Exploring the temporal lag between the structure and function of urban areas - Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment - page 148

11.1 Introduction

urban structural configuration from very high spatial resolu-
tion imagery - with a focus on pragmatic applications; and
in the other direction lie challenges for exploring the more
ontological questions surrounding the fusion of structural and
functional representations - focusing on more holistic views of
urban growth and urban economic and social sustainability. The
former includes examples of micro urban remote sensing and is
a domain commonly visited by photogrammetists and scientists
involved in civil engineering and planning applications, while the
latter constitutes macro urban remote sensing and is far more in
line with the construction of deductive and reductionist urban
geographic models.
The distinction between micro and macro remote sensing is
based on the scale of analysis and not necessarily on the mul-
tidimensionality of the remotely sensed data, in particular its
spatial resolution. Indeed, urban remote sensing was expected to
benefit in both micro and macro scales of analysis at the advent
and subsequent prevalence of higher spatial resolution satellite
sensor data (IKONOS, QuickBird, WorldView, etc.). Much of
the potential for new areas of research was to revolve around
precision mapping where space-borne imagery were anticipated
to aid the delineation of buildings and transport structures - very
much in the same manner as air-borne photography was tra-
ditionally used to update topographic maps (Couloigner and
Ranchin, 2000). However, to date, the level of expectation for
these high spatial resolution satellite sensor datasets seems to
have far exceeded the number of practical urban applications.
Despite the perceived advances in clarity and detail stemming
from pixels representing smaller instantaneous fields of view,
most of the criticism, in direct contrast, has been linked with the
increased spectral heterogeneity resulting from the finer scaled
spatial resolution. It means that urban classifications remain
highly tenuous and any reliable micro remote sensing, usually
in the form of precision mapping, is extracted directly from the
spatial orientation of pixels - in the similar vein to conventional
interpretation of aerial photography, but with slightly lower
clarity and with limited stereoscopic capabilities. However, the
spectral heterogeneity problem is less of a restriction for macro
remote sensing, which instead of measuring individual objects
such as buildings, roads and even side-walks, is more concerned
with a generalized view of an urban area such as neighborhoods,
zones or even the whole city. Classification accuracy is less impor-
tant, with the emphasis more on interpreting generalized land
cover/land use, measuring overall building density, and under-
standing urban processes such as growth, congestion/pollution,
and poverty. Arguably, it is this understanding of urban processes
that many researchers consider as the more important benefits
of remote sensing when applied to urban areas. However, to
fully appreciate the scale of dynamic urban changes remotely
sensed data need to be embellished with ancillary information
measuring socioeconomic characteristics, housing descriptors,
and zoning restrictions. But even the remote sensing-ancillary
data combination only provides an essentially empirically derived
model of a static city. What is needed is a theoretical basis from
which to interpret and understand urban land cover and land
use change; a theoretical basis built on the concept of a temporal
lag between what an urban society demands and what urban
physical consequences materialize.

Multitemporal analysis is one of the strengths of remote sensing.
By using sensor data across two or more points in time consistent
and frequent changes in land cover and land use can be measured
routinely and cost-effectively. When applying multitemporal
analysis to spectrally complex urban areas, further emphasis is
placed on establishing consistent temporal relationships between
remotely sensed data and ancillary information - normally used
to improve classification accuracy as well as to improve the
thematic description of urban classes. Much of the rationale
for this strict temporal consistency, where remotely sensed data
are used from a time period that is very similar to the date
that ancillary information is collected, is based on the notion
that urban areas are static surfaces and that its buildings and
citizens are intertwined and evolve at the same rate of change.
This is an assumption that seems to be grounded more on
convenience rather than on theory. This chapter will challenge
this assumption of static cities and open the debate on whether
there is instead a time-induced relationship between the physical
structures of urban areas and their human functionality. A time-
induced relationship implies the investigation of a so-called
temporal lag ; in other words a difference in time between when
urban structures appear and when decisions are made by their
inhabitants to implement those structural changes. To this end
the chapter calls for the inclusion of urban theory when using
remote sensing to measure multitemporal changes of land cover
and land use in urban areas. It investigates whether urban
theory - which embraces a more process-led dynamic city - can
be can be applied and tested on empirical datasets representing
urban structure from high spatial resolution IKONOS sensor
data, as well as urban functionality from point-based mailing
addresses and rasterized census surfaces. Preliminary evidence
is given of temporal differences between the three data sets,
highlighting possible temporal lags between physical structure
and socioeconomic function. A discussion follows on whether
these temporal differences can be related to urban processes
as predicted by theory or whether they are unrelated random
differences. The chapter concludes with calls for further testing
and a research agenda on how urban theory can be incorporated
directly into urban multitemporal analysis, specifically on how
temporal lags between urban structure and urban function can
predict urban change. First, it is important to establish the
appropriate scale of analysis for the investigation of temporal
lags; in other words, at what scale remote sensing should be
applied to urban theory.

11.2 Micro andmacro urban
remote sensing

Research focused on the remote sensing of urban areas using
satellite sensor data is at an intersection ( inter alia Mesev,
2003; Gamba, Dell'Acqua and Dasarathy, 2005; Weng and
Quattrochi, 2007; Xian, 2010). In one direction lie opportu-
nities for developing methodologies for precision mapping of