Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data

Remote Sensing of Environment

Volumn 113, Issue 8, 2009, Pages 1678-1690

  Esch, T ^a,b   Himmler, V ^b   Schorcht, G ^b   Thiel, M ^b   Wehrmann, T ^a   Bachofer, F ^c   Conrad, C ^b   Schmidt, M ^a,b   Dech, S ^a,b  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b UNIVERSITY OF WÜRZBURG   (Germany)

^c UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

Impervious surface; Modeling; Spatial planning; Urban areas

Indexed keywords

AREA-WIDE; AVERAGE ERRORS; BAVARIA; BUILT-UP AREAS; COMBINED ANALYSIS; DYNAMIC PROCESS; GEO-SPATIAL; GEOGRAPHIC INFORMATION; IMPERVIOUS SURFACE; INTEGRATED ANALYSIS; LANDSAT 7; LANDSAT IMAGES; LEIPZIG; MEAN ABSOLUTE ERROR; MODELING; REFERENCE DATA; ROAD NETWORK; SATELLITE IMAGES; SPATIAL PLANNING; STUDY AREAS; TRANSPORT INFRASTRUCTURE; URBAN AREAS; URBAN POPULATION; URBAN SPRAWL; VECTOR DATA;

AGGLOMERATION; LAND USE; MOTOR TRANSPORTATION; REGIONAL PLANNING;

VECTORS;

COST-BENEFIT ANALYSIS; DATA SET; ERROR ANALYSIS; FEDERAL SYSTEM; GIS; HUMAN SETTLEMENT; LAND USE CHANGE; LANDSAT; MODELING; RAILWAY TRANSPORT; ROAD TRANSPORT; SATELLITE IMAGERY; SPATIAL PLANNING; URBAN POPULATION; URBANIZATION;

CENTRAL EUROPE; EURASIA; EUROPE; GERMANY;

EID: 67349274187     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2009.03.012     Document Type: Article

References (55)

