Mapping impervious surface distribution with the integration of landsat tm and quickbird images in a complex urban-rural frontier in Brazil

Environmental Remote Sensing and Systems Analysis

Volumn , Issue , 2012, Pages 277-296

  Lu, Dengsheng ^a   Moran, Emilio ^a   Hetrick, Scott ^a   Li, Guiying ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84971593740     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b11702     Document Type: Chapter

References (73)

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 endmem-bers: Application to land cover change in the Brazilian Amazon. Remote Sensing of Environment, 52, 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(2), 243-258.
3. Bauer, M. E., Loffelholz, B. C., and Wilson, B. (2008). Estimating and mapping impervious surface area by regression analysis of Landsat imagery. In Remote Sensing of Impervious Surfaces, Q. Weng (ed.), Taylor & Francis Group, LLC, Boca Raton, FL, pp. 3-19.
4. Boardman, J. W. and Kruse, F. A. (1994). Automated spectral analysis: A geological example using AVIRIS data, North Grapevine Mountains, Nevada. In Proceedings, ERIM Tenth Thematic Conference on Geologic Remote Sensing, Ann Arbor, MI, pp. 407-418.
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, 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, 4627-4645.
7. Chander, G., Markham, B. L., and Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113, 893-903.
8. Chen, Y., Wen, D., Jing, L., and Shi, P. (2007). Shadow information recovery in urban areas from very high resolution satellite imagery. International Journal of Remote Sensing, 28, 3249-3254.
9. Civco, D. L., Hurd, J. D., Wilson, E. H., Arnold, C. L., and Prisloe, Jr., M. P. (2002). Quantifying and describing urbanizing landscapes in the northeast United States. Photogrammetric Engineering and Remote Sensing, 68, 1083-1090.
10. Cochrane, M. A. and Souza, Jr. C. M. (1998). Linear mixture model classification of burned forests in the eastern Amazon. International Journal of Remote Sensing, 19, 3433-3440.
11. Congalton, R. G. (1991). A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment, 37, 35-46.
12. Congalton, R. G. and Mead, R. A. (1983). A quantitative method to test for consistency and correct-ness in photo interpretation. Photogrammetric Engineering and Remote Sensing, 49, 69-74.
13. Congalton, R. G. and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition, CRC Press, Taylor & Francis Group, Boca Raton, FL, p. 183.
14. Dare, P. M. (2005). Shadow analysis in high-resolution satellite imagery of urban areas. Photogrammetric Engineering and Remote Sensing, 71, 169-177.
15. Elvidge, C. D., Tuttle, B. T., Sutton, P. C., Baugh, K. E., Howard, A. T., Milesi, C., Bhaduri, B., and Nemani R. (2007). Global distribution and density of constructed impervious surfaces. Sensors, 7, 1962-1979.
16. Esch, T., Himmler, V., Schorcht, G., Thiel, M., Wehrmann, T., Bachofer, F., Conrad, C., Schmidt, M., and Dech, S. (2009). Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data. Remote Sensing of Environment, 113, 1678-1690.
17. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185-201.
18. Garcia-Haro, F. J., Gilabert, M. A., and Melia, J. (1996). Linear spectral mixture modeling to estimate vegetation amount from optical spectral data. International Journal of Remote Sensing, 17, 3373-3400.
19. Goetz, S. J., Wright, R., Smith, A. J., Zinecker, E., and Schaub, E. (2003). IKONOS imagery for resource management: Tree cover, impervious surface, and riparian buffer analyses in the mid-Atlantic region. Remote Sensing of Environment, 88, 195-208.
20. Gillies, R. R., Box, J. B., Symanzik, J., and Rodemaker, E. J. (2003). Effects of urbaniza-tion on the aquatic fauna of the Line Creek watershed, Atlanta-A satellite perspective. Remote Sensing of Environment, 86, 411-422.
21. Green, A. A., Berman, M., Switzer, P., and Craig, M. D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 26, 65-74.
22. Greenfield, E. J., Nowak, D. J., and Walton, J. T. (2009). Assessment of 2001 NLCD per-cent tree and impervious cover estimates. Photogrammetric Engineering and Remote Sensing, 75, 1279-1286.
