Fraser Baker (United Kingdom) 1
1 - Manchester Metropolitan University
Climate change is projected to cause an increase in the frequency and severity of heat waves and flooding events across Europe. Urban greenspace provides urban cooling and absorbs rainwater reducing surface runoff, thus providing an effective solution to improving urban resilience to climate-related hazards. Effective climate change adaptation strategies need to consider the spatial distribution of urban greenspace and the flow of associated benefits (ecosystem services) to local residents, in order to identify areas where green infrastructure can further improve resilience. However studies typically model ecosystem services within administrative units, which do not reflect environmental phenomena, and do not consider variation in seasonal provision of ecosystem services associated with changes in climate and vegetation conditions. This study aims to improve the spatiotemporal resolution of ecosystem services modelling research by developing a modelling framework using high-resolution geospatial datasets.
High-resolution (1.5 m) multi-spectral remotely sensed imagery provided an improved classification of vegetation types and man-made surfaces in Manchester, UK, to input into models that simulate cooling potential and runoff attenuation. This detailed mapping of vegetation types allows examination of how changing vegetation conditions affects provision of cooling and runoff attenuation ecosystem services. Census population estimates are disaggregated to the building level to produce a fine scale (100 m2) model of ecosystem service demand. Comparison between the provision of, and demand for, ecosystem services, provides a novel high-resolution ecosystem service mapping product for spatiotemporal exploration. This is beneficial for informing urban planning practitioners on how spatially targeted urban green infrastructure interventions may improve climate change resilience for local residents. The consistent methodology applied and frequently updated datasets used mean that results can be easily updated with new data, to effectively monitor the effect of urban change both in terms of land cover and population density, to improve local scale urban resilience to climate change.