Advertisement

Applied Geomatics

, Volume 11, Issue 1, pp 81–96 | Cite as

The effect of user-defined parameters on DTM accuracydevelopment of a hybrid model

  • Ante ŠiljegEmail author
  • Mirko Barada
  • Ivan Marić
  • Vlatko Roland
Original Paper
  • 113 Downloads

Abstract

User-defined parameters (point density, interpolation, and pixel size) have a great influence on the accuracy of digital terrain model (DTM). Therefore, the optimal interpolation method (IM) and appropriate pixel size should be used to create a continuous surface. Pixel size or spatial resolution tends to be a compromise between the number of samples and the size of the study area, whereby input dataset is often devaluated. In this paper, the authors propose a new methodological approach to DTM production (hybrid DTM—HDTM) from an aero-photogrammetry dataset. Two different approaches to DTM production are presented: the usual (UDTM) and the hybrid (HDTM). HDTM is based on restructuring and refining the input dataset by generating contour lines, determining the optimal interpolation method and selecting the appropriate spatial resolution. The goal is to develop a qualitative DTM while minimalizing the devaluation of input data and error propagation. The accuracy of different IMs for UDTM was examined in a comparative analysis of six statistical parameters by applying cross-validation, with the focus on the root mean square error (RMSE) parameter. Four different methods of selecting optimal spatial resolution were tested for the same model. The UDTM and HDTM generated were compared by interior, exterior, and visual accuracy assessment and by the performance success of specific hydrological parameters: (1) flow accumulation—by applying a developed DNA concept and (2) watershed—by calculating volume and 3D surface. The reference value for exterior accuracy assessment was high-resolution airborne LiDAR DTM (LDTM). It was found that ordinary kriging (OK) was the best IM (RMSE, 1.9893 m). The spatial resolution of UDTM was calculated by combining two variants of the point pattern analysis method and was found to be 19 m. On the other hand, the complexity of terrain method was used to define the spatial resolution of HDTM (3 m). HDTM achieved better results than UDTM in all aspects of accuracy assessment. The exterior accuracy of HDTM was better by 1.483 m (RMSE). Finally, the results of the applied DNA concept showed that a stream generated from HDTM had a 2.587 m lower horizontal root mean square error (HRMSE) than the stream generated from UDTM.

