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Classification of Urban Areas: Inferring Land Use from the Interpretation of Land Cover

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Book cover Remote Sensing of Urban and Suburban Areas

Part of the book series: Remote Sensing and Digital Image Processing ((RDIP,volume 10))

Abstract

Ancillary data are vital for successful image classification of urban areas. This chapter explores the role of ancillary data (information from beyond remote sensing) for improving the contextual interpretation of satellite sensor imagery during spectral-based and spatial-based classification. In addition, careful consideration is given to the crucial distinctions between urban land cover and urban land use, and how the inherent heterogeneous structure of urban morphologies is statistically represented between hard and soft classifications.

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References

  • Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. LIS Geological Survey Professional Paper 964. USGS, Washington, DC

    Google Scholar 

  • Baraldi A, Parmiggiani F (1990) Urban area classification by multispectral SPOT images. IEEE Trans Geosci Remote Sens 28:674–680

    Article  Google Scholar 

  • Barnsley MJ, Steel AM, Barr SL (2003) Determining urban land use through an analysis of the spatial composition of buildings identified in LIDAR and multispectral image data. In: Mesev V (ed) Remotely sensed cities. Taylor & Francis, London, pp 83–108

    Google Scholar 

  • Batty M, Longley PA (1994) Fractal cities: a geometry of form and function. Academic, London

    Google Scholar 

  • Berthod M, Kato Z, Yu S, Zerubia L (1996) Bayesian image classification using Markov random fields. Image Vis Comput 14:285–295

    Article  Google Scholar 

  • Brugioni DA (1983) The Census: it can be done more accurately with space-age technology. Photogramm Eng Remote Sens 49:1337–1339

    Google Scholar 

  • Carr JR (1999) Classification of digital image texture using variograms. In: Atkinson PM, Tate NJ (eds) Advances in remote sensing and GIS analysis. Wiley, London, pp 135–146

    Google Scholar 

  • Chen K (2002) An approach to linking remotely sensed data and areal census data. Int J Remote Sens 23:37–48

    Article  Google Scholar 

  • Corbley KP (1996) One-meter satellites. Geo Inf Syst July:28–42

    Google Scholar 

  • Couloigner I, Ranchin T (2000) Mapping of urban areas: a multiresolution modelling approach for semi-automatic extraction of streets. Photogramm Eng Remote Sens 66:867–874

    Google Scholar 

  • Cowan DJ, Jensen JR (1998) Extraction and modeling of urban attributes using remote sensing technology. In: Liverman DM, Moran EF, Rindfuss EE, Stern PC (eds) People and pixels. National Academy Press, Washington, DC, pp 164–188

    Google Scholar 

  • Dobson JE (1993) Land cover, land use differences distinct. GIS World 6(2):20–22

    Google Scholar 

  • Donnay JP (1999) Use of remote sensing information in planning. In: Stillwell J, Geertman S, Openshaw S (eds) Geographical information and planning. Springer, Berlin, pp 242–260

    Chapter  Google Scholar 

  • Dreyer P (1993) Classification of land cover using optimized neural nets on SPOT data. Photogramm Eng Remote Sens 59:617–621

    Google Scholar 

  • Fisher PF (1997) The pixel: a snare and a delusion. Int J Remote Sens 18:679–685

    Article  Google Scholar 

  • Forster BC (1985) An examination of some problems and solutions in monitoring urban areas from satellite platforms. Int J Remote Sens 6:139–151

    Article  Google Scholar 

  • Geoghegan J, L P, Ogneva-Himmelberger Jr Y, Chowdhury RR, Sanderson S, Turner BL (1998) “Socializing the pixel” and “pixelizing the social” in land use and land cover-change. In: Liverman DM, Moran EF, Rindfuss EE, Stern PC (eds) People and pixels. National Academy Press, Washington, DC, pp 51–69

    Google Scholar 

  • Grey WMF, Luckman AJ, Holland D (2003) Mapping urban change in the UK using radar interferometry. Remote Sens Environ 87:16–22

    Article  Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Article  Google Scholar 

  • Harvey JT (2002) Estimating census district populations from satellite imagery: Some approaches and limitations. Int J Remote Sens 23:2071–2095

    Article  Google Scholar 

  • Hasse J, Lathrop RG (2003) A housing-level approach to characterizing residential sprawl. Photogramm Eng Remote Sens 69:1021–1030

    Google Scholar 

  • Heikkonen J, Varfis A (1998) A land cover/land use classification of urban areas: a remote sensing approach. Int J Pattern Recognit Artif Intell 12:475–489