23. 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, 95-108.
24. Hu, X. and Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolu-tion imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113, 2089-2102.
25. Janssen, L. F. J. and van der Wel, F. J. M. (1994). Accuracy assessment of satellite derived land-cover data: a review. Photogrammetric Engineering and Remote Sensing, 60, 419-426.
26. Ji, M. and Jensen, J. R. (1999). Effectiveness of subpixel analysis in detecting and quantify-ing urban imperviousness from Landsat Thematic Mapper. Geocarto International, 14, 31-39.
27. Li, Y., Gong, P., and Sasagawa, T. (2005). Integrated shadow removal based on photogram-metry and image analysis. International Journal of Remote Sensing, 26, 3911-3929.
28. Lohani, V., Kibler, D. F., and Chanat, J. (2002). Constructing a problem solving environ-ment tool for hydrologic assessment of land use change. Journal of the American Water Resources Association, 38, 439-452.
29. Lu, D., Mausel, P., Brondízio, E., and Moran, E. (2002). Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing, 23, 2651-2671.
30. Lu, D., Moran, E., and Batistella, M. (2003). Linear mixture model applied to Amazonian vegetation classification. Remote Sensing of Environment, 87, 456-469.
31. 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, 1053-1062.
32. Lu, D. and Weng, Q. (2006a). Use of impervious surface in urban land use classification. Remote Sensing of Environment, 102, 146-160.
33. Lu, D. and Weng, Q. (2006b). Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, United States. Remote Sensing of Environment, 104(2), 157-167.
34. 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(16), 3553-3570.
35. Lu, D., Batistella, M., Moran, E., and de Miranda, E. E. (2008). A comparative study of Landsat TM and SPOT HRG images for vegetation classification in the Brazilian Amazon. Photogrammetric Engineering and Remote Sensing, 70, 311-321.
36. Lu, D. and Weng, Q. (2009). Extraction of urban impervious surface from an IKONOS image. International Journal of Remote Sensing, 30(5), 1297-1311.
37. Lu, D., Hetrick, S., and Moran, E. (2011). Impervious surface mapping with QuickBird imag-ery. International Journal of Remote Sensing, 32(9), 2519-2533.
38. 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, 789-806.
39. Mohapatra, R. P. and Wu, C. (2008). Subpixel imperviousness estimation with IKONOS imag-ery: An artificial neural network approach. In Remote Sensing of Impervious Surfaces, Q. Weng (ed.), Taylor & Francis Group, LLC, Boca Raton, FL, pp. 21-37.
40. Mustard, J. F. and Sunshine, J. M. (1999). Spectral analysis for earth science: Investigations using remote sensing data. In Remote Sensing for the Earth Sciences: Manual of Remote Sensing, Third Edition, Volume 3, A. N. Rencz (ed.), John Wiley & Sons Inc., New York, pp. 251-307.
41. Phinn, S., Stanford, M., Scarth, P., Murray, A. T., and Shyy, P. T. (2002). Monitoring the com-position of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. International Journal of Remote Sensing, 23, 4131-4153.
42. Powell, S. L., Cohen, W. B., Yang, Z., Pierce, J. D., and Alberti, M. (2008). Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972-2006. Remote Sensing of Environment, 112, 1895-1908.
43. Rashed, T., Weeks, J. R., Gadalla, M. S., and Hill, A. G. (2001). Revealing the anatomy of cit-ies through spectral mixture analysis of multispectral satellite imagery: A case study of the Greater Cairo region, Egypt. Geocarto International, 16, 5-15.
44. 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, 1011-1020.
45. 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, 2165-2185.
46. Schueler, T. R. (1994). The importance of imperviousness. Watershed Protection Techniques, 1, 100-111.
47. Shaban, M. A. and Dikshit, O. (2001). Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh. International Journal of Remote Sensing, 22, 565-593.
48. Slonecker, E. T., Jennings, D., and Garofalo, D. (2001). Remote sensing of impervious sur-face: A review. Remote Sensing Reviews, 20, 227-255.
