, Volume 29, Issue 4, pp 1166–1178 | Cite as

Lidar intensity for improved detection of inundation below the forest canopy

  • Megan W. LangEmail author
  • Greg W. McCarty


Wetland hydrology is an important factor controlling wetland function and extent, and should therefore be a vital part of any wetland mapping program. Broad-scale forested wetland hydrology has been difficult to study with conventional remote sensing methods. Airborne Light Detection and Ranging (LiDAR) is a new and rapidly developing technology. LiDAR data have mainly been used to derive information on elevation. However, the intensity (amplitude) of the signal has the potential to significantly improve the ability to remotely monitor inundation — an important component of wetland hydrology. A comparison between LiDAR intensity data collected during peak hydrologic expression and detailedin situ data from a series of forested wetlands on the eastern shore of Maryland demonstrate the strong potential of LiDAR intensity data for this application (>96% overall accuracy). The relative ability of LiDAR intensity data for forest inundation mapping was compared with that of a false color near-infrared aerial photograph collected coincident with the LiDAR intensity (70% overall accuracy; currently the most commonly used method for wetland mapping) and a wetness index map derived from a digital elevation model. The potential of LiDAR intensity data is strong for addressing issues related to the regulatory status of wetlands and measuring the delivery of ecosystem services.

Key Words

forested wetlands hydrology inundation wetland mapping 


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Literature Cited

  1. Antonarakis, A. S., K. S. Richards, and J. Brasington. 2008. Object-based land cover classification using airborne LiDAR. Remote Sensing of Environment 112:2988–98.CrossRefGoogle Scholar
  2. Ator, S. W., J. M. Denver, D. E. Krantz, W. L. Neweell, and S. K. Martucci. 2005. A surficial hydrogeologic framework for the mid-Atlantic Coastal Plain. USGS Professional Paper, Reston, VA, USA, 1680:1–44.Google Scholar
  3. Böhner, J., R. Koethe, O. Conrad, J. Gross, A. Ringeler, and T. Selige. 2002. Soil regionalisation by means of terrain analysis and process parameterisation. p. 213–22.In E. Micheli, F. Nachtergaele and L. Montanarella (eds.) Soil Classification 2001. European Soil Bureau, Luxembourg, Research Report No. 7, EUR 20398.Google Scholar
  4. Brennan, R. and T. L. Webster. 2006. Object-oriented land cover classification of LIDAR-derived surfaces. Canadian Journal of Remote Sensing 32:162–72.Google Scholar
  5. Chust, G., I. Galparsoro, A. Borja, J. Franco, and A. Uriarte. 2008. Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery. Estuarine, Coastal and Shelf Science 78:633–43.CrossRefGoogle Scholar
  6. Cimmery, V. 2007. User guide for SAGA (version 2.0). Scholar
  7. Congalton, R. G. and Green, K. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press, Boca Raton, FL, USA.Google Scholar
  8. Donoghue, D. N. M., P. J. Watt, N. J. Cox, and J. Wilson. 2007. Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. Remote Sensing of Environment 110:509–22.CrossRefGoogle Scholar
  9. Duda, O. and P. E. Hart. 1973. Pattern Classification and Scene Analysis. John Wiley and Sons, Inc., New York, NY, USA.Google Scholar
  10. Fisher, T., J. Benitez, K. Lee, and A. Sutton. 2006. History of land cover change and biogeochemical impacts in the Choptank river basin in the mid-Atlantic region of the US. International Journal of Remote Sensing 27:3683–703.CrossRefGoogle Scholar
  11. Flood, M. 2001. Laser altimetry: from science to commercial LIDAR mapping. Photogrammetric Engineering and Remote Sensing 67:1209–27.Google Scholar
  12. Goodale, R., C. Hopkinson, D. Colville, and D. Amirault-Langlais. 2007. Mapping piping plover (Charadrius melodus melodus) habitat in coastal areas using airborne lidar data. Canadian Journal of Remote Sensing 33:519–33.Google Scholar
  13. Goodwin, N. R., N. C. Coops, and D. S. Culvenor. 2006. Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sensing of Environment 103:140–52.CrossRefGoogle Scholar
  14. Holmgren, J. and Å. Persson. 2004. Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment 90:415–23.CrossRefGoogle Scholar
  15. Homer, C., C. Huang, L. Yang, B. Wylie, and M. Coan. 2004. Development of a 2001 National Landcover Database for the United States. Photogrammetric Engineering and Remote Sensing 70:829–40.Google Scholar
  16. Hyyppä, J., H. Hyyppä, D. Leckie, F. Gougeon, X. Yu, and M. Maltamo. 2008. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing 29:1339–66.CrossRefGoogle Scholar
  17. Justice, C. O. and J. R. G. Townshend. 1981. Integrating ground data with remote sensing. p. 38–58.In J. R. G. Townshend (ed.) Terrain Analysis and Remote Sensing. Allen and Unwin, London, UK.Google Scholar
