The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.
References
[1]
Giambastiani, B.M.S.; Antonellini, M.; Gualbert, H.P.; Essink, O.; Stuurman, R.J. Saltwater intrusion in the unconfined coastal aquifer of Ravenna (Italy): A numerical model. J. Hydrol 2007, 340, 91–104.
Brodu, N.; Lague, D. 3D Terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology. ISPRS J. Photogramm. Remote Sens 2012, 68, 121–134.
[4]
Nield, J.M.; Wiggs, G.F.S.; Squirrell, R.S. Aeolian sand strip mobility and protodune development on a drying beach: Examining surface moisture and surface roughness patterns measured by terrestrial laser scanning. Earth Surf. Process. Landf 2011, 4, 513–522.
[5]
Nagihara, S.; Mulligan, K.R.; Xiong, W. Use of a three-dimensional laser scanner to digitally capture the topography of sand dunes in high spatial resolution. Earth Surf. Process. Landf 2004, 3, 391–398.
[6]
Nelson, A.; Reuter, H.I.; Gessler, P. DEM Production Methods and Sources. In Geomorphometry Concepts, Software, Applications; Hengl, T., Reuter, H.I., Eds.; Elsevier: Amsterdam, The Netherlands, 2009; pp. 65–85.
[7]
Houser, C.; Hapke, C.; Hamilton, S. Controls on coastal dune morphology, shoreline erosion and barrier island response to extreme storms. Geomorphology 2008, 3, 223–240.
[8]
Sallenger, A.H.; Krabill, W.B.; Swift, R.N.; Brock, J.; List, J.; Hansen, M.; Holman, R.A.; Manizade, S.; Sontag, J.; Meredith, A.; et al. Evaluation of airborne topographic lidar for quantifying beach changes. J. Coastal Res 2003, 1, 125–133.
[9]
Stockdonf, H.F.; Sallenger, A.H., Jr.; List, J.H.; Holman, R.A. Estimation of shoreline position and change using airborne topographic lidar data. J. Coast. Res 2002, 18, 502–513.
[10]
Snavely, N; Seitz, S.M; Szeliski, R. Photo tourism: Exploring photo collections in 3D. ACM Trans. Graph 2006, 25, 835–846.
[11]
Snavely, N. Scene Reconstruction and Visualization from Internet Photo CollectionsPh.D. Thesis, University of Washington, Seattle, WA, USA. 2008.
[12]
Ullman, S. The interpretation of structure from motion. Proc. R. Soc. Lond. B 1979, 203, 405–426.
[13]
Tomasi, C.; Kanade, T. Shape and motion from image streams under orthography: A factorization method. Int. J. Comput. Vis 1992, 9, 137–154.
[14]
Poelman, C.J.; Kanade, T. A paraperspective factorization method for shape and motion recovery. IEEE. Trans. Pattern Anal. Mach. Intell 1997, 19, 97–108.
[15]
Frahm, J-M.; Pollefeys, M.; Lazebnik, S.; Gallup, D.; Clipp, B.; Raguram, R.; Wu, C.; Zach, C.; Johnson, T. Fast robust large-scale mapping from video and internet photo collections. ISPRS J. Photogramm. Remote Sens 2010, 65, 538–549.
[16]
Lingua, A.; Marenchino, D.; Nex, F. Performance analysis of the SIFT operator for automatic feature extraction and matching in photogrammetric applications. Sensors 2009, 9, 3745–3766.
[17]
Barazzetti, L.; Remondino, F.; Scaioni, M.; Brumana, R. Fully Automatic UAV Image-Based Sensor Orientation. Proceedings of the 2010 Canadian Geomatics Conference and Symposium of Commission I, Calgary, AB, Canada, 15–18 June 2010.
[18]
Baltsavias, E.; Gruen, A.; Zhang, L.; Waser, L.T. High-quality image matching and automated generation of 3D tree models. Int. J. Remote Sens 2008, 29, 1243–1259.