49. Small, C. (2001). Estimation of urban vegetation abundance by spectral mixture analysis. International Journal of Remote Sensing, 22, 1305-1334.
50. Small, C. (2002). Multitemporal analysis of urban reflectance. Remote Sensing of Environment, 81, 427-442.
51. Small, C. (2004). The Landsat ETM+ spectral mixing space. Remote Sensing of Environment, 93, 1-17.
52. 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, 1-26.
53. Sutton, P. C., Anderson, S. A., Elvidge, C. D., Tuttle, B. T., and Ghosh, T. (2009). Paving the planet: Impervious surface as proxy measure of the human ecological footprint. Progress in Physical Geography, 33, 510-527.
54. 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, 1559-1575.
55. 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, 2781-2797.
56. Wang, Y. and Zhang, X. (2004). A SPLIT model for extraction of subpixel impervious surface information. Photogrammetric Engineering and Remote Sensing, 70, 821-828.
57. Wang, Y., Zhou, Y., and Zhang, X. (2008). The SPLIT and MASC models for extraction of impervious surface areas from multiple remote sensing data. In Remote Sensing of Impervious Surfaces, Q. Weng (ed.), Taylor & Francis Group, LLC, Boca Raton, FL, pp. 77-92.
58. Ward, D., Phinn, S. R., and Murray, A. L. (2000). Monitoring growth in rapidly urbanizing areas using remotely sensed data. Professional Geographer, 53, 371-386.
59. Weng, Q., Hu, X., and Lu, D. (2008). Extracting impervious surface from medium spatial res-olution multispectral and hyperspectral imagery: A comparison. International Journal of Remote Sensing, 29, 3209-3232.
60. Weng, Q., Hu, X., and Liu, H. (2009). Estimating impervious surfaces using linear spectral mixture analysis with multitemporal ASTER images. International Journal of Remote Sensing, 30(18), 4807-4830.
61. Wu, C. and Murray, A. T. (2003). Estimating impervious surface distribution by spectral mix-ture analysis. Remote Sensing of Environment, 84, 493-505.
62. Wu, C. (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93, 480-492.
63. Wu, C. and Murray, A. T. (2005). A cokriging method for estimating population density in urban areas. Computers, Environment and Urban Systems, 29, 558-579.
64. Wu, C. (2009). Quantifying high-resolution impervious surfaces using spectral mixture analy-sis. International Journal of Remote Sensing, 30(11), 2915-2932.
65. Xian, G. and Crane, M. (2005). Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment, 97, 203-215.
66. Xian, G. (2008). Mapping impervious surfaces using classification and regression tree algo-rithm. In Remote Sensing of Impervious Surfaces, Q. Weng (ed.), Taylor & Francis Group, LLC, Boca Raton, FL, pp. 39-58.
67. Xian, G., Crane, M. P., and McMahon, C. (2008). Quantifying multitemporal urban development characteristics in Las Vegas from Landsat and Aster data. Photogrammetric Engineering and Remote Sensing, 74, 473-481.
68. Yang, L., Xian, G., Klaver, J. M., and Deal, B. (2003a). Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing, 69, 1003-1010.
69. Yang, L., Huang, C., Homer, C., Wylie, B., and Coan, M. (2003b). An approach for mapping large-area impervious surface: synergistic use of Landsat 7 ETM+ and high spatial reso-lution imagery. Canadian Journal of Remote Sensing, 29, 230-240.
70. Yang, L., Jiang, L., Lin, H., and Liao, M. (2009). Quantifying sub-pixel urban impervious surface through fusion of optical and InSAR imagery. GIScience and Remote Sensing, 46(2), 161-171.
71. Yang, F., Matsushita, B., and Fukushima, T. (2010). A pre-screened and normalized multiple endmember spectral mixture analysis for mapping impervious surface area in Lake Kasumigaura Basin, Japan. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 479-490.
72. Zhou, W., Huang, G., Troy, A., and Cadenasso, M. L. (2009). Object-based land cover classi-fication of shaded areas in high spatial resolution imagery of urban areas: A comparison study. Remote Sensing of Environment, 113, 1769-1777.
73. 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, 17-24.