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Geosciences 2015, 5(3), 264-285; doi:10.3390/geosciences5030264

Snow Depth Retrieval with UAS Using Photogrammetric Techniques

1
School of Earth Sciences & Byrd Polar Research Center, The Ohio State University, Columbus, OH 43202, USA
2
School of Land and Food, University of Tasmania, Hobart, TAS 7001, Australia
3
Math and Geospatial Sciences, RMIT, Melbourne, VIC 3000, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Pavel Propastin
Received: 8 April 2015 / Accepted: 24 June 2015 / Published: 10 July 2015
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Geomorphological Mapping)
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Abstract

Alpine areas pose challenges for many existing remote sensing methods for snow depth retrieval, thus leading to uncertainty in water forecasting and budgeting. Herein, we present the results of a field campaign conducted in Tasmania, Australia in 2013 from which estimates of snow depth were derived using a low-cost photogrammetric approach on-board a micro unmanned aircraft system (UAS). Using commercial off-the-shelf (COTS) sensors mounted on a multi-rotor UAS and photogrammetric image processing techniques, the results demonstrate that snow depth can be accurately retrieved by differencing two surface models corresponding to the snow-free and snow-covered scenes, respectively. In addition to accurate snow depth retrieval, we show that high-resolution (50 cm) spatially continuous snow depth maps can be created using this methodology. Two types of photogrammetric bundle adjustment (BA) routines are implemented in this study to determine the optimal estimates of sensor position and orientation, in addition to 3D scene information; conventional BA (which relies on measured ground control points) and direct BA (which does not require ground control points). Error sources that affect the accuracy of the BA and subsequent snow depth reconstruction are discussed. The results indicate the UAS is capable of providing high-resolution and high-accuracy (<10 cm) estimates of snow depth over a small alpine area (~0.7 ha) with significant snow accumulation (depths greater than one meter) at a fraction of the cost of full-size aerial survey approaches. The RMSE of estimated snow depths using the conventional BA approach is 9.6 cm, whereas the direct BA is characterized by larger error, with an RMSE of 18.4 cm. If a simple affine transformation is applied to the point cloud derived from the direct BA, the overall RMSE is reduced to 8.8 cm RMSE. View Full-Text
Keywords: photogrammetry; cryosphere; unmanned aerial systems photogrammetry; cryosphere; unmanned aerial systems
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Vander Jagt, B.; Lucieer, A.; Wallace, L.; Turner, D.; Durand, M. Snow Depth Retrieval with UAS Using Photogrammetric Techniques. Geosciences 2015, 5, 264-285.

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