Advertisement

Light Detection And Ranging (LiDAR)

  • Joseph Awange
  • John Kiema
Chapter
Part of the Environmental Science and Engineering book series (ESE)

Abstract

Light Detection And Ranging (LiDAR) is an active laser measuring technology that combines laser scanning and Position and Orientation System (POS) in imaging for generation of accurate and dense 3D point clouds, Digital Elevation Models (DEMs) and Digital Surface Models (DSMs). Other value addition products such as contours, slope maps, tree and building height models and cut-and-fill models can also be produced from the primary LiDAR point cloud data.

References

  1. 1.
    Ackermann F (1999) Airborne laser scanning - present status and future expectations. ISPRS J Photogramm Remote Sens 54:64–67CrossRefGoogle Scholar
  2. 2.
    Rizaldy A, Firdaus W (2012) Direct georeferencing: a new standard in photogrammetry for high accuracy mapping. ISPRS-Int Arch Photogramm, Remote Sens Spat Inf Sci 39:59Google Scholar
  3. 3.
    Weng Q (2010) Remote sensing and GIS integration: theories, methods, and applications. McGraw-Hill, New York, 416pGoogle Scholar
  4. 4.
    Webster TL, Forbes DL, Dickie S, Shreenan R (2004) Using topographic LiDAR to map flood risk from storm-surge events for Charlottetown, Prince Edward Island, Canada. Can J Remote Sens 30(1):64–76CrossRefGoogle Scholar
  5. 5.
    Felix R (2015) An introduction to LiDAR. Retrieved January 18, 2018, from http://felix.rohrba.ch/en/2015/an-introduction-to-lidar/
  6. 6.
    Alharthy A, Bethel J (2002) Heuristic filtering and 3D feature extraction from LiDAR data. In: International archives of photogrammetry and remote sensing (IAPRS), Graz, Austria, Vol. XXXIV, Part 3A, pp 29–34Google Scholar
  7. 7.
    Kraus K (2007) Photogrammetry: geometry from images and laser scans (trans: Harley I and Kyle S) (2nd edn, Vol 1). Deutsche NationalbibliothekGoogle Scholar
  8. 8.
    Fieber KD, Davenport IJ, Ferryman JM, Gurney RJ, Walker JP, Hacker JM (2013) Analysis of full-waveform LiDAR data for classification of an orange orchard scene. ISPRS J Photogramm Remote Sens 82:63–82CrossRefGoogle Scholar
  9. 9.
    Mikhail EM, Bethel JS, McGlone JC (2015) Introduction to modern photogrammetry. Wiley, New YorkGoogle Scholar
  10. 10.
    Hudak AT, Evans JS, Smith AMS (2009) LiDAR utility for natural resource managers. Remote Sens 1:934–951.  https://doi.org/10.3390/rs1040934CrossRefGoogle Scholar
  11. 11.
    Haneberg WC, Cole WF, Kasali G (2009) Bull Eng Geol Environ 68:263.  https://doi.org/10.1007/s10064-009-0204-3CrossRefGoogle Scholar
  12. 12.
    Jaboyedoff M, Oppikofer T, Abelln A (2012) Use of LiDAR in landslide investigations: a review. Nat Hazards 61:5.  https://doi.org/10.1007/s11069-010-9634-2CrossRefGoogle Scholar
  13. 13.
    Jaffe JS, Moore KD, McLean J, Strand MP (2001) Underwater optical imaging: status and prospects. Oceanography 14(3):66CrossRefGoogle Scholar
  14. 14.
    McLeod D, Jacobson J, Hardy M (2013) Autonomous inspection using an underwater 3D LiDAR . https://doi.org/10.23919/OCEANS.2013.6741175
  15. 15.
    Agius C, Brearley J (2014) Can building footprint extraction from LiDAR be used productively in a topographic mapping context? https://www.um.edu.mt/library/oar//handle/123456789/9026
  16. 16.
    Dubayah RO, Drake JB (2000) LiDAR remote sensing for forestry. J For 98(6):44–46.  https://doi.org/10.1093/jof/98.6.44CrossRefGoogle Scholar
  17. 17.
    Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) LiDAR remote sensing for ecosystem studies: LiDAR, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. BioScience 52(1):19–30 (2002).  https://doi.org/10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2
  18. 18.
    Richardson JJ, Moskal M (2011) Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR. Remote Sens Environ 115(10):2640–2651.  https://doi.org/10.1016/j.rse.2011.05.020CrossRefGoogle Scholar
