Mapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach

Maps of regulating urban ecosystem services (UES) aid identification of priority areas for green–blue infrastructure investment to improve urban resilience to environmental hazards. Current mapping approaches however may present coarse spatial resolutions, and often fail to consider how UES flows se...

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Autores principales:	Fraser Baker, Graham R. Smith, Stuart J. Marsden, Gina Cavan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Regulating ecosystem services Mapping Deprivation Urban Environmental risk Uncertainty analysis Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/78fba1da6d71411aa52e913a2863e6e2
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spelling	oai:doaj.org-article:78fba1da6d71411aa52e913a2863e6e22021-12-01T04:33:44ZMapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach1470-160X10.1016/j.ecolind.2020.107058https://doaj.org/article/78fba1da6d71411aa52e913a2863e6e22021-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20309973https://doaj.org/toc/1470-160XMaps of regulating urban ecosystem services (UES) aid identification of priority areas for green–blue infrastructure investment to improve urban resilience to environmental hazards. Current mapping approaches however may present coarse spatial resolutions, and often fail to consider how UES flows serve resident demand at the appropriate micro-scale. In addition, prohibitive costs involved in collecting primary data to validate UES model parameters to local conditions may enforce the use of proxy methods, thereby inferring ambiguity in parameterisation and uncertainty in mapping outputs. This study examines both issues through the implementation of a high-spatial resolution approach to map multiple urban regulating ecosystem service (temperature regulation, stormwater absorption, and carbon storage) deprivation in Manchester, UK. Poorly performing UES areas are defined as the lowest 10% combined ecosystem service indicator values (‘coldspots’) at 100m grid resolution. Coldspots are compared to population demand levels, disaggregated from weighted population estimates, indicating neighbourhoods deprived of UES. Ambiguity in proxy method implementation is examined using combinations of UES parameter settings (n = 16) within various demand measures (n = 3) to measure changes in relationships between UES, and variation in final map outputs across the study area. Uncertainty is therefore quantified as an interactive process, whereby input parameter ambiguity affects local uncertainty in map outputs, due to varying landcover composition. As explicit sensitivity analysis in current UES mapping studies is limited, the study demonstrates how ambiguity in method parameterisation may impact UES mapping exercises. Complex interactions governing spatial variance in map uncertainty may therefore be addressed through identification of consistent areas of interest (e.g. hotspots, coldspots) by contrasting outputs realised from different parameterisations. As such, the study demonstrates the mapping approach as a transferable city-wide visualisation tool, using accessible data and methods, to investigate regulating UES deprivation at practical scales required to retrofit existing urban infrastructure with green-blue infrastructure investment.Fraser BakerGraham R. SmithStuart J. MarsdenGina CavanElsevierarticleRegulating ecosystem servicesMappingDeprivationUrbanEnvironmental riskUncertainty analysisEcologyQH540-549.5ENEcological Indicators, Vol 121, Iss , Pp 107058- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Regulating ecosystem services Mapping Deprivation Urban Environmental risk Uncertainty analysis Ecology QH540-549.5
spellingShingle	Regulating ecosystem services Mapping Deprivation Urban Environmental risk Uncertainty analysis Ecology QH540-549.5 Fraser Baker Graham R. Smith Stuart J. Marsden Gina Cavan Mapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach
description	Maps of regulating urban ecosystem services (UES) aid identification of priority areas for green–blue infrastructure investment to improve urban resilience to environmental hazards. Current mapping approaches however may present coarse spatial resolutions, and often fail to consider how UES flows serve resident demand at the appropriate micro-scale. In addition, prohibitive costs involved in collecting primary data to validate UES model parameters to local conditions may enforce the use of proxy methods, thereby inferring ambiguity in parameterisation and uncertainty in mapping outputs. This study examines both issues through the implementation of a high-spatial resolution approach to map multiple urban regulating ecosystem service (temperature regulation, stormwater absorption, and carbon storage) deprivation in Manchester, UK. Poorly performing UES areas are defined as the lowest 10% combined ecosystem service indicator values (‘coldspots’) at 100m grid resolution. Coldspots are compared to population demand levels, disaggregated from weighted population estimates, indicating neighbourhoods deprived of UES. Ambiguity in proxy method implementation is examined using combinations of UES parameter settings (n = 16) within various demand measures (n = 3) to measure changes in relationships between UES, and variation in final map outputs across the study area. Uncertainty is therefore quantified as an interactive process, whereby input parameter ambiguity affects local uncertainty in map outputs, due to varying landcover composition. As explicit sensitivity analysis in current UES mapping studies is limited, the study demonstrates how ambiguity in method parameterisation may impact UES mapping exercises. Complex interactions governing spatial variance in map uncertainty may therefore be addressed through identification of consistent areas of interest (e.g. hotspots, coldspots) by contrasting outputs realised from different parameterisations. As such, the study demonstrates the mapping approach as a transferable city-wide visualisation tool, using accessible data and methods, to investigate regulating UES deprivation at practical scales required to retrofit existing urban infrastructure with green-blue infrastructure investment.
format	article
author	Fraser Baker Graham R. Smith Stuart J. Marsden Gina Cavan
author_facet	Fraser Baker Graham R. Smith Stuart J. Marsden Gina Cavan
author_sort	Fraser Baker
title	Mapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach
title_short	Mapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach
title_full	Mapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach
title_fullStr	Mapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach
title_full_unstemmed	Mapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach
title_sort	mapping regulating ecosystem service deprivation in urban areas: a transferable high-spatial resolution uncertainty aware approach
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/78fba1da6d71411aa52e913a2863e6e2
work_keys_str_mv	AT fraserbaker mappingregulatingecosystemservicedeprivationinurbanareasatransferablehighspatialresolutionuncertaintyawareapproach AT grahamrsmith mappingregulatingecosystemservicedeprivationinurbanareasatransferablehighspatialresolutionuncertaintyawareapproach AT stuartjmarsden mappingregulatingecosystemservicedeprivationinurbanareasatransferablehighspatialresolutionuncertaintyawareapproach AT ginacavan mappingregulatingecosystemservicedeprivationinurbanareasatransferablehighspatialresolutionuncertaintyawareapproach
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Mapping regulating ecosystem service deprivation in urban areas: A transferable high-spatial resolution uncertainty aware approach

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