Keywords

DTM accuracy Sampling method Pixel size Spatial interpolation Hybrid DTM 

References

  1. Aguilar FJ, Agüera F, Aguilar MA, Carvajal F (2005) Effects of terrain morphology, sampling density, and interpolation methods on grid DTM accuracy. Photogramm Eng Remote Sens 71(7):805–816.  https://doi.org/10.14358/PERS.71.7.805 CrossRefGoogle Scholar
  2. Albani M, Klinkenberg B, Andison DW, Kimmins JP (2004) The choice of window size in approximating topographic surfaces from digital elevation models. Int J Geogr Inf Sci 18(6):577–593.  https://doi.org/10.1080/13658810410001701987 CrossRefGoogle Scholar
  3. Al-Yahyai S, Charabi Y, Gastli A (2013) Optimal micro-siting of small wind turbine using numerical simulation. In GCC Conference and Exhibition (GCC), 2013 7th IEEE (pp 28–32).  https://doi.org/10.1109/IEEEGCC.2013.6705743
  4. Anders NS, Seijmonsbergen AC, Bouten W (2013) Geomorphological change detection using object-based feature extraction from multi-temporal LiDAR data. IEEE Geosci Remote Sens Lett 10(6):1587–1591.  https://doi.org/10.1109/LGRS.2013.2262317 CrossRefGoogle Scholar
  5. Anderson ES, Thompson JA, Austin RE (2005) LiDAR density and linear interpolator effects on elevation estimates. Int J Remote Sens 26(18):3889–3900.  https://doi.org/10.1080/01431160500181671 CrossRefGoogle Scholar
  6. Anderson DL, Ames DP, Yang P (2014) Quantitative methods for comparing different polyline stream network models. J Geogr Inf Syst 6:88–98.  https://doi.org/10.4236/jgis.2014.62010 CrossRefGoogle Scholar
  7. Aricak B (2015) Using remote sensing data to predict road fill areas and areas affected by fill erosion with planned forest road construction: a case study in Kastamonu Regional Forest Directorate (Turkey). Environ Monit Assess 187(7):417.  https://doi.org/10.1007/s10661-015-4663-7 CrossRefGoogle Scholar
  8. Barada M (2017) Utjecaj korisničko-definiranih parametara na točnost digitalnog modela reljefa. Master Thesis, Department of Geography, University of Zadar, CroatiaGoogle Scholar
  9. Barker DM, Lawler DM, Knight DW, Morris DG, Davies HN, Stewart EJ (2009) Longitudinal distributions of river flood power: the combined automated flood, elevation and stream power (CAFES) methodology. Earth Surf Process Landf 34:280–290.  https://doi.org/10.1002/esp.1723 CrossRefGoogle Scholar
  10. Bashfield A, Keim A (2011) Continent-wide DEM creation for the European Union. In: 34th International Symposium on Remote Sensing of Environment. The GEOSS era: towards operational environmental monitoring. Sydney, Australia, pp 10–15Google Scholar
  11. Bater CW, Coops NC (2009) Evaluating error associated with lidar-derived DEM interpolation. Comput Geosci 35(2):289–300.  https://doi.org/10.1016/j.cageo.2008.09.001 CrossRefGoogle Scholar
  12. Biron PM, Choné G, Buffin-Bélanger T, Demers S, Olsen T (2013) Improvement of streams hydro-geomorphological assessment using LiDAR DTMs. Earth Surf Process Landf 38:1808–1821.  https://doi.org/10.1002/esp.3425 CrossRefGoogle Scholar
  13. Bishop MP, James LA, Shroder JF, Walsh SJ (2012) Geospatial technologies and digital geomorphological mapping: concepts, issues and research. Geomorphology 137(1):5–26.  https://doi.org/10.1016/j.geomorph.2011.06.027 CrossRefGoogle Scholar
  14. Böer J, Gonzalez C, Wecklich C, Bräutigam B, Schulze D, Bachmann M, Zink M (2016) Performance assessment of the final TanDTM-X DTM. In: ESA living planet symposium, Prague, Czech Republic, 9–13 May 2016Google Scholar
  15. Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, New YorkGoogle Scholar
  16. Callow JN, Van Niel KP, Boggs GS (2007) How does modifying a DTM to reflect known hydrology affect subsequent terrain analysis? J Hydrol 332(1–2):30–39.  https://doi.org/10.1016/j.jhydrol.2006.06.020 CrossRefGoogle Scholar
