Hyperspectral Sensing

Chapter

Abstract

Multispectral remote sensing has enjoyed widespread use for well over 40years and in the previous chapters we have explored the sensors and techniques that have been developed to exploit the remote measurement of environmental attributes using this technology. While multispectral analysis provides information to guide our assessment of environmental processes, the inherent limitations imposed by the comparatively broad spectral resolution of multispectral sensors restricts the level of detail that can be extracted from the data. As interest in environmental remote sensing continues to develop, imaging capabilities that extend measurement options beyond the wide wavelength bands common to multispectral sensors can expand environmental analysis and characterization efforts.

Keywords

Biomass Clay Nickel Dioxide Hydration 

References

  1. Aspinall, Richard (2002) A geographic information science perspective on hyperspectral remote sensing, Journal of Geographical Systems, 4, 127–140.Google Scholar
  2. Aspinall, R., Marcus, W., Boardman, J. (2002) Consideratons in Collecting, Processing and Analysing High Spatial Resolution Hyperspectral Data for Environmental Applications, Journal of Geographic Systems, 4, 15–29.Google Scholar
  3. Ben-Dor, E., Chabrillat, S., Dematte, J., Taylor, G., Hill, J., Whiting, M., and Sommer, S. (2009) Using Imaging Spectroscopy to Study Soil Properties, Remote Sensing of Environment, 113, S38-S55.Google Scholar
  4. Chaudhry, F., Wu, C., Liu, W., Chang, C. and Plaza, A. (2006) Pixel purity index-based algorithms for endmember extraction from hyperspectral imagery, in Chang, C. (ed) Recent Advances in Hyperspectral Signal and Image Processing, Transworld Research Network, 29–62.Google Scholar
  5. Clark, R. (1999) Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy, in Rencz, A. (ed) Manual of Remote Sensing, Volume 3, Remote Sensing for Earth Sciences, John Wiley and Sons, New York, 3–58.Google Scholar
  6. Clark, R., Swayze, G., Gallaher, A., King, T., Calvin, W. (1999) The U.S. Geological Survery, Digital Spectral Library, Version 1, U.S. Geological Survey Open File Report 93–592.Google Scholar
  7. Clark, R., Swayze, G., Wise, R., Livo, K., Hoefen, T., Kokaly, R. and Sutley, S. (2007) USGS Digital Spectral Library splib06a, U.S. Geological Survey, Data Series 231.Google Scholar
  8. Craig, S., Lohrenz, S., Lee, Z., Mahoney, K., Kirkpatrick, G., Schofield, O., Steward, G. (2006) Use of Hyperspectral Remote Sensing Reflectance for Detection and Assessment of the Harmful Alga, Karenia brevis, Applied Optics, 45, 5414–5425.Google Scholar
  9. Curran, P. (1994) Imaging Spectrometry, Progress in Physical Geography, 18, 247–266.Google Scholar
  10. Curran, P. (2001) Imaging Spectrometry for Ecological Applications, International Journal of Applied Earth Observation and Geoinformation, 3, 305–312.Google Scholar
  11. Dehaan, R. and Taylor, G. (2003) Image-derived spectral endmember as indicators of salinisation, International Journal of Remote Sensing, 24, 775–794.Google Scholar
  12. Fava, F.; Colombo, R.; Bocchi, S.; Meroni, M.; Sitzia, M.; Fois, N.; Zucca, C. (2009) Identification of hyperspectral vegetation indices for Mediterranean pasture characterization, International Journal of Applied Earth Observations and Geoinformation, 11, 233–243.Google Scholar
  13. Ferwerda, J. G.; Jones, S. D.; Du, Pei-Jun (2006) A Web-based open-source database for the distribution of hyperspectral signatures, Geoinformatics 2006: Geospatial Information Technology. Proceedings of the SPIE, 6421, 64210G-64210G-7.Google Scholar
  14. Gao, J. (2009) Digital Analysis of Remotely Sensed Imagery, McGraw-Hill, 645p.Google Scholar
  15. Garcia-Aro, F., Gilabert, M., Melia, J. (1999) Extraction of Endmembers from Spectral Mixtures, Remote Sensing of Environment, 68, 237–253.Google Scholar
  16. Govender, M., Chetty, K., Bulcock, H. (2007) A Review of Hyperspectral Remote Sensing and its Application in Vegetation and Water Resource Studies, Water SA, 33, 145–151.Google Scholar
  17. Goetz, Alexander F.H. (2009) Three decades of hyperspectral remote sensing of the Earth: A personal view, Remote Sensing of Environment, 113, S5–S16.Google Scholar
  18. Herold, M., Roberts, D., Gardner, M., Dennison, P. (2004) Spectrometry for Urban Area Remote Sensing- Development and Analysis of a Spectral Library from 350 to 2400 nm, Remote Sensing of Environment, 91, 304–319.Google Scholar
