INTEGRATION OF THE LAND-USE CHANGE MODEL INTO THE LAND-USE PLANNING PROCESS

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Land-use and land-cover change models

The objective of this section is to explore the existing modeling approaches that deal with Land-Use and land Cover Change (LUCC). Among them, the most popular ones are based on the use of spatial analyses using Geographical Information System (GIS) data, Markov Chain, Cellular Automata or Multi-Agent systems.
LUCC models have a long history in the spatial modeling domain (Dawn C. Parker, Berger, & Manson, 2002). We propose to classify these models in two, not exclusive, categories: descriptive models on one hand and explicative models on the other.

Descriptive and explicative models

The primary concern of Descriptive models is not to represent realistic mechanisms but to faithfully reproduce global-level dynamics of land-use changes. These models usually rely on a discretization of the space into identified spatial units that are often named “parcels” or “patches”. The evolution of these patches over time is driven by the aggregated influence of several global-level factors. The evolution rules can be written using various formalisms,  e.g. equations in mathematical models (Serneels & Lambin, 2001), transition rules in Cellular Automata models (Zhao & Peng, 2012; Subedi, Subedi, & Thapa, 2013), transition functions or matrices in Markov Chain (Kemeny & Snell, 1983), and so on. Individual decisions are usually not taken into account in these models.
Conversely, the second category of models, the explicative ones, are focusing on representing realistic dynamics of land-use change based on a more detailed and faithful representation of the possible factors. Rather than producing very accurate results, these kinds of models allow the modeler to find out the causes behind land-use changes. Therefore, these are more explicitly targeting decision-support system in which, for example, “What-if” experiments (Trickett & Trafton, 2007) can be investigated.
In this second category, some of the recent models rely on the agent-based approach. An Agent-Based Model (ABM) is built by identifying in a reference system the entities, their activities and interactions with other entities, the environment and its global dynamics. The joint execution of agent activities and global dynamics generate the studied phenomenon (Drogoul et al., 2002). ABM tools can now be used to design large-scale, data-driven, individual-based models that can become valuable Decision-Support System (Bonabeau, 2002; Sánchez-Maroño et al., 2013) for LUCC and Land-Use Planning (Villamor, van Noordwijkb, Troitzschc, & Vleka, 2012). They can also make valuable simulations for larger scales of geographic data (D. C. Parker, Manson, Janssen, Hoffman, & P, 2003; Valbuena, Verburg, Bregt, & Ligtenberg, 2010; Mena, Walsh, Frizzelle, Xiaozheng, & Malanson, 2011; Bakker, Alam, van Dijk, & Rounsevell, 2015). Nevertheless, these models use simple human behavioral models whereas some recent research works have proposed architectures to represent the stakeholders’ behaviors in more sophisticated ways. For example, (Taillandier & Therond, 2011) have proposed an approach based on the belief theory and on a multi-criteria decision-making process in yearly cropping plan decision-making.

