Urbanization in India – Spatiotemporal analysis using remote sensing data

With a spatial resolution of up to 1 m, the German radar satellite TerraSAR-X (TSX) has significantly increased the usability of spaceborne synthetic aperture radar (SAR) imagery in the context of urban applications. This paper presents an approach toward the semiautomated detection of built-up areas (BAs) based on single-polarized TSX images. The proposed methodology includes a specific preprocessing of the SAR data and an automated image analysis procedure. The preprocessing focuses on the analysis of local speckle characteristics in order to provide a texture layer that highlights BAs. In the context of an object-oriented image analysis, this texture layer is used along with the original intensity information to automatically extract settlements. The technique is tested on the basis of 12 TSX scenes covering representative urban agglomerations distributed throughout the world. Overall, accuracies between 76% and 96% for the derived city footprints demonstrate the high potential of both the TSX imagery and the proposed analysis approach in detecting BAs. In order to demonstrate the robustness and transferability of the image analysis concept, we finally transferred the classification strategy from the object-oriented domain to a more general and simplified pixel-based approach.

Delineation of Urban Footprints From TerraSAR-X Data by Analyzing Speckle Characteristics and Intensity Information

TerraSAR-X add-on for digital elevation measurement (TanDEM-X) is a German Earth observation mission collecting a total of two global coverages of very high resolution (VHR) synthetic aperture radar (SAR) X-band data with a spatial resolution of around three meters in the years 2011 and 2012. With these, the TanDEM-X mission (TDM) will provide a unique data set which is complementary to existing global coverages based on medium (MR) or high resolution (HR) optical imagery. The capabilities of the TDM in terms of supporting the analysis and monitoring of global human settlement patterns are explored and demonstrated. The basic methodology for a fully-operational detection and delineation of built-up areas from VHR SAR data is presented along with a description of the resulting geo-information product-the urban footprint (UF) mask-and the operational processing environment for the UF production. Moreover, potential follow-on analyses based on the intermediate products generated in the context of the UF analysis are introduced and discussed. The results of the study indicate the high potential of the TDM with respect to an analysis of urbanization patterns, peri-urbanization, spatio-temporal dynamics of settlement development as well as population estimation, vulnerability assessment and modeling of global change.

TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns

Urban land cover (ULC) has a considerable impact on local, regional, and global environmental change, and has significant ecological, biophysical, social, and climatic effects (Seto and Shepherd, 2009; DeFries et al., 2010). These effects are further amplified by the temporal duration of urban changes that tend to last for decades and are often irreversible. Optical sensors on board various satellite platforms play a significant role in urban monitoring and assessment. Two representative examples are indicative of the importance of optical sensors. First, since 2009 after USGS made the Landsat archive freely available, a 60-fold increase was observed in data downloads (NASA, 2013). Second, in the last decade, there has been a strong interest from the commercial sector to launch satellite optical sensors. This interest is clearly driven by the constantly increasing demand for such products from governmental, military, nonprofit, and commercial sectors. 4

Urban Observing Sensors

. Estimating flood risks and managing disasters combines knowledge in climatology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography – thus a complex multi-faceted problem. This study focuses on the capabilities of multi-source remote sensing data to support decision-making before, during and after a flood event. With our focus on urbanized areas, sample methods and applications show multi-scale products from the hazard and vulnerability perspective of the risk framework. From the hazard side, we present capabilities with which to assess flood-prone areas before an expected disaster. Then we map the spatial impact during or after a flood and finally, we analyze damage grades after a flood disaster. From the vulnerability side, we monitor urbanization over time on an urban footprint level, classify urban structures on an individual building level, assess building stability and quantify probably affected people. The results show a large database for sustainable development and for developing mitigation strategies, ad-hoc coordination of relief measures and organizing rehabilitation.

/pdf/flood-risks-in-urbanized-areas-multi-sensoral-approaches-55mnltm485.pdf

Flood risks in urbanized areas – multi-sensoral approaches using remotely sensed data for risk assessment

Classifying the complex structures of urban morphology from high resolution remote sensing imagery comprises difficulties due to their spectral and spatial heterogeneity. This paper presents a methodology allowing derivation of area-wide spatial information meaningful for city planning and management from IKONOS imagery. The initial point is a stable segmentation for an object-oriented approach to derive a thematic land cover classification. The classification methodology – which is predominantly based on shape and neighbourhood related features - is exemplified by the extraction of roads with a region-growing rule base. The approach follows the assumption that objects representing real world structures correspond in any urban area. Finally, the urban land cover classification is used to compute a spatial distribution of built-up densities within the city and to map homogeneous zones or structures of urban morphology. The aim apart from the information on urban morphology is to derive indirectly standard socio-economic data for further support of city management and planning. The result shows an allocation of different urban zones within the city of Istanbul with an accuracy of 82% compared to a digitized layer based on visual classification.

/pdf/an-urban-classification-approach-based-on-an-object-oriented-3mu858sj2c.pdf

An urban classification approach based on an object-oriented analysis of high resolution satellite imagery for a spatial structuring within urban areas

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.

