Advanced search
1 file | 2.68 MB

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

(2017) GEOSCIENTIFIC MODEL DEVELOPMENT. 10(5). p.1945-1960
Author
Organization
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

Downloads

  • KERMIT-A1-428.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 2.68 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Papagiannopoulou, Christina, Diego Gonzalez 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.
APA
Papagiannopoulou, C., Gonzalez 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.
Vancouver
1.
Papagiannopoulou C, Gonzalez 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.
MLA
Papagiannopoulou, Christina et al. “A Non-linear Granger-causality Framework to Investigate Climate-vegetation Dynamics.” GEOSCIENTIFIC MODEL DEVELOPMENT 10.5 (2017): 1945–1960. Print.
@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 Gonzalez 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},
  language     = {eng},
  number       = {5},
  pages        = {1945--1960},
  title        = {A non-linear Granger-causality framework to investigate climate-vegetation dynamics},
  url          = {http://dx.doi.org/10.5194/gmd-10-1945-2017},
  volume       = {10},
  year         = {2017},
}

Altmetric
View in Altmetric
Web of Science
Times cited: