Extraction of urban impervious surfaces from an IKONOS image

International Journal of Remote Sensing

Volumn 30, Issue 5, 2009, Pages 1297-1311

  Lu, Dengsheng ^a   Weng, Qihao ^b  

^a INDIANA UNIVERSITY   (United States)

^b INDIANA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; SATELLITE IMAGERY; TALL BUILDINGS;

CRITICAL STEPS; DECISION TREE CLASSIFIERS; EFFECTIVE APPROACHES; ENVIRONMENTAL ASSESSMENT; IMPERVIOUS SURFACE; LINEAR SPECTRAL MIXTURE ANALYSIS; QUANTITATIVE MEASURES; URBAN IMPERVIOUS SURFACES;

EXTRACTION;

ENVIRONMENTAL ASSESSMENT; IKONOS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; QUANTITATIVE ANALYSIS; SPECTRAL ANALYSIS; URBAN AREA;

EID: 67650258206     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431160802508985     Document Type: Article

References (50)

1. Adams, J. B., Sabol, D. E., Kapos, V., Filho, R. A., Roberts, D. A., Smith, M. O. and Gillespie, A. R. (1995) Classification of multispectral images based on fractions of endmembers: Application to land cover change in the Brazilian Amazon. Remote Sensing of Environment, 52, pp. 137-154.
2. Arnold, C. L. and Gibbons, C. J. (1996) Imperious surface coverage: The emergence of a key environmental indicator. Journal of the American Planning Association, 62, pp. 243-258.
3. Bauer, M. E., Heinert, N. J., Doyle, J. K. and Yuan, F. (2004) Impervious surface mapping and change monitoring using Landsat remote sensing. ASPRS Annual Conference Processings
4. Boardman, J. W. and Kruse, F. A. (1994) Automated spectral analysis: A geological example using AVIRIS data, north Grapevine Mountains, Nevada. Proceedings, ERIM Tenth Thematic Coference on Geologic Remote Sensing, pp. 407-418. Ann Arbor, MI
5. Brabec, E., Schulte, S. and Richards, P. L. (2002) Impervious surface and water quality: A review of current literature and its implications for watershed planning. Journal of Planning Literature, 16, pp. 499-514.
6. Cablk, M. E. and Minor, T. B. (2003) Detecting and discriminating impervious cover with high-resolution IKONOS data using principal component analysis and morphological operators. International Journal of Remote Sensing, 24, pp. 4627-4645.
7. Civico, D. L., Hurd, J. D., Wilson, E. H., Arnold, C. L. and Prisloe Jr, M. P. (2002) Quantifying and describing urbanising landscapes in the northeast United States. Photogrammetric Engineering and Remote Sensing, 68, pp. 1083-1090.
8. Congalton, R. G. (1991) A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment, 37, pp. 35-46.
9. Deguchi, C. and Sugio, S. (1994) Estimations for the percentage of impervious area by the use of satellite remote sensing imagery. Journal of Soil and Water Conservation, 29, pp. 135-144.
10. Dougherty, M., Dymond, R. L., Goetz, S. J., Jantz, C. A. and Goulet, N. (2004) Evaluation of impervious surface estimates in a rapidly urbanizing watershed. Photogrammetric Engineering and Remote Sensing, 70, pp. 1275-1284.
11. Fankhauser, R. (1999) Automatic determination of imperviousness in urban areas from digital orthophotos. Water Science and Technology, 39, pp. 81-86.
12. Foody, G. M. (2002) Hard and soft classifications by a neural network with a non-exhaustively defined set of classes. International Journal of Remote Sensing, 23, pp. 3853-3864.
13. Foody, G. M. (2002) Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, pp. 185-201.
14. Friedl, M. A. and Brodley, C. E. (1997) Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61, pp. 399-409.
15. Friedl, M. A., Brodley, C. E. and Strahler, A. H. (1999) Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing, 37, pp. 969-977.
16. Gillies, R. R., Box, J. B., Symanzik, J. and Rodemaker, E. J. (2003) Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta: A satellite perspective. Remote Sensing of Environment, 86, pp. 411-422.
17. Goetz, S. J., Wright, R. K., Smith, A. J., Zinecker, E. and Schaub, E. (2003) IKONOS imagery for resource management: Tree cover, impervious surfaces and riparian buffer analyses in the mid-Atlantic region. Remote Sensing of Environment, 88, pp. 195-208.
18. Harbor, J. M. (1994) A practical method for estimating the impact of land use change on surface runoff, groundwater recharge and wetland hydrology. American Planning Association, 60, pp. 95-108.
19. Hodgson, M. E., Jensen, J. R., Tullis, J. A., Riordan, K. D. and Archer, C. M. (2003) Synergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering and Remote Sensing, 69, pp. 973-980.
20. Jantz, P., Goetz, S. J. and Jantz, C. A. (2005) Urbanization and the loss of resource lands within the Chesapeake Bay watershed. Environmental Management, 36, pp. 808-825.
21. Jennings, D. B., Jarnagin, S. T. and Ebert, C. W. (2004) A modeling approach for estimating watershed impervious surface area from national land cover data 92. Photogrammetric Engineering and Remote Sensing, 70, pp. 1295-1307.
