Research Article
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Year 2017, Volume: 2 Issue: 1, 9 - 16, 01.02.2017
https://doi.org/10.26833/ijeg.286003

Abstract

References

  • Aguilar, F.J., Agüera, F., Aguilar, M.A., Carvajal, F. 2005. Effects of terrain morphology, sampling density and interpolation methods on grid DEM accuracy. Photogrammetric Engineering and Remote Sensing, 71 (7), 805-816.
  • Aguilar, F.J., Aguilar, M.A., Agüera, F., 2007. Accuracy assessment of digital elevation models using a non- parametric approach. International Journal of Geographical Information Science, 21 (6), 66-686.
  • Aguilar, F.J., Mills, J.P. 2008. Accuracy assessment of LiDAR-derived digital elevation models. The Photogrammetric Record, 23 (122), 148-169.
  • Anderson, E.S., Thompson, J.A., Austin, R.E. 2005 LIDAR density and linear interpolator effects on elevation estimates. International Journal of Remote Sensing, 26 (18), 3889-3900.
  • Anderson, E.S., Thompson, J.A., Crouse, D.A., Austin, R.E. 2006. Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM. Geoderma, 132 406– 415.
  • Arab-Sedze, M., Heggy, E., Bretar, F., Berveiller, D., Jacquemoud, S. 2014. Quantification of L-band InSAR coherence over volcanic areas using LiDAR and in situ measurements. Remote Sensing of Environment, 152, 202-216.
  • Arun, P.V. 2013. A comparative analysis of different DEM interpolation methods. The Egyptian Journal of Remote Sensing and Space Sciences, 16, 133-139.
  • Cavalli, M., Tarolli, P. 2011. Application of LiDAR technology for rivers analysis. Italian Journal of Engineering Geology and Environment, Special Issue (1), 33-44.
  • 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, 126-141.
  • Chen, C.F., Yue, T.X. 2010. A method of DEM construction and related error analysis. Computers and Geosciences, 36 (6), 717-725.
  • Chu, H.J., Chen, R.A., Tseng, Y.H., Wang, C.K. 2014. Identifying LiDAR sample uncertainty on terrain features from DEM simulation, Geomorphology, 204, 325-333.
  • Cressie, N.A.C. 1991. Statistics for Spatial Data. New York: John Wiley and Sons.
  • Dorn, H., Vetter, M., Höfle, B. 2014. GIS-based roughness derivation for flood simulations: a comparison of orthophotos, LiDAR and crowdsourced geodata. Remote Sensing, 6, 1739-1759.
  • Erdogan, S. 2010. Modelling the spatial distribution of DEM error with geographically weighted regression: an experimental study. Computers and Geosciences, 36, 34-43.
  • Fassnacht, F.E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P., Koch, B. 2014. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154, 102-114.
  • Fisher, P.F., Tate, N.J. 2006. Causes and consequences of error in digital elevation models. Progress in Physical Geography, 30 (4), 467-489.
  • Garnero, G., Godone, D. 2013. Comparisons between different interpolation techniques. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XL-5/W3, 139-144.
  • Geomagic Support Center 2014. Overview of the Point Sampling Commands. <support1.geomagic.com>
  • Gong, J., Li, Z., Zhu, Q., Sui, H., 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.
  • Gumus, K., Sen, A. 2013. Comparison of spatial interpolation methods and multi-layer neural networks for different point distributions on a digital elevation model. Geodetski Vesnik, 57 (3), 523-543.
  • Heckbert, P., Garland, M. 1997. Survey of polygonal surface simplification algorithms. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ´97, pp. 209-216.
  • Hodgson, M.E., Bresnahan, P. 2004. Accuracy of airborne LiDAR-derived elevation: empirical assessment and error budget. Photogrammetric Engineering & Remote Sensing, 70 (3), 331-339.
  • Immelman J., Scheepers L.G.C. 2011. The effects of data reduction on LiDAR-based digital elevation models, 4th International Congress on Image and Signal Processing, Shanghai, China, 1694-1698.
  • Joseph, V.R. 2006. Limit Kriging. Technometrics, 48 (4), 458-466.
  • Kraus, K., Mikhail, E. 1972. Linear least squares interpolation. Photogrammetric Engineering, 38, 1016- 1029.
  • Krige, D.G. 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Chemical, Metallurgical and Mining Society of South Africa, 52 (6), 119-139.
