TY - JOUR A1 - Taubenböck, H A1 - Wurm, M A1 - Netzband, M A1 - Zwenzner, H A1 - Roth, A A1 - Rahman, A A1 - Dech, S T1 - Flood risks in urbanized areas - multi-sensoral approaches using remotely sensed data for risk assessment JF - NATURAL HAZARDS AND EARTH SYSTEM SCIENCES N2 - 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. KW - damage assessment disaster KW - satellite data KW - management KW - radar KW - inundation KW - disaster KW - sar KW - gis KW - integration KW - earthquake Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-139605 VL - 11 IS - 2 ER - TY - JOUR A1 - Rauch, S. A1 - Taubenböck, H. A1 - Knopp, C. A1 - Rauh, J. T1 - Risk and space: modelling the accessibility of stroke centers using day- & nighttime population distribution and different transportation scenarios JF - International Journal of Health Geographics N2 - Purpose Rapid accessibility of (intensive) medical care can make the difference between life and death. Initial care in case of strokes is highly dependent on the location of the patient and the traffic situation for supply vehicles. In this methodologically oriented paper we want to determine the inequivalence of the risks in this respect. Methods Using GIS we calculate the driving time between Stroke Units in the district of Münster, Germany for the population distribution at day- & nighttime. Eight different speed scenarios are considered. In order to gain the highest possible spatial resolution, we disaggregate reported population counts from administrative units with respect to a variety of factors onto building level. Results The overall accessibility of urban areas is better than in less urban districts using the base scenario. In that scenario 6.5% of the population at daytime and 6.8% at nighttime cannot be reached within a 30-min limit for the first care. Assuming a worse traffic situation, which is realistic at daytime, 18.1% of the population fail the proposed limit. Conclusions In general, we reveal inequivalence of the risks in case of a stroke depending on locations and times of the day. The ability to drive at high average speeds is a crucial factor in emergency care. Further important factors are the different population distribution at day and night and the locations of health care facilities. With the increasing centralization of hospital locations, rural residents in particular will face a worse accessibility situation. KW - accessibility analysis KW - high resolution population data KW - public health Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261228 VL - 20 ER - TY - JOUR A1 - Taubenböck, H. A1 - Weigand, M. A1 - Esch, T. A1 - Staab, J. A1 - Wurm, M. A1 - Mast, J. A1 - Dech, S. T1 - A new ranking of the world's largest cities—Do administrative units obscure morphological realities? JF - Remote Sensing of Environment N2 - With 37 million inhabitants, Tokyo is the world's largest city in UN statistics. With this work we call this ranking into question. Usually, global city rankings are based on nationally collected population figures, which rely on administrative units. Sprawling urban growth, however, leads to morphological city extents that may surpass conventional administrative units. In order to detect spatial discrepancies between the physical and the administrative city, we present a methodology for delimiting Morphological Urban Areas (MUAs). We understand MUAs as a territorially contiguous settlement area that can be distinguished from low-density peripheral and rural hinterlands. We design a settlement index composed of three indicators (settlement area, settlement area proportion and density within the settlements) describing a gradient of built-up density from the urban center to the periphery applying a sectoral monocentric city model. We assume that the urban-rural transition can be defined along this gradient. With it, we re-territorialize the conventional administrative units. Our data basis are recent mapping products derived from multi-sensoral Earth observation (EO) data – namely the Global Urban Footprint (GUF) and the GUF Density (GUF-DenS) – providing globally consistent knowledge about settlement locations and densities. For the re-territorialized MUAs we calculate population numbers using WorldPop data. Overall, we cover the 1692 cities with >300,000 inhabitants on our planet. In our results we compare the consistently re-territorialized MUAs and the administrative units as well as their related population figures. We find the MUA in the Pearl River Delta the largest morphologically contiguous urban agglomeration in the world with a calculated population of 42.6 million. Tokyo, in this new list ranked number 2, loses its top position. In rank-size distributions we present the resulting deviations from previous city rankings. Although many MUAs outperform administrative units by area, we find that, contrary to what we assumed, in most cases MUAs are considerably smaller than administrative units. Only in Europe we find MUAs largely outweighing administrative units in extent. KW - city size KW - urban agglomeration KW - rank-size distribution KW - remote sensing KW - global urban footprint KW - urban morphology Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-240634 VL - 232 ER -