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Projecting the Urban Future: Contributions from Remote Sensing

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Abstract

Mapping urban extent accurately with remotely sensed imagery is challenging. In addition to the inherent difficulty of defining urban land use in terms of land cover (LC) at fixed spatial scales, robust identification of LC using remote sensing has its own challenges. Many of these challenges are related to the fact that the physical properties and conditions that influence scattering and emission of radiation vary in time, space, geography, geometry and wavelength. A second set of difficulties arises in the process of discretizing multiple scales of LC that often vary continuously in abundance over the range of spatial scales at which sensors integrate responses into individual pixels. These factors underscore the importance of spatial and spectral resolution for mapping human settlements and urban extent. The evolution of urban remote sensing is discussed in the context of changes in spatial and spectral resolution and in the tools used to classify land cover. A multi-sensor approach to urban extent mapping acknowledges the limitations of individual sensors and uses the intersection of multiple physical criteria to distinguish the built environment from other types of land cover. A simple example of multi-sensor mapping combines decameter-scale reflectance, derived from Landsat imagery, with kilometer-scale emission of night light, derived from DMSP-OLS composites. Current availability of intercalibrated, accurately co-registered Landsat imagery extending back to the 1970s allows for retrospective analyses of a variety of human modified landscapes—including urban. Combined with continuous field representation of land cover at subpixel scales, many of the limitations of single image discrete classification can be avoided. Global availability of Landsat allows for comparative analyses of human settlements in different environments and cultures. To facilitate comparison in terms of physical properties of land cover, we use a standardized spectral mixture model to convert the Landsat surface reflectance spectra to areal abundances of Substrate, Vegetation and Dark fractions. Because many land uses result in time-varying land cover properties we characterize the Landsat response across multiple seasons within a single year in terms of the seasonal mean and variability of each spectral endmember. The results of the multi-sensor characterization quantify both consistencies and inconsistencies in the combined response of modified landscapes in a variety of environments worldwide.

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References

  • Adams, J. B., & Gillespie, A. R. (2006). Remote sensing of landscapes with spectral images. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Adams, J. B., Sabol, D. E., Kapos, V., Filho, R. A., Roberts, D. A., Smith, M. O., & 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, 137–154.

    Article  Google Scholar 

  • Balk, D., Pozzi, F., Yetman, G., Nelson, A., & Deichmann, U. (2004). What can we say about urban extents? Methodologies to improve global population estimates in urban and rural areas? In Population association of America annual meeting. Boston, MA.

  • Chander, G., Markham, B. L., & 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.

    Article  Google Scholar 

  • Cinzano, P., Falchi, F., Elvidge, C. D., & Baugh, K. E. (2001). The artificial sky brightness in Europe derived from DMSP satellite data. In Preserving the astronomical sky (pp. 95–102).

  • Congalton, R., & Green, K. (2009). Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton, FL: CRC/Taylor & Francis.

    Google Scholar 

  • Dell’Acqua, F., & Gamba, P. (2002). Urban remote sensing and data fusion: a perfect’ wedding. In Procedings of the 3rd workshop on “Remote Sensing over Urban Areas (pp. 293–301) Istanbul, Turkey.

  • Elvidge, C. D., Baugh, K. E., Dietz, J. B., Bland, T., Sutton, P. C., & Kroehl, H. W. (1999). Radiance calibration of DMSP-OLS low-light imaging data of human settlements. Remote Sensing of Environment, 68, 77–88.

    Article  Google Scholar 

  • Elvidge, C. D., Baugh, K. E., Hobson, V. R., Kihn, E. A., Kroehl, H. W., Davis, E. R., & Cocero, D. (1997a). Satellite inventory of human settlements using nocturnal radiation emissions: A contribution for the global toolchest. Global Change Biology, 3, 387–395.

    Article  Google Scholar 

  • Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., & Davis, E. R. (1997b). Mapping city lights with nighttime data from the DMSP operational linescan system. Photogrammetric Engineering and Remote Sensing, 63, 727–734.

    Google Scholar 

  • Elvidge, C. D., Cinzano, P., Pettit, D. R., Arvesen, J., Sutton, P., Small, C., et al. (2007b). The Nightsat mission concept. International Journal of Remote Sensing, 28(12), 2645–2670.

  • Elvidge, C. D., Safran, J., Nelson, I. L., Tuttle, B. T., Hobson, V. R., Baugh, K. E., et al. (2004). Area and position accuracy of DMSP nighttime lights data. In Remote sensing and GIS accuracy assessment (pp. 281–292) Boca Raton: CRC Press.

  • Elvidge, C. D., Safran, J., Tuttle, B., Sutton, P., Cinzano, P., Pettit, D., et al. (2007a). Potential for global mapping of development via a Nightsat mission. GeoJournal, 69, 45–53.

  • Gamba, P. (1999). Data fusion over urban environments. In Proceedings of the 21st Urban Data Management Symposium (pp 7.1–7.4). Venice, Italy.

  • Gillespie, A. R., Smith, M. O., Adams, J. B., Willis, S. C., Fischer, A. F., & Sabol, D. E. (1990). Interpretation of residual images: spectral mixture analysis of AVIRIS images, Owens Valley, California. Proceedings of the 2nd Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop (pp. 243–270). Pasadena, CA: NASA Jet Propulsion Laboratory.

