Abstract
Interest in small unmanned aircraft systems (sUAS) for topographic mapping has significantly grown in recent years, driven in part by technological advancements that have made it possible to survey small- to medium-sized areas quickly and at low cost using sUAS aerial photography and digital photogrammetry. Although this approach can produce dense point clouds of topographic measurements, they have not been tested extensively to provide insights on accuracy levels for topographic mapping. This case study examines the accuracy of a sUAS-derived point cloud of a parking lot located at the Citizens Bank Arena (CBA) in Ontario, California, by comparing it to ground control points (GCPs) measured using global navigation satellite system (GNSS) data corrected with real-time kinematic (RTK) and to data from a terrestrial laser scanning (TLS) survey. We intentionally chose a flat surface due to the prevalence of flat scenes in sUAS mapping and the challenges they pose for accurately deriving vertical measurements. When the GNSS-RTK survey was compared to the sUAS point cloud, the residuals were found to be on average 18 mm and −20 mm for the horizontal and vertical components. Furthermore, when the sUAS point cloud was compared to the TLS point cloud, the average difference observed in the vertical component was 2 mm with a standard deviation of 31 mm. These results indicate that sUAS imagery can produce point clouds comparable to traditional topographic mapping methods and support other studies showing that sUAS photogrammetry provides a cost-effective, safe, efficient, and accurate solution for topographic mapping.
Highlights
Topographic maps are critical geospatial data products used in earth surface studies that provide insights on environmental conditions, geomorphology, and other earth surface processes and features.Typical methods for topographic mapping, small to medium spatial extents, consist of total station (TS), global navigation satellite system (GNSS), terrestrial laser scanning (TLS), or airborne light detection and ranging (LiDAR)
Doing so would lead to an ecological fallacy or false inference. This is one of the main contributions of this study: to evaluate the performance of a small unmanned aircraft systems (sUAS) point cloud comprehensively against GNSS-real-time kinematic (RTK) and TLS surveying techniques at areas beyond the ground control points (GCPs). Another contribution of our study is to provide a detailed workflow on data acquisition that may be replicated to support researchers, scientists, or practitioners in achieving high accuracy sUAS derived point clouds
The data acquisition workflow followed in this study may alleviate aforementioned issues with the low accuracy of point clouds derived from sUAS imagery
Summary
Typical methods for topographic mapping, small to medium spatial extents, consist of total station (TS), global navigation satellite system (GNSS), terrestrial laser scanning (TLS), or airborne light detection and ranging (LiDAR). Mapping professionals most often rely on TS and GNSS for producing as-built plans, site-monitoring, and general topographic surveys due to their high levels of accuracy; they are time-consuming and costly, especially for medium-to-large-sized topographic mapping projects (≥0.15 km). It is limited to line-of-sight and it is inefficient in areas with many physical barriers and moving objects. In those cases, airborne LiDAR may be preferred over TLS as it is able to capture complete site information rapidly. Airborne LiDAR can successfully evaluate large areas to perform agricultural, geomorphological, and environmental applications [1]
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