  17. Cavazzi S, Corstanje R, Mayr T, Hannam J, Fealy R (2013) Are fine resolution digital elevation models always the best choice in digital soil mapping? Geoderma 195:111–121.  https://doi.org/10.1016/j.geoderma.2012.11.020 CrossRefGoogle Scholar
  18. Chaplot V, Darboux F, Bourennane H, Leguédois S, Silvera N, Phachomphon K (2006) Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology 77(1–2):126–141.  https://doi.org/10.1016/j.geomorph.2005.12.010 CrossRefGoogle Scholar
  19. Chase AF, Chase DZ, Weishampel JF, Drake JB, Shrestha RL, Slatton KC, Awe JJ, Carter WE (2011) Airborne LiDAR, archaeology, and the ancient Maya landscape at Caracol, Belize. J Archaeol Sci 38(2):387–398.  https://doi.org/10.1016/j.jas.2010.09.018 CrossRefGoogle Scholar
  20. Chu HJ, Wang CK, Huang ML, Lee CC, Liu CY, Lin CC (2014) Effect of point density and interpolation of LiDAR-derived high-resolution DEMs on landscape scarp identification. GIScience & remote sensing 51(6):731–747.  https://doi.org/10.1080/15481603.2014.980086 CrossRefGoogle Scholar
  21. Claessens L, Heuvelink GBM, Schoorl JM, Veldkamp A (2005) DTM resolution effects on shallow landslide hazard and soil redistribution modelling. Earth Surf Process Landf 30:461–477.  https://doi.org/10.1002/esp.1155 CrossRefGoogle Scholar
  22. Contreras M, Aracena P, Chung W (2012) Improving accuracy in earthwork volume estimation for proposed forest roads using a high-resolution digital elevation model. Croatian Journal of Forest Engineering 33(1):125–142Google Scholar
  23. DGU RH (2014) Specifikacija proizvoda - Digitalni model reljefa 2. DGU RH, Zagreb, p 0Google Scholar
  24. Erdogan S (2009) A comparison of interpolation methods for producing digital elevation models at the field scale. Earth Surf Process Landf 34(3):366–376.  https://doi.org/10.1002/esp.1731 CrossRefGoogle Scholar
  25. ESRI (2016), ArcGIS Desktop 10.1 – Help, 2016Google Scholar
  26. Evans DH, Fletcher RJ, Pottier C, Chevance JB, Soutif D, Tan BS, Im S, Ea D, Tin T, Kim S, Cromarty C, de Greef S, Hanus K, Baty P, Kuszinger R, Shimoda I, Boornazian G (2013) Uncovering archaeological landscapes at Angkor using lidar. Proceedings of the National AcaDTMy of Sciences 110(31):12595–12600.  https://doi.org/10.1073/pnas.1306539110 CrossRefGoogle Scholar
  27. Feizizadeh B, Blaschke T (2016) Assessing uncertainties associated with digital elevation models for object based landslide delination. In: GEOBIA 2016: solutions and synergies, 14 September 2016–16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation. DOI:  https://doi.org/10.3990/2.390
  28. Fisher PF, Tate NJ (2006) Causes and consequences of error in digital elevation models. Prog Phys Geogr 30(4):467–489CrossRefGoogle Scholar
  29. Florinsky IV (2002) Errors of signal processing in digital terrain modelling. Int J Geogr Inf Sci 16(5):475–501.  https://doi.org/10.1080/13658810210129139 CrossRefGoogle Scholar
  30. Gamba P, Dell’Acqua F, Houshmand B (2003) Comparison and fusion of LIDAR and InSAR digital elevation models over urban areas. Int J Remote Sens 24(22):4289–4300.  https://doi.org/10.1080/014311603100009600 CrossRefGoogle Scholar
  31. Gonçalves A, Almeida J, Rua H (2016) Assessment of the permeability of historical defensive systems: the case of the lines of Torres Vedras. Int J Hist Archaeol 20(2):229–248.  https://doi.org/10.1007/s10761-016-0334-9 CrossRefGoogle Scholar
  32. Goulding CJ (1977) Cubic spline curves and calculation of volume of sectionally measured trees. Forest Research Institute 9 (1): 89–99, New Zealand Forest Service, RotoruaGoogle Scholar
