An integrative approach using remote sensing and social analysis to identify different settlement types and the specific living conditions of its inhabitants

Someday in 2007, the world population reached a historical
landmark: for the first time in human history, more than half of
the world´s population was urban. A stagnation of this urbanization
process is not in sight, so that by 2050, already 70 percent of
humankind is projected to live in urban settlements. Over the last
few decades, enormous migrations from rural hinterlands to steadily
growing cities could be witnessed coming along with a dramatic
growth of the world’s urban population. The speed and the scale of
this growth, particularly in the so called less developed regions,
are posing tremendous challenges to the countries concerned as well
as to the world community. Within mega cities the strongest trends
and the most extreme dimensions of the urbanization process can be
observed. Their rapid growth results in uncontrolled processes of
fragmentation which is often associated with pronounced poverty,
social inequality, socio-spatial and political fragmentation,
environmental degradation as well as population demands that
outstrip environmental service capacity. For the majority of the
mega cities a tremendous increase of informal structures and
processes has to be observed. Consequentially informal settlements
are growing, which represent those characteristic municipal areas
being subject to particularly high population density, dynamics as
well as marginalization. They have quickly become the most visible
expression of urban poverty in developing world cities. Due to the
extreme dynamics, the high complexity and huge spatial dimension of
mega cities, urban administrations often only have an obsolete or
not even existing data basis available to be at all informed about
developments, trends and dimensions of urban growth and change. The
knowledge about the living conditions of the residents is
correspondingly very limited, incomplete and not up to date.
Traditional methods such as statistical and regional analyses or
fieldwork are no longer capable to capture such urban process. New
data sources and monitoring methodologies are required in order to
provide an up to date information basis as well as planning
strate¬gies to enable sustainable developments and to simplify
planning processes in complex urban structures. This research shall
seize the described problem and aims to make a contribution to the
requirements of monitoring fast developing mega cities. Against
this background a methodology is developed to compensate the lack
of socio-economic data and to deduce meaningful information on the
living conditions of the inhabitants of mega cities. Neither social
science methods alone nor the exclusive analysis of remote sensing
data can solve the problem of the poor quality and outdated data
base. Conventional social science methods cannot cope with the
enormous developments and the tremendous growth as they are too
labor-, as well as too time- and too cost-intensive. On the other
hand, the physical discipline of remote sensing does not allow for
direct conclusions on social parameters out of remote sensing
images. The prime objective of this research is therefore the
development of an integrative approach − bridging remote sensing
and social analysis – in order to derive useful information about
the living conditions in this specific case of the mega city Delhi
and its inhabitants. Hence, this work is established in the
overlapping range of the research topics remote sensing, urban
areas and social science. Delhi, as India’s fast growing capital,
meanwhile with almost 25 million residents the second largest city
of the world, represents a prime example of a mega city. Since the
second half of the 20th century, Delhi has been transformed from a
modest town with mainly administrative and trade-related functions
to a complex metropolis with a steep socio-economic gradient. The
quality and amount of administrative and socio-economic data are
poor and the knowledge about the circumstances of Delhi’s residents
is correspondingly insufficient and outdated. Delhi represents
therefore a perfectly suited study area for this research. In order
to gather information about the living conditions within the
different settlement types a methodology was developed and
conducted to analyze the urban environment of the mega city Delhi.
To identify different settlement types within the urban area,
regarding the complex and heterogeneous appearance of the Delhi
area, a semi-automated, object-oriented classification approach,
based on segmentation derived image objects, was implemented. As
the complete conceptual framework of this research, the
classification methodology was developed based on a smaller
representative training area at first and applied to larger test
sites within Delhi afterwards. The object-oriented classification
of VHR satellite imagery of the QuickBird sensor allowed for the
identification of five different urban land cover classes within
the municipal area of Delhi. In the focus of the image analysis is
yet the identification of different settlement types and amongst
these of informal settlements in particular. The results presented
within this study demonstrate, that, based on density classes, the
developed methodology is suitable to identify different settlement
types and to detect informal settlements which are mega urban risk
areas and thus potential residential zones of vulnerable population
groups. The remote sensing derived land cover maps form the
foundation for the integrative analysis concept and deliver
there¬fore the general basis for the derivation of social
attributes out of remote sensing data. For this purpose settlement
characteristics (e.g., area of the settlement, average building
size, and number of houses) are estimated from the classified
QuickBird data and used to derive spatial information about the
population distribution. In a next step, the derived information is
combined with in-situ information on socio-economic conditions
(e.g., family size, mean water consumption per capita/family)
extracted from georeferenced questionnaires conducted during two
field trips in Delhi. This combined data is used to characterize a
given settlement type in terms of specific population and water
related variables (e.g., population density, total water
consumption). With this integrative methodology a catalogue can be
compiled, comprising the living conditions of Delhi’s inhabitants
living in specific settlement structures – and this in a quick,
large-scaled, cost effective, by random or regularly repeatable way
with a relatively small required data basis.The combined
application of remotely sensed imagery and socio-economic data
allows for the mapping, capturing and characterizing the
socio-economic structures and dynamics within the mega city of
Delhi, as well as it establishes a basis for the monitoring of the
mega city of Delhi or certain areas within the city respectively by
remote sensing. The opportunity to capture the condition of a mega
city and to monitor its development in general enables the persons
in charge to identify unbeneficial trends and to intervene
accordingly from an urban planning perspective and to countersteer
against a non-adequate supply of the inhabitants of different urban
districts, primarily of those of informal settlements. This study
is understood to be a first step to the development of methods
which will help to identify and understand the different forms,
actors and processes of urbanization in mega cities. It could
support a more proactive and sustainable urban planning and land
management – which in turn will increase the importance of urban
remote sensing techniques. In this regard, the most obvious and
direct beneficiaries are on the one hand the governmental agencies
and urban planners and on the other hand, and which is possibly the
most important goal, the inhabitants of the affected areas, whose
living conditions can be monitored and improved as required. Only
if the urban monitoring is quickly, inexpensively and easily
available, it will be accepted and applied by the authorities,
which in turn enables for the poorest to get the support they need.
All in all, the listed benefits are very convincing and corroborate
the combined use of remotely sensed and socio-economic data in mega
city research.