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Spatio-temporal statistical analysis of the carbon budget of the terrestrial ecosystem

Author

Listed:
  • Patrick Vetter

    (European University Viadrina)

  • Wolfgang Schmid

    (European University Viadrina)

  • Reimund Schwarze

    (European University Viadrina)

Abstract

The Net Ecosystem Exchange describes the net carbon dioxide flux between an ecosystem and the atmosphere and is a key quantity in climate change studies and in political negotiations. This paper provides a spatio-temporal statistical framework, which is able to infer the Net Ecosystem Exchange from remotely-sensed carbon dioxide ground concentrations together with data on the Normalized Difference Vegetation Index, the Gross Primary Production and the land cover classification. The model is based on spatial and temporal latent random effects, that act as space–time varying coefficients, which allows for a flexible modeling of the spatio-temporal auto- and cross-correlation structure. The intra- and inter-annual variations of the Net Ecosystem Exchange are evaluated and dynamic maps are provided on a nearly global grid and in intervals of 16 days.

Suggested Citation

  • Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2016. "Spatio-temporal statistical analysis of the carbon budget of the terrestrial ecosystem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 143-161, March.
    • Handle: RePEc:spr:stmapp:v:25:y:2016:i:1:d:10.1007_s10260-015-0342-7
    DOI: 10.1007/s10260-015-0342-7
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    References listed on IDEAS

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    1. Wenceslao González‐Manteiga & Rosa M. Crujeiras & Matthias Katzfuss & Noel Cressie, 2012. "Bayesian hierarchical spatio‐temporal smoothing for very large datasets," Environmetrics, John Wiley & Sons, Ltd., vol. 23(1), pages 94-107, February.
    2. Kanti Mardia & Colin Goodall & Edwin Redfern & Francisco Alonso, 1998. "The Kriged Kalman filter," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 7(2), pages 217-282, December.
    3. Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2013. "Efficient Approximation of the Spatial Covariance Function for Large Datasets - Analysis of Atmospheric CO2 Concentrations," Discussion Paper Series RECAP15 009, RECAP15, European University Viadrina, Frankfurt (Oder).
    4. Finazzi, Francesco & Fassò, Alessandro, 2014. "D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i06).
    5. Matthias Katzfuss & Noel Cressie, 2011. "Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets," Journal of Time Series Analysis, Wiley Blackwell, vol. 32, pages 430-446, July.
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    Cited by:

    1. Patrick Vetter & Wolfgang Schmid & Reimund Schwarze, 2016. "Spatio-temporal statistical assessment of anthropogenic CO2 emissions from satellite data," Discussion Paper Series RECAP15 24, RECAP15, European University Viadrina, Frankfurt (Oder).
    2. M. Bevilacqua & A. Fassò & C. Gaetan & E. Porcu & D. Velandia, 2016. "Covariance tapering for multivariate Gaussian random fields estimation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 21-37, March.
    3. Alessandro Fassò & Abdel El-Shaarawi & Maria Ranalli, 2016. "Advanced methods for space–time environmental data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 1-4, March.

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