Mapping imperviousness using NDVI and linear spectral unmixing of ASTER data in the Cologne-Bonn region (Germany)

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5239, Issue , 2004, Pages 274-284

  Matthias, Braun ^a   Martin, Herold ^b  

^a UNIVERSITY OF BONN   (Germany)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

ASTER; Bonn; Cologne; Germany; Imperviousness; Linear spectral unmixing; NDVI; Urban areas

Indexed keywords

CLIMATOLOGY; DATA REDUCTION; IMAGE SENSORS; LAND USE; LIGHT ABSORPTION; PHOTOMAPPING; URBAN PLANNING; VEGETATION;

ASTER; BONN; COLOGNE; GERMANY; IMPERVIOUSNESS; LINEAR SPECTRAL UNMIXING; NDVI; URBAN AREAS;

REMOTE SENSING;

EID: 1942438932     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.510978     Document Type: Conference Paper

References (17)

1. Arnold, C.L., and Gibbons, C.J., Impervious surface coverage: The emergence of a key environmental indicator. Journal of the American Planning Association, 62, 2, 243-258, 1996.
2. Civco, D.L. and Hurd, J.D., Impervious Surface Mapping for the State of Connecticut. Proceedings of the 1997 ASPRS Annual Conference, Seattle, WA, 3, 124135, 1997.
3. Fung, T. and Siu, W-L., Environmental quality and its changes, an analysis using NDVI, International Journal of Remote Sensing, 22, 305-1334, 2000.
4. Herold, M., Gardner, M., and Roberts, D., Spectral resolution requirements for mapping urban areas, IEEE Transactions on Geoscience and Remote Sensing, 2003, (in press).
5. Jenson, J. R., Introductory digital image processing: a remote sensing perspective, 316 p, Prentice Hall Series in Geographic Information Science, 1996.
6. Ji, M. and Jensen, J.R., Effectiveness of Subpixel Analysis in Detecting and Quantifying Urban Imperviousness from Landsat Thematic Mapper Imagery. Geocarto International, 14 (4), 33-41, 1999.
7. Morgan, K.M., Leo Newland, E. Weber, and Busbey, A.B., Using SPOT satellite data to map impervious cover for urban runoff predictions, Toxicology and Environmental Chemistry, 40, 11-16, 1994.
8. Netzband, M. Möglichkeiten und Grenzen der Fernerkundung zur Versiegelungskartierung in Siedlungsräumen, IÖR-Schriften, 28, Dresden, 151 p., 1998.
9. Oke, T. Boundary Layer Climates, Routledge, New York, 1987.
10. Rashed, T., Weeks, J. R., Gadalla, M. S. and Hill, A., 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, 2001.
11. Ridd, M.K., Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing, 16, 12, 2165-2185, 1995.
12. Roberts, D.A., Gardner, M., Church, R., Ustin, S., Scheer, G., and Green, R.O., Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models, Remote Sensing of Environment, 65, 267-279, 1998.
13. Sadler, G.J., Barnsley, M.J., and Barr, S.L., Information extraction from remotely sensed images for urban land analysis. Proceeding of the 2nd European Conference on Geographical Information Systems EGIS'91, Brussels, Belgium, April, EGIS Foundation, Utrecht, 955-964, 1991.
14. Schöttker, B., Over, M., Braun, M., Siegmund, A., and Menz, G., Remote Sensing as a tool to visualize land use/-cover change and to support environmental education - a case study in NRW (North Rhine-Westphalia, Germany) in context of the local Agenda 21 process. Proceedings of the EARSeL Symposium in Gent, 2-5 June 2003, 2003, (in press).
15. Schueler, T.R., The importance of imperviousness. Watershed Protection Techniques, 1, 3, 100-111, 1994.
16. Small, C., Estimation of urban vegetation abundance by spectral mixture analysis, International Journal of Remote Sensing, 22, 7, 1305-1334, 2001.
17. Wu, C. and Murray, A.T., Estimating impervious surface distribution by spectral mixture analysis, Remote Sensing of Environment, 84, 493-505, 2003.