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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
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.
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.
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.
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.
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25, 53–70.
Jensen, J. R. (2000). Remote sensing of the environment: an earth resource perspective. New Jersey: Prentice Hall.
Jensen, R. (2002). Spatial and temporal leaf area index dynamics in a north central Florida, USA Preserve. Geocarto International, 17[s](4), 47–54.
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.
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.
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.
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.
Lillesand, T. M., Kiefer, R. W., and Chipman, J. W. (2004). Remote Sensing and Image Interpretation (Fifth edn): John Wiley and Sons Inc.
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.
Lu, D., Mausel, P., Brondìzio, E., and Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401.
Lulla, V. O. (2005). Biomass estimation using statistical and neural network analysis of ASTER data. Indiana State University, Terre Haute, IN-47809 USA.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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
DOI: https://doi.org/10.1007/978-1-4020-9642-6_6
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-9641-9
Online ISBN: 978-1-4020-9642-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)