  • Krivoruchko, K., Gotway, C.A. 2004. Creating exposure maps using Kriging. Public Health GIS News and Information, 56, 11-16.
  • Lee, K.H., Woo, H., Suk, T. 2001. Point data reduction using 3D grids. The International Journal of Advanced Manufacturing Technology, 18 (3), 201-210.
  • Li, Z., Zhu, C., Gold, C. 2005. Digital Terrain Modeling: Principles and Methodology. Boca Raton: CRC Press.
  • Liu, X., Zhang, Z., Peterson, J., Chandra, S. 2007. The effect of LiDAR data density on DEM accuracy. International Congress on Modelling and Simulation: Land, Water and Environmental Management: Integrated Systems for Sustainability, Christchurch, New Zealand, 10-13 December 2007, pp. 1363-1369.
  • Liu, X. 2008. Airborne LiDAR for DEM generation: some critical issues. Progress in Physical Geography, 31 (1), 31-49.
  • Liu, X., Zhang, Z. 2008. LiDAR data reduction for efficient and high quality DEM generation, The International Archives of the Photogrammtery, Remote Sensing and Spatial Information Sciences, XXXVII, 173-178.
  • Liu, H., Kiesel, J., Hörmann, G., Fohrer, N. 2011. Effects of DEM horizontal resolution and methods on calculating the slope length factor in gently rolling landscapes. Catena, 87 (3), 368-375.
  • Ma, R., Meyer, W. 2005. DTM generation and building detection from LiDAR data. Photogrammetric Engineering and Remote Sensing, 71, 847-854.
  • Maune, D.F., Kopp, S.M., Crawford, .A., Zervas, C.E. 2007. Introduction. In D.F. Maune (Ed.), Digital Elevation Model Technologies and Applications: The DEM Users Manual (2nd ed.) (pp. 1-36). Bethesda: American Society for Photogrammetry and Remote Sensing.
  • Maune, D.F. 2008. Aerial mapping and surveying. In S.O. Dewberry, and L.N. Rauenzahn (Eds.), Land Development Handbook (3rd ed.) (pp. 877-910). New York: McGraw-Hill.
  • Mount St. Helens LiDAR Data 2006. <https://wagda.lib.washington.edu/data/type/elevation/li dar/st_helens/>
  • Mukherjee, S., Joshi, P.K., Mukherjee, S., Ghosh, A., Garg, R.D., Mukhopadhyay, A. 2013. Evaluation of vertical accuracy of open source digital elevation model (DEM). International Journal of Applied Earth Observation and Geoinformation, 21, 205-217.
  • Polat, N., Uysal, M. 2015. Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation. Measurement, 63, 61-68.
  • Rayburg, S., Thoms, M., Neave, M. 2009. A comparison of digital elevation models generated from different data sources. Geomorphology, 106, 261-270.
  • Razak, K.A., Straatsma, M.W., van Westen, C.J., Malet, J.P., de Jong, S.M. 2011. Airborne laser scanning of forested landslides characterization: Terrain model quality and visualization. Geomorphology, 126, 186- 200.
  • Renslow, M.S. 2012. Introduction. In M.S. Renslow (Ed.), Manual of Airborne Topographic LiDAR (pp. 1- 5). Bethesda: ASPRS.
  • Sailer, R., Rutzinger, M., Rieg, L. Wichmann, V. 2014. Digital elevation models derived from airborne laser scanning point clouds: appropriate spatial resolutions for multi-temporal characterization and quantification of geomorphological processes. Earth Surface Processes and Landforms, 39 (2), 272-284.
  • Tan, Q., Xu, X. 2014. Comparative analysis of spatial interpolation methods: an experimental study. Sensors and Transducers, 165 (2), 155-163.
  • Tarolli, P., Arrowsmith, J.R., Vivoni, E.R. 2009. Understanding earth surface processes from remotely sensed digital terrain models. Geomorphology, 113, 1-3.
  • Vianello, A., Cavalli, M., Tarolli, P. 2009. LiDAR- derived slopes for headwater channel network analysis. Catena, 76 (2), 97-106.
  • Wehr, A., Lohr, U. 1999. Airborne laser scanning-An introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing, 54, 68-82.
  • Weng, Q. 2006. An evaluation of spatial interpolation accuracy of elevation data. In A. Riedl, W. Kainz, and G.A. Elmes (Eds.), Progress in Spatial Data Handling (pp. 805-824). Berlin: Springer-Verlag.
  • Yan, W.Y., Shaker, A., El-Ashmawy, N. 2015. Urban land cover classification using airborne LiDAR data: A review. Remote Sensing of Environment, 158, 295-310.
  • Yilmaz, M., Gullu, M. 2014. A comparative study for the estimation of geodetic point velocity by artificial neural networks. Journal of Earth System Sciences, 123 (4), 791-808.

Comparing uniform and random data reduction methods for DTM accuracy

Year 2017, Volume: 2 Issue: 1, 9 - 16, 01.02.2017
https://doi.org/10.26833/ijeg.286003

Abstract

The digital cartographic representation of the elevation of the earth's surface created from discrete elevation points is defined as a digital terrain model (DTM). DTMs have been used in a wide range of applications, such as civil planning, flood control, transportation design, navigation, natural hazard risk assessment, hydraulic simulation, visibility analysis of the terrain, topographic change quantification, and forest characterization. Remote sensing, laser scanning, and radar interferometry become efficient sources for constructing high-accuracy DTMs by the developments in data processing technologies. The accuracy, the density, and the spatial distribution of elevation points, the terrain surface characteristics, and the interpolation methods have an influence on the accuracy of DTMs. In this study, uniform and random data reduction methods are compared for DTMs generated from airborne Light Detection and Ranging (LiDAR) data. The airborne LiDAR data set is reduced to subsets by using uniform and random methods, representing the 75%, 50%, and 25% of the original data set. Over the Mount St. Helens in southwest Washington State as the test area, DTM constructed from the original airborne LiDAR data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that uniform data reduction method can be used to reduce the LiDAR datasets to 50% density level while still maintaining the quality of DTM.

References

  • Aguilar, F.J., Agüera, F., Aguilar, M.A., Carvajal, F. 2005. Effects of terrain morphology, sampling density and interpolation methods on grid DEM accuracy. Photogrammetric Engineering and Remote Sensing, 71 (7), 805-816.
  • Aguilar, F.J., Aguilar, M.A., Agüera, F., 2007. Accuracy assessment of digital elevation models using a non- parametric approach. International Journal of Geographical Information Science, 21 (6), 66-686.
  • Aguilar, F.J., Mills, J.P. 2008. Accuracy assessment of LiDAR-derived digital elevation models. The Photogrammetric Record, 23 (122), 148-169.
  • Anderson, E.S., Thompson, J.A., Austin, R.E. 2005 LIDAR density and linear interpolator effects on elevation estimates. International Journal of Remote Sensing, 26 (18), 3889-3900.
  • Anderson, E.S., Thompson, J.A., Crouse, D.A., Austin, R.E. 2006. Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM. Geoderma, 132 406– 415.
  • Arab-Sedze, M., Heggy, E., Bretar, F., Berveiller, D., Jacquemoud, S. 2014. Quantification of L-band InSAR coherence over volcanic areas using LiDAR and in situ measurements. Remote Sensing of Environment, 152, 202-216.
  • Arun, P.V. 2013. A comparative analysis of different DEM interpolation methods. The Egyptian Journal of Remote Sensing and Space Sciences, 16, 133-139.
  • Cavalli, M., Tarolli, P. 2011. Application of LiDAR technology for rivers analysis. Italian Journal of Engineering Geology and Environment, Special Issue (1), 33-44.
  • 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, 126-141.
  • Chen, C.F., Yue, T.X. 2010. A method of DEM construction and related error analysis. Computers and Geosciences, 36 (6), 717-725.
  • Chu, H.J., Chen, R.A., Tseng, Y.H., Wang, C.K. 2014. Identifying LiDAR sample uncertainty on terrain features from DEM simulation, Geomorphology, 204, 325-333.
  • Cressie, N.A.C. 1991. Statistics for Spatial Data. New York: John Wiley and Sons.
  • Dorn, H., Vetter, M., Höfle, B. 2014. GIS-based roughness derivation for flood simulations: a comparison of orthophotos, LiDAR and crowdsourced geodata. Remote Sensing, 6, 1739-1759.
  • Erdogan, S. 2010. Modelling the spatial distribution of DEM error with geographically weighted regression: an experimental study. Computers and Geosciences, 36, 34-43.
  • Fassnacht, F.E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P., Koch, B. 2014. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154, 102-114.
  • Fisher, P.F., Tate, N.J. 2006. Causes and consequences of error in digital elevation models. Progress in Physical Geography, 30 (4), 467-489.
  • Garnero, G., Godone, D. 2013. Comparisons between different interpolation techniques. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XL-5/W3, 139-144.
  • Geomagic Support Center 2014. Overview of the Point Sampling Commands. <support1.geomagic.com>
  • Gong, J., Li, Z., Zhu, Q., Sui, H., 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.
  • Gumus, K., Sen, A. 2013. Comparison of spatial interpolation methods and multi-layer neural networks for different point distributions on a digital elevation model. Geodetski Vesnik, 57 (3), 523-543.
  • Heckbert, P., Garland, M. 1997. Survey of polygonal surface simplification algorithms. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ´97, pp. 209-216.
