IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i16p4334-d256657.html
   My bibliography  Save this article

Spatio-Temporal Patterns of Vegetation in the Yarlung Zangbo River, China during 1998–2014

Author

Listed:
  • Xiaowan Liu

    (College of Water Sciences, Beijing Normal University, Beijing 100875, China)

  • Zongxue Xu

    (College of Water Sciences, Beijing Normal University, Beijing 100875, China)

  • Dingzhi Peng

    (College of Water Sciences, Beijing Normal University, Beijing 100875, China)

Abstract

Spatiotemporal vegetation patterns are of great importance for regional development. As one of the largest transnational rivers in China, the Yarlung Zangbo River in the Qinghai-Tibetan Plateau was selected as the study site, and the spatiotemporal patterns of vegetation during 1998–2014 were analyzed using the normalized difference vegetation index (NDVI). The results show that the NDVI increased with decreasing elevation, and the largest value was observed for the broadleaf forest. The lag time of NDVI to precipitation for most of the vegetation units was distinguished as approximately one month. In the region with an elevation of over 5000 m, the NDVI for the alpine vegetation was negatively correlated with the precipitation. Most NDVI variations were due to precipitation and temperature (approximately 75%). These results could provide a reference for ecological protection at a similar high elevation in the future.

Suggested Citation

  • Xiaowan Liu & Zongxue Xu & Dingzhi Peng, 2019. "Spatio-Temporal Patterns of Vegetation in the Yarlung Zangbo River, China during 1998–2014," Sustainability, MDPI, vol. 11(16), pages 1-11, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4334-:d:256657
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/16/4334/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/16/4334/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hubert, M. & Vandervieren, E., 2008. "An adjusted boxplot for skewed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5186-5201, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Biyun Guo & Taiping Xie & M.V. Subrahmanyam, 2019. "The Impact of China’s Grain for Green Program on Rural Economy and Precipitation: A Case Study of Yan River Basin in the Loess Plateau," Sustainability, MDPI, vol. 11(19), pages 1-18, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    2. Vincenzo Verardi, 2013. "Semiparametric regression in Stata," United Kingdom Stata Users' Group Meetings 2013 14, Stata Users Group.
    3. Finger, Robert, 2012. "Biases in Farm-Level Yield Risk Analysis due to Data Aggregation," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 61(01), pages 1-14, February.
    4. Shirin Enshaeifar & Ahmed Zoha & Andreas Markides & Severin Skillman & Sahr Thomas Acton & Tarek Elsaleh & Masoud Hassanpour & Alireza Ahrabian & Mark Kenny & Stuart Klein & Helen Rostill & Ramin Nilf, 2018. "Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    5. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    6. M. Hubert & P. Rousseeuw & K. Vakili, 2014. "Shape bias of robust covariance estimators: an empirical study," Statistical Papers, Springer, vol. 55(1), pages 15-28, February.
    7. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
    8. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    9. Kudraszow, Nadia L. & Vahnovan, Alejandra V. & Ferrario, Julieta & Fasano, M. Victoria, 2025. "Robust generalized canonical correlation analysis based on scatter matrices," Computational Statistics & Data Analysis, Elsevier, vol. 206(C).
    10. Arnab Hazra & Abhik Ghosh, 2024. "Robust Statistical Modeling of Monthly Rainfall: The Minimum Density Power Divergence Approach," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 241-279, May.
    11. Hervé Cardot & Antonio Musolesi, 2019. "Modeling temporal treatment effects with zero inflated semi-parametric regression models: the case of local development policies in France," SEEDS Working Papers 0219, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jan 2019.
    12. Karin Alexandra van Nes & L Andries van der Ark & Cor van Loveren & Irene Helena Adriana Aartman, 2023. "Construction of a questionnaire based on the Health Action Process Approach for psycho-social cognitive determinants of parents in brushing children’s teeth in the Netherlands," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-20, August.
    13. Silvia L. P. Ferrari & Giovana Fumes, 2017. "Box–Cox symmetric distributions and applications to nutritional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 321-344, July.
    14. Olivia D'Aoust & Olivier Sterck & Philip Verwimp, 2013. "Buying Peace: The Mirage of Demobilizing Rebels," Working Papers ECARES ECARES 2013-22, ULB -- Universite Libre de Bruxelles.
    15. Mia Hubert & Irène Gijbels & Dina Vanpaemel, 2013. "Reducing the mean squared error of quantile-based estimators by smoothing," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 448-465, September.
    16. Małgorzata Kobylińska, 2021. "Spatial Diversity of Organic Farming in Poland," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    17. Robert Finger, 2010. "Review of ‘Robustbase’ software for R," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(7), pages 1205-1210, November/.
    18. Graciela Boente & Matías Salibian-Barrera, 2015. "S -Estimators for Functional Principal Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1100-1111, September.
    19. Guillermo Vinue & Irene Epifanio, 2021. "Robust archetypoids for anomaly detection in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 437-462, June.
    20. Fábio M. Bayer & Francisco Cribari‐Neto & Jéssica Santos, 2021. "Inflated Kumaraswamy regressions with application to water supply and sanitation in Brazil," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(4), pages 453-481, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4334-:d:256657. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.