Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model

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Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model. / Boke-Olen, Niklas; Lehsten, Veiko; Ardo, Jonas; Beringer, Jason; Eklundh, Lars; Holst, Thomas; Veenendaal, Elmar; Tagesson, Håkan Torbern.

I: PLoS ONE, Bind 11, Nr. 4, e0154615, 29.04.2016.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Boke-Olen, N, Lehsten, V, Ardo, J, Beringer, J, Eklundh, L, Holst, T, Veenendaal, E & Tagesson, HT 2016, 'Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model', PLoS ONE, bind 11, nr. 4, e0154615. https://doi.org/10.1371/journal.pone.0154615

APA

Boke-Olen, N., Lehsten, V., Ardo, J., Beringer, J., Eklundh, L., Holst, T., Veenendaal, E., & Tagesson, H. T. (2016). Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model. PLoS ONE, 11(4), [e0154615]. https://doi.org/10.1371/journal.pone.0154615

Vancouver

Boke-Olen N, Lehsten V, Ardo J, Beringer J, Eklundh L, Holst T o.a. Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model. PLoS ONE. 2016 apr. 29;11(4). e0154615. https://doi.org/10.1371/journal.pone.0154615

Author

Boke-Olen, Niklas ; Lehsten, Veiko ; Ardo, Jonas ; Beringer, Jason ; Eklundh, Lars ; Holst, Thomas ; Veenendaal, Elmar ; Tagesson, Håkan Torbern. / Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model. I: PLoS ONE. 2016 ; Bind 11, Nr. 4.

Bibtex

@article{20ed309e4b1f4147985e378a947652b5,
title = "Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model",
abstract = "Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.Figures",
author = "Niklas Boke-Olen and Veiko Lehsten and Jonas Ardo and Jason Beringer and Lars Eklundh and Thomas Holst and Elmar Veenendaal and Tagesson, {H{\aa}kan Torbern}",
year = "2016",
month = apr,
day = "29",
doi = "10.1371/journal.pone.0154615",
language = "English",
volume = "11",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "4",

}

RIS

TY - JOUR

T1 - Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model

AU - Boke-Olen, Niklas

AU - Lehsten, Veiko

AU - Ardo, Jonas

AU - Beringer, Jason

AU - Eklundh, Lars

AU - Holst, Thomas

AU - Veenendaal, Elmar

AU - Tagesson, Håkan Torbern

PY - 2016/4/29

Y1 - 2016/4/29

N2 - Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.Figures

AB - Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.Figures

U2 - 10.1371/journal.pone.0154615

DO - 10.1371/journal.pone.0154615

M3 - Journal article

C2 - 27128678

VL - 11

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 4

M1 - e0154615

ER -

ID: 167886466