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Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam

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  • Castella, Jean-Christophe
  • Verburg, Peter H.

Abstract

The tools and methods developed by different scientific communities to simulate the dynamics of land use have emphasised either processes or patterns of changes. Agent-based models (ABM) belong to the former category while many spatially explicit simulation models belong to the latter. These two different modelling approaches were jointly implemented at a study site in Vietnam to assess their respective strengths and weaknesses with respect to their capacity to support the formulation of land-use policy and to influence decision-making by multiple groups of stakeholders. SAMBA is a people-centred approach combining an ABM, a role-playing game and a geographic information system. Participatory simulations help elicit the rules of the ABM and calibrate the model, while the model supports the participatory exploration of land-use change scenarios over longer time periods. CLUE-s is a spatial simulation model which explores changes in land-use patterns within user-specified rules of permissible change and rates of change. Driving factors that influence changes from one land-use type to another are defined by combining spatially explicit data on land use and supposed driving factors in a logistical regression analysis. Alternatively, the decision rules that were revealed during the participatory simulations – with the role plays and the multi-agent modelling of the SAMBA approach – were incorporated in the CLUE-s model to provide more realistic estimates for the varying influence of land-use drivers. We checked the respective validity of the two models by applying them at the same site and comparing their outputs. As a result, no single approach was obviously superior according to the validation statistics. The three approaches turned out to be complementary in simulating land-use patterns, while providing different types of information. Integration of the two models into a rule-based version of CLUE-s helped reconciling data-driven statistical models and process-driven agent-based models in LUCC research. This new model reinforced the overall framework by facilitating the partnership between researchers from different scientific communities and between researchers and multiple groups of stakeholders. It may also better respond to the expectations of land users at different levels of the decision-making hierarchy.

Suggested Citation

  • Castella, Jean-Christophe & Verburg, Peter H., 2007. "Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam," Ecological Modelling, Elsevier, vol. 202(3), pages 410-420.
    • Handle: RePEc:eee:ecomod:v:202:y:2007:i:3:p:410-420
    DOI: 10.1016/j.ecolmodel.2006.11.011
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    2. Fondevilla, Cristian & Àngels Colomer, M. & Fillat, Federico & Tappeiner, Ulrike, 2016. "Using a new PDP modelling approach for land-use and land-cover change predictions: A case study in the Stubai Valley (Central Alps)," Ecological Modelling, Elsevier, vol. 322(C), pages 101-114.
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    4. Smajgl, Alex & House, Alan P.N. & Butler, James R.A., 2011. "Implications of ecological data constraints for integrated policy and livelihoods modelling: An example from East Kalimantan, Indonesia," Ecological Modelling, Elsevier, vol. 222(3), pages 888-896.
    5. Hone-Jay Chu & Chen-Fa Wu & Yu-Pin Lin, 2013. "Incorporating Spatial Autocorrelation with Neural Networks in Empirical Land-Use Change Models," Environment and Planning B, , vol. 40(3), pages 384-404, June.
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    9. de Freitas, Marcos Wellausen Dias & Muñoz, Pablo & dos Santos, João Roberto & Alves, Diógenes Salas, 2018. "Land use and cover change modelling and scenarios in the Upper Uruguay Basin (Brazil)," Ecological Modelling, Elsevier, vol. 384(C), pages 128-144.
    10. Holger Cammerer & Annegret Thieken & Peter Verburg, 2013. "Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria)," 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. 68(3), pages 1243-1270, September.
    11. 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.
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    13. Menglin Ou & Jian Gong, 2021. "Farmland Transfers in China: From Theoretic Framework to Practice," IJERPH, MDPI, vol. 19(1), pages 1-14, December.
    14. Melvin Lippe & Thomas Hilger & Sureeporn Sudchalee & Naruthep Wechpibal & Attachai Jintrawet & Georg Cadisch, 2017. "Simulating Stakeholder-Based Land-Use Change Scenarios and Their Implication on Above-Ground Carbon and Environmental Management in Northern Thailand," Land, MDPI, vol. 6(4), pages 1-18, December.
    15. Noelia Guaita García & Julia Martínez Fernández & Carl Fitz, 2020. "Environmental Scenario Analysis on Natural and Social-Ecological Systems: A Review of Methods, Approaches and Applications," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    16. Lippe, Melvin & Rummel, Lisa & Günter, Sven, 2022. "Simulating land use and land cover change under contrasting levels of policy enforcement and its spatially-explicit impact on tropical forest landscapes in Ecuador," Land Use Policy, Elsevier, vol. 119(C).
    17. Deng Ding & David Bennett & Silvia Secchi, 2015. "Investigating Impacts of Alternative Crop Market Scenarios on Land Use Change with an Agent-Based Model," Land, MDPI, vol. 4(4), pages 1-28, November.
    18. Yang, Xin & Zheng, Xin-Qi & Lv, Li-Na, 2012. "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, Elsevier, vol. 233(C), pages 11-19.
    19. Xindong He & Xianmin Mai & Guoqiang Shen, 2019. "Delineation of Urban Growth Boundaries with SD and CLUE-s Models under Multi-Scenarios in Chengdu Metropolitan Area," Sustainability, MDPI, vol. 11(21), pages 1-13, October.
    20. Shoyama, Kikuko & Yamagata, Yoshiki, 2014. "Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem services in a watershed in Japan," Ecosystem Services, Elsevier, vol. 8(C), pages 25-34.
    21. Farid El Wahidi & Julien Radoux & Quentin Ponette & Pierre Defourny, 2015. "Entity-Based Landscape Modelling to Assess the Impacts of Different Incentives Mechanisms on Argan Forest Dynamics," Land, MDPI, vol. 4(4), pages 1-27, November.
    22. Shivangi S. Somvanshi & Oshin Bhalla & Phool Kunwar & Madhulika Singh & Prafull Singh, 2020. "Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 1073-1091, February.
    23. Aquilué, Núria & De Cáceres, Miquel & Fortin, Marie-Josée & Fall, Andrew & Brotons, Lluís, 2017. "A spatial allocation procedure to model land-use/land-cover changes: Accounting for occurrence and spread processes," Ecological Modelling, Elsevier, vol. 344(C), pages 73-86.

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