IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i7p1072-d1436651.html
   My bibliography  Save this article

Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022

Author

Listed:
  • Jose Antonio Sobrino

    (Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain)

  • Sergio Gimeno

    (Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain)

  • Virginia Crisafulli

    (Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain)

  • Álvaro Sobrino-Gómez

    (Global Change Unit, Image Processing Laboratory (IPL), University of Valencia, E-46980 Paterna, Spain)

Abstract

Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: ‘Urban Areas’, ‘Dense Vegetation’, ‘Sparse Vegetation’, ‘Water Bodies’, and Other’. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications.

Suggested Citation

  • Jose Antonio Sobrino & Sergio Gimeno & Virginia Crisafulli & Álvaro Sobrino-Gómez, 2024. "Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022," Land, MDPI, vol. 13(7), pages 1-25, July.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:7:p:1072-:d:1436651
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/7/1072/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/13/7/1072/
    Download Restriction: no
    ---><---

    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:jlands:v:13:y:2024:i:7:p:1072-:d:1436651. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.