  33. Grindle C, Lewis M, Glinton R, Giampapa J, Owens S, Sycara K (2004) Automating terrain analysis: algorithms for intelligence preparation of the battlefield. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 48, No. 3, pp. 533–537), Sage CA: Los Angeles, DOI:  https://doi.org/10.1177/154193120404800355
  34. Guptill SC, Morrison JL (2002) Elementi kvalitete prostornih podataka, O’Alster, Ipswich. Translated by: D. Tutić and M. Lapaine, Hrvatsko kartografsko društvo, Zagreb, 1–11Google Scholar
  35. Hengl T (2006) Finding the right pixel size. Comput Geosci 32(9):1283–1298.  https://doi.org/10.1016/j.cageo.2005.11.008 CrossRefGoogle Scholar
  36. Hengl T, Evans IS (2009) Mathematical and digital models of the land surface. In: Hengl T, Reuter HI (eds) Geomorphometry: concepts, software, applications. Elsevier, Amsterdam, pp 31–63.  https://doi.org/10.1016/S0166-2481(08)00002-0 CrossRefGoogle Scholar
  37. Hengl T, Gruber S, Shrestha DP (2003) Digital terrain analysis in ILWIS: lecture notes and user guide. In: International institute for geo-information science and earth observation (ITC). Enschede, NetherlandsGoogle Scholar
  38. Hoober D, Svoray T, Cohen S (2017) Using a landform evolution model to study ephemeral gullying in agricultural fields: the effects of rainfall patterns on ephemeral gully dynamics. Earth Surf Process Landf 42:1213–1226CrossRefGoogle Scholar
  39. Humme A, Lindenbergh R, Sueur C (2006) Revealing celtic fields from lidar data using kriging based filtering. In: Symposium V, Maas H-G, Schneider D (eds) Proceedings of the ISPRS Commission. Dresden, Germany, pp 25–27, 2006Google Scholar
  40. Hutchinson MF (1989) A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. J Hydrol 106:211–232.  https://doi.org/10.1016/0022-1694(89)90073-5 CrossRefGoogle Scholar
  41. Hutchinson MF (1996) A locally adaptive approach to the interpolation of digital elevation models. In: Third International Conference/Workshop on Integrating GIS and Environmental Modeling, Santa Fe, NM, January 21–26, 1996. Santa Barbara, CA: National Center for Geographic Information and AnalysisGoogle Scholar
  42. Hutchinson MF, Xu T, Stein JA (2011) Recent progress in the ANUDTM elevation gridding procedure. Geomorphometry 2011, 19–22. ISO 690Google Scholar
  43. Hynek BM, Beach M, Hoke MR (2010) Updated global map of Martian valley networks and implications for climate and hydrologic processes. Journal of Geophysical Research: Planets, 115(E9). DOI:  https://doi.org/10.1029/2009JE003548
  44. Ismail Z, Rahman MZA, Salleh MRM, Yusof ARM (2015) Accuracy assessment of LIDAR-derived elevation value over vegetated terrain in tropical region. Jornal Teknologi 73(5). DOI:  https://doi.org/10.11113/jt.v73.4335
  45. James LA, Watson DG, Hansen WF (2007) Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA. Catena 71(1):132–144.  https://doi.org/10.1016/j.catena.2006.10.010 CrossRefGoogle Scholar
  46. Jonkman SN, Maaskant B, Boyd E, Levitan ML (2009) Loss of life caused by the flooding of New Orleans after hurricane Katrina: analysis of the relationship between flood characteristics and mortality. Risk Anal 29(5):676–698.  https://doi.org/10.1111/j.1539-6924.2008.01190.x CrossRefGoogle Scholar
  47. Karel W, Pfeifer N, Briese C (2006) DTM quality assessment, in: ISPRS Technical Commission Symposium. In: ISPRS Technical Commission II Symposium (2006), International Archives of the ISPRS, XXXVI/2 (2006), Wien, 1682-1750, 7Google Scholar
  48. Kienzle S (2004) The effect of DEM raster resolution on first order, second order and compound terrain derivatives. Trans GIS 8(1):83–111CrossRefGoogle Scholar
  49. Korzeniowska K, Łącka M (2011) Generating DEM from LiDAR data–comparison of available software tools. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 22Google Scholar