    Article  Google Scholar 

  • Herold M, Scepan J, Clarke KC (2002) The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ Plann A 34:1443–1458

    Article  Google Scholar 

  • Ji M, Jensen JR (1999) Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper imagery. Geocarto Int 14(4):33–41

    Article  Google Scholar 

  • Lam NSN, Quattrochi DA, Qiu HL, Zhao W (1998) Environmental assessment and monitoring with image characterization and modeling system using multi-scale remote sensing data. Appl Geogr Stud 2:77–93

    Article  Google Scholar 

  • Mesev V (1997) Remote sensing of urban systems: hierarchical integration with GIS. Comput Environ Urban Syst 21:175–187

    Article  Google Scholar 

  • Mesev V (1998) The use of census data in urban image classification. Photogramm Eng Remote Sens 64:431–438

    Google Scholar 

  • Mesev V (2001) Modified maximum likelihood classifications of urban land use: spatial segmentation of prior probabilities. Geocarto Int 16(4):39–46

    Article  Google Scholar 

  • Mesev V (ed) (2003) Remotely sensed cities. Taylor & Francis, London

    Google Scholar 

  • Mesev V (2007) Fusion of point-based postal urban data with IKONOS imagery. Int J Inf Fusion 8:157–167.

    Article  Google Scholar 

  • Mesev V, McKenzie P (2005) Urban neighbourhood patterns: links between high spatial resolution remotely sensed data and point-based GIS data sources. In: Wise S, Craglia M (eds) GIS and evidence based policy making. Taylor & Francis, London

    Google Scholar 

  • Myint SW (2003) The use of wavelet for feature extraction of cities from satellite sensor images. In: Mesev V (ed) Remotely sensed cities. Taylor & Francis, London, pp 109–134

    Google Scholar 

  • Ogrosky CE (1975) Population estimates from satellite imagery. Photogramm Eng Remote Sens 41:707–712

    Google Scholar 

  • Pesaresi M, Bianchin A (2001) Recognizing settlement structure using mathematical morphology and image texture. In: Donnay JP, Barnsley MJ, Longley PA (eds) Remote sensing and urban analysis. Taylor & Francis, London, pp 55–68

    Chapter  Google Scholar 

  • Pinder DA, Witherick ME (1973) Nearest neighbour analysis of linear point patterns. Tijdschift voor Economische an Sociale Geographie 64

    Google Scholar 

  • Pinder DA, Witherick ME (1975) A modification of the nearest-neighbour analysis for use in linear situations. Geography 60:16–23

    Google Scholar 

  • Rashed T, Weeks JR, Roberts DA, Rogan J, Powell R (2003) Measuring the physical composition of urban morphology using multiple endmember spectral mixture models. Photogramm Eng Remote Sens 69:1011–1020

    Google Scholar 

  • Ridd M (1995) Exploring a V-I-S (Vegetation-Impervious Surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy of cities. Int J Remote Sens 16:2165–2185

    Article  Google Scholar 

  • Strahler AH (1980) The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sens Environ 10:135–163

    Article  Google Scholar 

  • Webster CJ (1995) Urban morphological fingerprints. Environ Plann B 22:279–297

    Google Scholar 

  • Welch R (1982) Spatial resolution requirements for urban studies. Int J Remote Sens 3:139–146

    Article  Google Scholar 

  • Woodcock CE, Strahler AH (1987) The factor of scale in remote sensing. Remote Sens Environ 21:311–332

    Article  Google Scholar 

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Acknowledgements

ADDRESS-POINTTM and COMPASTM are Crown Copyright (www.ordsvy.gov.uk and www.osni.gov.uk respectively). Cities Revealed is the copyright of the GeoInformation Group (www.crworld.co.uk). IKONOS image was provided by Space Imaging. The author would like to thank Paul McKenzie for the production of Fig. 8.4.

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Authors and Affiliations

  1. Department of Geography, Florida State University, Tallahassee, FL, 32306-2190, USA

    Victor Mesev

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  1. Victor Mesev
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Correspondence to Victor Mesev .

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Editors and Affiliations

  1. House (GSAREH), Geospatial Applied Research Expert, Bescott Drive 14924, Austin, 78728, USA

    Tarek Rashed

  2. Geography Department, Ruhr-University Bochum, Universitätsstr. 150, Bochum, 44801, Germany

    Carsten Jürgens

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Mesev, V. (2010). Classification of Urban Areas: Inferring Land Use from the Interpretation of Land Cover. In: Rashed, T., Jürgens, C. (eds) Remote Sensing of Urban and Suburban Areas. Remote Sensing and Digital Image Processing, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-4385-7_8

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