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Models and Methods for Rural Land Use Planning and their

Applicability in Galicia (Spain)

Santé, I & Crecente R Land Laboratory, Department of Agroforestry Engineering

University of Santiago de Compostela, isante@lugo.usc.es & rcrecente@lugo.usc.es Phone number: +34-982252303-23292/23260 Fax number: +34-982285926

Keywords: land use planning; land use model; rural land use; land evaluation; land use allocation.

1 Introduction In this paper, land use planning techniques and methodologies with different objectives, applications, and land uses have been identified. The existing literature in this field is dispersed throughout different subjects such as planning, GIS, decision-making, or computer systems. The compilation and analysis of different models have allowed us to draw a series of conclusions for the future development of a strategic rural land use plan in Galicia (Spain). The changes experienced in rural areas - depopulation, loss of agricultural activity - or the appearance of new activities or expectations placed upon the land require support tools for the decision-making process in terms of connecting activities and territories. This need is reflected in the Galician Strategy for Rural Development, which considers necessary the creation and application of Strategic Plans for Rural Space Use in Galicia as a priority action topic in rural development. Reviewing the existing land use planning models included the analysis and evaluation of their main characteristics. Based on these characteristics, the possibility of applying the different methods to Galicia has been established. The characteristics and requirements of each method were contrasted with the specific conditions in Galicia in relation to land use planning: i) a mixed economy, which derives a great variety of land uses and high competition among them, ii) the need to consider socioeconomic factors as determining elements of land suitability, iii) the shortage of information about crop yields, areas requested for each land use, etc., and iv) the lack of previous experience in this type of planning, and, consequently, of models or systems adapted to the region. The methods not applicable to the context of land use planning in Galicia have been excluded, and the conditions for the implementation of the remaining methods have been defined. Currently, the development of GIS is acquiring increasing importance in land use planning (Jacobs, 2000). The use of new technologies such as GIS opens up new possibilities for land use planning processes. Traditionally, GIS has been used for generating and storing the model´s input information and/or displaying the results obtained by it. However, the new systems are evolving towards a tighter integration between GIS and the planning model, and towards overcoming the more or less static character of GIS by means of dynamic simulation models. This development explains why special attention has been paid to land use planning models integrated into GIS. Two phases have been considered for defining a land use planning model: land evaluation, in which the suitability of the land for the uses considered is evaluated, and land use allocation, from which the optimum allocation of uses to land units is decided according to the results of the previous phase.

2 Land Evaluation Since 1950, land evaluation has evolved from methods focused on the edaphic component towards more

quantified assessments, with the increasing use of nonsoil factors (Van Diepen, 1991). However, models such as the USDA Land Capability Classification system (Klingebiel & Montgomery, 1961) or the USBR Land Classification for Irrigated Land Use, designed more than forty years ago, are still widely applied. In the 1930s, mathematical models began to be applied to determine soil production capacity (i.e., Storie, 1933); these models are known as parametric indices. In 1976 FAO published A Framework for Land Evaluation which allowed standardization of methodology and terminology. The central process of this framework is the comparison of the land qualities in each unit with the requirements of each land use type, which concludes assigning a suitability

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level (S1, S2, S3, N1 or N2) to each cartographic unit. Some authors (Burrough et al., 1992; Hall et al., 1992) propose a fuzzy logic method for continuous land suitability classification in FAO framework. This type of analysis reduces the loss of information and provides results that contribute to a greater discrimination between areas for land use planning. Another land suitability assessment model that has been incorporated into the FAO framework for obtaining continuous land suitability maps is that developed by Triantafilis et al. (2001). This model considers five degrees of limitation for the land characteristics that are relevant for each use. The first land evaluation methods, previous to the FAO framework, focused on the edaphic component of land evaluation. Nowadays, land evaluation methods must consider new uses and factors that require the evaluation of natural, economic, and social resources. These needs were contemplated during the development of the FAO framework and other more recent land evaluation systems such as the Land Evaluation and Site Assessment (LESA) (Coughlin et al., 1994). Other systems developed after the FAO framework are the Soil Potential Ratings (McCormack, 1986), the Fertility Capability Classification (FCC) (Sánchez et al., 2003), or the Agro-Ecological Zoning (AEZ) (FAO, 1997). Other techniques employed for land evaluation are crop simulation models (i.e., De Wit & Van Keulen, 1987; Jones et al., 2003; Stöckle et al., 2003), expert systems (Diamond & Wright, 1988; Yialouris et al., 1997), and artificial neuronal networks (Wang, 1994). These land evaluation methods were evaluated and compared on the base of four characteristics, which will determine wether they are applicable to a particular planning situation (table I):