Bridging the gap: toward hybrid models

These two categories of LUCC models have remained for a long time somehow separated, firstly because they had different objectives and secondly because they relied on different modeling paradigms. However, their objectives are in fact quite convergent: explaining and predicting large-scale area changes in land-use and especially their variability over time. The fact that human activities are not taken into account casts doubt on the ability of the first category of models to produce realistic predictive models; conversely, the « environment » of the human agents cannot be considered solely as a product of their activity.
Especially in countries (like Vietnam) that are threatened by climate change, land-cover changes as well as other stressors (economy, innovations) need to be taken into account and the first category of models can become essential in that respect, in conjunction, of course, with models of the second category. These reasons have led to the emergence of a new type of models, known in the literature as « hybrid models » (Parrott, 2011), which basically combine different sub-models into one to produce richer insights, at the price, however, of an increased complexity: a complexity in the design of these combinations of models and a complexity in their exploration.
LUDAS (Le, Park, Vlek, & Cremers, 2008), built in NetLogo, or Aporia (Murray- Rust, Robinson, Guillem, Karali, & Rounsevell, 2014), built on top of the Repast Symphony platform (Michael J. North, Collier, & Vos, 2006), are two good examples of this trend, and underline both the potentialities of this new modeling approach, but also its drawbacks, which are summarized in the four following issues.
Lack of genericity. Until now, despite the similarity between the objects, processes or actors that can be found across different LUCC case studies, a model developed for one case study usually remains specific to it. In particular, no real effort has been made to generalize and share methodological outcomes (architectures, sub-models, patterns) because they rely on assumptions that cannot be easily translated to other contexts; Aporia (Murray-Rust et al., 2014), for instance, is dedicated to European farmers and their environment, while LUDAS (Le et al., 2008) remains restricted to highlands and mountainous areas in Vietnam.
Lack of flexibility. With the notable exception of Aporia (which partially supports the change of sub-models), most of the existing hybrid LUCC models are designed as a static composition of carefully chosen (or written) sub-models. This does not allow considering sub-models as possible parameters of experiments, something that can be necessary to explore different configurations or scenarios. In our case, given the variety of identified factors, explaining LUCC in the Mekong Delta with an integrated model requires that we explore several causes, some of them represented not only by parameters but by entire sub-models or specific combinations of them. The underlying software architecture thus needs to provide a high degree of modularity and flexibility, in order to easily add, remove or change submodels, and also to change their way of interacting, exchanging data and contributing to the overall outcome.
Lack of « necessary complexity ». Despite their goal, most of the hybrid LUCC models (Zhao & Peng, 2012; Subedi et al., 2013) tend to not treat the different dynamics equally: some are well represented whereas others remain superficial. When the environmental factors are represented with great details, the behavior of stakeholders remains simple (e.g. Lambin and Geist, 2007). Conversely, when their behavior is modeled using advanced mechanisms, like the BDI architecture (Taillandier & Therond, 2011), the environment lacks a proper representation. Of course, simple models have many advantages, e.g. being easier to understand and more tractable from a simulation point of view, but a “necessary complexity” is, sometimes, necessary to provide LUCC models their heuristic power in terms of decision-support (Edmonds & Moss, 2005).
Lack of representation of high-resolution spatial data. Most of the existing LUCC models lack genericity, flexibility and the necessary complexity. In addition, almost all these models are built on raster data with a low resolution. Each cell in a raster model represents a large area that contains many parcels with several land-use types inside. The uncertainty of these data could thus produce very uncertain prediction results at a higher resolution.
Transforming small-scale models into large-scale models requires taking into account human decisions to get accurate simulation results.
The limitations of existing models are quite clear for socio-environmental system modelers. Even in the socio-ecological modeling design, Ostrom (2009) and then McGinnis and Ostrom (2014) have presented a general framework with the purpose of analyzing the sustainability of socio-ecological systems (SES). Developing and integrating complex interactions into real complex SES are still challenging with the current SES framework.
Thus, integrating cognitive agents to represent social actors could be a very important step to improve these models (Singh, Padgham, & Logan, 2016). However, cognitive agents’ architectures are quite difficult to understand and to implement, even for computer scientists. In the two next sections, I provide some details about the decision-making process of farmers concerning land-use change, which highlights the needs to improve the cognitive agent architecture for farmers in my model and gives some clues to choose the most appropriate architecture among all the existing ones.

