Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Asal, F. F. (2016)
Publisher: Copernicus Publications
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology
With continuous developments in LiDAR technologies high point cloud densities have been attainable but accompanied by challenges for processing big volumes of data. Reductions in high point cloud densities are expected to lower data acquisition and data processing costs; however this could affect the characteristics of the generated Digital Elevation Models (DEMs). This research aimed to evaluate the effects of reductions in airborne LiDAR point cloud data densities on the visual and statistical characteristics of the generated DEMs. DEMs have been created from a dataset which constitutes last returns of raw LiDAR data that was acquired at bare lands for Gilmer County, USA between March and April 2004, where qualitative and quantitative testing analyses have been performed. Visual analysis has shown that the DEM can withstand a considerable degree of quality with reduced densities down to 0.128 pts/m2 (47 % of the data remaining), however degradations in the DEM visual characteristics appeared in coarser tones and rougher textures have occurred with more reductions. Additionally, the statistical analysis has indicated that the standard deviations of the DEM elevations have decreased by only 22 % of the total decrease with data density reductions down to 0.101 pts/m2 (37 % of the data remaining) while greater rate of decreasing in the standard deviations has occurred with more reductions referring to greater rate of surface smoothing and elevation approximating. Furthermore, the accuracy analysis testing has given that the DEM accuracy has degraded by only 4.83 % of the total degradations with data density reductions down to 0.128 pts/m2, however great deteriorations in the DEM accuracy have occurred with more data reductions. Finally, it is recommended that LiDAR data can withstand point density reductions down to 0.128 pts/m2 (about 50 % of the data) without big deteriorations in the visual and statistical characteristics of the generated DEMs.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Anderson, E. S., Thompson, J. A. and Austin, R. E., 2005.
    • LiDAR density and linear interpolator effects on elevation estimates. The International Journal of Remote Sensing, 26 (18), pp.3889-3900.
    • Bilskie, M. V. and Hagen, S. C. 2013. Topographic Accuracy Assessment of Bare Earth LiDAR-Derived Unstructured Meshes. Advances in Water Resources 52 (2013) 165-177 Elsevier.
    • Guo, Q., Li, W., Yu, H. and Alvarez, O. 2010. Effects of Topographic Variability and LiDAR Sampling Density on Several DEM Interpolation Methods. Photogrammetric Engineering & Remote Sensing Vol. 76, No. 6, June 2010, Habib, A., Ghanma, M., Morgan, M. and Al-Ruzouq, R., 2005.
    • Photogrammetric and LiDAR Data Registration Using Linear Features. Photogrammetric Engineering and Remote Sensing, 71 (6), pp.699-707.
    • Jensen, J. 2005. Introductory Digital Image Processing - a Remote Sensing Perspective. Third Edition. Pearson Prentice Hall, Upper Saddle River, New Jersey, 07458. USA.
    • Jensen, J. 2000. Remote Sensing of the Environment: An Earth Resource Perspective. Pearson Prentice Hall, Upper Saddle River, New Jersey 07458.
    • Karel, W., Pfeifer, N. and Briese, C. 2006. DTM Quality Assessment. Comm. II Symposium. Vienna; 12-14 July, 2006.
    • International Archives of ISPRS, XXXVI/2, 1682-1750: 7 -12.
    • Lillesand, T. M. and Keifer, R. W. 2000. Remote Sensing and Image Interpretation. Fourth Edition, John Wiley & Sons, Inc.
    • Liu, X., Zhang, Z., Peterson, J., and Chandra, S. 2007. The Effect of LiDAR Data Density on DEM Accuracy, Proc. of the International congress on modelling and simulation (MODSIM07), Christchurch, New Zealand, 2007a: 1363-1369.
    • Liu, X. and Zhang Z. 2008. LiDAR Data Reduction for Efficient and High Quality DEM Generation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008.
    • Liu, X., 2008. Airborne LiDAR for DEM Generation: Some Critical Issues. Progress in Physical Geography 32: 31-49.
    • Marin, R. M., Revilla, E. L., Manrique, J. C. O. and Sacristan, M. M. 2013. Handling Low-Density LiDAR Data: Calculating the Heights of Civil Constructions and the Accuracy Expected.
    • Hindawi Publishing Corporation, Advances in Civil Engineering Volume 2013, Article ID 602364, 5 pages.
    • Olsen, R., Puetz, A. and Anderson, B. 2009. Effects of LiDAR Point Density on Bare Earth Extraction and DEM Creation.
    • ASPRS 2009 Annual Con. Baltimore, Maryland, March 9-13.
    • K. 2015. Effects of LiDAR Point Density and Landscape Context on Estimates of Urban Forest Biomass. ISPRS Journal of Photogrammetry and Remote Sensing 101 (2015) 310-322.
    • Smith, M.J. and Clark, C.D.2005, Methods for the visualization of digital elevation models for landform mapping. Earth Surface Processes and Landforms 30: 885-900.
    • Watt, M. S., Adams, T., Aracil, S. G., Marshall, H., and Watt., P. 2013. The Influence of LiDAR Pulse Density and Plot Size on the Accuracy of New Zealand Plantation Sand Volume Equations. New Zealand Journal of Forestry Science, 43:15 Springer, 10.1186/1179-5395-43-15.
    • Wehr, A., and Lohr, U. 1999. Airborne Laser Scanning-an Introduction and Overview. ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 68-82.
    • Zhu, C., Shi, W., Li, Q., Wang, G., Cheung, T., Dai, E. and Shea, G. 2005. Estimation of Average DEM Accuracy under Linear Interpolation Considering Random Errors at the Nodes of TIN Model, International Journal of Remote Sensing, Volume 26, Number 24,01D2005, pp. 5509-5523(15).
  • No related research data.
  • No similar publications.