/pdf/urban-structure-analysis-of-mega-city-mexico-city-using-674noy8o2w.pdf

Urban structure analysis of mega city Mexico City using multisensoral remote sensing data

TerraSAR-X is a new German radar satellite that shall be launched in mid 2006. The expected lifetime is 5 years. It carries a high frequency X-band Synthetic Aperture Radar (SAR) sensor that can be operated in different modes and polarisation. The Spotlight-, Stripmapand ScanSAR-modes provide high resolution SAR images for detailed analysis as well as wide swath data whenever a larger coverage is required. Imaging will be possible in single, dual and quad-polarisation. TerraSAR-X will be an operational SARsystem for scientific and commercial applications. Today’s space borne SAR systems usually provide 25 m resolution that is suitable to separate built-up areas from other land use and therewith the delineation of the city bodies. The high resolution SAR data of the upcoming new SAR sensors like TerraSAR-X and Radarsat-2 will increase dramatically the level of details visible in the SAR images. The detection and mapping of buildings, city structures and infrastructure like roads and railways will be possible. These features are of interest for mapping and monitoring purposes. The high resolution further enables to derive meaningful texture measures. Polarimetry can be used to separate different backscattering mechanisms and therewith to improve classification results The very short revisit time together with the all weather capability make TerraSAR-X to a very useful source to assess disaster damage. SAR interferometry is one of the major applications of today’s SAR data and can also be applied to TerraSAR-X data. Here the range differences between the ground targets and the antenna’s position during the acquisition are evaluated. Ground motions can be derived by applying differential interferometry and persistent scatterer techniques. A prerequisite for interferometry is the correlation of the radar echoes. The degree of correlation can be estimated from the SAR data and can be utilized for classification and change detection purposes. High resolution, short revisit times and a ± 250m repeat orbit service will open new perspectives for the observation of urban areas.

TerraSAR-X: How can high resolution SAR data support the observation of urban areas?

Almost two thirds of the world’s population will live in cities by 2030. Thus, human settlements typify the most dynamic regions on earth. To cope with this development urban planning and management requires up-to-date information about the various processes taking place within the urbanised zones. As radar remote sensing allows for the collection of areal data under almost all weather and environmental conditions it is predestined for a frequent and near-term retrieval of geo-information. 
In view of the future TerraSAR-X mission the German Remote Sensing Data Center (DFD) researches into the potential use of high resolution radar imagery. In this context our main objective is to develop concepts for a mostly automated detection and analysis of human settlements by means of high resolution SAR data. The studies are based on multi-frequency single-, dual- and quad-polarised SAR data recorded by the airborne Experimental Synthetic Aperture Radar (E-SAR) system of the German Aerospace Center (DLR). This paper presents first results of an object-oriented approach towards a semi-automated identification of built-up areas based on single-polarised E-SAR X-band imagery. The basic concept includes an optimised speckle suppression procedure in order to improve and stabilise subsequent image analysis as well as the development of an object-oriented classification scheme for an automated detection of built-up areas from high resolution X-band imagery.
The developed concept for a semi-automated extraction of urban areas from single polarised X-band data yields promising results. Built-up areas could be detected with an accuracy of 86%, 85% and 91% for three flight tracks. While the main body of the settlements could be identified with an accuracy of more than 90% inaccuracies were mainly associated with flanking parks, recreation areas and allotments.

/pdf/semi-automated-classification-of-urban-areas-by-means-of-19yn1xo342.pdf

Semi-automated classification of urban areas by means of high resolution radar data

The actual process of rapid urbanisation is associated with various ecological, social and economic changes in both, the urban area and the surrounding natural environment. In order to keep up with the effects and impacts of this development, effective urban and regional planning requires accurate and up-to-date information on the urban dynamics. Recent studies have demonstrated the applicability of high resolution optical satellite data for the acquisition of spatial and socio-economic information. In contrast, radar imagery has hardly been employed for these purposes so far. The future TerraSAR-X will provide radar data with a ground resolution comparable to existing high resolution optical satellite sensors. Thus, it will afford detailed urban analysis based on spaceborne radar imagery for the first time. The detection concept presented here serves as a preliminary investigation of the potential use of TerraSAR-X data in the context of urban applications. It introduces a robust approach towards an automated detection of built-up areas using data acquired by the Experimental Synthetic Aperture Radar (E-SAR) system of the German Aerospace Center (DLR). For that purpose different data sets of single-polarised Xband imagery are analysed in an object-oriented classification. A robust object-oriented analysis strongly depends on accurate and reliable image segmentation. Thus, a classification-based optimisation procedure to stabilise and improve the initial image segmentation step is introduced. Subsequently the identification of built-up areas is performed on the basis of three image segmentation levels in different spatial scales. Here, contextual and textural features along with shape-related and hierarchical characteristics play a major role. Finally, the transferability and robustness of the presented approach is illustrated by applying the developed classification scheme to E-SAR data of three complete flight tracks.

/pdf/robust-approach-towards-an-automated-detection-of-built-up-4epkczzu0j.pdf

Robust approach towards an automated detection of built-up areas from high resolution radar imagery

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