Propagation of vertical and horizontal source data errors into a TIN with linear interpolation

International Journal of Geographical Information Science

Volumn 28, Issue 7, 2014, Pages 1378-1400

  Fan, Lei ^a   Smethurst, Joel ^a   Atkinson, Peter ^a   Powrie, William ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

DEM accuracy; error propagation; spatial interpolation; topographical survey; triangulated irregular network

Indexed keywords

EID: 84901268947     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2014.889299     Document Type: Article

References (44)

1. Aguilar, F.J., et al., 2005. Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogrammetric Engineering & Remote Sensing, 71 (7), 805-816. doi:10.14358/PERS.71.7.805
2. Aguilar, F.J., et al., 2006. The accuracy of grid digital elevation models linearly constructed from scattered sample data. International Journal of Geographical Information Science, 20 (2), 169-192. doi:10.1080/13658810500399670
3. American Society for Photogrammetry and Remote Sensing, 2004. ASPRS guidelines: vertical accuracy reporting for lidar data [online]. In: M. Flood, eds. Draft ASPRS guidelines: Horizontal accuracy reporting for lidar data [online]. Available from: http://www.asprs.org/a/society/committees/standards/Vertical_Accuracy_Reporting_for_Lidar_Data.pdf [Accessed 16 September 2013]; http://www.asprs.org/a/society/committees/standards/Horizontal_Accuracy_Reporting_for_Lidar_Data.pdf [Accessed 16 September 2013].
4. Bater, C.W. and Coops, N.C., 2009. Evaluating error associated with lidar-derived DEM interpolation. Computers and Geosciences, 35, 289-300. doi:10.1016/j.cageo.2008.09.001
5. Bruin, S.D., 2008. Modelling positional uncertainty of line features by accounting for stochastic deviations from straight line segments. Transactions in GIS, 12 (2), 165-177. doi:10.1111/j.1467-9671.2008.01093.x
6. Brunori, C.A., et al., 2013. Characterization of active fault scarps from LiDAR data: a case study from Central Apennines (Italy). International Journal of Geographical Information Science, 27 (7), 1405-1416. doi:10.1080/13658816.2012.684385
7. Chen, Y.M. and Zhou, Q.M., 2013. A scale-adaptive DEM for multi-scale terrain analysis. International Journal of Geographical Information Science, 27 (7), 1329-1348. doi:10.1080/13658816.2012.739690
8. Coveney, S., 2013. Association of elevation error with surface type, vegetation class and data origin in discrete-returns airborne LiDAR. International Journal of Geographical Information Science, 27 (3), 467-483. doi:10.1080/13658816.2012.695794
9. Cox, M.G. and Harris, P.M., 2006. Software support for metrology best practice guide no. 6: uncertainty evaluation. Teddington: National Physical Laboratory.
10. Day, S.S., et al., 2013. Measuring bluff erosion part 1: terrestrial laser scanning methods for change detection. Earth Surface Processes Landforms, 38, 1055-1067. doi:10.1002/esp.3353
11. Erdogan, S., 2009. A comparison of interpolation methods for producing digital elevation models at the field scale. Earth Surface Processes and Landforms, 34, 366-376. doi:10.1002/esp.1731
12. Fisher, P.F. and Tate, N.J., 2006. Causes and consequences of error in digital elevation models. Progress in Physical Geography, 30 (4), 467-489. doi:10.1191/0309133306pp492ra
13. Fonstad, M.A., et al., 2013. Topographic structure from motion: a new development in photogrammetric measurement. Earth Surface Processes Landforms, 38, 421-430. doi:10.1002/esp.3366
14. Gallay, M., et al., 2013. Assessing modern ground survey methods and airborne laser scanning for digital terrain modelling: a case study from the Lake District, England. Computers & Geosciences, 51, 216-227. doi:10.1016/j.cageo.2012.08.015
15. Gessesse, G.D., et al., 2010. Assessment of erosion, deposition and rill development on irregular soil surfaces using close range digital photogrammetry. The Photogrammetric Record, 25, 299-318. doi:10.1111/j.1477-9730.2010.00588.x
16. Goodchild, M.F., 1999. Measurement-based GIS. In: W.Z. Shi, et al., eds. Proceedings of the international symposium on spatial data quality '99. Hong Kong Polytechnic University, 1-9.
17. Heritage, G.L., et al., 2009. Influence of survey strategy and interpolation model on DEM quality. Geomorphology, 112 (3-4), 334-344.
18. Heuvelink, G.B.M., 1998. Error propagation in environmental modelling with GIS. London: Taylor & Francis.
19. Hodge, R.A., 2010. Using simulated terrestrial laser scanning to analyse errors in high-resolution scan data of irregular surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 65 (2), 227-240. doi:10.1016/j.isprsjprs.2010.01.001
20. Hodgson, M.E. and Bresnahan, P., 2004. Accuracy of airborne LiDAR-derived elevation: empirical assessment and error budget. Photogrammetric Engineering & Remote Sensing, 70, 331-339. doi:10.14358/PERS.70.3.331
