Skip to main content

Hyperspectral Applications in Urban Geography

  • Chapter
Planning and Socioeconomic Applications

Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 1))

Abstract

This chapter examines the scale and scope of hyperspectral remote sensing applications and presents a brief case study. As the case study demonstrates, hyperspectral approaches expand the range and accuracy of very fine scale urban studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Anderson, J. R., Hardy, E. E., Roach, J. T., and Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. Paper presented at the Geological Survey Professional Paper.

    Google Scholar 

  • Chou, T. Y., Lei, T. C., Wan, S., and Yang, L. S. (2005). Spatial knowledge databases as applied to the detection of changes in urban land use. International Journal of Remote Sensing, 26[s](14), 3047–3068.

    Article  Google Scholar 

  • Clark, R. N., Swayze, G. A., Wise, R. A., Livo, K. E., Hoefen, T. M., Kokaly, R. F., et al. (2007). USGS Digital Spectral Library splib06a.

    Google Scholar 

  • Hardin, P. J., and Jensen, R. R. (2005). Neural network estimation of urban leaf area index. GIScience and Remote Sensing, 42[s](3), 229–252.

    Google Scholar 

  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25, 53–70.

    Article  Google Scholar 

  • Jensen, J. R. (2000). Remote sensing of the environment: an earth resource perspective. New Jersey: Prentice Hall.

    Google Scholar 

  • Jensen, R. (2002). Spatial and temporal leaf area index dynamics in a north central Florida, USA Preserve. Geocarto International, 17[s](4), 47–54.

    Article  Google Scholar 

  • Jensen, R. R., and Binford, M. W. (2004). Measurement and comparison of Leaf Area Index estimators derived from satellite remote sensing techniques. International Journal of Remote Sensing, 25[s](20), 4251–4265.

    Article  Google Scholar 

  • Jensen, R. R., Boulton, J. R., and Harper, B. T. (2003). The relationship between urban leaf area index and household energy usage in Terre Haute, Indiana, U.S. Journal of Arboriculture, 29[s](4), 226–229.

    Google Scholar 

  • Jensen, R.R., Hardin, P. J., Bekker, M. F., Farnes, D. A., Lulla, V., and Hardin, A. (in press). Modeling urban leaf area index with AISA${+}$ hyperspectral data. Applied Geography.

    Google Scholar 

  • Li, G., and Weng, Q. (2007). Measuring the quality of life in city of Indianapolis by integration of remote sensing and census data. International Journal of Remote Sensing, 28[s](2), 249–267.

    Article  Google Scholar 

  • Lillesand, T. M., Kiefer, R. W., and Chipman, J. W. (2004). Remote Sensing and Image Interpretation (Fifth edn): John Wiley and Sons Inc.

    Google Scholar 

  • Liu, X., and Lathrop, R. G. (2002). Urban change detection based on an artificial neural network. International Journal of Remote Sensing, 23(12), 2513–2518.

    Article  Google Scholar 

  • Lu, D., Mausel, P., Brondìzio, E., and Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401.

    Article  Google Scholar 

  • Lulla, V. O. (2005). Biomass estimation using statistical and neural network analysis of ASTER data. Indiana State University, Terre Haute, IN-47809 USA.

    Google Scholar 

  • Mundia, C. N., and Aniya, M. (2005). Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS. International Journal of Remote Sensing, 26(13), 2831–2849.

    Article  Google Scholar 

  • Muttitanon,W., and Tripathi, N. K. (2005). Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. International Journal of Remote Sensing, 26(11), 2311–2323.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Lulla, V. (2009). Hyperspectral Applications in Urban Geography. In: Gatrell, J.D., Jensen, R.R. (eds) Planning and Socioeconomic Applications. Geotechnologies and the Environment, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9642-6_6

Download citation

Publish with us

Policies and ethics