which has been overlaid at the previously vectorialized
classification. Then it was applied a clip between mesh and
classification of impervious areas, and for each square cell it
was computed the indices. It was also implemented a filter of
spatial correlation of 200m, through the use of a buffer. The
effectiveness of the filter consists in reaching a homogenization
of values, based on the generalized concept that nearby objects
tend to similarity. It also serves to avoid an unsuitable
fragmentation of data. Once obtained the final results of indices
for every impervious region in the cells, it was applied a cluster
analysis to automatically classify homogeneous areas,
consistently with those indicators and based on the conceptual
models of texture types previously theorized: continuous,
discontinuous, and scattered. Depending on these categories it
was merged all the polygons belonging to the same class, and
then exploded, to obtain the final database of polygons with
their own morphological characteristics (Figure 7). Further
analysis have been undertaken which aimed to quantify the
amount of every typology of urban texture, and the proportions
between them and the total amount of land consumption.
BH CONTINUOUS
EER DISCONTINUOUS
SCATTERED
Figure 7. Result of cluster analysis about urban texture types
4. CONCLUSIONS
For collecting the necessary data to provide analysis of urban
growth phenomena, the data which can be derived through
remote sensing is inherently suited to offer essential
information about the characteristics of different land cover
categories at different spatial and temporary scales. The
development of processing algorithms for satellite imagery and
techniques for getting information, accurately and consistently,
together with the development of analytical techniques and
methods for obtaining indicators of specific attributes for urban
growth modelling are essential tool to generate synthetic system
to cover all the main aspects, morphological, environmental and
socio-economical, about the dynamics of urban growth. The
relevance of this work is the possibility to analyze those areas
affected for high levels of land consumption, due to the
urbanization, and which kind of urban texture is being more
developed, it means, if the urban sprawl phenomena is leading
the urban policies, or if the cities are following a typical
Mediterranean model mostly based on the compactness.
5. BIBLIOGRAPHY
Crist E. P. and Cicone R. C., 1984, A physically-based
transformation of Thematic Mapper data -- the TM Tasseled
Cap, IEEE Trans. on Geosciences and Remote Sensing, GE-22:
256-263.
Crocetto N., Tarantino E., 2009. A class-oriented strategy for
features extraction from multidate ASTER imagery. Remote
Sensing, 1, 1171-1189.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
Frenkel A., Ashkenazi M. 2008. Measuring urban srawl; how
can we deal with it? Environment and Planning B: Planning
and Design, 35 (1): 56-79
Huang, C., B. Wylie, L. Yang, C. Homer, and G. Zylstra.
Derivation of a Tasseled Cap Transformation Based on Landsat
7 At-Satellite Reflectance. USGS EROS Data Center White
Paper.
Huete, A.R., H. Liu, K. Batchily, and W. van Leeuwen, 1997.
A Comparison of Vegetation Indices Over a Global Set of TM
Images for EOS-MODIS. Remote Sensing of Environment
59(3):440-451.
Jaeger J. A. G. 2000. Landscape division, splitting index, and
effective mesh size: new measures of landscape fragmentation.
Landscape Ecology, 15: 115-130
Kasimu A., Tateishi R., 2010. Extracting area at risk of
desertification using MODIS and geophysical data: in Xinjiang
Uyghur Autonomous Region of China. [International
Conference of Multimedia Technology (ICMT).
Kaufman Y.J. and D. Tanre, 1996. Strategy for Direct and
Indirect Methods for Correcting the Aerosol Effect on Remote
Sensing: from AVHRR to EOS-MODIS. Remote Sensing of
Environment, 55:65-79.
Lin W., Wang Q., Zha S., Li J., 2010. Construction and
application of characteristic bands of typical land cover based
on spectrum-photometric method. 18” International Conference
on Geoinformatics.
Richards J. A., 1999, Remote Sensing Digital Image Analysis,
Springer-Verlag, Berlin, p. 240.
Qi J., Chehbouni A., Huete A. R., Kerr Y. H., Sorooshian S.,
1994. A modified soil adjusted vegetation index. Remote
Sensing of Environment, vol. 48, issue 2, pp.119-126.
XU H. Q., 2005. Fast information extraction of urban built-up
land based on the analysis of spectral signature and normalized
difference index. Geographical Research, vol. 24, pp. 311-318.
XU H. Q., 2006. Modification of normalized difference water
index (NDWI) to enhance open water features in remotely
sensed imagery. International Journal of Remote Sensing, vol.
27, n° 14, pp. 3025-3033.
Zha Y., Ni S. X., Yang S., 2003. An effective approach to
automatically extract urban land-use from TM imagery. Journal
of Remote Sensing, vol.7 , pp.37-40.
6. ACKNOWLEDGEMENTS
The authors of this paper acknowledge the research funding
provided by the Spanish Ministry of Education and Science
(SEJ2006-09630), the Spanish Ministry of Science and
Innovation (CSO2009-09057), the Spanish Ministry of
Development (E08/08), and the Spanish Ministry of Housing.
Acknowledgements are also due to the European Union through
the INTERREG IIIB Program (South Western Europe). For
technical support the authors strongly acknowledge Montserrat
Moix, Carlos Marmolejo, Jorge Cerda, staff members at Centre
of Land Policy and Valuations (CPSV) of the Technical
University of Catalonia (UPC) (Barcelona TECH).