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A non-linear Granger-causality framework to investigate climate-vegetation dynamics

Christina Papagiannopoulou (UGent) , Diego Miralles (UGent) , Stijn Decubber (UGent) , Matthias Demuzere (UGent) , Niko Verhoest (UGent) , Wouter A Dorigo and Willem Waegeman (UGent)
(2017) GEOSCIENTIFIC MODEL DEVELOPMENT. 10(5). p.1945-1960
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Abstract
Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics.
Keywords
GLOBAL TERRESTRIAL ECOSYSTEMS, SOIL-MOISTURE, RANDOM FORESTS, SURFACE-TEMPERATURE, CARBON-DIOXIDE, SAMPLE TESTS, TIME-SERIES, NDVI DATA, PRECIPITATION, SATELLITE

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MLA
Papagiannopoulou, Christina, et al. “A Non-Linear Granger-Causality Framework to Investigate Climate-Vegetation Dynamics.” GEOSCIENTIFIC MODEL DEVELOPMENT, vol. 10, no. 5, 2017, pp. 1945–60, doi:10.5194/gmd-10-1945-2017.
APA
Papagiannopoulou, C., Miralles, D., Decubber, S., Demuzere, M., Verhoest, N., Dorigo, W. A., & Waegeman, W. (2017). A non-linear Granger-causality framework to investigate climate-vegetation dynamics. GEOSCIENTIFIC MODEL DEVELOPMENT, 10(5), 1945–1960. https://doi.org/10.5194/gmd-10-1945-2017
Chicago author-date
Papagiannopoulou, Christina, Diego Miralles, Stijn Decubber, Matthias Demuzere, Niko Verhoest, Wouter A Dorigo, and Willem Waegeman. 2017. “A Non-Linear Granger-Causality Framework to Investigate Climate-Vegetation Dynamics.” GEOSCIENTIFIC MODEL DEVELOPMENT 10 (5): 1945–60. https://doi.org/10.5194/gmd-10-1945-2017.
Chicago author-date (all authors)
Papagiannopoulou, Christina, Diego Miralles, Stijn Decubber, Matthias Demuzere, Niko Verhoest, Wouter A Dorigo, and Willem Waegeman. 2017. “A Non-Linear Granger-Causality Framework to Investigate Climate-Vegetation Dynamics.” GEOSCIENTIFIC MODEL DEVELOPMENT 10 (5): 1945–1960. doi:10.5194/gmd-10-1945-2017.
Vancouver
1.
Papagiannopoulou C, Miralles D, Decubber S, Demuzere M, Verhoest N, Dorigo WA, et al. A non-linear Granger-causality framework to investigate climate-vegetation dynamics. GEOSCIENTIFIC MODEL DEVELOPMENT. 2017;10(5):1945–60.
IEEE
[1]
C. Papagiannopoulou et al., “A non-linear Granger-causality framework to investigate climate-vegetation dynamics,” GEOSCIENTIFIC MODEL DEVELOPMENT, vol. 10, no. 5, pp. 1945–1960, 2017.
@article{8528009,
  abstract     = {{Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics.}},
  author       = {{Papagiannopoulou, Christina and Miralles, Diego and Decubber, Stijn and Demuzere, Matthias and Verhoest, Niko and Dorigo, Wouter A and Waegeman, Willem}},
  issn         = {{1991-9603}},
  journal      = {{GEOSCIENTIFIC MODEL DEVELOPMENT}},
  keywords     = {{GLOBAL TERRESTRIAL ECOSYSTEMS,SOIL-MOISTURE,RANDOM FORESTS,SURFACE-TEMPERATURE,CARBON-DIOXIDE,SAMPLE TESTS,TIME-SERIES,NDVI DATA,PRECIPITATION,SATELLITE}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{1945--1960}},
  title        = {{A non-linear Granger-causality framework to investigate climate-vegetation dynamics}},
  url          = {{http://doi.org/10.5194/gmd-10-1945-2017}},
  volume       = {{10}},
  year         = {{2017}},
}
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