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Estimation of leaf color variances of Cotinus coggygria based on geographic and environmental variables

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Abstract

Capturing leaf color variances over space is important for diagnosing plant nutrient and health status, estimating water availability as well as improving ornamental and tourism values of plants. In this study, leaf color variances of the Eurasian smoke tree, Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net (GELnet) and support vector machine (SVM) algorithms. Results reveal that leaf color varied over space, and the variances were the result of geography due to its effect on solar radiation, temperature, illumination and moisture of the shrub environment, whereas the influence of climate were not obvious. The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables, and demonstrates that both techniques have the potential to estimate leaf color variances of C. coggygria in a shrubbery with a complex geographical environment in the absence of human activity.

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Acknowledgements

We thank the Chongqing Meteorological Information and Technical Support Center for providing climate data.

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Authors and Affiliations

  1. College of Resources and Environment, Southwest University, Chongqing, 400716, People’s Republic of China

    Xing Tan, Jiaojiao Wu, Yun Liu, Shixia Huang, Lan Gao & Wen Zhang

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  1. Xing Tan
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  2. Jiaojiao Wu
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  3. Yun Liu
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  4. Shixia Huang
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  5. Lan Gao
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  6. Wen Zhang
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Correspondence to Yun Liu.

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Project funding: The work was supported by the Fundamental Research Funds for the Central Universities (Grant No. XDJK2019D041), the Research Innovation Programs for graduate student of Chongqing, China (Grant No. CYS19123), and the National Undergraduate Innovation and Entrepreneurship Training Programs (Grant No. 201810635015).

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Corresponding editor: Tao Xu.

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Tan, X., Wu, J., Liu, Y. et al. Estimation of leaf color variances of Cotinus coggygria based on geographic and environmental variables. J. For. Res. 32, 609–622 (2021). https://doi.org/10.1007/s11676-020-01118-6

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  • DOI: https://doi.org/10.1007/s11676-020-01118-6

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