critical thresholds or phenomenological breakpoints that can be used to uncover the
specific process having the greatest impact on the urban structure in the real world.
However, we should be aware that the use of FD values from the TP algorithm alone
may be insufficient in providing enough insights into this topic. The TP algorithm was
designed to measure spatial complexity by a single FD across scales, which is
inadequate, especially when one realizes that many natural and anthropogenic processes
do not have the same influence on urban landscape at different scales in the real world.
The application of multifractal models is thus recommended to better address this issue
(De Cola, 1993).
REFERENCES
Brown, S. R. 1995. Measuring the dimension of self-affine fractals: Examples of rough
surfaces. In C. C. Barton and P. R. LaPointe (Eds.), Fractals in the Earth Sciences.
New York: Plenum, pp. 77–87.
Clarke, K. C. 1986. Computation of the fractal dimension of topographic surfaces using the
triangular prism surface area method. Computers and Geosciences 12:713–722.
De Cola, L. 1989. Fractal analysis of a classified Landsat scene. Photogrammetric Engineering
and Remote Sensing 55:601–610.
De Cola, L. 1993. Multifractals in image processing and process imagine. In N. S. N. Lam and
L. De Cola (Eds.), Fractals in Geography. Englewood Cliffs, NJ: Prentice Hall; pp. 280–304.
De Jong, S. M., and Burrough, P. A. 1995. A fractal approach to the classification of
Mediterranean vegetation types in remotely sensed images. Photogrammetric Engineering
and Remote Sensing 61:1041–1053.
Dell’Acqua, F., and Gamba, P. 2006. Discriminating urban environments using multiscale
texture and multiple SAR images. International Journal of Remote Sensing 27:3797–3812.
Emerson, C. W., Lam, N. S. N., and Quattrochi, D. A. 1999. Multiscale fractal analysis of image
texture and pattern. Photogrammetric Engineering and Remote Sensing 65:51–61.
Emerson, C. W., Lam, N. S. N., and Quattrochi, D. A. 2005. A comparison of local variance,
fractal dimension, and Moran’s I as aids to multispectral image classification. International
Journal of Remote Sensing 26:1575–1588.
Emerson, C. W., Quattrochi, D. A., and Lam, N. S. N. 2002. Spatial metadata for global change
investigations using remote sensing. The GIScience 2002 2nd International Conference on
Geographic Information Science, Boulder, CO.
Frohn, R. C. 1998. Remote Sensing for Landscape Ecology: New Metric Indicators for
Monitoring, Modeling, and Assessment of Ecosystems. Boca Raton, FL: Lewis Publishers,
CRS Press.
Jaggi, S., Quattrochi, D. A., and Lam, N. S. N. 1993. Implementation and operation of three
fractal measurement algorithms for analysis of remote-sensing data. Computers and Geosciences 19:745–767.
Lam, N. S. N. 1990. Description and measurement of Landsat TM images using fractals.
Photogrammetric Engineering and Remote Sensing 56:187–195.
Lam, N. S. N., and De Cola, L. 1993. Fractals in Geography. Englewood Cliffs, NJ: Prentice
Hall.
REFERENCES
251
specific process having the greatest impact on the urban structure in the real world.
However, we should be aware that the use of FD values from the TP algorithm alone
may be insufficient in providing enough insights into this topic. The TP algorithm was
designed to measure spatial complexity by a single FD across scales, which is
inadequate, especially when one realizes that many natural and anthropogenic processes
do not have the same influence on urban landscape at different scales in the real world.
The application of multifractal models is thus recommended to better address this issue
(De Cola, 1993).
REFERENCES
Brown, S. R. 1995. Measuring the dimension of self-affine fractals: Examples of rough
surfaces. In C. C. Barton and P. R. LaPointe (Eds.), Fractals in the Earth Sciences.
New York: Plenum, pp. 77–87.
Clarke, K. C. 1986. Computation of the fractal dimension of topographic surfaces using the
triangular prism surface area method. Computers and Geosciences 12:713–722.
De Cola, L. 1989. Fractal analysis of a classified Landsat scene. Photogrammetric Engineering
and Remote Sensing 55:601–610.
De Cola, L. 1993. Multifractals in image processing and process imagine. In N. S. N. Lam and
L. De Cola (Eds.), Fractals in Geography. Englewood Cliffs, NJ: Prentice Hall; pp. 280–304.
De Jong, S. M., and Burrough, P. A. 1995. A fractal approach to the classification of
Mediterranean vegetation types in remotely sensed images. Photogrammetric Engineering
and Remote Sensing 61:1041–1053.
Dell’Acqua, F., and Gamba, P. 2006. Discriminating urban environments using multiscale
texture and multiple SAR images. International Journal of Remote Sensing 27:3797–3812.
Emerson, C. W., Lam, N. S. N., and Quattrochi, D. A. 1999. Multiscale fractal analysis of image
texture and pattern. Photogrammetric Engineering and Remote Sensing 65:51–61.
Emerson, C. W., Lam, N. S. N., and Quattrochi, D. A. 2005. A comparison of local variance,
fractal dimension, and Moran’s I as aids to multispectral image classification. International
Journal of Remote Sensing 26:1575–1588.
Emerson, C. W., Quattrochi, D. A., and Lam, N. S. N. 2002. Spatial metadata for global change
investigations using remote sensing. The GIScience 2002 2nd International Conference on
Geographic Information Science, Boulder, CO.
Frohn, R. C. 1998. Remote Sensing for Landscape Ecology: New Metric Indicators for
Monitoring, Modeling, and Assessment of Ecosystems. Boca Raton, FL: Lewis Publishers,
CRS Press.
Jaggi, S., Quattrochi, D. A., and Lam, N. S. N. 1993. Implementation and operation of three
fractal measurement algorithms for analysis of remote-sensing data. Computers and Geosciences 19:745–767.
Lam, N. S. N. 1990. Description and measurement of Landsat TM images using fractals.
Photogrammetric Engineering and Remote Sensing 56:187–195.
Lam, N. S. N., and De Cola, L. 1993. Fractals in Geography. Englewood Cliffs, NJ: Prentice
Hall.
REFERENCES
251