  19. 19.
    Tansey K, Selmes N, Anstee A, Tate NT, Denniss A (2009) Estimating tree and stand variables in a Corsican Pine woodland from terrestrial laser scanner data. Int J Remote Sens 30(2009):5195–5209CrossRefGoogle Scholar
  20. 20.
    Garca M, Riao D, Chuvieco E, Danson FM (2010) Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens Environ 114(4):816–830CrossRefGoogle Scholar
  21. 21.
    Morsy S, Shaker A, El-Rabbany A (2017) Multispectral LiDAR data for land cover classification of urban areas. Sensors (Basel, Switzerland) 17(5).  https://doi.org/10.3390/s17050958
  22. 22.
    Cicimol A (2010) Classification of full-waveform airborne laser scanning data and extraction of attributes of vegetation for topographic mapping. PhD Thesis University of LeicesterGoogle Scholar
  23. 23.
    Ussyshkin V, Theriault L (2011) Airborne LiDAR: advances in discrete return technology for 3D vegetation mapping. Remote Sens 3(3):416–434.  https://doi.org/10.3390/rs3030416CrossRefGoogle Scholar
  24. 24.
    Brunn A, Weidner U (1997) Extracting buildings from digital surface models. Int Arch Photogramm Remote Sens 32:27–34Google Scholar
  25. 25.
    Clode S, Rottensteinerb F, Kootsookosc P, Zelniker E (2007) Detection and vectorization of roads from LiDAR data. Photogramm Eng Remote Sens 73:517–535CrossRefGoogle Scholar
  26. 26.
    Forlani G, Nardinocchi C, Scaioni M, Zingaretti P (2006) Complete classification of raw LiDAR data and 3D reconstruction of buildings. Pattern Anal Appl 8:357–374CrossRefGoogle Scholar
  27. 27.
    Haala N, Brenner C (1999) Extraction of buildings and trees in urban environments. ISPRS J Photogramm Remote Sens 54:130–137CrossRefGoogle Scholar
  28. 28.
    Miliaresis G, Kokkas N (2007) Segmentation and object-based classification for the extraction of the building class from LiDAR DEMs. Comput Geosci 33:1076–1087CrossRefGoogle Scholar
  29. 29.
    Kokkas N, (2005) City modeling and building reconstruction with Socet Set vol 5.2 BAE Systems. Customer presentation at the 2005 GXP Regional User Conference. Cambridge, England, pp 19–21Google Scholar
  30. 30.
    Lee DH, Lee KM, Lee SU (2008) Fusion of LiDAR and imagery for reliable building extraction. Photogramm Eng Remote Sens 74:215–225CrossRefGoogle Scholar
  31. 31.
    Secord J, Zakhor A (2007) Tree detection in urban regions using aerial LiDAR and image data. IEEE Geosci Remote Sens Lett 4:196–200CrossRefGoogle Scholar
  32. 32.
    Sohn G, Dowman I (2005) Data fusion of high-resolution satellite imagery and LiDAR data for automatic building extraction. ISPRS J Photogramm Remote Sens 62(1):43–63CrossRefGoogle Scholar
  33. 33.
    Voss M, Sugumaran R (2008) Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach. Sensors 8:3020–3036CrossRefGoogle Scholar
  34. 34.
    Kinzel PJ, Legleiter CJ and Nelson, JM (2012) Mapping river bathymetry with a small footprint green LiDAR: applications and challenges. J Am Water Resour Assoc (JAWRA) 1–22.  https://doi.org/10.1111/jawr.12008
  35. 35.
    Monfort CL, Lippmann TC (2011) Assimilation of airborne imagery with a wave model for bathymetric estimation. J Coast Res 62:40–49.  https://doi.org/10.2112/SI_62_5CrossRefGoogle Scholar
  36. 36.
    Bowen ZH, Waltermire RG (2002) Evaluation of light detection and ranging (LiDAR) for measuring river corridor topography. J Am Water Resour Assoc.  https://doi.org/10.1111/j.1752-1688.2002.tb01532.x
  37. 37.
    Uddin W (2002) Evaluation of airborne LiDAR digital terrain mapping for highway corridor planning and design. Proc. Pecora. www.isprs.org
  38. 38.
    Veneziano D, Souleyrette R, Hallmark S (2014) Transportation research record.  https://doi.org/10.3141/1836-01. ISSN: 0361-1981
  39. 39.
