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Gross primary productivity of a large metropolitan region in midsummer using high spatial resolution satellite imagery

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Abstract

Although gross primary productivity (GPP) is estimated with remote sensing over large regions of Earth, urban areas are usually excluded due to the lack of light use efficiency (LUE) parameters for urban vegetation and the spatial heterogeneity of urban land cover. Here, we estimated midsummer GPP, both within and among vegetation and land-use types, across the Minneapolis-Saint Paul, Minnesota metropolitan region. We derived LUE parameters for urban vegetation types using estimates of GPP from tree sap flow and eddy covariance CO2 flux observations, and from fraction of absorbed photosynthetically active radiation based on 2 m resolution WorldView-2 satellite imagery. Mean GPP per unit land area (including vegetation, impervious surfaces, and soil) was 2.64 g C m−2 d−1, and was 4.45 g C m−2 d−1 per unit vegetated area. Mapped GPP estimates were within 11.4% of estimates from independent tall tower eddy covariance measurements. Turf grass GPP had a larger coefficient of variation (0.18) than other vegetation classes (~0.10). Vegetation composition was largely consistent across the study area. Excluding golf courses, mean land-use GPP for the total study area varied more by percent vegetation cover (R2 = 0.98, p < 0.001) than by variability within vegetation classes (R2 = 0.21, p = 0.19). Urban GPP in general was less than half that of natural forests and grasslands in the same climate zone.

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Acknowledgements

The field measurements at KUOM were funded by a grant from the NASA Earth Science Division (NNG04GN80G) as a component of the North American Carbon Program (NACP), and the acquisition of the WorldView-2 imagery was funded by a grant from the NSF Dynamics of Coupled Natural and Human Systems Program (BCS-0908549).

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Correspondence to David L. Miller.

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Miller, D.L., Roberts, D.A., Clarke, K.C. et al. Gross primary productivity of a large metropolitan region in midsummer using high spatial resolution satellite imagery. Urban Ecosyst 21, 831–850 (2018). https://doi.org/10.1007/s11252-018-0769-3

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  • DOI: https://doi.org/10.1007/s11252-018-0769-3

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