  50. Leitão JP, de Vitry MM, Scheidegger A, Rieckermann J (2016) Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas. Hydrol Earth Syst Sci 20(4):1637.  https://doi.org/10.5194/hess-20-1637-2016 CrossRefGoogle Scholar
  51. Li J, Heap AD (2008) A review of spatial interpolation methods for environmental scientists. Geoscience Australia, Record 2008/23, CanberraGoogle Scholar
  52. Li J, Heap AD (2014) Spatial interpolation methods applied in the environmental sciences: a review. Environ Model Softw 53:173–189.  https://doi.org/10.1016/j.envsoft.2013.12.008 CrossRefGoogle Scholar
  53. Li Z, Zhu Q, Gold C (2005) Digital terrain modeling. CRC Press, LondonGoogle Scholar
  54. Lin S, Jing C, Coles NA, Chaplot V, Moore NJ, Wu J (2013) Evaluating DTM source and resolution uncertainties in the soil and water assessment tool. Stoch Env Res Risk A 27(1):1–13.  https://doi.org/10.1007/s00477-012-0577-x CrossRefGoogle Scholar
  55. Liu X (2008) Airborne LiDAR for DEM generation: some critical issues. Prog Phys Geogr 32(1):31–49.  https://doi.org/10.1177/0309133308089496 CrossRefGoogle Scholar
  56. Lo Curzio S, Magliulo P (2010) Soil erosion assessment using geomorphological remote sensing techniques: an example from southern Italy. Earth Surf Process Landf 35:262–271.  https://doi.org/10.1002/esp.1905 CrossRefGoogle Scholar
  57. Longley PA (2005) Geographical information systems and science, 2nd edn. Wiley, ChichesterGoogle Scholar
  58. Maio CV, Tenenbaum DE, Brown CJ, Mastone VT, Gontz AM (2013) Application of geographic information technologies to historical landscape reconstruction and military terrain analysis of an American Revolution Battlefield: preservation potential of historic lands in urbanized settings, Boston, Massachusetts, USA. J Cult Herit 14(4):317–331.  https://doi.org/10.1016/j.culher.2012.08.002 CrossRefGoogle Scholar
  59. Malone BP, Minasny B, Odgers NP, McBratney AB (2014) Using model averaging to combine soil property rasters from legacy soil maps and from point data. Geoderma 232:34–44. ISO 690.  https://doi.org/10.1016/j.geoderma.2014.04.033 CrossRefGoogle Scholar
  60. Malvić T (2008) Primjena geostatistike u analizi geoloških podataka, Udžbenici Sveučilišta u Zagrebu, INA-Industrija nafte d.d., Zagreb, 2008Google Scholar
  61. McCullagh MJ (1988) Terrain and surface modelling systems: theory and practice. Fotogrammetric Record 12(72):747–779CrossRefGoogle Scholar
  62. Medved I, Pribicević B, Medak D, Kuzmanić I (2010) Usporedba metoda interpolacije batimetrijskih mjerenja za praćenje promjena volumena jezera. Geodetski List 64(2):71–86Google Scholar
  63. Meng X, Currit N, Zhao K (2010) Ground filtering algorithms for airborne LiDAR data: a review of critical issues. Remote Sens 2(3):833–860.  https://doi.org/10.3390/rs2030833 CrossRefGoogle Scholar
  64. Milan DJ, Heritage GL. (2012) LiDAR and ADCP use in gravel bed rivers: advances since GBR6. Gravel-bed rivers: processes, tools, environments, 286–302. DOI:  https://doi.org/10.1002/9781119952497.ch22
  65. Minasny B, McBratney AB (2007) Spatial prediction of soil properties using EBLUP with the Matérn covariance function. Geoderma 140(4):324–336.  https://doi.org/10.1016/j.geoderma.2007.04.028 CrossRefGoogle Scholar
  66. Mitas L, Mitasova H (1999) Spatial interpolation. In: Longley P, Goodchild MF, Maguire DJ, Rhind DW (eds) Geographical information systems: principles, techniques, management and applications, Second edn. Wiley, Chichester, pp 481–492Google Scholar
  67. Nagesh H, Goil S, Choudhary A. (2001) Adaptive grids for clustering massive data sets. In Proceedings of the 2001 SIAM International Conference on Data Mining (pp. 1–17). Society for Industrial and Applied MathematicsGoogle Scholar
