%0 Journal Article %1 Taubenböck2012162 %A Taubenböck, H. %A Esch, T. %A Felbier, A. %A Wiesner, M. %A Roth, A. %A Dech, S. %D 2012 %J Remote Sensing of Environment %K article dech %P 162 - 176 %R http://dx.doi.org/10.1016/j.rse.2011.09.015 %T Monitoring urbanization in mega cities from space %U http://www.sciencedirect.com/science/article/pii/S0034425711003427 %V 117 %X Mega cities, the largest category of urban agglomerations, attract considerable attention because of their population size, economic, socio-cultural, environmental and political influence and geographical complexity. Until 1975 there were just three mega cities in the world: New York, Tokyo and Mexico City — today there are 27 cities having more than the defined 10 million inhabitants. This paper presents a straight forward, application-oriented approach using multi-temporal remotely sensed data to systematically monitor the spatiotemporal dynamics of the world's urban giants. Object-oriented and pixel-based classification image analysis techniques are applied to Landsat as well as to TerraSAR-X data in order to define urbanized areas of the mega cities at different points of time. Subsequently post-classification change detection is performed on urban footprint level. With time intervals of about 10 years almost 40 years of urbanization are monitored, showing different dimensions, dynamics and patterns across the analyzed cities. The generated urban footprint products show accuracies consistently higher than 80%, allowing for further applications in fields such as urban planning, risk management, or population assessment.

@article{Taubenböck2012162, abstract = {Mega cities, the largest category of urban agglomerations, attract considerable attention because of their population size, economic, socio-cultural, environmental and political influence and geographical complexity. Until 1975 there were just three mega cities in the world: New York, Tokyo and Mexico City — today there are 27 cities having more than the defined 10 million inhabitants. This paper presents a straight forward, application-oriented approach using multi-temporal remotely sensed data to systematically monitor the spatiotemporal dynamics of the world's urban giants. Object-oriented and pixel-based classification image analysis techniques are applied to Landsat as well as to TerraSAR-X data in order to define urbanized areas of the mega cities at different points of time. Subsequently post-classification change detection is performed on urban footprint level. With time intervals of about 10 years almost 40 years of urbanization are monitored, showing different dimensions, dynamics and patterns across the analyzed cities. The generated urban footprint products show accuracies consistently higher than 80%, allowing for further applications in fields such as urban planning, risk management, or population assessment. }, added-at = {2015-10-12T13:23:08.000+0200}, author = {Taubenböck, H. and Esch, T. and Felbier, A. and Wiesner, M. and Roth, A. and Dech, S.}, biburl = {https://www.bibsonomy.org/bibtex/2bab252b8971342cc6a961aa992d0bf4d/mschramm}, doi = {http://dx.doi.org/10.1016/j.rse.2011.09.015}, interhash = {1362aeb3881eff88dcf732ef14527338}, intrahash = {bab252b8971342cc6a961aa992d0bf4d}, issn = {0034-4257}, journal = {Remote Sensing of Environment }, keywords = {article dech}, note = {Remote Sensing of Urban Environments }, pages = {162 - 176}, timestamp = {2015-10-12T13:23:08.000+0200}, title = {Monitoring urbanization in mega cities from space }, url = {http://www.sciencedirect.com/science/article/pii/S0034425711003427}, volume = 117, year = 2012 }