Elsevier

Environmental Pollution

Volume 183, December 2013, Pages 133-142
Environmental Pollution

Identifying potential sources of variability between vegetation carbon storage estimates for urban areas

https://doi.org/10.1016/j.envpol.2013.06.005Get rights and content

Highlights

  • Broad-scale spatial data fail to capture the fine-grained urban landcover mosaic.

  • This can have a large impact when extrapolating carbon densities up to a city-wide value.

  • We review how methodologies to estimate vegetation carbon vary in the literature.

  • We need a more uniform method for estimating carbon stores to aid inter-city comparisons.

  • This will allow the drivers underpinning urban carbon stock variability to be identified.

Abstract

Although urbanisation is a major cause of land-use change worldwide, towns and cities remain relatively understudied ecosystems. Research into urban ecosystem service provision is still an emerging field, yet evidence is accumulating rapidly to suggest that the biological carbon stores in cities are more substantial than previously assumed. However, as more vegetation carbon densities are derived, substantial variability between these estimates is becoming apparent. Here, we review procedural differences evident in the literature, which may be drivers of variation in carbon storage assessments. Additionally, we quantify the impact that some of these different approaches may have when extrapolating carbon figures derived from surveys up to a city-wide scale. To understand how/why carbon stocks vary within and between cities, researchers need to use more uniform methods to estimate stores and relate this quantitatively to standardised ‘urbanisation’ metrics, in order to facilitate comparisons.

Introduction

Globally, the urban human population has expanded rapidly in recent decades, with over half of people now living in towns and cities (United Nations, 2012). In turn, this has been accompanied by high rates of land conversion to urban areas (Seto et al., 2012). With urbanisation set to continue, the need to understand and quantify ecosystem service provision within cities is increasingly acknowledged as being highly apposite to the lives of inhabitants, and essential in helping to tackle the environmental and social challenges they experience (Gaston, 2010a).

One particular ecosystem service that has become a high-profile feature of climate change mitigation efforts is carbon storage within soils and vegetation (e.g., Schimel, 1995, Grimm et al., 2008). Indeed, to fulfil international reporting obligations (e.g., UN Convention on Climate Change and Kyoto protocol) and national reduction targets, many countries must produce inventories of greenhouse gas emissions by sources and removal by sinks, including accounting for biological carbon losses and sequestration arising from different land-uses and their conversion (Dyson et al., 2009). As the bulk of carbon emissions can be attributed to urban areas (International Energy Agency, 2008, Satterthwaite, 2008, Kennedy et al., 2010), the policies and actions of the local authorities that administer towns and cities are central to meeting the required cuts. However, in order to achieve measureable reductions in the long-term, reliable baseline assessments of carbon stocks need to be available. Only then can it be established whether interventions such as tree planting strategies and land development policies (e.g., Churkina et al., 2010, Escobedo et al., 2011, Pataki et al., 2011, Raciti et al., 2012a) can be advocated as effective tools that go some way to offsetting the emissions of urban inhabitants.

Although considerably smaller than carbon emissions per unit area, there a is growing consensus that urban biological carbon stocks warrant further investigation, as they are more substantial than previously assumed (e.g., Nowak and Crane, 2002, Pataki et al., 2006, Davies et al., 2011, Hutyra et al., 2011, Raciti et al., 2012b). However, as this relatively new field of research begins to expand and more urban carbon density measurements are derived, variability between estimates is becoming apparent. Whilst this is not unexpected, because carbon densities will be influenced by a range of intrinsic and extrinsic spatio-temporal factors (e.g., interactions with the prevailing climate, regional patterns and histories of urbanisation, human population densities, land management), it is currently difficult to compare values across studies meaningfully, due to the assortment of methodological approaches used. A recent study has illustrated the problem by highlighting the discrepancies that may arise as a result of inconsistent definitions of ‘urban’ land-use (Raciti et al., 2012b). In this paper, we review additional procedural differences, evident in the literature, which are potential sources of variability in vegetation carbon density assessments. Furthermore, we quantify the impact that some of these different approaches may have when extrapolating carbon estimates derived from surveys up to a city-wide scale.

Section snippets

Review of the urban vegetation carbon storage literature

To identify potential sources of variability in published urban vegetation carbon density estimates, we review the procedural differences evident in the methods sections of peer-reviewed literature (e.g., resolution of the underlying spatial data, definitions of urban areas, use of correction factors to be applied to urban tree biomass estimates). The following search terms and Boolean operators were used in the ISI Web of Science database to identify studies suitable for inclusion: urban* AND

Results and discussion

Across the city of Leicester, the principal landcover classes within the high-resolution LandBase vector dataset were Herbaceous Vegetation, Artificial Surface, Buildings and Trees, with city-wide areal extents of 37.5, 27.4, 15.2 and 10.6% respectively (Fig. 1). Landcover categories with smaller percentage contributions included Shrubs (7.1%), Tall Shrubs (1.3%), Inland Water (0.6%) and Bare Ground (0.3%). If the landcover classes throughout the city were derived using increasingly coarse

Acknowledgements

This work was supported by EPSRC grant EP/F007604/1 to the 4M consortium: Measurement, Modelling, Mapping and Management: an Evidence Based Methodology for Understanding and Shrinking the Urban Carbon Footprint. The 4M consortium has five UK partners: Loughborough University, De Montfort University, Newcastle University, University of Sheffield and University of Exeter. M. Dallimer holds an EU-FP7 Marie-Curie Fellowship (Grant Number 273547). Infoterra kindly provided access to LandBase. We

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