  • Hodgson, M.E., Bresnahan, P. 2004. Accuracy of airborne LiDAR-derived elevation: empirical assessment and error budget. Photogrammetric Engineering & Remote Sensing, 70 (3), 331-339.
  • Immelman J., Scheepers L.G.C. 2011. The effects of data reduction on LiDAR-based digital elevation models, 4th International Congress on Image and Signal Processing, Shanghai, China, 1694-1698.
  • Joseph, V.R. 2006. Limit Kriging. Technometrics, 48 (4), 458-466.
  • Kraus, K., Mikhail, E. 1972. Linear least squares interpolation. Photogrammetric Engineering, 38, 1016- 1029.
  • Krige, D.G. 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Chemical, Metallurgical and Mining Society of South Africa, 52 (6), 119-139.
  • Krivoruchko, K., Gotway, C.A. 2004. Creating exposure maps using Kriging. Public Health GIS News and Information, 56, 11-16.
  • Lee, K.H., Woo, H., Suk, T. 2001. Point data reduction using 3D grids. The International Journal of Advanced Manufacturing Technology, 18 (3), 201-210.
  • Li, Z., Zhu, C., Gold, C. 2005. Digital Terrain Modeling: Principles and Methodology. Boca Raton: CRC Press.
  • Liu, X., Zhang, Z., Peterson, J., Chandra, S. 2007. The effect of LiDAR data density on DEM accuracy. International Congress on Modelling and Simulation: Land, Water and Environmental Management: Integrated Systems for Sustainability, Christchurch, New Zealand, 10-13 December 2007, pp. 1363-1369.
  • Liu, X. 2008. Airborne LiDAR for DEM generation: some critical issues. Progress in Physical Geography, 31 (1), 31-49.
  • Liu, X., Zhang, Z. 2008. LiDAR data reduction for efficient and high quality DEM generation, The International Archives of the Photogrammtery, Remote Sensing and Spatial Information Sciences, XXXVII, 173-178.
  • Liu, H., Kiesel, J., Hörmann, G., Fohrer, N. 2011. Effects of DEM horizontal resolution and methods on calculating the slope length factor in gently rolling landscapes. Catena, 87 (3), 368-375.
  • Ma, R., Meyer, W. 2005. DTM generation and building detection from LiDAR data. Photogrammetric Engineering and Remote Sensing, 71, 847-854.
  • Maune, D.F., Kopp, S.M., Crawford, .A., Zervas, C.E. 2007. Introduction. In D.F. Maune (Ed.), Digital Elevation Model Technologies and Applications: The DEM Users Manual (2nd ed.) (pp. 1-36). Bethesda: American Society for Photogrammetry and Remote Sensing.
  • Maune, D.F. 2008. Aerial mapping and surveying. In S.O. Dewberry, and L.N. Rauenzahn (Eds.), Land Development Handbook (3rd ed.) (pp. 877-910). New York: McGraw-Hill.
  • Mount St. Helens LiDAR Data 2006. <https://wagda.lib.washington.edu/data/type/elevation/li dar/st_helens/>
  • Mukherjee, S., Joshi, P.K., Mukherjee, S., Ghosh, A., Garg, R.D., Mukhopadhyay, A. 2013. Evaluation of vertical accuracy of open source digital elevation model (DEM). International Journal of Applied Earth Observation and Geoinformation, 21, 205-217.
  • Polat, N., Uysal, M. 2015. Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation. Measurement, 63, 61-68.
  • Rayburg, S., Thoms, M., Neave, M. 2009. A comparison of digital elevation models generated from different data sources. Geomorphology, 106, 261-270.
  • Razak, K.A., Straatsma, M.W., van Westen, C.J., Malet, J.P., de Jong, S.M. 2011. Airborne laser scanning of forested landslides characterization: Terrain model quality and visualization. Geomorphology, 126, 186- 200.
  • Renslow, M.S. 2012. Introduction. In M.S. Renslow (Ed.), Manual of Airborne Topographic LiDAR (pp. 1- 5). Bethesda: ASPRS.
  • Sailer, R., Rutzinger, M., Rieg, L. Wichmann, V. 2014. Digital elevation models derived from airborne laser scanning point clouds: appropriate spatial resolutions for multi-temporal characterization and quantification of geomorphological processes. Earth Surface Processes and Landforms, 39 (2), 272-284.
  • Tan, Q., Xu, X. 2014. Comparative analysis of spatial interpolation methods: an experimental study. Sensors and Transducers, 165 (2), 155-163.
  • Tarolli, P., Arrowsmith, J.R., Vivoni, E.R. 2009. Understanding earth surface processes from remotely sensed digital terrain models. Geomorphology, 113, 1-3.
  • Vianello, A., Cavalli, M., Tarolli, P. 2009. LiDAR- derived slopes for headwater channel network analysis. Catena, 76 (2), 97-106.
  • Wehr, A., Lohr, U. 1999. Airborne laser scanning-An introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing, 54, 68-82.
  • Weng, Q. 2006. An evaluation of spatial interpolation accuracy of elevation data. In A. Riedl, W. Kainz, and G.A. Elmes (Eds.), Progress in Spatial Data Handling (pp. 805-824). Berlin: Springer-Verlag.
  • Yan, W.Y., Shaker, A., El-Ashmawy, N. 2015. Urban land cover classification using airborne LiDAR data: A review. Remote Sensing of Environment, 158, 295-310.
  • Yilmaz, M., Gullu, M. 2014. A comparative study for the estimation of geodetic point velocity by artificial neural networks. Journal of Earth System Sciences, 123 (4), 791-808.
There are 50 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Mustafa Yilmaz

