A Framework for the Long-term Monitoring of Urban Green Volume Based on Multi-temporal and Multi-sensoral Remote Sensing Data

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Green urban infrastructure is of key importance for many aspects of urban life and urban planning. Valid and comprehensive databases with very high spatial and temporal resolution are needed to monitor changes and to detect negative trends. This paper presents an approach to assess urban indicators such as green volume and soil sealing with very high accuracy and based on a wide range of different sensors (aerial stereo images, QuickBird, WorldView 2 and 3, Sentinel 2, HRSC, LIDAR). A framework using regression tree methods was developed and successfully applied in a case study (the city of Potsdam, Germany) resulting in a long time series dating back 25 years. The methodology offers the opportunity to analyze urban development in detail and to understand the functional relationships of urban planning processes. Demands for effective climate change adaptation, especially in terms of reducing heat stress, can thus be better defined.