[en] Spatial metrics derived from satellite imagery are useful measures to quantify structural characteristics of expanding cities, and can provide indications of functional land use types. Images of medium resolution are cheap, widely available and are often part of extensive historic archives. Their lower resolution, on the other hand, inhibits studying urban morphology and change processes at a more detailed, intra-urban level. In this study, we develop spatial metrics for use on continuous sealed surface data produced by a sub-pixel classification of Landsat ETM+ imagery. The metrics characterise the shape of the cumulative frequency distribution of the estimated sub-pixel fractions within a building block by fitting an exponential and a sigmoid function with a least-squares approach. A classification tree is then used to relate the metric variables to urban land-use classes selected from the European MOLAND topology. This approach shows promising results, but still needs improvement which may be achieved by including spatially explicit metrics in the analysis.
Research Center/Unit :
Laboratoire SURFACES
Disciplines :
Earth sciences & physical geography
Author, co-author :
Van de Voorde, Tim; Vrij Universiteit Brussel - VUB > geografie > Cartography and GIS Research Unit (CGIS)
van der Kwast, Johannes; Vlaamse Instelling voor Technologisch Onderzoek - VITO
Engelen, Guy; Vlaamse Instelling voor Technologisch Onderzoek - VITO
Binard, Marc ; Université de Liège - ULiège > Département de géographie > Laboratoire SURFACES - Unité de Géomatique - Geomatics Unit
Cornet, Yves ; Université de Liège - ULiège > Département de géographie > Laboratoire SURFACES - Unité de Géomatique - Geomatics Unit
Canters, Frank; Vrij Universiteit Brussel - VUB > geografie > Cartography and GIS Research Unit (CGIS)
Language :
English
Title :
Quantifying intra-urban morphology of the Greater Dublin area with spatial metrics derived from medium resolution remote sensing data
Publication date :
2009
Event name :
7th international Urban Remote Sensing conference (URS 2009)
Event organizer :
IEEE
Event place :
Shanghai, China
Event date :
du 20 mai 2009 au 22 mai 2009
Audience :
International
Main work title :
IEEE Proceedings of the 7th International Urban Remote Sensing Conference : Shanghai, May 20-22, 2009
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Bibliography
G. Martine, The State of the World Population 2007. New York: United Nations Population Fund, 2008.
European Environment Agency (EAA), Urban Sprawl in Europe - the Ignored Challenge. Brussels: OOPEC, EAA report 10, 2006.
M. Herold, H. Couclelis, and K.C. Clarke, "The role of spatial metrics in the analysis and modelling of urban land use change," Comput. Environ. Urban, vol. 29, pp. 369-399, 2005
P.M. Torrens, "A toolkit for measuring sprawl," Applied Spatial Analysis, vol. 1, pp. 5-36, 2008
W. Ji, J. Ma, R.W. Twibell, and K. Underhill, "Characterising urban sprawl using multi-stage remote sensing images and landscape metrics," Comput. Environ. Urban, vol. 30, pp. 861-879, 2006.
S. Barr and M. Barnsley, "A region-based, graph-theoretic data model for the inference of second-order thematic information from remotely sensed images," Int. J. Geogr. Inf. Sci., vol. 11, pp. 555-576, 1997.
P. Kitchen, "Identifying changes of urban social change in Dublin - 1986 to 1996," Irish Geogr., vol 35, pp. 156-174, 2002.
M.J. Bannon, "The Greater Dublin Region: planning for its transformation and development," in Dublin: Contemporary Trends and Issues for the Twenty-first Century, J. Killen and A. MacLaran, Eds. Dublin: Geographical Society of Ireland, 1999, pp. 1-19.
F. van der Meer, "Image classification through spectral unmixing," in: Spatial Statistics for Remote Sensing, A. Stein, F. van der Meer, and B. Gorte, Eds. Dordrecht: Kluwer Academic Publishers, 1999, pp. 185-193.
T. Rashed, J.R. Weeks, D. Stow, and D. Fugate, "Measuring temporal composition of urban morphology through spectral mixture analysis: towards a soft approach of change analysis in crowded cities," Int. J. Remote Sens., vol 26, pp. 699-718, 2005.
M.K. Ridd, "Exploring a V-I-S (Vegetation-Impervious Surface-Soil) model for urban ecosystem analysis through remote sensing - Comparative anatomy for cities," Int. J. Remote Sens., vol. 16, pp. 2165-2185, 1995.
C. Wu, "Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery," Remote Sens. Environ., vol. 93, pp. 480-492, 2004.
S. Phinn, M. Stanford, P. Scarth, A.T. Murray and P.T. Shyy, "Monitoring the composition of urban environments based on the vegetation-impervious-soil (VIS) model by subpixel analysis techniques," Int. J. Remote Sens., vol. 23, pp. 4131-4153, 2002.
T. Van de Voorde, T. De Roeck and F. Canters, "A comparison of two spectral mixture modelling approaches for impervious surface mapping in urban areas," Int. J. Remote Sens., in press.
T. Kohonen, Self-Organzing Maps, 3 rd ed. Berlin: Springer, 2001.
T. Van de Voorde, W. De Genst, and F. Canters, "Improving pixel-based VHR land-cover classifications of urban areas with post-classification techniques," Photogramm. Eng. Remote Sens., vol. 73, pp. 1017-1027, 2007.
J. Huang, X.X. Lu, and J.M. Sellers, "A global comparative analysis of urban form: applying spatial metrics and remote sensing," Landscape Urban Plan., vol. 82, pp. 184-197, 2007.
K. C. Seto and M. Fragkias, "Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics," Landscape Ecol., vol. 20, pp. 871-888, 2005.
Y.C. Weng, "Spatiotemporal changes of landscape pattern in response to urbanization," Landscape Urban Plan., vol. 81, pp. 341-353, 2007.
L. Breiman, J.H. Freidman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees. Belmont, CA: Wadsworth International Group, 1984.
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