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With global changes such as climate change and urbanization, land cover is prone to changing rapidly in cities around the globe. Urban management and planning is challenged with development pressure to house increasing numbers of people. Most up-to date continuous land use and land cover change data are needed to make informed decisions on where to develop new residential areas while ensuring sufficient open and green spaces for a sustainable urban development. Optical remote sensing data provide important information to detect changes in heterogeneous urban landscapes over long time periods in contrast to conventional approaches such as cadastral and construction data.

However, data from individual sensors may fail to provide useful images in the required temporal density, which is particularly the case in mid-latitudes due to relatively abundant cloud coverage. Furthermore, the data of a single sensor may be unavailable for an extended period of time or to the public at no cost. In this paper, we present an integrated, standardized approach that aims at combining remote sensing data in a high resolution that are provided by different sensors, are publicly available for a long-term period of more than ten years (2005-2017) and provide a high temporal resolution if combined. This multi-sensor and multi-temporal approach detects urban land cover changes within the highly dynamic city of Leipzig, Germany as a case. Landsat, Sentinel and RapidEye data are combined in a robust and normalized procedure to offset the variation and disturbances of different sensor characteristics. To apply the approach for detecting land cover changes, the Normalized Difference Vegetation Index (NDVI) is calculated and transferred into a classified NDVI (Classified Vegetation Cover-CVC). Small scale vegetation development in heterogeneous complex areas of a European compact city are highlighted. Results of this procedure show successfully that the presented approach is applicable with divers sensors' combinations for a longer time period and thus, provides an option for urban planning to update their land use and land cover information timely and on a small scale when using publicly available no cost data.