1. AdV. Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland. Erläuterungen zum ATKIS-Objektartenkatalog der Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (2008). http://www.atkis.de Available URL: http://www.atkis.de (accessed 14 October, 2008)
2. Aizerman M., Braveman E., and Rozonoer L. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25 (1964) 821-837
3. Alberti, M., Coe, S. & Jiang, Y. (2006). Operational Remote Sensing Solutions for Estimating Total Impervious Surface Areas. Final Report prepared for The Washington State Department of Transportation (T2695-72). Washington, University of Washington.
4. Bauer M.E., Loffelholz B.C., and Wilson B. Estimating and mapping impervious surface area by regression analysis of landsat imagery. In: Weng Q. (Ed). Remote Sensing of Impervious Sufaces (2008), Taylor & Francis Group, Boca Raton 3-19
5. Braun M., and Herold M. Mapping impervious surface using NDVI and linear spectral unmixing of ASTER data in the Cologne-Bonn region (Germany). Proceedings of SPIE, February 2004 5239 (2004) 274-284
6. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 no. 2 (1998) 1-47
7. Cablk M.E., and Minor T. Detecting and discriminating impervious cover with high-resolution IKONOS data using principal component analysis and morphological operators. International Journal of Remote Sensing 24 23 (2003) 4627-4645
8. Chang C.C., and Lin C.J. LIBSVM: a library for support vector machines (2001). http://www.csie.ntu.edu.tw/~cjlin/libsvm Available URL: http://www.csie.ntu.edu.tw/~cjlin/libsvm (accessed 14 October, 2008)
9. Cervero R. Shapeless, spread out, skipped over and scattershot - sprawl sweeps the globe. The World Paper, March/April 2000 (2000) 5-6
10. Cieslewicz D.J. The environmental impacts of sprawl. In: Squires G.D. (Ed). Urban Sprawl. Causes, Consequences & Policy Responses (2002), The Urban Institute Press, Washington DC 23-38
11. Cortes C., and Vapnik V. Support vector networks. Machine Learning 20 (1995) 1-25
12. DESTATIS (2007). Umweltnutzung und Wirtschaft. Bericht zu den Umweltökonomischen Gesamtrechnungen. Wiesbaden, Statistisches Bundesamt. (pp. 120).
13. EEA (2006). Urban sprawl in Europe - The ignored challenge. Publications of the European Communities, 2006. ISBN 92-9167-887-2, ISSN 1725-9177, Copenhagen, EEA.
14. Esch T., Roth A., Thiel M., Schmidt M., and Dech S. Comparison of ALOS-PALSAR and TerraSAR-X data in terms of detecting settlements - first results. Proceedings of the ALOS PI Symposium 2008, 3-7 November 2008, Rhodes, Greece (2008)
15. Ewing R. Characteristics, causes, and effects of sprawl: a literature review. Environmental and Urban Issues 21 no. 2 (1994) 1-15
16. Frie, B., & Hensel, R. (2007). Schätzverfahren zur Bodenversiegelung: der UGRdL-Ansatz. Statistische Analysen und Studien Nordrhein-Westfalen, Band 44.
17. Hearst M.A. Support vector machines. IEEE Intelligent Systems & Their Applications 13 no. 4 (1998) 18-21
18. Heiden U., Segl K., Roessner S., and Kaufmann H. Ecological evaluation of urban biotope types using airborne hyperspectral HyMap data. Proceedings of the 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Berlin, Germany (2003) 18-22
19. Heldens W., Esch T., Heiden U., and Dech S. Potential of hyperspectral remote sensing for characterisation of urban structure in Munich. In: Carsten Jürgens (Ed). Remote Sensing - New Challenges of High Resolution. Proceedings of the EARSeL Joint Workshop, Bochum, Germany, March 5-7 (2008) 94-103
20. Herold M., Gardner M.E., and Roberts D.A. Spectral resolution requirements for mapping urban areas. IEEE Transactions on Geoscience and Remote Sensing 41 9 (2003) 1907-1919
21. Ji M., and Jensen J.R. Effectiveness of subpixel analysis in detecting and quantifying urban impervious surface from Landsat thematic mapper imagery. Geocarto International 14 4 (1999) 33-41
22. Johnson M.P. Environmental impacts of urban sprawl: a survey of the literature and proposed research agenda. Environment and Planning 33 4 (2001) 717-736
23. Jones P.D., Groisman P.Ya., Coughlan M., Plummer N., Wang W.C., and Karl T.R. Assessment of urbanization effects in time series of surface air temperature over land. Nature 347 (1990) 169-172
24. Kampouraki, M., Wood, G.A. & Brewer, T. (2006). The application of remote sensing to identify and measure sealed areas in urban environments. Proceedings of 1st International Conference on Object-based Image Analysis, Salzburg, Austria, July 4-5. CD-ROM.
25. Kuntz S. Mapping the degree of sealing in Berlin by combining ERS SAR interferometry and optical high resolution data. Regensburger Geographische Schriften 35 (2001) 55-56
26. Lopez R., and Hynes H.P. Sprawl in the 1990s. Measurement, distribution and trends. Urban Affairs Review 38 3 (2003) 325-355
27. Lu D., and Weng Q. Mapping urban impervious surfaces from medium and high spatial resolution multispectral imagery. In: Weng Q. (Ed). Remote Sensing of Impervious Sufaces (2008), Taylor & Francis Group, Boca Raton 59-73
28. Lu D., and Weng Q. Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment 104/2006 (2006) 157-167
