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Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey

Author

Listed:
  • Isabel Nicholson Thomas

    (EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland)

  • Gregory Giuliani

    (EnviroSPACE Laboratory, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland
    GRID-Geneva, Institute for Environmental Sciences, University of Geneva, Bd. Carl-Vogt 66, 1205 Geneva, Switzerland)

Abstract

Timely and reliable Land Use and Cover change information is crucial to efficiently mitigate the negative impact of environmental changes. Switzerland has the ambitious objective of being a sustainable country while remaining an attractive business location with a high level of well-being. However, this aspiration is hampered by increasing pressures that are significantly impacting the environment and putting serious demands on land. In the present study, we used the national Land Cover (LC) dataset, named ArealStatistik , produced by the Federal Statistical Office, to explore the spatiotemporal patterns of Land Cover in Switzerland, providing a comprehensive assessment of land cover change at the national scale. Results indicate that, in general, Switzerland has undergone small, spatially dispersed, dynamic, and gradual change trends, with high rates of transition between low growing Brush Vegetation and forest LC classes in recent years. These pixel-level trends are more important in the lower altitude plateau and Jura regions, while greater changes in the spatial configuration of LC are observed in the alpine regions. However, findings also suggest that identifying drivers and understanding the rate of change are limited by the spatial resolution and temporal update frequency of the ArealStatistik . The ability to understand these drivers would benefit from a high-resolution annual LC dataset. Such a data product can be produced using the ArealStatistik together with dense satellite data time-series and Machine/Deep Learning techniques.

Suggested Citation

  • Isabel Nicholson Thomas & Gregory Giuliani, 2023. "Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey," Land, MDPI, vol. 12(7), pages 1-20, July.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1386-:d:1191578
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    References listed on IDEAS

    as
    1. Rutherford, Gillian N. & Bebi, Peter & Edwards, Peter J. & Zimmermann, Niklaus E., 2008. "Assessing land-use statistics to model land cover change in a mountainous landscape in the European Alps," Ecological Modelling, Elsevier, vol. 212(3), pages 460-471.
    2. Gregory Giuliani & Gilberto Camara & Brian Killough & Stuart Minchin, 2019. "Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes," Data, MDPI, vol. 4(4), pages 1-6, November.
    3. Martina Artmann & Olaf Bastian & Karsten Grunewald, 2017. "Using the Concepts of Green Infrastructure and Ecosystem Services to Specify Leitbilder for Compact and Green Cities—The Example of the Landscape Plan of Dresden (Germany)," Sustainability, MDPI, vol. 9(2), pages 1-26, February.
    4. Gregory Giuliani & Elvire Egger & Julie Italiano & Charlotte Poussin & Jean-Philippe Richard & Bruno Chatenoux, 2020. "Essential Variables for Environmental Monitoring: What Are the Possible Contributions of Earth Observation Data Cubes?," Data, MDPI, vol. 5(4), pages 1-25, October.
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