1. Purpose and land uses considered: The reviewed methods can be divided into ‘suitability systems’ and ‘capability systems’ (McRae & Burnham, 1981). The difference between these systems is based on the fact that land capability includes all land uses, in this case, agricultural uses, while suitability refers to a specific use.

2. Information required: The crop simulation models stand out because they require detailed data. The Land Capability Classification, the parametric indices, and the fuzzy-based systems are also based on accurate soil information. In other methods, such as the USBR Land Suitability system or the Soil Potential Ratings, the data needed are basically economic. In the most flexible systems, such as the FAO framework or the AEZ, the amount, type, and accuracy of the information depend on each specific application. Only the FAO framework and LESA consider socioeconomic factors.

3. Procedure: In a quantitative evaluation, results are expressed as quantified estimates, while in a qualitative evaluation, qualifying adjectives are used for the variables considered (Dent & Young, 1981). Land evaluation is increasingly based on quantitative procedures.

4. Results: Land suitability classification into discrete groups, defined by sharp bounds, involves a considerable loss of information. The fuzzy approach, among others, overcomes this limitation by providing results in a continuous scale.

Table I: Characteristics of the main land evaluation systems Purpose Land Uses Information

Required Procedure Results

FAO framework Suitability Specific Uses Physical Socioeconomic

Quantitative/ Qualitative 5 suitability classes

Land Capability Classification Capability General

agricultural use Physical Qualitative 8 capability classes

USBR Land Suitability for Irrigation Capability Irrigation

projects Physical

Economic Qualitative 6 suitability classes Pre-FAO

Parametric Indices Capability General agricultural use Physical Quantitative Continuous capability

classification

LESA Capability General agricultural use

Physical Socioeconomic Quantitative Continuous capability

classification

Soil Potential Ratings Suitability Specific Uses Physical Economic Quantitative Continuous suitability

classification

FCC Capability General agricultural use Physical Qualitative Several capability classes

AEZ Suitability Specific crops Physical Quantitative/ Qualitative 5 suitability classes

Dynamic simulation models Suitability Specific crops Physical Quantitative Crop yield predictions

Fuzzy-based systems Suitability Specific uses Physical Quantitative Continuous suitability classification

Post-FAO

Expert systems Variable Variable Variable Qualitative Several suitability classes

3 Land Use Allocation In land use allocation the possible land use patterns are analyzed to select the best to achieve specific goals. The techniques most commonly applied to spatial land use allocation correspond to expert systems, multi-criteria evaluation methods, linear programming or spatial simulation models, frequently integrated into GIS.

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3.1 Expert Systems Expert Systems or Knowledge-Based Systems consist of a computer program that gathers knowledge from human experts, and tries to simulate his or her reasoning process to generate, in a quick and reliable way, a solution to a complex problem. Many expert systems have been developed for dealing with spatial problems such as resource management, regional planning, land suitability analysis, or land use allocation. Yialouris et al. (1997) described the development of a Geographical Information Expert which evaluates the suitability for several crops and selects the optimum one for each land unit. Zhu et al. (1996) designed ILUDSS to allow planners to design a specific model for each land use, and to evaluate the model automatically, obtaining a suitability map for each land use.