  19. Im, J. and Jensen, J. (2008) Hyperspectral Remote Sensing of Vegetation, Geography Compass 2/6 1943–1961.Google Scholar
  20. Kalacska, Margaret E.; Bell, Lynne S.; Arturo Sanchez-Azofeifa, G.; Caelli, Terry (2009)The Application of Remote Sensing for Detecting Mass Graves: An Experimental Animal Case Study from Costa Rica*, Journal of Forensic Sciences, 54, I159–166.Google Scholar
  21. Kruse, F. (2004) Comparison of ATREM, ACORN, and FLAASH Atmospheric Corrections sing Low Altitude AVIRIS Data of Boulder, Colorado, Proceedings of the 204 AVIRIS Earth Science and Applications Workshop, Jet Propulsion Laboratory, Pasadena, CA.Google Scholar
  22. Madden, Marguerite (2004) Remote Sensing and Geographic Information System Operations for Vegetation Mapping of Invasive Exotics1, Weed Technology, 18, 1457–1463.Google Scholar
  23. Martinez, P., Perez, R., Plaza, A., Aguilar, P., Cantero, M., Plaza, J. (2006) Endmember Extraction Algorithms from Hyperspectral Images, Annals of Geophysics, 49, 93–101.Google Scholar
  24. Milton, E., Schaepman, M., Anderson. K., Kneubühler, M., and Fox, N. (2009) Progress in field spectroscopy Remote Sensing of Environment, 113, Supplement 1, s92–s109.Google Scholar
  25. Nidamanuri, Rama Rao; Zbell, Bernd (2010) A method for selecting optimal spectral resolution and comparison metric for material mapping by spectral library search, Progress in Physical Geography, 34, 47–58.Google Scholar
  26. Plaza, Antonio; Benediktsson, Jon Atli; Boardman, Joseph W.; Brazile, Jason; Bruzzone, Lorenzo; Camps-Valls, Gustavo; Chanussot, Jocelyn; Fauvel, Mathieu (2009) Recent advances in techniques for hyperspectral image processing, Remote Sensing of Environment, 113, S110–S122.Google Scholar
  27. Pontius, Jennifer; Martin, Mary; Plourde, Lucie; Hallett, Richard (2008) Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies, Remote Sensing of Environment, 112, 2665–2676.Google Scholar
  28. Price, J. (1995) Examples of high resolution visible to near-infrared reflectance spectra and a standardized collection for remote sensing studies, International Journal of Remote Sensing, 16, 993–1000.Google Scholar
  29. Price, J. (1998) An Approach FOR Analysis of Reflectance Spectra, Remote Sensing of Environment, 4, 316–335.Google Scholar
  30. Rogge, D.M.; Rivard, B.; Zhang, J.; Sanchez, A.; Harris, J.; Feng, J. (2007) Integration of spatial-spectral information for the improved extraction of endmembers, Remote Sensing of Environment, 110, 287–303.Google Scholar
  31. Schaepman, Michael E.; Ustin, Susan L.; Plaza, Antonio J.; Painter, Thomas H.; Verrelst, Jochem; Liang, Shunlin (2009) Earth system science related imaging spectroscopy-An assessment, Remote Sensing of Environment, 113, S123–S137.Google Scholar
  32. Schmidtlein, S. (2005) Imaging Spectroscopy as a Tool for Mapping Ellenberg Indicator Values, Journal of Applied Ecology, 42, 966–974.Google Scholar
  33. Shaw, G. and Burke, H. (2003) Spectral Imaging for Remote Sensing, Lincoln Laboratory Journal, 14, 3–28.Google Scholar
  34. Swayze, G. (2000) Using Imaging Spectroscopy to Map Acid Mine Waste, Environmental Science and Technology, 34, 47–57.Google Scholar
  35. Treitz, P.; Howarth, P. (1999) Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems Progress in Physical Geography, 23, 359–390.Google Scholar
  36. Underwood, Emma; Ustin, Susan; DiPietro, Deanne (2003) Mapping nonnative plants using hyperspectral imagery, Remote Sensing of Environment, 86, 150–161.Google Scholar
  37. Van der Meer, F. (2004) Analysis of Spectral Absorption Features in Hyperspectral Imagery, International Journal of Applied Earth Observation and Geoinformation, 5, 55–68.Google Scholar
  38. Van der Meer, F. and Jong, S. (2002) Imaging Spectroscopy: Basic Principles and Prospective Applications, Springer, 425p.Google Scholar
  39. Vane, G and Goetz, A. (1988) Proceedings, Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Performance Evaluation Workshop, National Aeronautics and Space Administration, Jet Propulsion Laboratory Publication, Pasadena, CA.Google Scholar
  40. Younan, N. H.; King, R. L.; Bennett Jr, H. H. (2004) Classification of Hyperspectral Data: A Comparative Study Precision Agriculture, 5, 41–53.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of GeographyOhio UniversityAthensUSA

Personalised recommendations