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Determine stormwater pond geometrics and hydraulics using remote sensing technologies: A comparison between airborne-LiDAR and UAV-photogrammetry field validation against RTK-GNSS

Journal of Hydroinformatics 2023 3 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Guohan Zhao, Michael R. Rasmussen, Kim G. Larsen, Jiřı́ Srba, Thomas D. Nielsen, Martijn A. Goorden, Weizhu Qian, Jesper Ellerbæk Nielsen

Summary

Researchers compared UAV-photogrammetry and airborne-LiDAR against RTK-GNSS ground truth for measuring stormwater pond geometry and hydraulics across six ponds. UAV-photogrammetry outperformed infrared airborne-LiDAR for wet ponds, while correction methods for vegetation penetration improved dry pond performance, establishing UAV photogrammetry as the preferred cost-effective approach for pond monitoring.

Abstract Flow-regulated stormwater ponds providing safe outflow discharges prevail as the primary stormwater management tool for stream protections. Detailed pond geometries are essential metrics in pond monitoring technologies, which convert the point-based water level measurements to areal/volumetric ponding water estimations. Unlike labour-intensive surveys (e.g., RTK-GNSS or total stations), UAV-photogrammetry and airborne-LiDAR have been advocated as cost-effective alternatives to acquire high-quality datasets. In this paper, we compare the use of these two approaches for stormwater pond surveys. With reference to RTK-GNSS in-situ observations, we identify their geometric and hydraulic discrepancies based on six stormwater ponds from three aspects: (i) DEMs, (ii) stage-curves and (iii) outflow discharges. Three main findings are outlined: (i) for wet ponds where moisture environments are dominant, UAV-photogrammetry outperforms (infrared) airborne-LiDAR, where airborne-LiDAR yields 0.15–0.54 NSEoutflow, which is unacceptable; (ii) for dry ponds, UAV-photogrammetry obtains 0.88–0.89 NSEoutflow as poor vegetation penetrations; two correction methods (i.e., grass removal and shifted stage-curves) are proposed, indicating good alignment to RTK-GNSS observations and (iii) UAV-photogrammetry delivers <0.1 m resolution in outlining break-line features for stormwater pond structures. With significant economic advantages, the multi-UAV collaborative photogrammetry would address the shortcomings of a single UAV and thus pave the way for large urban catchment/watershed survey applications.

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