  18. Kaasalainen, S., E. Ahokas, J. Hyyppä, and J. Suomalainen. 2005. Study of surface brightness from backscattered laser intensity: calibration of laser data. IEEE Geoscience and Remote Sensing Letters 2:255–59.CrossRefGoogle Scholar
  19. Kaasalainen, S., A. Kukko, T. Lindroos, P. Litkey, H. Kaartinen, J. Hyyppä, and E. Ahokas. 2008. Brightness measurements and calibration with airborne and terrestrial laser scanners. IEEE Transactions on Geoscience and Remote Sensing 46:528–34.CrossRefGoogle Scholar
  20. Kudray, G. M. and M. R. Gale. 2000. Evaluation of national wetland inventory maps in a heavily forested region in the upper great lakes. Wetlands 20:581–87.CrossRefGoogle Scholar
  21. Kusler, J., P. Parenteau, and E. A. Thomas. 2007. “Significant nexus” and Clean Water Act jurisdiction. Association of State Wetland Managers, Scholar
  22. Lang, M. and G. McCarty. 2008. Wetland Mapping: History and Trends. p. 74–112.In R. E. Russo (ed.) Wetlands: Ecology, Conservation and Management. Nova Publishers, New York, NY, USA.Google Scholar
  23. Lang, M., G. McCarty, J. Ritchie, A. Sadeghi, D. Hively, and D. Eckles. 2008. Radar monitoring of wetland hydrology: dynamic information for the assessment of ecosystem services. Proceedings of the 2008 IEEE International Geoscience & Remote Sensing Symposium, Boston, MA, USA.Google Scholar
  24. Lee, J. S. 1980. Digital image enhancement and noise filtering by use of the local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence 2:165–168.CrossRefGoogle Scholar
  25. Lemmens, M. 2007. Airborne lidar sensors. GIM International 21(2):24–27.Google Scholar
  26. Luzum, B. J., K. C. Slatton, and R. L. Shrestha. 2005. Analysis of spatial and temporal stability of airborne laser swath mapping data in feature space. IEEE Transactions on Geoscience and Remote Sensing 43:1403–20.CrossRefGoogle Scholar
  27. Luzum, B., M. Starek, and K. C. Slatton. 2004. Normalizing ALSM Intensities. Geosensing Engineering and Mapping (GEM) Civil and Coastal Engineering Department, University of Florida, USA. GEM Center Report No. Rep 2004-07-001.Google Scholar
  28. Mitsch, W. J. and J. G. Gosselink. 2007. Wetlands, fourth edition. John Wiley and Sons, Inc., New York, NY, USA.Google Scholar
  29. Murphy, P. N. C., J. Ogilvie, K. Connor, and P. A. Arp. 2007. Mapping wetlands: a comparison of two different approaches for New Brunswick, Canada. Wetlands 27:845–54.CrossRefGoogle Scholar
  30. Rolband, M. S. 1995. A comparison of wetland areas in northern Virginia: National wetland inventory maps versus field delineated wetlands under the 1987 manual. Wetland Journal: Research, Restoration, Education 7:10–14.Google Scholar
  31. Rosso, P. H., S. L. Ustin, and A. Hastings. 2006. Use of lidar to study changes associated withSpartina invasion in San Francisco Bay marshes. Remote Sensing of Environment 100:295–306.CrossRefGoogle Scholar
  32. Silva, T. S. F., M. P. F. Costa, J. M. Melack, and E. M. L. M. Novo. 2008. Remote sensing of aquatic vegetation: theory and applications. Environmental Monitoring and Assessment 140:131–45.CrossRefPubMedGoogle Scholar
  33. Song, J. H., S. H. Han, K. Y. Yong, and Y. I. Kim. 2002. Assessing the possibility of land-cover classification using LiDAR intensity data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 34:259–62.Google Scholar
  34. Stolt, M. H. and J. C. Baker. 1995. Evaluation of National Wetland Inventory maps to inventory wetlands in the southern Blue Ridge of Virginia. Wetlands 15:346–53.CrossRefGoogle Scholar
  35. Tiner, R. W. 1990. Use of high-altitude aerial photography for inventorying forested wetlands in the United States. Forest Ecology and Management 33:593–604.CrossRefGoogle Scholar
  36. Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8:127–50.CrossRefGoogle Scholar
  37. U.S. Fish and Wildlife Service. 2002. National wetlands inventory: A strategy for the 21st century. US Department of the Interior, Fish and Wildlife Service, Washington, DC, USA.Google Scholar
  38. Vierling, K., L. Vierling, W. Gould, S. Martinuzzi, and R. Clawges. 2008. Lidar: shedding new light on habitat characterization and modeling. Frontiers in Ecology and the Environment 6:90–98.CrossRefGoogle Scholar
  39. Wehr, A. and U. Lohr. 1999. Airborne laser scanning — an introduction and overview. ISPRS Journal of Photogrammetry & Remote Sensing 54:68–82.CrossRefGoogle Scholar
  40. Wright, C. and A. Gallant. 2007. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment 107:582–605.CrossRefGoogle Scholar
  41. Yu, K., S. H. Han, H. Chang, and T. Ha. 2002. Potential of reflected intensity of airborne laser scanning systems in roadway features identification. Geomatica 56:363–74.Google Scholar

Copyright information

© Society of Wetland Scientists 2009

Authors and Affiliations

  1. 1.Remote Sensing and Hydrology Laboratory, Beltsville Agricultural Research CenterUSDA-ARSBeltsvilleUSA

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