Classifying Urban Green Spaces using a combined Sentinel-2 and Random Forest approach

Abstract

Abstract. Environmental and human benefits of Urban Green Spaces (UGSs) have been known for a long time. However, the definition of a reasonable greening strategy still remains challenging due to the lack of sufficient baseline information as well as a lack of consensus what constitutes a UGS. Therefore, accurate identification of the existing green spaces in cities is crucial for developing UGS inventories for urban planning and resource management activities. In this paper we explore the potential of freely available highest resolution multi-spectral remote sensing imagery to identify large homogeneous as well small heterogeneous UGSs. The approach of using a Random Forest classification on Sentinel-2 imagery is shown to be useful to identify various UGSs with a 97 % accuracy. Freely available data and a relatively straightforward implementation of the proposed approach makes it a valuable tool for decision and policy makers.