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    Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10259/11238

    Título
    Characterising spatiotemporal vegetation variations using LANDSAT time‐series and Hurst exponent index in the Mekong River Delta
    Autor
    Tran, Thuong V.
    Tran, Duy X.
    Nguyen, Ho
    Latorre Carmona, PedroAutoridad UBU Orcid
    Myint, Soe W.
    Publicado en
    Land Degradation & Development. 2021, V. 32, n. 13, p. 3469-3797
    Editorial
    Wiley
    Fecha de publicación
    2021-08
    ISSN
    1085-3278
    DOI
    10.1002/ldr.3934
    Résumé
    Spatiotemporal analysis and monitoring of vegetation help us investigate ecological health and guide better forest conservation and land management practices for sustainable development. This paper proposes the use of spatial analysis approaches (i.e., ordinary least squares [OLS] and the Hurst exponent) combined with time-series analysis using enhanced vegetation index (EVI) data, derived from LANDSAT via the Google Earth Engine, to estimate the trends and sustainability of vegetation dynamics in the Tra Vinh Province in the Mekong River Delta. We also assessed the EVI changes connected to land change issues to examine the influence of land use conversion on vegetation dynamics. Results show that a large portion of the study area was covered by abundant vegetation (over 50% of the total area), and the increased EVI area was about 5.5-times greater than the area of EVI reduction. Additionally, vegetation sustainability was being seriously compromised (e.g., a decrease in the total area of 8,275 ha) due to several land conversion drivers such as shrimp farming, urbanisation, and industrialisation. Furthermore, results obtained from this research provide insight into the spatiotemporal dynamics of vegetation coverage and reveal the consistency of future vegetation trends. Moreover, the study also quantitatively assessed the positive impacts of Buddhist doctrines on reducing the negative trend of vegetation change in the study area. These findings can lay the ground to formulate sustainable land and environmental plans that meet the 11th, 13th and 15th Sustainable Development Goals (SDGs) (i.e., the sustainable cities and communities, the climate actions, and the life on land). Besides, the analytical procedure adopted in this study can also be applicable to any other coastal areas that require the accurate assessment of vegetation status over time.
    Palabras clave
    Coastal area
    EVI
    Linear regression model
    Spatial analysis
    Tra Vinh
    Materia
    Biotecnología
    Biotechnology
    Tecnología de la información
    Information technology
    URI
    https://hdl.handle.net/10259/11238
    Versión del editor
    https://doi.org/10.1002/ldr.3934
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