Detection of impervious surface change with multitemporal Landsat images in an urban-rural frontier

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 66, Issue 3, 2011, Pages 298-306

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

^a INDIANA UNIVERSITY   (United States)

Author keywords

Impervious surfaces; Landsat; QuickBird; Regression analysis; Urban rural frontier

Indexed keywords

ENVIRONMENTAL CONDITIONS; HIGH SPATIAL RESOLUTION IMAGES; IMPERVIOUS SURFACE; IMPERVIOUS SURFACE AREA; IMPERVIOUS SURFACE ESTIMATION; LAND COVER; LANDSAT; LANDSAT IMAGES; MATO GROSSO; MIXED PIXEL; MULTI-TEMPORAL; PIXEL BASED CLASSIFICATIONS; PIXEL-BASED METHODS; QUICKBIRD; QUICKBIRD IMAGES; REGRESSION MODEL; SPATIAL RESOLUTION IMAGES; URBAN-RURAL;

ESTIMATION; FEATURE EXTRACTION; IMAGE RESOLUTION; PHOTOMAPPING; PIXELS; RESEARCH;

REGRESSION ANALYSIS;

CALIBRATION; GEOLOGICAL MAPPING; LAND COVER; LANDSAT; PIXEL; QUICKBIRD; REGRESSION ANALYSIS; RURAL AREA; SATELLITE IMAGERY; SPATIAL RESOLUTION; SURFACE AREA;

BRAZIL; LUCAS DO RIO VERDE; MATO GROSSO;

EID: 79951957617     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2010.10.010     Document Type: Article

References (55)

