Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system. I. Structure and theoretical specification

Le, Quang Bao; Park, Soo‐Jin; Vlek, Paul L. G.; Cremers, Armin B.

doi:10.1016/j.ecoinf.2008.04.003

Cited by 146 publications

(92 citation statements)

References 26 publications

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“…Because of this complexity, the data requirements and the diversity of farming systems within agricultural regions, ABM has mainly been implemented in simulating local scale LUCC processes (e.g. Acosta-Michlik and Espaldon 2008; Le et al 2008). When modelling regional LUCC processes, models are normally parameterised with artificial data (e.g.…”

Section: Introductionmentioning

confidence: 99%

An agent-based approach to model land-use change at a regional scale

et al. 2009

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Land-use/cover change (LUCC) is a complex process that includes actors and factors at different social and spatial levels. A common approach to analyse and simulate LUCC as the result of individual decisions is agent-based modelling (ABM). However, ABM is often applied to simulate processes at local scales, while its application in regional studies is limited. This paper describes first a conceptual framework for ABM to analyse and explore regional LUCC processes. Second, the conceptual framework is represented by combining different concepts including agent typologies, farm trajectories and probabilistic decision-making processes. Finally, the framework is illustrated through a case study in the Netherlands, where processes of farm cessation, farm expansion and farm diversification are shaping the structure of the landscape. The framework is a generic, straightforward approach to analyse and explore regional LUCC with an explicit link to empirical approaches for parameterization of ABM.

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Section: Introductionmentioning

confidence: 99%

An agent-based approach to model land-use change at a regional scale

et al. 2009

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“…Household agents calculate the utility (expressed in terms of probability) for all land-use and location options within their landholdings (except for unavailable land, being tree plantations and shifting sandy pasture), and select the option with the highest utility. They may not, however, select the best land use or location, as often seen in reality, on the basis of an ordered-choice algorithm (Le et al 2008). The ordered-choice algorithm consists of the following steps: (1) rank the probabilities calculated for all land-use options at a given location, in descending order; (2) try the option ranked first with the success rate of the probability in itself; and (3) decide the first option or pick the second one and repeat the process, depending on the success of the first try.…”

Section: Fig 2 Schematic Overview Of Key Human-environment Dynamics mentioning

confidence: 99%

“…Main procedures and sub-models are detailed in the Appendices, but the rest are described in detail in Le (2005) and Le et al (2008) feedback loops-which can induce delayed, dislocated, and unintended system changes (Scholz 2011b)-is crucial to understand the spatio-temporal trade-offs of ecosystem services. We also employed the Land Use Dynamic Simulator (LUDAS) (Le 2005;Le et al 2008Le et al , 2010Le et al , 2012 as the initial modeling framework for further development and context-based specification. LUDAS's agent-based structure incorporates social-ecological heterogeneity and interactions, bridging organizational, spatial, and temporal couplings (Liu et al 2007b).…”

Section: Design Conceptsmentioning

confidence: 99%

“…The initialization of the household population consists of three steps (Import Sampled Household Data, Generate Remaining Population, and Define Landholding Coverage procedures), which are detailed in Le et al (2008Le et al ( , 2010. The initial SLCP plots are spatially decided according to the actual policy rule in the study area (see the descriptions of the study area and Define Initial SLCP Plot procedure in Appendix 1 for details).…”

Section: Detailsmentioning

confidence: 99%

“…Various social and ecological processes also occur heterogeneously on the basis of the diverse components in both types of system. Social and ecological heterogeneity, as well as hierarchy and feedback mechanisms, are therefore essential factors of complex systems (Liu et al 2007a, b;Le et al 2008Le et al , 2010Verburg and Overmars 2009;Villamor et al 2011;Le et al 2012). At the same time, the combination of heterogeneity, hierarchy, and feedback mechanisms complicates H-E systems and makes them difficult to understand.…”

Section: Introductionmentioning

confidence: 99%

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Agent-based modeling of complex social–ecological feedback loops to assess multi-dimensional trade-offs in dryland ecosystem services

Miyasaka

Le²,

Okuro

et al. 2017

Landscape Ecol

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Context Recent conceptual developments in ecosystem services research have revealed the need to elucidate the complex and unintended relationships between humans and the environment if we are to better understand and manage ecosystem services in practice. Objectives This study aimed to develop a model that spatially represents a complex human-environment (H-E) system consisting of heterogeneous socialecological components and feedback mechanisms at multiple scales, in order to assess multi-dimensional (spatial, temporal, and social) trade-offs in ecosystem services. Methods We constructed an agent-based model and empirically calibrated it for a semi-arid region in Northeast China, and examined ecosystem service trade-offs derived from the Sloping Land Conversion Program (SLCP), which is based on payment for ecosystem services. This paper describes our model, named Inner Mongolia Land Use Dynamic Simulator (IM-LUDAS), using the overview, design concepts, and details ? decision (ODD ? D) protocol and demonstrates the capabilities of IM-LUDAS through simulations. Results IM-LUDAS represented typical characteristics of complex H-E systems, such as secondary and cross-scale feedback loops, time lags, and threshold 123Landscape Ecol (2017) 32:707-727 DOI 10.1007 change, revealing the following results: tree plantations expanded by the SLCP facilitated vegetation and soil restoration and household change toward off-farm livelihoods, as expected by the government; conversely, the program caused further land degradation outside the implementation plots; moreover, the livelihood changes were not large enough to compensate for income deterioration by policy-induced reduction in cropland. Conclusions IM-LUDAS proved itself to be an advanced empirical model that can recreate essential features of complex H-E systems and assess multidimensional trade-offs in ecosystem services.

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Integrative geospatial approaches for the comprehensive monitoring and assessment of land management sustainability: Rationale, Potentials, and Characteristics

et al. 2010

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Sustainable dryland management seeks to improve the conditions of people and ecosystems affected by degradation, but it is often unclear which land management strategies work, which ones do not and why. Monitoring and assessment (M&A) can support decision-making by providing this information. As implied by the 10-year Strategy of the United Nations Convention to Combat Desertification (UNCCD), however, M&A efforts have thus far been insufficient or inadequate. We argue that integrative geospatial approaches should be implemented to enhance dryland management decision-making. By assimilating and linking human and environmental data, qualitative and quantitative data, as well as field and remotely sensed data in a spatially explicit framework, such approaches facilitate assessments of both the complexities and place-specificities inherent to sustainability. In addition, they help represent different stakeholder perspectives, promote communication among scientists from diverse backgrounds as well as between scientific and local experts, facilitate inter-institutional knowledge sharing, and create synergy between the UNCCD and other Conventions. Due to these benefits as well as the rapid evolution and increasing availability and affordability of geospatial data and technologies in all countries, it is appropriate to begin capitalizing more fully on them now for the M&A of land management sustainability. In order for integrative geospatial approaches to become more central to M&A efforts, however, capacities and infrastructure must be improved and standards and protocols developed for the collection, analysis, and modeling of data, for the evaluation of outputs, and for the reporting of results.

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Land-Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system. I. Structure and theoretical specification

Cited by 146 publications

References 26 publications

An agent-based approach to model land-use change at a regional scale

An agent-based approach to model land-use change at a regional scale

Agent-based modeling of complex social–ecological feedback loops to assess multi-dimensional trade-offs in dryland ecosystem services

Integrative geospatial approaches for the comprehensive monitoring and assessment of land management sustainability: Rationale, Potentials, and Characteristics

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