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ISSN:2454-4116

International Journal of New Technology and Research

Impact Factor 3.953

(An ISO 9001:2008 Certified Online Journal)
India | Germany | France | Japan

An Evaluation DEM Accuracy Acquired Using a Small Unmanned Aerial Vehicle Across a Riverine Environment

( Volume 3 Issue 7,July 2017 ) OPEN ACCESS
Author(s):

Entwistle N.S. ,Heritage G.L.

Abstract:

Fluvial systems offer a challenging and varied environment for topographic survey, displaying a rapidly varying morphology, diversevegetation assemblage and varying degree of submergence. Traditionally theodolite or GPS based systems have been used to capture cross-section and break of slope based data which has subsequently been interpolated to generate a topographic surface. Advances in survey technology has resulted in an improved ability to capture larger volumes of data with infrared terrestrial and aerial LiDAR systems capturing high-density (<0.02m) data across terrestrial surfaces but instruments are expensive and cumbersome and fail to survey through water.

The rise of Structure from Motion (SfM) photogrammetry, coupled with unmanned aerial vehicles (UAVs), has potential to rapidly record information needed to derive elevation data at reach scale with sub decimetre density. The approach has the additional advantage over LiDAR of seeing through clear water to capture bed detail, whilst also generating orthorectified photographic mosaics of the survey reach.  However, the accuracy of the data has received comparatively little attention. Here we present a survey protocol for UAV deployment and provide a reach scale comparison between a Terrestrial LiDAR Survey (TLS) and SfM UAV survey on the River Sprint near Kendal in England.. Comparative analysis of elevation data between TLS and SfM suggest comparable accuracy and precision across terrestrial surfaces with error lowest over solid surfaces, increasing with vegetation complexity.  Submerged SfM data captured bed levels generally to within ±0.2 with only a weak relationship recorded between error and flow depth.

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