1. Adams J.B., Sabol D.E., Kapos V., Filho R.A., Roberts D.A., Smith M.O., Gillespie A.R. Classification of multispectral images based on fractions of endmembers: application to land cover change in the Brazilian Amazon. Remote Sensing of Environment 1995, 52(2):137-154.
2. Bauer M.E., Loffelholz B.C., Wilson B. Estimating and mapping impervious surface area by regression analysis of Landsat imagery. Remote Sensing of Impervious Surfaces 2008, 3-19. Taylor & Francis Group, LLC, Boca Raton, FL. Q. Weng (Ed.).
3. Brabec E., Schulte S., Richards P.L. Impervious surface and water quality: a review of current literature and its implications for watershed planning. Journal of Planning Literature 2002, 16(4):499-514.
4. Chander G., Markham B.L., Helder D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 2009, 113(5):893-903.
5. Coppin P.R., Bauer M.E. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews 1996, 13(3):207-234.
6. Coppin P., Jonckheere I., Nackaerts K., Muys B., Lambin E. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 2004, 25(9):1565-1596.
7. Cracknell A.P. Synergy in remote sensing - what's in a pixel?. International Journal of Remote Sensing 1998, 19(11):2025-2047.
8. Deguchi C., Sugio S. Estimations for the percentage of impervious area by the use of satellite remote sensing imagery. Journal of Soil and Water Conservation 1994, 29(1-2):135-144.
9. Dougherty M., Dymond R.L., Goetz S.J., Jantz C.A., Goulet N. Evaluation of impervious surface estimates in a rapidly urbanizing watershed. Photogrammetric Engineering and Remote Sensing 2004, 70(11):1275-1284.
10. Elvidge C.D., Tuttle B.T., Sutton P.C., Baugh K.E., Howard A.T., Milesi C., Bhaduri B., Nemani R. Global distribution and density of constructed impervious surfaces. Sensors 2007, 7(9):1962-1979.
11. Esch T., Himmler V., Schorcht G., Thiel M., Wehrmann T., Bachofer F., Conrad C., Schmidt M., Dech S. Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data. Remote Sensing of Environment 2009, 113(8):1678-1690.
12. Fisher P. The pixel: a snare and a delusion. International Journal of Remote Sensing 1997, 18(3):679-685.
13. Gillies R.R., Box J.B., Symanzik J., Rodemaker E.J. Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta-a satellite perspective. Remote Sensing of Environment 2003, 86(3):411-422.
14. Greenfield E.J., Nowak D.J., Walton J.T. Assessment of 2001 NLCD percent tree and impervious cover estimates. Photogrammetric Engineering & Remote Sensing 2009, 75(11):1279-1286.
15. Hodgson M.E., Jensen J.R., Tullis J.A., Riordan K.D., Archer C.M. Synergistic use of Lidar ad color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering & Remote Sensing 2003, 69(9):973-980.
16. Hu X., Weng Q. Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment 2009, 113(10):2089-2102.
17. Jennings D.B., Jarnagin S.T., Ebert C.W. A modeling approach for estimating watershed impervious surface area from national land cover data 92. Photogrammetric Engineering & Remote Sensing 2004, 70(11):1295-1307.
18. Ji M., Jensen J.R. Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper. Geocarto International 1999, 14(4):31-39.
19. Kennedy R.E., Townsend P.A., Gross J.E., Cohen W.B., Bolstad P., Wang Y.Q., Adams P. Remote sensing change detection tools for natural resource managers: understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sensing of Environment 2009, 113(7):1382-1396.
20. Lu D., Mausel P., Brondízio E., Moran E. Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing 2002, 23(13):2651-2671.
21. Lu D., Mausel P., Brondizio E., Moran E. Change detection techniques. International Journal of Remote Sensing 2004, 25(12):2365-2407.
22. Lu D., Weng Q. Spectral mixture analysis of the urban landscapes in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering & Remote Sensing 2004, 70(9):1053-1062.
23. Lu D., Weng Q. Use of impervious surface in urban land use classification. Remote Sensing of Environment 2006, 102(1-2):146-160.
24. Lu D., Weng Q. 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 2006, 104(2):157-167.
25. Lu D., Batistella M., Moran E., de Miranda E.E. A comparative study of Landsat TM and SPOT HRG images for vegetation classification in the Brazilian Amazon. Photogrammetric Engineering & Remote Sensing 2008, 74(3):311-321.
26. Lu D., Weng Q. Extraction of urban impervious surface from an IKONOS image. International Journal of Remote Sensing 2009, 30(5):1297-1311.
27. Lu, D., Hetrick, S., Moran, E., 2010. Impervious surface mapping with QuickBird imagery. International Journal of Remote Sensing. doi:10.1080/01431161003698393.
28. Madhavan B.B., Kubo S., Kurisaki N., Sivakumar T.V.L.N. 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 2001, 22(5):789-806.
29. Mohapatra R.P., Wu C. Subpixel imperviousness estimation with IKONOS imagery: an artificial neural network approach. Remote Sensing of Impervious Surfaces 2008, 21-37. Taylor & Francis Group, LLC, Boca Raton, FL. Q. Weng (Ed.).
30. Phinn S., Stanford M., Scarth P., Murray A.T., Shyy P.T. Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. International Journal of Remote Sensing 2002, 23(20):4131-4153.
31. Powell S.L., Cohen W.B., Yang Z., Pierce J.D., Alberti M. Quantification of impervious surface in the Snohomish water resources inventory area of western Washington from 1972-2006. Remote Sensing of Environment 2008, 112(4):1895-1908.
32. Rashed T., Weeks J.R., Gadalla M.S., Hill A.G. Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: a case study of the Greater Cairo region. Egypt. Geocarto International 2001, 16(4):7-18.
33. Rashed T., Weeks J.R., Roberts D., Rogan J., Powell R. Measuring the physical composition of urban morphology using multiple endmember spectral mixture models. Photogrammetric Engineering & Remote Sensing 2003, 69(9):1011-1020.
34. 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 1995, 16(12):2165-2185.
35. Seto K.C., Liu W. Comparing ARTMAP neural network with the maximum-likelihood classifier for detecting urban change. Photogrammetric Engineering and Remote Sensing 2003, 69(9):981-990.
36. Shaban M.A., Dikshit O. 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 2001, 22(4):565-593.
37. Singh A. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 1989, 10(6):989-1003.
38. Slonecker E.T., Jennings D., Garofalo D. Remote sensing of impervious surface: a review. Remote Sensing Reviews 2001, 20(3):227-255.
39. Smith M.O., Ustin S.L., Adams J.B., Gillespie A.R. Vegetation in Deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment 1990, 31(1):1-26.
40. Sutton P.C., Anderson S.A., Elvidge C.D., Tuttle B.T., Ghosh T. Paving the planet: impervious surface as proxy measure of the human ecological footprint. Progress in Physical Geography 2009, 33(4):510-527.
41. Wang Y., Zhou Y., Zhang X. The SPLIT and MASC models for extraction of impervious surface areas from multiple remote sensing data. Remote Sensing of Impervious Surfaces 2008, 77-92. Taylor & Francis Group, LLC, Boca Raton, FL. Q. Weng (Ed.).
42. Remote Sensing of Impervious Surfaces 2007, Taylor & Francis Group, LLC, Boca Raton, FL, 454 p. Q. Weng (Ed.).
43. Weng Q., Hu X., Lu D. Extracting impervious surface from medium spatial resolution multispectral and hyperspectral imagery: a comparison. International Journal of Remote Sensing 2008, 29(11):3209-3232.
44. Weng Q., Hu X., Liu H. Estimating impervious surfaces using linear spectral mixture analysis with multitemporal ASTER images. International Journal of Remote Sensing 2009, 30(18):4807-4830.
45. Wu C., Murray A.T. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment 2003, 84(4):493-505.
46. Wu C. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment 2004, 93(4):480-492.
47. Wu C., Yuan F. Seasonal sensitivity analysis of impervious surface estimation with satellite imagery. Photogrammetric Engineering & Remote Sensing 2008, 73(12):1393-1402.
48. Wu C. Quantifying high-resolution impervious surfaces using spectral mixture analysis. International Journal of Remote Sensing 2009, 30(11):2915-2932.
49. Xian G., Crane M. Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment 2005, 97(2):203-215.
50. Xian G. Assessing urban growth with subpixel impervious surface coverage. Urban Remote Sensing 2007, 179-200. Taylor & Francis Group, LLC, Boca Raton, FL. Q. Weng, D.A. Quattrochi (Eds.).
51. Xian G. Mapping impervious surfaces using classification and regression tree algorithm. Remote Sensing of Impervious Surfaces 2008, 39-58. Taylor & Francis Group, LLC, Boca Raton, FL. Q. Weng (Ed.).
52. Xian G., Crane M.P., McMahon C. Quantifying multitemporal urban development characteristics in Las Vegas from Landsat and Aster data. Photogrammetric Engineering & Remote Sensing 2008, 74(4):473-481.
53. Yang L., Xian G., Klaver J.M., Deal B. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering & Remote Sensing 2003, 69(9):1003-1010.
54. Yang L., Huang C., Homer C., Wylie B., Coan M. An approach for mapping large-area impervious surface: synergistic use of Landsat 7 ETM+ and high spatial resolution imagery. Canadian Journal of Remote Sensing 2003, 29(2):230-240.
55. Yang L., Jiang L., Lin H., Liao M. Quantifying sub-pixel urban impervious surface through fusion of optical and InSAR imagery. GIScience and Remote Sensing 2009, 46(2):161-171.