22. Jensen, J. R. and Cowen, D. C. (1999) Remote sensing of urban/suburban infrastructure and socioeconomic attributes. Photogrammetric Engineering and Remote Sensing, 65, pp. 611-622.
23. Ji, M. and Jensen, J. R. (1999) Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper. Geocarto International, 14, pp. 31-39.
24. Lohani, V., Kibler, D. F. and Chanat, J. (2002) Constructing a problem solving environment tool for hydrologic assessment of land use change. Journal of the American Water Resources Association, 38, pp. 439-452.
25. Lu, D. and Weng, Q. (2004) Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering and Remote Sensing, 70, pp. 1053-1062.
26. Lu, D. and Weng, Q. (2005) Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana. Photogrammetric Engineering and Remote Sensing, 71, pp. 1275-1284.
27. Lu, D. and Weng, Q. (2006) Use of impervious surface in urban land use classification. Remote Sensing of Environment, 102, pp. 146-160.
28. Lu, D., Weng, Q. and Li, G. (2006) Residential population estimation using a remote sensing derived impervious surface approach. International Journal of Remote Sensing, 27, pp. 3553-3570.
29. Madhavan, B. B., Kubo, S., Kurisaki, N. and Sivakumar, T. V. L. N. (2001) Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing. International Journal of Remote Sensing, 22, pp. 789-806.
30. Mustard, J. F. and Sunshine, J. M. (1999) Spectral analysis for earth science: Investigations using remote sensing data. Remote Sensing for the Earth Sciences: Manual of Remote Sensing (3rd edition), pp. 251-307. Wiley, New York
31. Pal, M. and Mather, P. M. (2003) An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86, pp. 554-565.
32. Phinn, S., Stanford, M., Scarth, P., Murray, A. T. and Shyy, P. T. (2002) Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. International Journal of Remote Sensing, 23, pp. 4131-4153.
33. Rashed, T., Weeks, J. R., Gadalla, M. S. and Hill, A. G. (2001) Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: A case study of the Greater Cairo region, Egypt. Geocarto International, 16, pp. 5-15.
34. Rashed, T., Weeks, J. R., Roberts, D., Rogan, J. and Powell, R. (2003) Measuring the physical composition of urban morphology using multiple endmember spectral mixture models. Photogrammetric Engineering and Remote Sensing, 69, pp. 1011-1020.
35. Ridd, M. K. (1995) 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, pp. 2165-2185.
36. Schueler, T. R. (1994) The importance of imperviousness. Watershed Protection Techniques, 1, pp. 100-111.
37. Slonecker, E. T., Jennings, D. and Garofalo, D. (2001) Remote sensing of impervious surface: A review. Remote Sensing Reviews, 20, pp. 227-255.
38. Small, C. (2001) Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 22, pp. 1305-1334.
39. Smith, M. O., Ustin, S. L., Adams, J. B. and Gillespie, A. R. (1990) Vegetation in Deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment, 31, pp. 1-26.
40. Smits, P. C., Dellepiane, S. G. and Schowengerdt, R. A. (1999) Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20, pp. 1461-1486.
41. Theseira, M. A., Thomas, G., Taylor, J. C., Gemmell, F. and Varjo, J. (2003) Sensitivity of mixture modeling to endmember selection. International Journal of Remote Sensing, 24, pp. 1559-1575.
42. Van der Meer, F. and de Jong, S. M. (2000) Improving the results of spectral unmixing of Landsat Thematic Mapper imagery by enhancing the orthogonality of end-members. International Journal of Remote Sensing, 21, pp. 2781-2797.
43. Weng, Q. (2001) Modeling urban growth effect on surface runoff with the integration of remote sensing and GIS. Environmental Management, 28, pp. 737-748.
44. Weng, Q., Lu, D. and Schubring, J. (2004) Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89, pp. 467-483.
45. Weng, Q., Lu, D. and Liang, B. (2006) Urban surface biophysical descriptors and land surface temperature variations. Photogrammetric Engineering and Remote Sensing, 72, pp. 1275-1286.
46. Wu, C. and Murray, A. T. (2003) Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 84, pp. 493-505.
47. Wu, C. (2004) Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93, pp. 480-492.
48. Yang, L., Huang, C., Homer, C., Wylie, B. and Coan, M. (2003) An approach for mapping large-area impervious surface: Synergistic use of Landsat 7 ETM+ and high spatial resolution imagery. Canadian Journal of Remote Sensing, 29, pp. 230-240.
49. Yang, L., Xian, G., Klaver, J. M. and Deal, B. (2003) Urban land cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing, 69, pp. 1003-1010.
50. Zug, M., Phan, L., Bellefleur, D. and Scrivener, O. (1999) Pollution wash-off modeling on impervious surface: Calibration, validation and transposition. Water Science and Technology, 39, pp. 17-24.