    Google Scholar 

  • Heiden, U., Roessner, S., Segl, K., & Kaufmann, H. (2001). Analysis of spectral signatures of urban surfaces for theiridentification using hyperspectral HyMap data. iRemote Sensing and Data Fusion over Urban Areas, IEEE/ISPRS Joint Workshop (pp. 173–177). IEEE: Rome, Italy.

    Google Scholar 

  • Henderson, F. M., & Xia, Z. G. (1998). Radar applications in urban analysis, settlement detection and population analysis. In F. M. Henderson & A. J. Lewis (Eds.), Principles and applications of imaging radar. New York: John Wiley and Sons.

    Google Scholar 

  • Henderson, M., Yeh, E. T., Gong, P., Elvidge, C., & Baugh, K. (2003). Validation of urban boundaries derived from global night-time satellite imagery. International Journal of Remote Sensing, 24, 595–609.

    Article  Google Scholar 

  • Herold, M., Roberts, D. A., Gardner, M. E., & Dennison, P. E. (2004). Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm. Remote Sensing of Environment, 91, 304–319.

    Article  Google Scholar 

  • Imhoff, M. L., Bounoua, L., DeFries, R., Lawrence, W. T., Stutzer, D., Tucker, C. J., & Ricketts, T. (2004). The consequences of urban land transformation on net primary productivity in the United States. Remote Sensing of Environment, 89, 434–443.

    Article  Google Scholar 

  • Kressler, F., & Steinnocher, K. (1996). Change detection in urban areas using satellite images and spectral mixture analysis. International Archives of Photogrammetry and Remote Sensing, XXXI(Part B7), 379–383.

    Google Scholar 

  • Nghiem, S. V., Balk, D., Rodriguez, E., Neumann, G., Sorichetta, A., Small, C., & Elvidge, C. D. (2009). Observations of urban and suburban environments with global satellite scatterometer data. Isprs Journal of Photogrammetry and Remote Sensing, 64, 367–380.

    Article  Google Scholar 

  • Powell, R., Roberts, D., Dennison, P., & Hess, L. (2007). Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil. Remote Sensing of Environment, 106, 253–267.

    Article  Google Scholar 

  • Rashed, T., Weeks, J., Gadalla, M., & Hill, A. (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, 5–16.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Small, C. (2009). The Color of Cities: An Overview of Urban Spectral Diversity. In M. Herold & P. Gamba (Eds.), Global mapping of human settlements (pp. 59–106). New York: Taylor and Francis.

    Google Scholar 

  • Small, C., & Elvidge, C. D. (2013). Night on Earth: Mapping decadal changes of night light in Asia. International Journal of Applied Earth Observation and Geoinformation, 22, 40–52.

  • Small, C., & Milesi, C. (2013). Multi-scale standardized spectral mixture models. Remote Sensing of Environment, 136, 442–454.

    Article  Google Scholar 

  • Smith, M. O., Johnson, P. E., & Adams, J. B. (1985). Quantitative determination of mineral types and abundances from reflectance spectra using principal component analysis. Journal of Geophysical Research, 90, 792–804.

    Google Scholar 

  • Sutton, P. C. (2003). A scale-adjusted measure of “Urban sprawl” using nighttime satellite imagery. Remote Sensing of Environment, 86, 353–369.

    Article  Google Scholar 

  • Sutton, P., Roberts, D., Elvidge, C., & Baugh, K. (2001). Census from Heaven: An estimate of the global human population using night-time satellite imagery. International Journal of Remote Sensing, 22, 3061–3076.

    Article  Google Scholar 

  • Sutton, P., Roberts, C., Elvidge, C., & Meij, H. (1997). A comparison of nighttime satellite imagery and population density for the continental United States. Photogrammetric Engineering and Remote Sensing, 63, 1303–1313.

    Google Scholar 

  • Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., & Dech, S. (2012). Monitoring urbanization in mega cities from space. Remote Sensing of Environment, 117, 162–176.

    Article  Google Scholar 

  • van der Linden, S. (2008). Investigating the potential of hyperspectral remote sensing data for the analysis of urban imperviousness—a Berlin case study. In, Humboldt-Universität zu Berlin—Geographisches Institut (p. 153). Berlin, Germany: Humboldt-Universität zu Berlin.

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Acknowledgments

Copious thanks to Deborah Balk for inviting and urging me to contribute this chapter. Thanks also to Chris Elvidge, Kim Baugh and the Earth Observation Group at NGDC for their efforts to continuously refine and distribute the DMSP-OLS and VIIRS-DNB night light data. Thanks also to the Landsat team at the USGS EROS data center for their efforts to continuosly improve the volume and quality of Landsat data. Thanks also to the anonymous reviewers for many helpful suggestions. This work was funded by the NASA Socioeconomic Data and Applications Center (SEDAC) (NASA contract NNG13HQ04C). Figures 1 and 5 include copyrighted material of DigitalGlobe Inc., All rights reserved.

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  1. Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, 10964, USA

    Christopher Small

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Correspondence to Christopher Small.

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Small, C. Projecting the Urban Future: Contributions from Remote Sensing. Spat Demogr 4, 17–37 (2016). https://doi.org/10.1007/s40980-015-0002-4

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