3.2 Multi-criteria Evaluation Voogd (1983) presented the application of several multi-criteria evaluation techniques to land planning, where the number of spatial units evaluated was limited. The integration of multi-criteria methods and GIS allows to overcome this limitation and provides a tool with great potential for obtaining land suitability maps or selecting sites for a particular activity (Mendoza, 1997; Eastman et al., 1995; Jun, 2000). While GIS provide an appropriate framework for the application of multi-criteria evaluation methods, which are not capable of managing spatial data, the multi-criteria evaluation procedures add to GIS the means of performing trade-offs on conflicting objectives, while taking into account multiple criteria and the knowledge of the decision maker (Carver, 1991). Multi-criteria evaluation techniques based on the ideal point analysis are the techniques that have been more frequently integrated in a GIS with this aim (i.e., Carver, 1991; Pereira & Duckstein, 1993; Jankowski & Richard, 1994; Malczewski, 1996; Vatalis & Manoliadis, 2002). Another multi-criteria evaluation method frequently integrated into GIS to perform land suitability analyses is the Analytic Hierarchy Process (Banai, 1993; Jun, 2000). The AHP can also be used to generate the weights assigned to the land suitability criteria (Weerakon, 2002) or to the suitability maps to calculate a ‘compound’ suitability score (Mendoza, 1997). The IDRISI software provides two multi-criteria evaluation tools for obtaining suitability maps; the weighted linear combination (WLC) and the ordered weighted average (OWA) (Eastman, 1995). Multi-criteria evaluation techniques have also been applied to generate multiple land use scenarios by selecting the optimum use for each land unit. Amongst them, hierarchical optimization (Carver, 1991) involves allocating the maximum area to the highest priority land use, excluding it from the remaining uses, and repeating the process until the total area is allocated. When the hierarchy of the objectives is not known, a compromise solution can be determined by using the ideal point method to assign to each spatial unit the land use for which its suitability is the highest, minimizing the suitability of the remaining uses (Barredo, 1996). Eastman et al. (1995) developed the Multi Objective Land Allocation method, based on the ideal point concept, and implemented in the MOLA module of IDRISI. Examples of the application of this technique to multiple land use allocation are provided in Van der Merwe (1997) and Eastman et al. (1998).

3.3 Mathematical Programming Mathematical programming provides the combination of land uses that optimizes one or more objective functions subject to a series of constraints. Chuvieco (1993) developed a model for the minimization of rural unemployment, formulated as the maximization of the areas with the most labour-intensive land uses. Another example is the combined application of GIS and linear programming to strategic planning of agricultural uses carried out by (Campbell et al., 1992). However, land use planning often requires multi-objective models. Giupponi & Rosato (1998) developed a model in which goal programming was used to reach a compromise between the maximization of the gross margin and the minimization of the expected risk for a land use plan. Another goal programming model is described in Oliveira et al. (2003) to plan several forest land uses. Interactive Multiple Goal Linear Programming (IMGLP) allows an exchange of information between the decision-maker and the system, and has been applied to land use planning (Ive & Cocks, 1983; Suhaedi et al., 2002), to the analysis of agricultural development policies (De Wit et al., 1988), and has even been implemented in a software application (GOAL-QUASI) intended to explore future land use options in the European Union (Ittersum, 1995). A similar technique, known as ARBDS, was developed by Fischer & Makowski (1996). Another iterative process has been developed by Lu et al. (2004) to evaluate different land use strategies. Aerts et al. (2003) and Diamond & Wright (1989) describe integer linear programming models for optimal spatial land use allocation. These models offer the possibility of translating their results onto a map of optimum land use allocation to the spatial units. Some interesting examples of land use planning models based on the combination of multi-criteria evaluation techniques and mathematical programming can be found in Janssen & Rietveld (1990), and Ridgley & Heil (1998).

3.4 Spatial Simulation Models The most recent technique for land use allocation are the spatial simulation models, based on simulated annealing, genetic algorithms, cellular automata, or agent based models.

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Genetic algorithms are one of the types of algorithms that have been applied to land use optimization. Matthews et al. (1999; 2000) proposed two genetic algorithms applied to land use planning that were incorporated into a Land Allocation Decision Support System (LADSS). In the first algorithm, the genes directly represented the use of a parcel of land; in the second one, the genotype encoded the objective percentage and the priority of each land use (Matthews, 2001). Aerts & Heuvelink (2002) used an optimization procedure based on simulated annealing to solve a land use planning model, in which an equivalence between the energy function and the development costs of the land use plan was established. Similarly, Alier et al. (1996) used a variation of simulated annealing to optimize land use allocation, in which the energy function for a land use was characterized by the carrying capacity, the environmental impact and the cost of changing land use. Other simulation models use the concept of cellular automata. Engelen et al. (1999) integrated a GIS and a cellular automata to evaluate different land use scenarios. Each cell's potential for transition to a state (use) is calculated based on the cell's suitability for that state, on the zoning regulation for that use in the cell's area, and on the neighborhood effect. The development of simulation models based on cellular automata is more common in urban planning (i.e., Wu, 1998; Wu & Webster, 1998; Li & Yeh, 2002; Barredo et al., 2004).