Murat Uysal

Publication Date February 1, 2017
Published in Issue Year 2017 Volume: 2 Issue: 1

Cite

APA Yilmaz, M., & Uysal, M. (2017). Comparing uniform and random data reduction methods for DTM accuracy. International Journal of Engineering and Geosciences, 2(1), 9-16. https://doi.org/10.26833/ijeg.286003
AMA Yilmaz M, Uysal M. Comparing uniform and random data reduction methods for DTM accuracy. IJEG. February 2017;2(1):9-16. doi:10.26833/ijeg.286003
Chicago Yilmaz, Mustafa, and Murat Uysal. “Comparing Uniform and Random Data Reduction Methods for DTM Accuracy”. International Journal of Engineering and Geosciences 2, no. 1 (February 2017): 9-16. https://doi.org/10.26833/ijeg.286003.
EndNote Yilmaz M, Uysal M (February 1, 2017) Comparing uniform and random data reduction methods for DTM accuracy. International Journal of Engineering and Geosciences 2 1 9–16.
IEEE M. Yilmaz and M. Uysal, “Comparing uniform and random data reduction methods for DTM accuracy”, IJEG, vol. 2, no. 1, pp. 9–16, 2017, doi: 10.26833/ijeg.286003.
ISNAD Yilmaz, Mustafa - Uysal, Murat. “Comparing Uniform and Random Data Reduction Methods for DTM Accuracy”. International Journal of Engineering and Geosciences 2/1 (February 2017), 9-16. https://doi.org/10.26833/ijeg.286003.
JAMA Yilmaz M, Uysal M. Comparing uniform and random data reduction methods for DTM accuracy. IJEG. 2017;2:9–16.
MLA Yilmaz, Mustafa and Murat Uysal. “Comparing Uniform and Random Data Reduction Methods for DTM Accuracy”. International Journal of Engineering and Geosciences, vol. 2, no. 1, 2017, pp. 9-16, doi:10.26833/ijeg.286003.
Vancouver Yilmaz M, Uysal M. Comparing uniform and random data reduction methods for DTM accuracy. IJEG. 2017;2(1):9-16.

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