  68. Nelson A, Reuter HI, Gessler P (2009) DTM production methods and sources. In: Hengl T, Reuter HI (eds) Geomorphometry: concepts, software, and applications. Elsevier, Amsterdam, pp 65–85CrossRefGoogle Scholar
  69. Nitsche M, Turowski JM, Badoux A, Rickenmann D, Kohoutek TK, Pauli M, Kirchner JW (2013) Range imaging: a new method for high-resolution topographic measurements in small-and medium-scale field sites. Earth Surf Process Landf 38(8):810–825.  https://doi.org/10.1002/esp.3322 CrossRefGoogle Scholar
  70. Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Syst 4(3):313–332CrossRefGoogle Scholar
  71. Pajarola R, Gobbetti E (2007) Survey of semi-regular multiresolution models for interactive terrain rendering. Vis Comput 23(8):583–605.  https://doi.org/10.1007/s00371-007-0163-2 CrossRefGoogle Scholar
  72. Parrot JF, Nunez CR (2016) LiDAR DTM: artifacts, and correction for river altitudes. Investigaciones Geográficas. Boletín del Instituto de Geografía 2016(90):28–39Google Scholar
  73. Passalacqua P, Belmont P, Foufoula Georgiou E (2012) Automatic geomorphic feature extraction from lidar in flat and engineered landscapes. Water Resour Res, 48(3): DOI:  https://doi.org/10.1029/2011WR010958
  74. Pereira P, Oliva M, Misiune I (2016) Spatial interpolation of precipitation indexes in Sierra Nevada (Spain): comparing the performance of some interpolation methods. Theor Appl Climatol 126(3–4):683–698. ISO 690.  https://doi.org/10.1007/s00704-015-1606-8 CrossRefGoogle Scholar
  75. Petrie G, Toth C (2009) Introduction to laser ranging, profiling, and scanning. In: Shan J, Toth CK (ed)Google Scholar
  76. Pike RJ (1995) Geomorphometry – progress, practice and prospect. Zeitschrift für Geomorphologie, Supplement band 101:221–238Google Scholar
  77. Pike RJ (2000) Geomorphometry - diversity in quantitative surface analysis. Prog Phys Geogr 24(1):1–20.  https://doi.org/10.1191/030913300674449511 CrossRefGoogle Scholar
  78. Pike RJ, Evans IS, Hengl T (2009) Geomorphometry: a brief guide. In: Hengl T & Reuter H I (ed) Geomorphometry: concepts, software, applications (pp. 3.30). Elsevier, Amsterdam, The Netherlands, ser. Development in Soil Science, 3–30. DOI:  https://doi.org/10.1016/S0166-2481(08)00001-9
  79. Pirotti F, Tarolli P (2010) Suitability of LiDAR point density and derived landform curvature maps for channel network extraction. Hydrological Processes: An International Journal 24(9):1187–1197.  https://doi.org/10.1002/hyp.7582 CrossRefGoogle Scholar
  80. Podobnikar T (2009) Methods for visual quality assessment of a digital terrain model, SAPIENS 2 (3)Google Scholar
  81. Prosdocimi M, Calligaro S, Sofia G, Dalla Fontana G, Tarolli P (2015) Bank erosion in agricultural drainage networks: new challenges from structure-from-motion photogrammetry for post-event analysis. Earth Surf Process Landf 40:1891–1906.  https://doi.org/10.1002/esp.3767 CrossRefGoogle Scholar
  82. Qi P, Hu S, Cui Y (2013) On the suitability of the SRTM DTM for simulating potential insolation. In: Geoinformatics (GEOINFORMATICS) 2013, 21st International Conference on (pp. 1–5). IEEE. DOI:  https://doi.org/10.1109/Geoinformatics.2013.6626144
  83. Raaflaub LD, Collins MJ (2006) The effect of error in gridded digital elevation models on the estimation of topographic parameters. Environ Model Softw 21(5):710–732.  https://doi.org/10.1016/j.envsoft.2005.02.003 CrossRefGoogle Scholar
  84. Raber GT, Jensen JR, Hodgson ME, Tullis JA, Davis BA, Berglund J (2007) Impact of LiDAR nominal post-spacing on DEM accuracy and flood zone delineation. Photogramm Eng Remote Sens 73(7):793–804.  https://doi.org/10.14358/PERS.73.7.793 CrossRefGoogle Scholar
