Today's mega cities could serve as good predictors of future urbanization processes in incipient mega cities. Measuring
and analysing the past effects of urban growth in the largest category of urban agglomerations aims at understanding
spatial dynamics. In this study we use remote sensing, landscape metrics and gradient analysis to measure, quantify, and
analyze spatiotemporal effects of massive urbanization in 10 sample mega cities throughout the world. By using timeseries
of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban
patterns, sprawl and densification processes and various types of urban development. A multi-scale analysis starts at city
level using landscape metrics to quantify spatial urban patterns. We relate the metrics, like e.g. landscape shape index,
edge density or class area to each other in spider charts. Furthermore, we use gradient analysis to provide insight into
spatial pattern development from the urban core to the periphery. The results paint a characteristic picture of
spatiotemporal urbanization for the individual mega cites and enable comparison of all cities across the board. Spatial
characteristics of urbanization dynamics allow indirectly conclusions on causes or future consequences.
Dynamics of urban environments are a challenge to a sustainable development. Urban areas promise wealth, realization
of individual dreams and power. Hence, many cities are characterized by a population growth as well as physical
development. Traditional, visual mapping and updating of urban structure information of cities is a very laborious and
cost-intensive task, especially for large urban areas. For this purpose, we developed a workflow for the extraction of the
relevant information by means of object-based image classification. In this manner, multisensoral remote sensing data
has been analyzed in terms of very high resolution optical satellite imagery together with height information by a digital
surface model to retrieve a detailed 3D city model with the relevant land-use / land-cover information. This information
has been aggregated on the level of the building block to describe the urban structure by physical indicators. A
comparison between the indicators derived by the classification and a reference classification has been accomplished to
show the correlation between the individual indicators and a reference classification of urban structure types. The
indicators have been used to apply a cluster analysis to group the individual blocks into similar clusters.
The occurrence of a tsunami, a set of oceans waves caused by any large, abrupt disturbance of the sea surface, hitting a
vulnerable system on land can cause massive loss of life, destruction of coastal infrastructure and disruption of economic
activity. Vulnerability assessment and risk modelling are important components for an effective end-to-end hazard early
warning system and therefore contribute significantly to disaster risk reduction. The focus of this study is on the
capabilities and synergistic usage of multisensoral remotely sensed data to contribute to these complex tasks. We use
medium and high resolution optical satellite data (Landsat and Ikonos), high resolution radar data from TerraSAR-X as
well as a digital elevation model to provide multiple products for the assessment of spatial vulnerability in case of a
tsunami impact on the heterogeneous and highly structured coastal urban area of Padang, Indonesia. Results include
physical indicators like dimension and location of urbanization, quantification of potentially affected buildings, the
identification of safe areas as well as a time-dependent population assessment.
Against the background of massive urban development, area-wide and up-to-date spatial information is in demand.
However, for many reasons this detailed information on the entire urban area is often not available or just not valid
anymore. In the event of a natural hazard - e.g. a river flood - it is a crucial piece of information for relief units to have
knowledge about the quantity and the distribution of the affected population. In this paper we demonstrate the abilities of
remotely sensed data towards vulnerability assessment or disaster management in case of such an event. By means of
very high resolution optical satellite imagery and surface information derived by airborne laser scanning, we generate a
precise, three-dimensional representation of the landcover and the urban morphology. An automatic, object-oriented
approach detects single buildings and derives morphological information - e.g. building size, height and shape - for a
further classification of each building into various building types. Subsequently, a top-down approach is applied to
distribute the total population of the city or the district on each individual building. In combination with information of
potentially affected areas, the methodology is applied on two German cities to estimate potentially affected population
with a high level of accuracy.
Mega city Mexico City is ranked the third largest urban agglomeration to date around the globe. The large extension as
well as dynamic urban transformation and sprawl processes lead to a lack of up-to-date and area-wide data and
information to measure, monitor, and understand the urban situation. This paper focuses on the capabilities of multisensoral
remotely sensed data to provide a broad range of products derived from one scientific field - remote sensing - to support urban managing and planning. Therefore optical data sets from the Landsat and Quickbird sensors as well as
radar data from the Shuttle Radar Topography Mission (SRTM) and the TerraSAR-X sensor are utilised. Using the
multi-sensoral data sets the analysis are scale-dependent. On the one hand change detection on city level utilising the
derived urban footprints enables to monitor and to assess spatiotemporal urban transformation, areal dimension of urban
sprawl, its direction, and the built-up density distribution over time. On the other hand, structural characteristics of an
urban landscape - the alignment and types of buildings, streets and open spaces - provide insight in the very detailed
physical pattern of urban morphology on higher scale. The results show high accuracies of the derived multi-scale
products. The multi-scale analysis allows quantifying urban processes and thus leading to an assessment and
interpretation of urban trends.
Urban areas represent one of the most dynamic regions on earth. To solve the problems which are associated with the
rapid changes in those regions urban and spatial planning relies on up-to-date information about the urban sprawl.
Remote sensing data and in particular Synthetic Aperture Radar (SAR) images can provide valuable information about
the characteristics of urban sprawl with a short repetition rate. In previous studies we could demonstrate the ability to
classify built-up areas with a high accuracy using TerraSAR-X images. This paper focuses on the transfer of this method
to ALOS/PALSAR images. First results of our study show that the method developed for X-band imagery can be
transferred to L-band SAR images. However, the analysis of L-band data still requires some modifications of the
proposed procedure in order to increase the accuracy.
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