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Decision-making of farmers concerning land-use change

To analyze the impact of human decisions on land-use change, I first describe the main activities of farmers in the coastal regions of Vietnam (see Figure 6, Section 1.2, integrate human decision-making behaviors into LUCC models.
First of all, what I call a farmer represents a human being who performs all the necessary activities to raise living organisms or raw materials for food on a parcel. In his/her parcel, he/she can choose one among a few popular land-use types (in this particular area): rice, rice + other crops, fruit, vegetable, aquaculture, and rice + aquaculture. As analyzed in Chapter 1, people in the coastal area tend to shift from rice cultivation to aquaculture (or rice + aquaculture). The higher income of these new land-use types is the main motivation of this change. As far as rice production is concerned, it demands a low capital but it gives the lowest income whereas aquaculture activities give the highest income but demand a large capital investment. Indeed, the income of rice production in the Summer- Autumn season of 2006 is 246USD/ha (832 VND3/kg) (Thanh, 2010) while the income of rice-shrimp farming in 1997 is 317USD/ha with an estimated cost of 455USD/ha (Brennan, 2003).
Given a land-use plan, authorities try to make land-use changes fit their plan by indirectly influencing the environment through the building of irrigational infrastructure (dikes, sluice gates, fresh water canals, etc.). However, at the end, farmers remain the final decision-makers. Before changing their land-use, farmers have to take into account the constraints of the environmental conditions (such as soil, salinity…), economic conditions (price and cost of products, investment for a new type…), and cultivation techniques. Some factors such as the financial capital can prevent a farmer changing his production and make him wait many years to have enough money to be able to change.
Considering the environmental factors (including soil properties, water salinity and temperatures), some farmers follow their own knowledge to decide whether their parcels are suitable for a new land-use type. Some others follow their neighbors by watching their landuse and their changes or by asking information about their experience. Farmers can also exchange cultivation techniques. Environmental conditions are not the only constraints in the farmers’ decision-making; they also have to take into account economic conditions. Although aquaculture activities give a high income, they also demand a large capital investment. Most of the farmers do not have enough money for this investment. Farmers should thus take into account their capital (and the ways to increase it if needed) and also the cost and price of the production. The money for investments can come from a loan from a bank located in each district (in the form of mortgagee) with a limited budget of disbursements each year. Beside loans from official banks, there is also a black market for loans (which are often easier to get). Official loan interest rates are always lower than black market ones. However, black market offers more flexibility (with of course much more risk). This flexibility could lead the farmers to have many objectives at the same time.

Brief introduction to decision-making in socio-ecological systems

The previous section showed the importance to integrate human decision-making processes in LUCC models. For this purpose, in this section, I propose an overview of the decision-making approaches used in socio-ecological models. I start this overview with a brief introduction to the Markov chain and the Multi-criteria decision-making (MCDM) approaches that are the most popular ones for designing agents in land-use models. Then, I review the cognitive decision-making approaches that are commonly used to represent humans in agent-based modeling.

Decision-making approaches for reactive agents

Reactive decision-making processes have been modeled with a huge variety of approaches, even in ecological or environmental modeling. In this section, I introduce the Markov theory and MCDM for representing the decision-making process when they are integrated in existing LUCC models and LUP process.

The Markov chain approach

A Markov process (Kemeny and Snell, 1983) is a random process where the decision for the next state only depends on the current state and on a probability distribution. The decision is totally independent of the sequence of events that preceded it. As an example, in Figure 7, a system can be in two states A and E. If the system is in state A, the probability to stay in state A is 0.6, and the one to move to state E is 0.4. These probabilities are not dependent at all on the states in which the systems was before moving to A. Markov chains combined with Cellular Automata (Gutowitz, 1991) is an appropriate method for predicting and distributing spatial phenomena. A Cellular Automaton consists of a grid of cells, each cell having a value and a set of neighbor cells. The functionality of each cell is based on some fixed rules (a mathematical equation or a Markov chain). This method is
mostly applied at the macro level for land use change models with the definition of a global transition probability matrix between the different land-use types. The LUCC models cited in Section 2.2.1 are good examples of use of this method.