  85. Remondino F (2003) From point cloud to surface: the modeling and visualization problem. International Archives of photogrammetry, remote sensing and spatial information sciences, 34Google Scholar
  86. Saksena S, Merwade V (2015) Incorporating the effect of DTM resolution and accuracy for improved flood inundation mapping. J Hydrol 530:180–194.  https://doi.org/10.1016/j.jhydrol.2015.09.069 CrossRefGoogle Scholar
  87. Sanders BF (2007) Evaluation of on-line DTMs for flood inundation modeling. Adv Water Resour 30(8):1831–1843.  https://doi.org/10.1016/j.advwatres.2007.02.005 CrossRefGoogle Scholar
  88. Satge F, Denezine M, Pillco R, Timouk F, Pinel S, Molina J, Garnier J, Seyler F, Bonnet MP (2016) Absolute and relative height-pixel accuracy of SRTM-GL1 over the South American Andean Plateau. ISPRS J Photogramm Remote Sens 121:157–166.  https://doi.org/10.1016/j.isprsjprs.2016.09.003 CrossRefGoogle Scholar
  89. Schneider A, Gerke HH, Maurer T, Nenov R (2013) Initial hydro-geomorphic development and rill network evolution in an artificial catchment. Earth Surf Process Landf 38:1496–1512.  https://doi.org/10.1002/esp.3384 CrossRefGoogle Scholar
  90. Shannon CE (1949) Communication in the presence of noise. Proceedings of the Institute of Radio Engineers 37(1):10–21Google Scholar
  91. Shary PA, Sharaya LS, Mitusov AV (2002) Fundamental quantitative methods of land surface analysis. Geoderma 107(2):1–32.  https://doi.org/10.1016/S0016-7061(01)00136-7 CrossRefGoogle Scholar
  92. Shi WZ, Tian Y (2006) A hybrid interpolation method for the refinement of a regular grid digital elevation model. Int J Geogr Inf Sci 20(1):53–67.  https://doi.org/10.1080/13658810500286943 CrossRefGoogle Scholar
  93. Siebert S, Teizer J (2014) Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system. Autom Constr 41:1–14.  https://doi.org/10.1016/j.autcon.2014.01.004 CrossRefGoogle Scholar
  94. Šiljeg A (2013) Digitalni model reljefa u analizi geomorfometrijskih parametara–primjer PP Vransko jezero. Doktorski rad, PMF, Sveucilište u ZagrebuGoogle Scholar
  95. Šiljeg A, Lozić S, Šiljeg S (2015) A comparison of interpolation methods on the basis of data obtained from a bathymetric survey of Lake Vrana, Croatia. Hydrol Earth Syst Sci 9:3653–3666.  https://doi.org/10.5194/hess-19-3653-2015 CrossRefGoogle Scholar
  96. Slattery KT, Slattery KD, Peterson JP (2012) Road construction earthwork volume calculation using three-dimensional laser scanning. J Surv Eng 138(2):96–99.  https://doi.org/10.1061/(ASCE)SU.1943-5428.0000073 CrossRefGoogle Scholar
  97. Smelik RM, Tutenel T, de Kraker KJ, Bidarra R (2010) Declarative terrain modeling for military training games. International journal of computer games technology 2:2–11.  https://doi.org/10.1155/2010/360458 CrossRefGoogle Scholar
  98. Steinitz C (2011) On scale and complexity and the need for spatial analysis, ArcNews (http://www.esri.com/news/arcnews/spring11articles/on-scale-and-complexityand-the-need-for-spatial-analysis.html)
  99. Stereńczak K, Ciesielski M, Balazy R, Zawiła-Niedźwiecki T (2016) Comparison of various algorithms for DTM interpolation from LIDAR data in dense mountain forests. European Journal of Remote Sensing 49(1):599–621.  https://doi.org/10.5721/EuJRS20164932 CrossRefGoogle Scholar
  100. Su J, Bork E (2006) Influence of vegetation, slope, and lidar sampling angle on DEM accuracy. Photogramm Eng Remote Sens 72(11):1265–1274.  https://doi.org/10.14358/PERS.72.11.1265 CrossRefGoogle Scholar
  101. Sulebak JR, Hjelle Ø (2003) Multiresolution spline models and their applications in geomorphology. Concepts and Modelling in Geomorphology: International Perspectives. 221–237Google Scholar
