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Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada

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Abstract

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta’s smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen’s slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta’s sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982–2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

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Acknowledgments

The study was partly supported by the National Natural Science Foundation of China (Grant Nos. 51509201,41501231,51479160,41471451), Scientific Research Program Funded by Shaanxi Provincial Education Department (Grant No. 15JK1503), Research Foundation of State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area (Grant No. 2013ZZKT-5), and Dr. Start-up Foundation of Xi’an University of Technology (Grant No. 118-211413). CANGRD data sets were provided by Ewa Milewska at the Climate Research Branch of the Meteorological Service of Canada. The weekly smoothed NDVI data is available at the NOAA’s Satellite and Information Service (NRSDIS) (http://www.star.nesdis.noaa.gov/star/index.php). The authors thank Profs. Thian Yew Gan, Felix Kogan, and Dr. Xuezhi Tan for their assistance on this study. The valuable comments and suggestions of Prof. Scott C. Sheridan and two anonymous reviewers have greatly improved our manuscript.

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Authors and Affiliations

  1. State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, 710048, China

    Rengui Jiang, Jiancang Xie & Jiwei Zhu

  2. College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, China

    Hailong He

  3. Department of Civil & Environmental Engineering, University of Alberta, Edmonton, T6G 1H9, Canada

    Chun-Chao Kuo

  4. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China

    Mingxiang Yang

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  1. Rengui Jiang
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Correspondence to Rengui Jiang, Jiancang Xie or Hailong He.

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Jiang, R., Xie, J., He, H. et al. Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada. Int J Biometeorol 60, 1389–1403 (2016). https://doi.org/10.1007/s00484-015-1132-5

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  • DOI: https://doi.org/10.1007/s00484-015-1132-5

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