Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

Abstract

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

Urban Nature: Does Green Infrastructure Relate to the Cultural and Creative Vitality of European Cities?

“…For instance, Taylor et al [50] used Google Earth to measure the quality of urban green spaces. Similarly, Ludwig et al [49] showed that combining OSM data with Sentinel-2 satellite imagery improves assessment of urban green spaces.…”

“…Their study shows that the degree of completeness and the quantity of contributions for urban green spaces vary between cities, with a tendency of greater data completeness in city centres and lower data completeness on the city outskirts. A more recent study by Ludwig et al [49] summarizes the challenges encountered by researchers of urban green spaces such as the lack of consensus on the definition of "urban green space", the unequal sensitivity of data on private and small green spaces, and the differences in land cover indicators between municipal, national, and other geographic information systems datasets.…”

Sensitivity analysis in multi-criteria evaluation of the suitability of urban green spaces for recreational activities

“…Given the definition of SDSS (Malczewski, 1999), urban green spaces can be assessed with an SDSS approach in terms of their suitability for several activities by taking user preferences into account. As users expect a change in the results upon their personal preferences, the OAT sensitivity analysis method as a followup step investigates how sensitive the outcome is to different user preferences, which are the criteria weights.…”

“…Spatial decision support systems (SDSS) with their various approaches offer the possibility to evaluate the alternatives based on computed characteristics (Keenan and Jankowski, 2019) by integrating spatial data processing and multi-criteria decision making (MCDM) into a computer-based system. They are designed to enable people to make more efficient choices, especially when dealing with spatial decision-making issues (Malczewski, 1999). As, for example in the present case, to choose urban green spaces according to a range of weighted criteria under certain activities.…”

Mapping Global Urban Impervious Surface and Green Space Fractions Using Google Earth Engine

“…In contrast, the development of urban GS datasets does not have sufficient resolution and completeness, due to varied definitions of green space and different monitoring methods [30]. The pan-European CORINE Land Cover data set contains a designated class "Green Urban Areas," including GS larger than 25 ha [31].The Urban Atlas [32] for the EU and the Trust for Public Land's ParkServe data set [33] for the US contains land-use information only for selected cities.…”