[19]
Fonstad, M.A.; Dietrich, J.T.; Courville, B.C.; Jensen, J.L; Carbonneau, P.E. Topographic structure from motion: A new development in photogrammetric measurement. Earth Surf. Process. Landf 2013, 38, 421–430.
[20]
Rango, A.; Laliberte, A.; Herrick, J.E.; Winters, C.; Havstad, K.; Steele, C.; Browning, D. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. J. Appl. Remote Sens 2009, 3, 033542.
[21]
Verhoeven, G. Providing an archaeological bird’s eye view—An overall picture of ground-based means to execute low-altitude aerial photography (LAAP) in archaeology. Archaeol. Prospect 2009, 16, 233–249.
[22]
Verhoeven, G.; Loenders, J.; Vermeulen, F.; Docter, R. Helikite aerial photography (HAP)—A versatile means of unmanned, radio-controlled, low-altitude aerial archaeology. Archaeol. Prospect 2009, 16, 125–138.
[23]
Mathews, A.; Jensen, J. Visualizing and quantifying vineyard canopy LAI using an unmanned aerial vehicle (UAV) collected high density structure from motion point cloud. Remote Sens 2013, 5, 2164–2183.
[24]
D’Oleire-Oltmanns, S.; Marzolff, I.; Peter, K.; Ries, J. Unmanned aerial vehicle (UAV) for monitoring soil erosion in Morocco. Remote Sens 2012, 4, 3390–3416.
[25]
Wallace, L.; Lucieer, A.; Watson, C.; Turner, D. Development of a UAV-LiDAR system with application to forest inventory. Remote Sens 2012, 4, 1519–1543.
[26]
Hunt, E.; Hively, W.; Fujikawa, S.; Linden, D.; Daughtry, C.; McCarty, G. Acquisition of NIR-Green-Blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens 2010, 2, 290–305.
[27]
Fonstad, M.A.; Marcus, W.A. High resolution, basin extent observations and implications for understanding river form and process. Earth Surf. Process. Landf 2010, 35, 680–698.
[28]
Turner, D.; Lucieer, A.; Watson, C. An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds. Remote Sens 2012, 4, 1392–1410.
[29]
Harwin, S.; Lucieer, A. Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sens 2012, 4, 1573–1599.
[30]
Rosnell, T.; Honkavaara, E. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors 2012, 12, 453–480.
[31]
Bryson, M.; Johnson-Roberson, M.; Murphy, R.J.; Bongiorno, D. Kite aerial photography for low-cost, ultra-high spatial resolution multi-spectral mapping of intertidal landscapes. PloS One 2013, 8, e73550.
[32]
Wheaton, J.M.; Brasington, J.; Darby, S.E.; Sear, D.A. Accounting for uncertainty in DEMs from repeat topographic surveys: Improved sediment budgets. Earth Surf. Process. Landf 2010, 35, 136–156.
[33]
Teatini, P.; Ferronato, M.; Gambolati, G.; Bertoni, W.; Gonella, M. A century of land subsidence in Ravenna, Italy. Env. Geol 2005, 47, 831–846.
[34]
Carbognin, L.; Tosi, L. Interaction between climate changes, eustacy and land subsidence in the North Adriatic Region, Italy. Mar. Ecol. Prog. Ser 2002, 23, 38–50.
[35]
Agisoft PhotoScan. User Manual: Professional Edition. Version 0.9.1; AgiSoft LLC: Petersburg, Russia, 2013.
[36]
Seitz, S.; Curless, B.; Diebel, J.; Scharstein, D.; Szeliski, R. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern, Washington, DC, USA, 17–22 June 2006; pp. 519–528.
[37]
Szeliski, R. Computer Vision: Algorithms and Applications; Springer-Verlag: London, UK, 2010.
[38]
Verhoeven, G. Taking computer vision aloft—Archaeological three–dimensional reconstructions from aerial photographs with PhotoScan. Archaeol. Prospect 2011, 18, 67–73.
[39]
Montreuil, A.; Joanna, B.; Chandler, J. Detecting seasonal variations in embryo dune morphology using a terrestrial laser scanner. J. Coast. Res 2013, 65, 1313–1318.