Mapping impervious surfaces using classification and regression tree algorithm

Remote Sensing of Impervious Surfaces

Volumn , Issue , 2007, Pages 39-58

  Xian, George ^a  

^a EARTH RESOURCES OBSERVATION AND SCIENCE EROS CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 70350158748     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (35)

1. Arnfield, A.J., Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island, International Journal of Climatology, 231,1, 2003.
2. Breiman, L., Friedman, J., Olshen, R., and Stone, C., Classification and Regression Trees, Wadsworth International Group, Belmont, CA, 1984, p. 358.
3. Carlson, T.N., Applications of remote sensing to urban problems, Remote Sensing of Environment, 86, 273, 2003.
4. Carlson, T.N., Analysis and prediction of surface runoff in an urbanization watershed using satellite imagery, Journal of the American Water Resources Association, 40, 1087, 2004.
5. Clapham, Jr. W.B. Continuum-based classification of remotely sensed imagery to describe urban sprawl on a watershed scale, Remote Sensing of Environment, 86, 322, 2003.
6. Clark County, Comprehensive Planning, Clark County, Nevada. Accessed on 16 August 2006 at http://www.co.clark.nv.us/Comprehensive-planning.
7. Davis, C. and Schaub, T., A transboundary study of urban sprawl in the Pacific Coast region of North America: The benefit of multiple measurement methods, International Journal of Applied Earth Observation and Geoinformation, 7, 268, 2005.
8. Douglass, M., Mega-urban regions and world city formations: Globalization, the economic crisis and urban policy issues in Pacific Asia, Urban Studies, 37, 2315, 2000.
9. Frey, W.H., Metro America in the New Century: Metropolitan and Central City Demographic Shifts since 2000, Brookings Institution, Washington, 2005. Accessed on September 16, 2005 at http://www.brookings.edu/metro/pubs/.
10. Gillies, R.R., Box, J.B., Symanzik, J., and Rodemaker, E.J., Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta-a satellite perspective, Remote Sensing of Environment, 86, 411, 2003.
11. Hebble, E.E., Carlson, T.N., and Daniel, K., Impervious surface area and residential housing density: A satellite perspective, Geocarto International, 16, 13, 2001.
12. Ji, M.H. and Jensen, J.R., Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper imagery, Geocarto International, 14, 31,1999.
13. Kalnay, E. and Cai, M., Impact of urbanization and land-use change on climate, Natural, 423, 528, 2003.
14. KCORPP (King County Office of Regional Policy and Planning), The Annual Growth Report 2000, King County, Washington. Accessed on August 10, 2006 at http://www.metrokc.gov/budget/agr/agr00/.
15. Landsat Project Science Office, Landsat 7 Science Data Users Handbook, 2006. URL: http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html; last accessed June, 2006.
16. Lu, D. and Weng, Q., Use of impervious surface in urban land-use classification, Remote Sensing of Environment, 102, 146, 2006.
17. Morgan, K.M., Newland, L.W., Weber, E., and Busbey, A.B., Using spot satellite data to map impervious cover for urban runoff predictions, Toxicological and Environmental Chemistry, 40, 11, 1993.
18. NLCD 2001, National Land Cover Database 2001. Accessed on August 16, 2006 at http://www.mrlc.gov/mrlc2k.nlcd.asp.
19. Plunk, D.E., Morgan, K., and Newland, L., Mapping impervious cover using Landsat TM data, Journal of Soil and Water Conservation, 45, 589, 1990.
20. Quinlan, J.R., C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993, p. 302.
21. Robinson, L., Newell, J.P., and Marzluff, J.M., Twenty-five year of sprawl in the Seattle region: Growth management responses and implication for conservation, Landscape and Urban Planning, 71, 51, 2005.
22. Schueler, T.R., The importance of imperviousness, Watershed Protection Techniques, 1, 100, 1994.
23. Seto, K. and Liu, W., Comparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change, Photogrammetric Engineering and Remote Sensing, 69, 981, 2003.
24. Slonecker, E.T., Jennings, D.B., and Garofalo, D., Remote sensing of impervious surfaces: A review, Remote Sensing Review, 20, 227, 2001.
25. Small, C., Estimation of urban vegetation abundance by spectral mixture analysis, International Journal of Remote Sensing, 22,1305, 2001.
26. Small, C., Pozzi, F., and Elvidge, C.D., Spatial analysis of global urban extent from DMSP-OLS night lights, Remote Sensing of Environment, 96, 277, 2005.
27. Voogt, J.A. and Oke, T.R., Thermal remote sensing of urban climates, Remote Sensing of Environment, 86, 370, 2003.
28. Ward, D., Phinn, S.R., and Murray, A.T., Monitoring growth in rapidly urbanized areas using remotely sensed data, Professional Geographer, 52, 371, 2000.
29. Weng, Q., A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China, International Journal of Remote Sensing, 22, 1999, 2001.
30. Wu, C., Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery, Remote Sensing of Environment, 93, 480, 2004.
31. Xian, G., Assessing urban growth with sub-pixel impervious surface coverage, Urban Remote Sensing, Weng and Quattrochi, Eds., Taylor & Francis Group, Boca Raton, FL, 2006, p. 179.
32. Xian, G. and Crane, M., Assessments of urban growth in the Tampa Bay watershed using remote sensing data, Remote Sensing of Environment, 97, 203, 2005.
33. Xian, G. and Crane, M., An analysis of urban thermal characteristics and associated urban land cover in Tampa Bay and Las Vegas using Landsat satellite data, Remote Sensing of Environment, 104, 2,147, 2006.
34. Xian, G., Yang, L., Klaver, J.M., and Hossain, N., Measuring urban sprawl and extent through multi-temporal imperviousness mapping, Rates, Trends, Causes, and Consequences of Urban Land-Use Change in the United States, U.S. Geological Survey Professional Paper, 1726, 2006, p. 59.
35. Yang, L., Xian, G., Klaver, J.M., and Deal, B., Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data, Photogrammetric Engineering and Remote Sensing, 69, 1003, 2003.