21. Hormann, K. and Agathos, A., 2001. The point in polygon problem for arbitrary polygons. Computational Geometry: Theory and Applications, 20 (3), 131-144. doi:10.1016/S0925-7721(01)00012-8
22. Hu, P., Liu, X.H., and Hu, H., 2009. Accuracy assessment of digital elevation models based on approximation theory. Photogrammetric Engineering & Remote Sensing, 75 (1), 49-56. doi:10.14358/PERS.75.1.49
23. Huang, Y.D., 2000. Evaluation of information loss in digital elevation models with digital photogrammetric systems. Photogrammetric Record, 16 (95), 781-791. doi:10.1111/0031-868X.00151
24. Kyriakidis, P.C. and Goodchild, M.F., 2006. On the prediction error variance of three common spatial interpolation schemes. International Journal of Geographical Information Science, 20 (8), 823-855. doi:10.1080/13658810600711279
25. Kyriakidis, P.C., Shortridge, A.M., and Goodchild, M.F., 1999. Geostatistics for conflation and accuracy assessment of digital elevation models. International Journal of Geographical Information Science, 13 (7), 677-707. doi:10.1080/136588199241067
26. Leica HDS8810 Datasheet [online]. Available from: http://www.leica-geosystems.co.uk/downloads123/zz/hds/HDS8800/brochures/HDS8800_BRO_en.pdf [Accessed 23 September 2013].
27. Leung, Y., Ma, J.H., and Goodchild, M.F., 2004a. A general framework for error analysis in measurement-based GIS Part 1: The basic measurement-error model and related concepts. Journal of Geographical Systems, 6, 325-354. doi:10.1007/s10109-004-0141-4
28. Leung, Y., Ma, J.H., and Goodchild, M.F., 2004b. A general framework for error analysis in measurement-based GIS Part 3: Error analysis in intersections and overlays. Journal of Geographical Systems, 6, 381-402. doi:10.1007/s10109-004-0143-2
29. Li, Z.L., 1993. Mathematical models of the accuracy of digital terrain model surfaces linearly constructed from square gridded data. The Photogrammetric Record, 14 (82), 661-674. doi:10.1111/j.1477-9730.1993.tb00776.x
30. Lloyd, C.D. and Atkinson, P.M., 2002. Non-stationary approaches for mapping terrain and assessing prediction uncertainty. Transactions in GIS, 6 (1), 17-30. doi:10.1111/1467-9671.00092
31. Lloyd, C.D. and Atkinson, P.M., 2006. Deriving ground surface digital elevation models from LiDAR data with geostatistics. International Journal of Geographical Information Science, 20 (5), 535-563. doi:10.1080/13658810600607337
32. Negishi, J.N., et al., 2012. Using airborne scanning laser altimetry (LiDAR) to estimate surface connectivity of floodplain water bodies. River Research and Applications, 28, 258-267. doi:10.1002/rra.1442
33. Oksanen, J. and Sarjakoski, T., 2005. Error propagation of DEM-based surface derivatives. Computers and Geosciences, 31, 1015-1027. doi:10.1016/j.cageo.2005.02.014
34. RIEGEL VZ-6000 Datasheet [online]. Available from: http://www.riegl.com/uploads/tx_pxpriegldownloads/DataSheet_VZ-6000_23-09-2013_PRELIMINARY.pdf [Accessed 23 September 2013].
35. Schürch, P., et al., 2011. Detection of surface change in complex topography using terrestrial laser scanning: application to the Illgraben debris-flow channel. Earth Surface Processes and Landforms, 36, 1847-1859. doi:10.1002/esp.2206
36. Shi, W.Z., Li, Q.Q., and Zhu, C.Q., 2005. Estimating the propagation error of DEM from higher-order interpolation algorithms. International Journal of Remote Sensing, 26 (14), 3069-3084. doi:10.1080/01431160500057905
37. Shortridge, A., 2001. Characterizing uncertainty in digital elevation models. In C.T. Hunsaker, et al., eds. Spatial uncertainty in ecology: implications for remote sensing and GIS applications. New York: Springer, 238-257.
38. Spaete, L.P., et al., 2011. Vegetation and slope effects on accuracy of a LiDAR-derived DEM in the sagebrush steppe. Remote Sensing Letters, 2 (4), 317-326. doi:10.1080/01431161.2010.515267
39. Su, J.G. and Bork, E.W., 2006. Influence of vegetation, slope and LiDAR sampling angle on DEM accuracy. Photogrammetric Engineering and Remote Sensing, 72, 1265-1274. doi:10.14358/PERS.72.11.1265
40. Taylor, J.R., 1982. An introduction to error analysis: the study of uncertainties in physical measurements. 1st ed. Mill Valley, CA: University Science Books.
41. Webster, T.L., et al., 2006. Flood-risk mapping for storm-surge events and sea-level rise using LIDAR for southeast New Brunswick. Canadian Journal of Remote Sensing, 32 (2), 194-211. doi:10.5589/m06-016
42. Wechsler, S.P. and Kroll, C.N., 2006. Quantifying DEM uncertainty and its effect on topographic parameters. Photogrammetric Engineering & Remote Sensing, 72 (9), 1081-1090. doi:10.14358/PERS.72.9.1081
43. Wheaton, J.M., et al., 2010. Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surface Processes Landforms, 35, 136-156.
44. Zhu, C., et al., 2005. Estimation of average DEM accuracy under linear interpolation considering random error at the nodes of TIN model. International Journal of Remote Sensing, 26 (24), 5509-5523. doi:10.1080/10245330500169029