29. Meinel G., and Hernig A. Erhebung der Bodenversiegelung auf Grundlage des ATKIS-Basis-DLM - Möglichkeiten und Grenzen. Photogrammetrie-Fernerkundung-Geoinformation 3 (2006) 195-204
30. Mohapatra R.P., and Wu C. Subpixel imperviousness estimation with IKONOS imagery: an artificial neural network approach. In: Weng Q. (Ed). Remote Sensing of Impervious Sufaces (2008), Taylor & Francis Group, Boca Raton 21-37
31. MRLC. National Land Cover Database 2001 (NLCD 2001) (2009). http://www.mrlc.gov/nlcd.php# Available URL: http://www.mrlc.gov/nlcd.php# (accessed 27 February, 2009)
32. Netzband M., Stefanov W.L., and Redman C.L. Remote sensing as a tool for urban planning and sustainability. In: Netzband M., Stefanov W.L., and Redman C.L. (Eds). Applied remote sensing for urban planning, governance and sustainability (2007), Springer, Berlin 1-19
33. Phinn S., Stanford M., Scarth P., Murray A.T., and Shyy T. Monitoring the composition and form of urban environments based on the vegetation-impervious surface-soil (VIS) model by sub-pixel analysis techniques. International Journal of Remote Sensing 23 20 (2002) 4131-4153
34. Powell R.L., Roberts D.A., Dennison P.E., and Hess L.L. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sensing of Environment 106 2007 (2007) 253-267
35. Richter R. Atmospheric/Topographic Correction for Satellite Imagery. ATCOR-2/3 User Guide, Version 6.4 (2006). http://www.rese.ch/pdf/atcor23_manual.pdf Available URL: http://www.rese.ch/pdf/atcor23_manual.pdf (accessed 14 October, 2008)
36. 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 (1995) 2165-2185
37. Sachs, N., Kaule, G., Klamt, C., Krewitt, W. & Friedrich, R. (2000). Vorprojekt zur Entwicklung und Anwendung eines räumlich differenzierten Indikatorsystems zur Messung einer nachhaltigen Entwicklung in Baden-Württemberg. Final report.
38. Sawaya K., Olmanson L., Heinert N., Brezonik P., and Bauer M. Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery. Remote Sensing of Environment 88 (2003) 144-156
39. Schiller G., and Siedentop S. Preserving cost-efficient infrastructure supply in shrinking cities. Proceedings of 2nd International Conference on Smart and Sustainable Built Environments, Shanghai, China, 15.-17. November (2006) 134-141
40. Schoelkopf B., Burges C., and Vapnik V. Extracting support data for a given task. In: Fayyad U.M., and Uthurusamy R. (Eds). Proceedings, First International Conference on Knowledge Discovery & Data Mining (1995), AAAI Press, Menlo Park, CA, USA
41. Smola, A. J., & Schölkopf, B. (1998). A Tutorial on Support Vector Regression. NeuroCOLT Technical Report NC-TR-98-030. Available URL: http://www.neurocolt.com/abs/1998/abs98030.html (accessed 14 October, 2008).
42. StMWIVT. Online portal of the indicator catalog for regional planning (INKA) provided by the Bavarian Ministry of Economic Affairs, Infrastructure, Transport and Technology (2008). http://www.inka.bayern.de/ Available URL: http://www.inka.bayern.de/ (accessed 14 October, 2008)
43. Tzotsos, A. (2006). A support vector machine approach for object based image analysis. Proceedings of 1st International Conference on Object-based Image Analysis, Salzburg, Austria. CD-ROM.
44. USGS. Landsat Enhanced Thematic Mapper Plus ETM+). Unites States Geological Survey (2007). http://edc.usgs.gov/products/satellite/landsat7.html Available URL: http://edc.usgs.gov/products/satellite/landsat7.html (accessed 14 October, 2008)
45. Vapnik V.N. Statistical learning theory. Wiley Series on Adaptive and Learning Systems (1998), Wiley, New York
46. Vapnik V.N. The nature of statistical learning theory (1995), Springer, New York
47. Wehrmann, T. (2006). Automatisierte Klassifikation von Landnutzung durch Objekterkennung am Beispiel von CORINE Land Cover. Ph.D. thesis, University of Würzburg.
48. Weng Q., Hu X., and Lu D. Extracting impervious surface from hyperspectral imagery with linear spectral mixture analysis. In: Weng Q. (Ed). Remote Sensing of Impervious Sufaces (2008), Taylor & Francis Group, Boca Raton 93-118
49. Wu C. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment 93 4 (2004) 480-492
50. Wu C., and Murray A.T. Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment 84 (2003) 493-505
51. Xian G. Assessing urban growth with sub-pixel impervious surface coverage. In: Weng Q., and Quattrochi D.A. (Eds). Urban Remote Sensing (2006), Taylor & Francis Group, Boca Raton 179-199
52. Xian G. Mapping urban impervious surfaces from medium and high spatial resolution multispectral imagery. In: Weng Q. (Ed). Remote Sensing of Impervious Sufaces (2008), Taylor & Francis Group, Boca Raton 59-73
53. Yang L., Huang C., Homer C.G., Wylie B.K., and Coan M.J. An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Canadian Journal of Remote Sensing 29 no. 2 (2003) 230-240
54. Yang X., and Liu Z. Use of satellite-derived landscape impervious surface index to characterize urban spatial growth. Computers, Environment and Urban Systems 29 (2005) 524-540
55. Yuan F., and Bauer M.E. Comparison of impervious surface area and Normalized difference vegetation index as indicators of surface urban heat island effects in Landsat Imagery. Remote Sensing of Environment 106 2 (2007) 375-386