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Urban spectrometry
Since the development of imaging spectrometry in the early 1980s, hyperspectral remote sensing has become an important tool for earth observations. The main advantage of hyperspectral remote sensing is the amount of spectral detail it provides. A large number of contiguous bands allows for precise identification of chemical and physical material properties. To date, the majority of research has focused on natural targets such as vegetation and minerals. Far less research was tailored on urban areas using hyperspectral data with few published spectra and limited analysis of their characteristics and separability. Urban environments are characterized by a large variety of different types of materials and land cover surfaces than found in natural landscapes including roofing materials, pavement types, soil and water surfaces, and vegetated areas. It is only recently that researcher have started exploration the use of spectrometry and hyperspectral remote sensing in urban areas. The main areas of research are:
1) Investigation and understanding of spectral characteristics of urban materials and land cover types
2) Spectral discrimination and important spectral features needed for material separation or identification of surface conditions
3) Capabilities and limitations of hyperspectral urban mapping
The Santa Barbara urban spectral library was developed to investigate these objectives. In conjuction with exploring this spectral library it is suggested to explore the following resources related to imaging spectrometry and urban areas.
Resources on urban spectrometry / spectral libraries:
1) Imaging spectrometry of urban materials by Dar A. Roberts and Martin Herold
2) Introduction to spectroscopy of rocks/minerals, and principles of spectroscopy by Roger N. Clarke
3) ASTER spectral library of manmade materials
4) Viewable spectral library of urban features, University of Utah
5) Bidirectional reflectance of urban surfaces – Dissertation by G. Meister
6) Interesting links to hyperspectral resources
Additional references:
Ben-Dor, E., Levin, N. and H. Saaroni 2001. A spectral based recognition of the urban environment using the visible and near-infrared spectral region (0.4-1.1 m). A case study over Tel-Aviv, International Journal of Remote Sensing, 22, 11, 2193-2218.
Donnay, J.P., Barnsley, M.J. and P.A. Longley (eds.) 2001. Remote sensing and urban analysis, Taylor and Francis, London and New York.
Green, R.O., Eastwood, M.L., Sarture, C.M. and T.G. Chrien et al. 1998. Imaging spectroscopy and the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). Remote Sensing of Environment, 65, 3, 227-248.
Hepner, G. F., Houshmand, B., Kulikov, I. and Bryant, N., 1998. Investigation of the integration of AVIRIS and IFSAR for urban analysis, Photogrammetric Engineering and Remote Sensing, 64 (8): 813 – 820.
Jensen, J.R. and D.C. Cowen 1999. Remote sensing of urban/suburban infrastructure and socio-economic attributes, Photogrammetric Engineering and Remote Sensing, 65, 5, 611-622.
Meer, F.D. van der and Jong, S.M. de (eds.) 2001. Imaging spectrometry: basic principles and prospective applications / Kluwer Academic, 403p.
Rashed, T., Weeks, J.R., Gadalla, M.S. and A. Hill 2001. Revealing the Anatomy of Cities through Spectral Mixture Analysis of Multispectral Imagery: A Case Study of the Greater Cairo Region, Egypt. Geocarto International, 16, 4, 5-16.
Roessner, S., Segl, K., Heiden, U. and H. Kaufmann 2001. Automated differentiation of urban surfaces based on airborne hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 39, 7, 1525–1532.
Clark, R. N., R. O. Green, G. A. Swayze, G. Meeker, S. Sutley, T. M. Hoefen, K. E. Livo, G. Plumlee, B. Pavri, C. Sarture, S. Wilson, P. Hageman, P. Lamothe, J. S. Vance, J. Boardman I. Brownfield, C. Gent, L. C. Morath, J. Taggart, P. M. Theodorakos, and M. Adams, 2001.Environmental Studies of the World Trade Center area after the September 11, 2001 attack. U. S. Geological Survey, Open File Report OFR-01-0429, URL: http://speclab.cr.usgs.gov/wtc/ (access: February 2004).
Wu, C. and A.T. Murray 2003. Estimating impervious surface distribution by spectral mixture analysis, Remote Sensing of Environment, 84, 493-505.
by Martin Herold, NCRST, University of California Santa Barbara, June 2004
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