the survey. The survey of the 100ha study area at the Kameelzynkraal farm, Gauteng Province, South Africa took about 2½ hours and the generation of the point cloud about 18 hours. Ground control points (GCPs) were positioned across the site to achieve geometrical precision and georeferencing accuracy. Structure from Motion (SfM) computer vision techniques were used to reconstruct the camera positions, terrain features and to derive ultra-high resolution point clouds, orthophotos and 3D models from the multi-view photos. The results of the geometric accuracy of the data based on the 20 GCPs were 0.018m for the overall and 0.0025m for the vertical root mean squared error (RMSE). The results exceeded our expectations and provided valuable, rapid and accurate mapping of wetlands that can be used for wetland studies and thereby support and enhance associated decision making to secure our future.
Highlights
The advent of photogrammetry using Unmanned Aerial Vehicle (UAV) has proved a cost effective and efficient alternative to traditional remote sensing techniques (Shabazi et al, 2014)
The flying height determined for the UAV survey was 120m above ground level (AGL) which is the legal requirement in terms of the Civil Aviation Authority (CAA) and this ensured rapid acquisition of images with a sufficient level of detail of the study area which equates to a lower cost
A total amount of 989 images was used for creating the initial point cloud which resulted in 861 296 939 points which required 36 hours of processing to complete the generation of the dense point cloud, export of the point cloud and initial 5 and 10cm ground pixel resolution orthophotos
Summary
The advent of photogrammetry using UAV has proved a cost effective and efficient alternative to traditional remote sensing techniques (Shabazi et al, 2014). Progress in computer vision and computing power has led to the advancement of UAV photogrammetry This includes key advancements such as operational solutions for 3D data acquisition based on Structure-fromMotion (SfM) photogrammetry and multi-view stereo (MVS) (James and Robson 2012, Westoby et al, 2012, Fonstad et al, 2013). The use of computer vision software is an alternative technique to create 3D models from photographs that evolved considerably in recent years. This alternative is a cost effective and easy to use method compared to expensive laser scanners or rigorous photogrammetry. Computer vision software integrates state-of-the-art SfM and MVS algorithms to generate/reconstruct very dense and accurate point clouds from a series of overlapping photographs as indicated in Verhoeven, (2011), James and Robson, (2012) and Westoby et al, (2012). Complex wetland vegetation information at a community scale can be identified (Li et al, 2010, Lechner et al, 2012), delineated and classified (Marcaccio et al, 2015, Zweig et al, 2015) through high resolution orthophotos acquired using UAVs
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