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Improving the predictive performance of CLUE-S by extending demand to land transitions: The trans-CLUE-S model

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  • Kiziridis, Diogenis A.
  • Mastrogianni, Anna
  • Pleniou, Magdalini
  • Tsiftsis, Spyros
  • Xystrakis, Fotios
  • Tsiripidis, Ioannis

Abstract

The CLUE-S model is a popular choice for modelling land use and land cover change from local to regional scales, but it spatially allocates the demand for only the total cover of each land class in the predicted map. In the present work, we introduce a CLUE-S variant that allocates demand at the more detailed level of land type transitions: the trans-CLUE-S model. We implemented this extension algorithmically in R, without the need of new parameters. By processing each row of the land transition matrix separately, the model allocates the demand of each land category's transitions via the CLUE-S allocation routine for only the cells which were of that category in the map of the previous time step. We found that the trans-CLUE-S model had half the total and configuration disagreement of the CLUE-S predictions in an empirical landscape, and in simulated landscapes of different characteristics. Moreover, the trans-CLUE-S performance was less sensitive to the number of environmental predictors of land type suitability for allocating demand. Although trans-CLUE-S is computationally more demanding due to running a CLUE-S allocation for each land class, we appended the solution of a land-use assignment optimisation problem that facilitates the convergence and acceleration of allocation. We additionally provide R functions for: CLUE-S variants at other levels of demand resolution; random instead of environment-based allocation; and for simulating landscapes of desired characteristics. Our R code for the models and functions can contribute to more reproducible, transparent and accurate modelling, analysis and interpretation of land cover change.

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  • 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).
    • Handle: RePEc:eee:ecomod:v:478:y:2023:i:c:s0304380023000352
    DOI: 10.1016/j.ecolmodel.2023.110307
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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. 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.
    3. 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.
    4. Gaucherel, C., 2008. "Neutral models for polygonal landscapes with linear networks," Ecological Modelling, Elsevier, vol. 219(1), pages 39-48.
    5. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
    6. 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.
    7. 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.
    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. 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.
    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.
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