    White RA, Dietterick BC, Mastin T, Strohman R (2010) Forest roads mapped using LiDAR in steep forested terrain. Remote Sens 2(4):1120–1141.  https://doi.org/10.3390/rs2041120CrossRefGoogle Scholar
  40. 40.
    Briggman KA (2010) Differential absorption LiDAR for the detection and quantification of greenhouse gases. Retrieved January 11, 2018, from https://www.nist.gov/programs-projects/differential-absorption-lidar-detection-and-quantification-greenhouse-gases
  41. 41.
    Grebby S, Naden J, Cunningham D, Tansey K (2011) Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain. Remote Sens Environ 115(1):214–226CrossRefGoogle Scholar
  42. 42.
    Williams K, Olsen M, Roe G, Glennie C (2013) Synthesis of transportation applications of mobile LiDAR. Remote Sens 5(9):4652–4692.  https://doi.org/10.3390/rs5094652CrossRefGoogle Scholar
  43. 43.
    Wang Y, Liang X, Flener C, Kukko A, Kaartinen H, Kurkela M, Vaaja M, Hyyppa H, Alho P (2013) 3D modeling of coarse fluvial sediments based on mobile laser scanning data. Remote Sens 5(9):4571–4592.  https://doi.org/10.3390/rs5094571CrossRefGoogle Scholar
  44. 44.
    Renslow M, Greenfield P, Guay T (2000) Evaluation of multi-return LiDAR for forestry applications. Project Report for the Inventory and Monitoring Steering Committee, RSAC-2060/4810-LSP-0001-RPT1Google Scholar
  45. 45.
    Yang B, Chen C (2015) Automatic registration of UAV-borne sequent images and LiDAR data. ISPRS J Photogramm Remote Sens 101:262–274.  https://doi.org/10.1016/j.isprsjprs.2014.12.025CrossRefGoogle Scholar
  46. 46.
    Wichmann V, Bremer M, Lindenberger J, Rutzinger M, Georges C, Petrini-Monteferri, F (2015) Evaluating the potential of multispectral airborne LiDAR for topographic mapping and land cover classification. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3/W5Google Scholar
  47. 47.
    Spinhirne JD (1993) Micro pulse LiDAR. IEEE Trans Geosci Remote Sens 31(1):48CrossRefGoogle Scholar
  48. 48.
    Konecny G (2003) Geoinformation: remote sensing, photogrammetry, geographic informaGoogle Scholar
  49. 49.
    Renslow M (2005) The status of LiDAR today and future directions . Proceedings of ISPRS WG 1/2 ISPRS WG 1/2, Banff WorkshopGoogle Scholar
  50. 50.
    Aplin P, Atkinson PM, Curran PJ (1997) Fine spatial resolution satellite sensors for the next decade. Int J Remote Sens 18:3873–3881CrossRefGoogle Scholar
  51. 51.
    Bähr H-P, Vögtle T (eds) (1998) Erderkundungssatelliten und ihre Produkte. Digitale Bildverarbeitung, vol 3. Wichmann Verlag, Heidelberg, pp 29–43Google Scholar
  52. 52.
    Baltsavias E (1999) A comparison between photogrammetry and laser scanning. ISPRS J Photogramm Remote Sens 54:83–94CrossRefGoogle Scholar
  53. 53.
    Campbell JB (2007) Introduction to remote sensing, 4th edn. Guilford Press, New YorkGoogle Scholar
  54. 54.
    Congalton R, Green K (2009) Assessing the accuracy of remotely sensed data: principles and practices. Taylor & Francis Group, LondonGoogle Scholar
  55. 55.
    Devillers R, Jeansoulin R (eds) (2006) Fundamentals of spatial data quality. ISTE Ltd, Washington, D.CGoogle Scholar
  56. 56.
    Forshaw MRB, Haskell A, Miller PF, Stanley DJ, Townshend JRG (1983) Spatial resolution of remotely sensed imagery: a review paper. Int J Remote Sens 4(3):497–520CrossRefGoogle Scholar
  57. 57.
    Fritz LW (1996) The Era of commercial earth observation satellites. Photogramm Eng Remote Sens 1:39–45Google Scholar
  58. 58.
    Groot R, McLaughlin J (eds) (2000) Geospatial data infrastructure: concepts, cases and good practice. Oxford University Press, OxfordGoogle Scholar
  59. 59.
    Guelman M, Ortenberg F (2009) Small satellite’s role in future hyperspectral earth observation missions. Acta Astronautica 64:1252–1263CrossRefGoogle Scholar
  60. 60.
