IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i1p253-d1035579.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/1/253/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/1/253/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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. 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.

    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. 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. 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.
    3. 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.
    4. Raman Pall & Yvan Gauthier & Sofia Auer & Walid Mowaswes, 2023. "Predicting drug shortages using pharmacy data and machine learning," Health Care Management Science, Springer, vol. 26(3), pages 395-411, September.
    5. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    6. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    7. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    8. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    9. Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
    10. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    11. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    12. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    13. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    14. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    15. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    16. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    17. Hunish Bansal & Basavraj Chinagundi & Prashant Singh Rana & Neeraj Kumar, 2022. "An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    18. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    19. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    20. Sylwester Bejger, 2024. "Machine Learning in Cartel Screening—The Case of Parallel Pricing in a Fuel Wholesale Market," Energies, MDPI, vol. 17(16), pages 1-17, August.

    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:12:y:2023:i:1:p:253-:d:1035579. 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.