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Simulating Future Land Use and Cover of a Mediterranean Mountainous Area: The Effect of Socioeconomic Demands and Climatic Changes

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  • Diogenis A. Kiziridis

    (Department of Botany, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Anna Mastrogianni

    (Department of Botany, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Magdalini Pleniou

    (Forest Research Institute, Hellenic Agricultural Organization “DIMITRA”, 57006 Vassilika, Greece)

  • Spyros Tsiftsis

    (Department of Forest and Natural Environment Sciences, International Hellenic University, 1st km Drama-Mikrochori, 66132 Drama, Greece)

  • Fotios Xystrakis

    (Forest Research Institute, Hellenic Agricultural Organization “DIMITRA”, 57006 Vassilika, Greece)

  • Ioannis Tsiripidis

    (Department of Botany, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

Land use and cover (LUC) of southern European mountains is dramatically changing, mainly due to observed socioeconomic demands and climatic changes. It is therefore important to understand LUC changes to accurately predict future landscapes and their threats. Simulation models of LUC change are ideal for this task because they allow the in silico experimentation under different socioeconomic and climatic scenarios. In the present study, we employed the trans-CLUE-S model, to predict for 2055 the LUC of a typical southern European sub-mountainous area, which has experienced widespread abandonment until recently. Four demand scenarios were tested, and under each demand scenario, we compared three climatic scenarios, ranging from less to more warm and dry conditions. We found that farmland declined from 3.2% of the landscape in 2015 to 0.4% in 2055 under the business-as-usual demand scenario, whereas forest further increased from 62.6% to 79%. For any demand scenario, differences in LUC between maps predicted under different climatic scenarios constituted less than 10% of the landscape. In the less than 10% that differed, mainly farmland and forest shifted to higher elevation under a warmer and drier climate, whereas grassland and scrubland to lower. Such insights by modelling analyses like the present study’s can improve the planning and implementation of management and restoration policies which will attempt to conserve ecosystem services and mitigate the negative effects of socioeconomic and climatic changes in the mountainous regions of southern Europe.

Suggested Citation

  • Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2023. "Simulating Future Land Use and Cover of a Mediterranean Mountainous Area: The Effect of Socioeconomic Demands and Climatic Changes," Land, MDPI, vol. 12(1), pages 1-23, January.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:1:p:253-:d:1035579
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    References listed on IDEAS

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    1. J. Ronald Eastman & Jiena He, 2020. "A Regression-Based Procedure for Markov Transition Probability Estimation in Land Change Modeling," Land, MDPI, vol. 9(11), pages 1-12, October.
    2. Marc Hanewinkel & Dominik A. Cullmann & Mart-Jan Schelhaas & Gert-Jan Nabuurs & Niklaus E. Zimmermann, 2013. "Climate change may cause severe loss in the economic value of European forest land," Nature Climate Change, Nature, vol. 3(3), pages 203-207, March.
    3. Paula A. Harrison & Robert W. Dunford & Ian P. Holman & Mark D. A. Rounsevell, 2016. "Climate change impact modelling needs to include cross-sectoral interactions," Nature Climate Change, Nature, vol. 6(9), pages 885-890, September.
    4. Robert Pontius & Wideke Boersma & Jean-Christophe Castella & Keith Clarke & Ton Nijs & Charles Dietzel & Zengqiang Duan & Eric Fotsing & Noah Goldstein & Kasper Kok & Eric Koomen & Christopher Lippitt, 2008. "Comparing the input, output, and validation maps for several models of land change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 11-37, March.
    5. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    6. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    7. Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Elpida Karadimou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2022. "Acceleration and Relocation of Abandonment in a Mediterranean Mountainous Landscape: Drivers, Consequences, and Management Implications," Land, MDPI, vol. 11(3), pages 1-23, March.
    8. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    9. García-Ruiz, J.M. & Lasanta, T. & Nadal-Romero, E. & Lana-Renault, N. & Álvarez-Farizo, B., 2020. "Rewilding and restoring cultural landscapes in Mediterranean mountains: Opportunities and challenges," Land Use Policy, Elsevier, vol. 99(C).
    10. Holman, I.P. & Brown, C & Janes, V & Sandars, D, 2017. "Can we be certain about future land use change in Europe? A multi-scenario, integrated-assessment analysis," Agricultural Systems, Elsevier, vol. 151(C), pages 126-135.
    11. Luis Garrote & Ana Iglesias & Alfredo Granados & Luis Mediero & Francisco Martin-Carrasco, 2015. "Quantitative Assessment of Climate Change Vulnerability of Irrigation Demands in Mediterranean Europe," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 325-338, January.
    12. Ana Iglesias & Luis Garrote & Sonia Quiroga & Marta Moneo, 2012. "A regional comparison of the effects of climate change on agricultural crops in Europe," Climatic Change, Springer, vol. 112(1), pages 29-46, May.
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    1. Kiziridis, Diogenis A. & Mastrogianni, Anna & Pleniou, Magdalini & Tsiftsis, Spyros & Xystrakis, Fotios & Tsiripidis, Ioannis, 2023. "Improving the predictive performance of CLUE-S by extending demand to land transitions: The trans-CLUE-S model," Ecological Modelling, Elsevier, vol. 478(C).
    2. Jinsen Mou & Zhaofang Chen & Junda Huang, 2023. "Predicting Urban Expansion to Assess the Change of Landscape Character Types and Its Driving Factors in the Mountain City," Land, MDPI, vol. 12(4), pages 1-20, April.

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