    Ho C, Robinson A, Millerm D, Davis M (2005) Overview of sensors and needs for environmental monitoring. Sensors 5:4–37CrossRefGoogle Scholar
  61. 61.
    Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Prentice-Hall, Upper Saddle River, NJGoogle Scholar
  62. 62.
    Joseph G (2000) How well do we understand earth observation electro-optical sensor parameters? ISPRS J Photogramm Remote Sens 55:9–12CrossRefGoogle Scholar
  63. 63.
    Kiema JBK (2001) Multi-source data fusion and image compression in urban remote sensing. Doctor of Engineering. Dissertation. University of Karlsruhe (ISBN3-8265-9312-X) Shaker Verlag, 130pGoogle Scholar
  64. 64.
    Kramer HJ, Cracknell A (2008) An overview of small satellites. Int J Remote Sens 29:4285–4337CrossRefGoogle Scholar
  65. 65.
    Kumi-Boateng B (2012) A spatio-temporal based estimation of vegetation changes in the Tarkwa mining area of Ghana. Doctor of Philosophy. Dissertation. University of Mines and Technology, Ghana, 165pGoogle Scholar
  66. 66.
    Kussul N, Shelestov A, Skakun S (2009) Grid and sensor web technologies for environmental monitoring. Earth Sci Inform 2(1–2):37–51CrossRefGoogle Scholar
  67. 67.
    Liu X (2008) Airborne LiDAR for DEM generation: some critical issues. Prog Phys Geogr 32(1):31–49.  https://doi.org/10.1177/0309133308089496CrossRefGoogle Scholar
  68. 68.
    Ma R (2005) DEM generation and building detection from LiDAR data. Photogramm Eng & Remote Sens 71(7):847–854CrossRefGoogle Scholar
  69. 69.
    McCoy R (2005) Field methods in remote sensing. The Guilford Press, New York, 158pGoogle Scholar
  70. 70.
    Murai S (2004) Remote sensing and GIS courses - distance education. Japan International Cooperation Agency (JICA)-NetGoogle Scholar
  71. 71.
    Phinn SR (1998) A framework for selecting appropriate remote sensed data dimensions for environmental monitoring and management. Int J Remote Sens 19:3457–3463CrossRefGoogle Scholar
  72. 72.
    Popescue SC, Wynne RH, Nelson RF (2003) Measuring individual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass. Can J Remote Sens 29:564–577CrossRefGoogle Scholar
  73. 73.
    Porter J, Arzberger P, Braun H, Brynat P, Gage S, Hansen T, Lin C, Lin F, Kratz T, Michener W, Shapiro S, Williams T (2005) Wireless sensor networks for ecology. BioScience 55:561–572CrossRefGoogle Scholar
  74. 74.
    Quattrochi DA, Goodchild MF (1997) Scale in remote sensing and GIS. Lewis Publishers, New YorkGoogle Scholar
  75. 75.
    Ramapriyan HK (2002) Satellite imagery in earth science applications. In: Castelli V, Bergman LD (eds) Image databases: search and retrieval of digital imagery. Wiley, New YorkGoogle Scholar
  76. 76.
    Schenk T, Csatho B (2002) Fusion of LiDAR data and aerial imagery for a more complete surface description. Archives of photogrammetryGoogle Scholar
  77. 77.
    Schiewe J (1998) Experiences from the MOMS-02-project for future developments. Int Arch Photogramm Remote Sens 32:533–539Google Scholar
  78. 78.
    Ussyshkin RV, Smith RB (2007) A new approach for assessing LiDAR data accuracy for corridor mapping applications. In: Proceedings of the 5th international symposium on mobile mapping technology, Padua, ItalyGoogle Scholar
  79. 79.
    Xue Y, Li Y, Guang J, Zhang X, Guo J (2008) Small satellite remote sensing applications - history, current and future. Int J Remote Sens 29:4339–4372CrossRefGoogle Scholar
  80. 80.
    Zhang Y, Xie P, Li H (2007) An online colour 2D and 3D image system for disaster management. In: Li J, Zlatanova S, Fabbri A (eds) Geomatics solutions for disaster management. Lecture notes in geoinformation and cartography. Springer, Berlin, pp 1–15Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Spatial SciencesCurtin UniversityPerthAustralia
  2. 2.Department of Geospatial and Space TechnologyUniversity of Nairobi NairobiKenya

Personalised recommendations