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

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. (c) 2006 Elsevier B.V. All rights reserved.