Evaluation of accuracy of DEMs obtained from uav-point clouds for different topographical areas

Year 2017, , 110 - 117, 01.10.2017

Alper Akar

https://doi.org/10.26833/ijeg.329717

Cited By: 23

Abstract

The main objective of the study was to examine accuracies of DEMs (Digital Elevation Models) with different topographical structures generated by using the Unmanned Aerial Vehicle (UAV) point clouds. Two different terrains with flat and sloping topographical structures were selected for the study, and DEMs of these terrains were generated using eight interpolation techniques (Kriging, Natural Neighbor, Radial Basis Function Triangulation with Linear interpolation, Nearest Neighbor, Invers Distance to a Power, Local Polynomial and Minimum Curvature). The accuracies of DEMs were tested by calculating the statistic methods with the help of the control points obtained by land surveying techniques. At the end of the study, it was observed that in DEMs prepared for both flat (study area 1) and sloping (study area 2) terrains, Kriging interpolation method yields the best results as study area 1 and 2, respectively. In addition, the results were examined using Shapiro-Wilk and ANOVA: Friedman tests. After observing with the Shapiro- Wilk test that the data has a normal distribution, it was statistically determined through the parametric ANOVA: Friedman test that there is no difference between the variables.

Keywords

Digital Elevation Model, Unmanned Aerial Vehicles, Kriging, ANOVA

References

• Jain, S.K. and Singh, V.P., 2003. Water Resources Systems Planning and Management.Fist ed. Elsevier, Amsterdam.
• Honghai, Q.I. and Altinakar, M.S., 2011. A GIS-based decision support system for integrated flood management under uncertainty with two dimensional numerical simulations. Environmental Modelling and Software, 26 (6), 817–821.
• Ramlal, B. and Baban, S., 2008. Developing a GIS based integrated approach to flood management in Trinidad. West Indies, Journal of Environmental Management, 88 (4), 1131-1140.
• Spark, R.N. and Williams, P.F., 1996. Digital Terrain Models and the visualization of structural geology, Computer Methods in the Geosciences, 15, 421–446.
• Januchowski, S.R., Pressey, R.L., Van Der Wal, J. and Edwards, A., 2010. Characterizing errors in digital elevation models and estimating the financial costs of accuracy. International Journal of Geographical Information Science, 24 (9), 1327–1347.
• Hohle, J., 2009. DEM generation using a digital large format frame camera. Photogrammetric Engineering and Remote Sensing, 75 (1), 87–93.
• Kraus, K., 2007. Photogrammetry - Geometry from Images and Laser Scans, Walter de Gruyter, Goettingen, Germany, p. 459.
• Vosselman, G. and Maas, H.G., 2010. Airborne and Terrestrial Laser Scanning. CRC: Boca Raton, FL, USA, p. 318.
• Arun, P.V., 2013. A comparative analysis of different DEM interpolation methods. Geodesy and Cartography, 39 (4), 171-177.
• Wilson, J.P., Gallant, J.C., 2000. Secondary Topographic Attribute. in: Wilson, J.P.,Gallant, J.C. (Eds.), Terrain Analysis: Principles and Applications, John Wiley andSons., New York, pp. 87–131.
• Uysal, M., Toprak, A.S. and Polat, N., 2015. DEM generation with UAV photogrammetry and accuracy analysis in Sahitler Hill, Measurement, 73, 539-543.
• Remondino, F., Barazzetti, L., Nex, F., Scaioni, M. and Sarazzi, D., 2011. UAV Photogrammetry for mapping and 3D modelling current status and future perspectives. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-1- C22, pp. 25-31.
• Ruzgienė, B., Berteška, T., Gečyte, S., Jakubauskienė, E. and Aksamitauskas, V.Č., 2015. The surface modelling based on UAV photogrammetry and qualitativeestimation. Measurement, 73, 619–627.
• Mesas-Carrascosa, F.J., Notario-García, M.D., de Larriva, J.E.M., de la Orden, M.S. and Porras, A.G.F., 2014. Validation of measurements of land plot area using UAV imagery. International Journal of Applied Earth Observation and Geoinformation, 33, 270–279.
• Austin, R., 2010. Unmanned Aircraft Systems: UAVs Design, Development and Deployment, John Wiley and Son Ltd. Publication, Hoboken, Chichester.
• Colomina, I. and Molina, P., 2014. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97.
• Sauerbier, M. and Eisenbeiss, H., 2010. UAVs for the documentation of archaeological excavations. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII, Part 5, pp. 526-531.
• Aguilar, F.J., Agüera, F., Aguilar, M.A. and Fernando, C., 2005. Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogrammetric Engineering and Remote Sensing, 71, (7), 805–816.
• Gong, J., Li,. Z, Zhu, Q., Sui, H. and Zhou, Y., 2000. Effects of various factors on the accuracy of DEMs: an intensive experimental investigation. Photogrammetric Engineering and Remote Sensing, 66 (9), 1113–1117.
• Kienzle, S., 2004. The effect of DEM raster resolution on first order, second order and compound terrain derivatives, Transactions in GIS, 8, (1), 83–111.
• Surfer guide 8.0., 2002. Surfer 8.0 user’s guide, Golden Software, Inc. Colorado, USA.
• Vohat, P., Gupta, V., Bordoloi, T.K., Naswa, H., Singh, G. and Singh, M., 2013. Analysis of different interpolation methods for up hole data using Surfer software. 10th Biennial International Conference & Exposition, 23-25 November, Kochi, Kerala, India.
• Zhu, P., Zhang, L.W., Liew, K.M., 2014. Geometrically nonlinear thermomechanical analysis of moderately thick functionally graded plates using a local Petrov– Galerkin approach with moving Kriging interpolation, Compos. Struct., 107, 298–314.
• Yılmaz, I., 2009. A research on the accuracy of landform volumes determined using different interpolation methods. Scientific Research and Essay, 4 (11), 1248–1259.
• Driscoll, T.A. and Heryudono, A., 2007. Adaptive residual subsampling methods for radial basis function interpolation and collocation problems. Comput Math Appl 53, 927–939.
• Gutmann, H.M., 2001. A radial basis function method for global optimization. Journal of Global Optimization, 19 (3), 201–227.
• Prasantha, H.S., Shashidhara, H.L. and Balasubramanya, M.K.N., 2009. Image Scaling Comparison Using Universal Image Quality Index, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, 28-29 December, Trivandrum, Kerala, India, pp.859-863.
• Yang, C.S., Kao, S.P., Lee, F.B. and Hung, P.S., 2004. Twelve different interpolation methods: a case study of Surfer 8.0. In: Proceedings of ISPRS Congress 20, pp. 778–785.
• Briggs, I.C., 1974. Machine contouring using minimum curvature, Geophysics, 39, 39–48.
• Cooke, R., Mostaghimi, S. and Parker, J.C., 1993. Estimating oil spill characteristics from oil heads in scattered monitoring wells. Environmental Monitoring and Assessment, 28, 33–51.
• Shepard, D., 1968. A two-dimensional interpolation for irregularly-spaced data function.
• Lazzaro, D. and Montefusco, L.B., 2002. Radial basis functions for the multivariate interpolation of large scattered data sets. J. Comput. Appl. Math. 140, 521– 536.
• Siebert, S. and Teizer, J., 2014. Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Automation in Construction. 41, 1–14.
• Tabachnick, B.G. and Fidell, L.S., 2013. Using Multivariate Statistics, fifth ed.Pearson, Boston.