Table of contents :

RÉSUMÉ
ABSTRACT
ACKNOWLEDGEMENTS
TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLES
CHAPTER 1 INTRODUCTION
1.1 Agricultural Land-Use Planning in Vietnam
1.2 Anlyzis of the recent land-use plans issues in the Mekong Delta
1.3 Research questions
1.4 Objectives of the current research
1.5 Contribution of the thesis
1.6 Structure of the thesis
CHAPTER 2 STATE OF THE ART
2.1 Land-use and land-cover change models
2.1.1 Descriptive and explicative models
2.1.2 Bridging the gap: toward hybrid models
2.2 Decision-making of farmers concerning land-use change
2.3 Brief introduction to decision-making in socio-ecological systems
2.3.1 Decision-making approaches for reactive agents
2.3.2 Decision-making approaches for cognitive agents
2.4 Agent architectures embedding decision-making processes
2.4.1 Cognitive agent architectures
2.4.2 BDI architectures
2.5 BDI architectures and platforms to simulate farmer behaviors
2.5.1 Agent architectures for representing farmer behaviors
2.5.2 BDI architecture in existing ABM platforms
2.6 Conclusion
CHAPTER 3 THE BASIC MULTI-AGENT BASED MODEL OF LAND-USE CHANGE (MAB-LUC)
3.1 Basic integrated model for the land-use change
3.1.1 The conceptual model of the MAB-LUC
3.1.2 Modularity of the MAB-LUC
3.2 Definition of the MAB-LUC
3.2.1 Economic Sub-model
3.2.2 Environmental sub-model
3.2.3 Sub-model of farmers’ social influence
3.2.4 Farmer sub-model
3.2.5 Discussion about the farmer decision-making agent
3.3 Conclusion
CHAPTER 4 INTEGRATING A HUMAN DECISION-MAKING MODEL INTO AN AGENT BASED MODEL
4.1 Principles of the human decision-making architecture
4.2 Presentation of the GAMA BDI plug-in
4.2.1 Representation of knowledge of GAMA BDI agents
4.2.1.1 Declaration of a BDI agent
4.2.1.2 Predicates
4.2.2 Behavior of agents
4.3 Integrating the BDI architecture into the sub-model of Farmers
4.3.1 Conceptual model of the farmers based on the BDI architecture
4.3.2 Desires base of farmers
4.3.3 Intentions base of farmers
4.3.4 Set of plans defined for farmers
4.4 Conclusion
CHAPTER 5 VALIDATION OF THE COGNITIVE AGENT IN LAND-USE CHANGE MODELS
5.1 Description of experiments
5.1.1 Experiment data
5.1.2 Indicators for simulation assessment
5.2 Calibration of the sub-model of the MAB-LUC
5.2.1 Calibration of the model of farmers using Markov-based decision approach
5.2.2 Calibration of the model of farmers using MCDM approach
5.2.3 Calibration of the model of Farmers using the BDI-based decision approach
5.3 Evaluation the MAB-LUC
5.3.1 Experiment 1: The MAB-LUC using Markov-based decision approach
5.3.2 Experiment 2: The MAB-LUC using the MCDM approach
5.3.3 Experiment 3: The MAB-LUC model using the BDI – based decision approach .
5.4 Assessment
5.5 Conclusion
CHAPTER 6 INTEGRATION OF THE LAND-USE CHANGE MODEL INTO THE LAND-USE PLANNING PROCESS
6.1 Integration of the MAB-LUC into the land-use planning process
6.2 Appraisal of socio-economic factors for land-use plans
6.3 Appraisal of both socio-economic and environmental factors for land-use plans
6.4 Assessment of land-use plans under climate change
6.5 Conclusion
CHAPTER 7 CONCLUSION
7.1 Contributions
7.1.1 Contributions to agent-based modeling
7.1.2 Contributions for LUCC, LUP and assessment on impact of climate change .
7.2 Perspectives
7.2.1 Improving the integrated model regarding the usage of uncertain data
7.2.2 Extending the integrated model to similar works
A APPENDIX A: GLOSSARY
B APPENDIX B: PUBLICATIONS
REFERENCES

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