Hannes Taubenböck

Urbanization in India – Spatiotemporal analysis using remote sensing data

Urbanization is arguably the most dramatic form of irreversible land transformation. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. In this uncontrolled situation, city planners lack tools to measure, monitor, and understand urban sprawl processes. Multitemporal remote sensing has become an important data-gathering tool for analysing these changes. By using time-series of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban sprawl, redensification and urban development in the tremendously growing 12 largest Indian urban agglomerations. A multi-scale analysis aims to identify spatiotemporal urban types. At city level, the combination of absolute parameters (e.g. areal growth or built-up density) and landscape metrics (e.g. SHAPE index) quantitatively characterise the spatial pattern of the cities. Spider charts can display the spatial urban types at three time stages, showing temporal development and helping the reader compare all cities based on normalized scales. In addition, gradient analysis provides insight into location-based spatiotemporal patterns of urbanization. Therefore, we analyse zones defining the urban core versus the urban edges. The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations. These cities in the same cultural area range from 2.5 million inhabitants to 20 million (in the metropolitan region of Mumbai). The results paint a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India.

Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission

The German TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) mission (TDM) collects two global data sets of very high resolution (VHR) synthetic aperture radar (SAR) images between 2011 and 2013. Such imagery provides a unique information source for the identification of built-up areas in a so far unique spatial detail. This letter presents the novel implementation of a fully automated processing system for the delineation of human settlements worldwide based on the SAR data acquired in the context of the TDM. The proposed Urban Footprint Processor (UFP) includes three main processing stages dedicated to: i) the extraction of texture information suitable for highlighting regions characterized by highly structured and heterogeneous built-up areas; ii) the generation of a binary settlement layer (built-up, non-built-up) based on an unsupervised classification scheme accounting for both the original backscattering amplitude and the extracted texture; and iii) a final post-editing and mosaicking phase aimed at providing the final Urban Footprint (UF) product for arbitrary geographical regions. Experimental results assess the high potential of the TDM data and the proposed UFP to provide highly accurate geo-data for an improved global mapping of human settlements.

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

Abstract. 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.

Object-based feature extraction using high spatial resolution satellite data of urban areas

Urban morphology is characterized by a complex and variable coexistence of diverse, spatially and spectrally heterogeneous objects. Built-up areas are among the most rapidly changing and expanding elements of the landscape. Thus, remote sensing becomes an essential field for up-to-date and area-wide data acquisition, especially in explosively sprawling cities of developing countries. The urban heterogeneity requires high spatial resolution image data for an accurate geometric differentiation of the small-scale physical features. This study proposes an object-based, multi-level, hierarchical classification framework combining shape, spectral, hierarchical and contextual information for the extraction of urban features. The particular focus is on high class accuracies and stable transferability by fast and easy adjustments on varying urban structures or sensor characteristics. The framework is based on a modular concept following a chronological workflow from a bottom-up segmentation optimization to a hierarchical, fuzzy-based decision fusion top-down classification. The workflow has been developed on IKONOS data for the megacity Istanbul, Turkey. Transferability is tested based on Quickbird data from the various urban structures of the incipient megacity Hyderabad, India. The validation of both land-cover classifications shows an overall accuracy of more than 81 percent.

Performance Evaluation for 3-D City Model Generation of Six Different DSMs From Air- and Spaceborne Sensors

Since remote sensing provides more and more sensors and techniques to accumulate data on urban regions, three-dimensional representations of these complex environments gained much interest for various applications. In order to obtain three-dimensional representations, one of the most practical ways is to generate Digital Surface Models (DSMs) using very high resolution remotely sensed images from two or more viewing directions, or by using LIDAR sensors. Due to occlusions, matching errors and interpolation techniques these DSMs do not exhibit completely steep walls, and in order to obtain real three-dimensional urban models including objects like buildings from these DSMs, advanced methods are needed. A novel approach based on building shape detection, height estimation, and rooftop reconstruction is proposed to achieve realistic three-dimensional building representations. Our automatic approach consists of three main modules as; detection of complex building shapes, understanding rooftop type, and three-dimensional building model reconstruction based on detected shape and rooftop type. Besides the development of the methodology, the goal is to investigate the applicability and accuracy which can be accomplished in this context for different stereo sensor data. We use DSMs of Munich city which are obtained from different satellite (Cartosat-1, Ikonos, WorldView-2) and airborne sensors (3K camera, HRSC, and LIDAR). The paper later focuses on a quantitative comparisons of the outputs from the different multi-view sensors for a better understanding of qualities, capabilities and possibilities for applications. Results look very promising even for the DSMs derived from satellite data.

Object-based image information fusion using multisensor earth observation data over urban areas

At present, the majority of the worlds population is living in urban areas. Cities undergo constant development in their morphology. The latter is always a turned-into-stone representation of the coetaneous social, economical and technical values. The technical developments in recent years of very high-resolution spaceborne earth observation methods enable mapping of large urban areas with a decent level of detail. Additionally, detailed elevation information of urban areas in developed countries is widely available. Digital surface models (DSMs) support the classification of urban structures beyond two-dimensional classifications. We present a hierarchical, object-based and transferable framework to extract the urban structure on a high level of geometric detail for two test sites in Germany. The results show accuracies of above 90% for the land-use/land-cover classification for both test sites applying the same routines. DSMs from various sources have been utilised for the extraction of the individual building structures with accuracies of 90% and 80%, respectively. The methodology is suited to extract the urban structure on the level of individual buildings and the results can be utilised as 3D city model for the purpose of decision-making, urban planning and further analyses.

Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap

Remote sensing data and methods are widely deployed in order to contribute to the assessment of numerous components of earthquake risk. While for earthquake hazard-related investigations, the use of remotely sensed data is an established methodological element with a long research tradition, earthquake vulnerability–centred assessments incorporating remote sensing data are increasing primarily in recent years. This goes along with a changing perspective of the scientific community which considers the assessment of vulnerability and its constituent elements as a pivotal part of a comprehensive risk analysis. Thereby, the availability of new sensors systems enables an appreciable share of remote sensing first. In this manner, a survey of the interdisciplinary conceptual literature dealing with the scientific perception of risk, hazard and vulnerability reveals the demand for a comprehensive description of earthquake hazards as well as an assessment of the present and future conditions of the elements exposed. A review of earthquake-related remote sensing literature, realized both in a qualitative and quantitative manner, shows the already existing and published manifold capabilities of remote sensing contributing to assess earthquake risk. These include earthquake hazard-related analysis such as detection and measurement of lineaments and surface deformations in pre- and post-event applications. Furthermore, pre-event seismic vulnerability–centred assessment of the built and natural environment and damage assessments for post-event applications are presented. Based on the review and the discussion of scientific trends and current research projects, first steps towards a roadmap for remote sensing are drawn, explicitly taking scientific, technical, multi- and transdisciplinary as well as political perspectives into account, which is intended to open possible future research activities.

Emergency Preparedness in the Case of a Tsunami—Evacuation Analysis and Traffic Optimization for the Indonesian City of Padang

The “Last-Mile Evacuation” research project develops a numerical last mile tsunami early warning and evacuation information system on the basis of detailed earth observation data and techniques as well as unsteady, hydraulic numerical modeling of small-scale flooding and inundation dynamics of the tsunami including evacuation simulations in the urban coastal hinterland for the city of Padang, West Sumatra, Indonesia. It is well documented that Sumatra’s third largest city with almost one million inhabitants is located directly on the coast and partially sited beneath the sea level, and thus, is located in a zone of extreme risk due to severe earthquakes and potential triggered tsunamis. “Last-Mile” takes the inundation dynamics into account and additionally assesses the physical-technical susceptibility and the socio-economic vulnerability of the population with the objective to mitigate human and material losses due to possible tsunamis. By means of discrete multi-agent techniques risk-based, time- and site-dependent forecasts of the evacuation behavior of the population and the flow of traffic in large parts of the road system in the urban coastal strip are simulated and concurrently linked with the other components.

Risk reduction at the “ Last - Mile ”: an attempt to turn science into action by the example of Padang, Indonesia

More than ever before, the last decade revealed the immense vulnerability of the world’s cities to natural hazards. Neither the tsunami in the Indian Ocean in 2004, the hurricane Katrina in 2005, the cyclone Nargis in 2008 nor the earthquakes in Sichuan in 2008 or in Haiti 2010 found the people, the city administrations or the national or international organizations well prepared in the advent of anticipated but to a large extent disregarded natural disasters. It is evident that the lack of tailor-made disaster management plans and standard operational procedures are often the crucial point in proper risk reduction approaches. This study presents an approach to transfer knowledge of an extensive multidisciplinary scientific study on risk identification into recommendations for risk reduction strategies. The study has been conducted by means of a combination of experts from different scientific communities coming from civil and coastal engineering, remote sensing, social sciences, evacuation modelling and capacity development. The paper presents the results of this research approach and interweaves key findings with recent experiences from an eyewitness on a previous hazard event. Thus, necessary tsunami hazard and vulnerability information as well as valuable insights into preparedness activities have been derived for initiating updated infrastructural designs and practical recommendations for emergency management as well as strategic spatial planning activities at the local scale. The approach was applied in the context of tsunami early warning and evacuation planning in the coastal city of Padang, Western Sumatra, Republic of Indonesia.

Investigating the Applicability of Cartosat-1 DEMs and Topographic Maps to Localize Large-Area Urban Mass concentrations

Building models are a valuable information source for urban studies and in particular for analyses of urban mass concentrations (UMCS). Most commonly, light detection and ranging (LiDAR) is used for their generation. The trade-off for the high geometric detail of these data is the low spatial coverage, comparably high costs and low actualization rates. Spaceborne stereo data from Cartosat-1 are able to cover large areas on the one hand, but hold a lower geometric resolution on the other hand. In this paper, we investigate to which extent the geometric shortcomings of Cartosat-1 can be overcome integrating building footprints from topographic maps for the derivation of large-area building models. Therefore, we describe the methodology to derive digital surface models (DSMs) from Cartosat-1 data and the derivation of building footprints from topographic maps at 1:25 000 (DTK25). Both data are fused to generate building block models for four metropolitan regions in Germany with an area of ~ 16 000 km2. Building block models are further aggregated to 1 × 1 km grid cells and volume densities are computed. Volume densities are classified to various levels of UMCs. Performance evaluation of the building block models reveals that the building footprints are larger in the DTK-25, and building heights are lower with a mean absolute error of 3.21 m. Both factors influence the building volume, which is linearly lower than the reference. However, this error does not affect the classification of UMC, which can be classified with accuracies between 77% and 97%.