Urban areas are a challenging environment because of their ever changing structure and the different temporal behaviors and spatial patterns. In this chapter a detailed analysis of some of the questions arising from the use of remotely sensed data in urban area for change detection are addressed. Specifically, the role of very high resolution sensors and their relevance with respect to either fast or slow changes in human settlement is analyzed, with specific stress on rapid mapping in specific sites (hotspots), e.g. for post-disaster damage assessment. Similarly, the possibility to exploit long temporal sequences of coarser resolution data is also explored and discussed, since the availability of huge archives is nowadays a reality that may be used to look for interesting interrelationships between urban area pattern changes and environmental changes, at both the local (town), regional and global level. Examples related to a so-called “hypertemporal” sequences of EO data are offered, and show the great potentials of these data sets. © Springer International Publishing AG 2016.

Change detection in urban areas: Spatial and temporal scales

GAMBA, PAOLO ETTORE;DELL'ACQUA, FABIO
2016-01-01

Abstract

Urban areas are a challenging environment because of their ever changing structure and the different temporal behaviors and spatial patterns. In this chapter a detailed analysis of some of the questions arising from the use of remotely sensed data in urban area for change detection are addressed. Specifically, the role of very high resolution sensors and their relevance with respect to either fast or slow changes in human settlement is analyzed, with specific stress on rapid mapping in specific sites (hotspots), e.g. for post-disaster damage assessment. Similarly, the possibility to exploit long temporal sequences of coarser resolution data is also explored and discussed, since the availability of huge archives is nowadays a reality that may be used to look for interesting interrelationships between urban area pattern changes and environmental changes, at both the local (town), regional and global level. Examples related to a so-called “hypertemporal” sequences of EO data are offered, and show the great potentials of these data sets. © Springer International Publishing AG 2016.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1186478
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