  102. Tan Q, Xu X (2014) Comparative analysis of spatial interpolation methods: an experimental study. Sensors & Transducers 165:155–163Google Scholar
  103. Van Haaren R, Fthenakis V (2011) GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): evaluating the case for New York state. Renew Sust Energ Rev 15(7):3332–3340.  https://doi.org/10.1016/j.rser.2011.04.010 CrossRefGoogle Scholar
  104. Wang L, Wang K (2015) Impacts of DTM uncertainty on estimated surface solar radiation and extracted river network. Bull Am Meteorol Soc 96(2):297–304CrossRefGoogle Scholar
  105. Wasklewicz T, Staley DM, Reavis K, Oguchi T (2013) Digital terrain modeling. In: Shroder J, Bishop MP (eds) Treatise on geomorphology, vol 3. Academic Press, San Diego pp 130–61CrossRefGoogle Scholar
  106. Watson DF (1992) Contouring: a guide to the analysis and display of spatial data. Pergamon Press, Oxford, UKGoogle Scholar
  107. Wechsler SP (2003) Perceptions of digital elevation model uncertainty by DTM users, URISA Journal 15 (2), 57–64. Washington DCGoogle Scholar
  108. Wilson J (2012) Digital terrain modelling. Geomorphology 137(1):269–297CrossRefGoogle Scholar
  109. Wilson JP, Bishop MP (2013) Geomorphometry. J. Shroder (editor in chief), M. P. Bishop (ed.), Treatise on Geomorphology 3: 162–186. DOI:  https://doi.org/10.1016/B978-0-12-374739-6.00049-X
  110. Wilson JP, Gallant JC (2000) Digital terrain analysis. In: Wilson JP, Gallant JC (eds) Terrain analysis: principles and applications. John Wiley and Sons, New York, pp 1–27Google Scholar
  111. Wong WSD, Lee J (2005) Statistical analysis of geographic information with ArcView GIS and ArcGIS. Wiley, HobokenGoogle Scholar
  112. Yakar M, Yilmaz HM, Mutluoglu O (2010) Comparative evaluation of excavation volume by TLS and total topographic station based methods. Lasers in Engineering 19(5–6):331–345Google Scholar
  113. Yan WY, Shaker A, El-Ashmawy N (2015) Urban land cover classification using airborne LiDAR data: a review. Remote Sens Environ 158:295–310.  https://doi.org/10.1016/j.rse.2014.11.001 CrossRefGoogle Scholar
  114. Yang CT, Stall JB (1971) Note on the map scale effect in the study of stream morphology. Water Resour Res 7(3):709–712.  https://doi.org/10.1029/WR007i003p00709 CrossRefGoogle Scholar
  115. Yang P, Ames PA, Fonseca A, Anderson D, Shrestha R, Glenn NF, Cao Y (2014) What is the effect of LiDAR-derived DTM resolution on large-scale watershed model results? Environ Model Softw 58:48–57.  https://doi.org/10.1016/j.envsoft.2014.04.005 CrossRefGoogle Scholar
  116. Yao X, Fu B, Lü Y, Sun F, Wang S, Liu M (2013) Comparison of four spatial interpolation methods for estimating soil moisture in a complex terrain catchment. PLoS One 8(1):e54660.  https://doi.org/10.1371/journal.pone.0054660 CrossRefGoogle Scholar
  117. Yilmaz HM (2007) The effect of interpolation methods in surface definition: an experimental study. Earth Surf Process Landf 32(9):1346–1361.  https://doi.org/10.1002/esp.1473 CrossRefGoogle Scholar
  118. Zhang P, Liu R, Bao Y, Wang J, Yu W, Shen Z (2014) Uncertainty of SWAT model at different DTM resolutions in a large mountainous watershed. Water Res 53:132–144.  https://doi.org/10.1016/j.watres.2014.01.018 CrossRefGoogle Scholar
  119. Zhao C, Jensen J, Deng X, Dede-Bamfo N (2016) Impacts of LiDAR sampling methods and point spacing density on DEM generation. Papers in Applied Geography 2(3):261–270.  https://doi.org/10.1080/23754931.2015.1121405 CrossRefGoogle Scholar

Copyright information

© Società Italiana di Fotogrammetria e Topografia (SIFET) 2018

Authors and Affiliations

  1. 1.Department of GeographyUniversity of ZadarZadarCroatia
  2. 2.Prehnit d.o.o.ZagrebCroatia

Personalised recommendations