3.5 Analysis of Land Use Allocation Methods The most significant characteristics of the land use allocation methods were summarized in five points:

1. Aim and results: The first applications of multi-criteria evaluation to regional planning were not implemented in GIS, and attempted to rank the different alternatives (regions, planning policies, etc.). The integration of multi-criteria evaluation techniques into GIS allowed the conversion of these rankings into land use allocation maps. The techniques used for this purpose vary depending on the planning aim. While the purpose of TOPSIS, compromise programming, or of the techniques implemented by Carver (1991) was to obtain a suitability map for one single land use, the ideal point analysis (Barredo, 1996) and MOLA were designed to select the optimum land use for each spatial unit. This spatial allocation of multiple land uses can be achieved also through integer programming models (Aerts et al., 2003), in which the variables (spatial units) take a value equal to 1 or 0 depending on whether they are allocated to a particular land use or not. However, most mathematical programming models provide only the optimum area for each land use without information on spatial distribution of the results. In the case of the spatial simulation models studied, the result is always a land use map.

2. Information required: Multi-criteria evaluation techniques demand information about the criteria considered in the assessment of suitability for a specific activity. When these techniques are implemented in GIS, this information is required as evaluation criteria maps. The multi-criteria evaluation techniques aimed at selecting the optimum use for each land unit use the suitability maps for each land use as evaluation criteria. Mathematical programming models require very diverse alphanumeric information, depending on their specific formulation. Spatial simulation models may use evaluation factor maps as input information, or, directly, suitability maps for each land use. Spatial simulation models and some multi-criteria evaluation techniques need, in addition, the area destined to each land use as external data.

3. Integration with GIS: Among the techniques fully integrated into GIS are TOPSIS, analysis of concordance-discordance, hierarchical optimization, compromise programming, MOLA, or ideal point analysis. The application of complex techniques based on pair-wise comparisons in a raster GIS, where each cell represents one choice alternative, is limited by the computer processing time, so that performance depends to a great extent on the size of the evaluation matrix, and, therefore, on the expanse of the application area. In most mathematical programming models, integration with GIS usually takes place in two phases. The first phase corresponds to the acquisition of data from the GIS to feed the model, and the second phase corresponds to the use of GIS to map the results. In the case of integer programming models, these results correspond to a land use allocation map. However, the application of these models is limited by the size of the study area due to the resolution time needed. The spatial simulation models described in this work present a tight integration between GIS and the land use allocation model used.

4. Flexibility and performance: Mathematical programming models show a rigid logic, in which the objectives and constraints are formulated strictly quantitatively, assuming a linear relationship between the variables that may not be real. However, the main difficulty of these models is to design the algorithm, which must be specific to each particular situation. This specificity demands reviewing the model every time that the application conditions or the information sources vary. The main advantages of these models are the explicit and efficient procedure, and the possibility of performing sensitivity analysis. In multi-criteria evaluation techniques, there is also the possibility of performing sensitivity analysis of the weighting factors, or of the p index in the calculation of the distance to the ideal point (Pereira & Duckstein, 1993). These techniques have further advantages such as the analysis of multiple alternatives and the consideration of the opinion of different stakeholders (Xiang et al., 1992). In terms

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of the disadvantages, Voogd (1983) pointed out the complexity of the mathematical operations, the implicit assumptions of different methods and the impossibility of quantifying many criteria.

5. Group decision making: Among the reviewed models, the interactive programming techniques establish more opportunities for achieving a compromise between the different individuals or interest groups involved in the planning process. The model developed by Malczewski (1996) also considered multiple decision-makers throughout the entire decision making process.

4 Discussion When it comes to assessing and comparing different land evaluation systems, it is essential to consider the purpose for which they are going to be used, and the results that are expected from them. Within the context of land use planning, the choice of the land evaluation method depends, to a large extent, on the land uses considered in the planning process. When the objective is to design a model for all the land uses present in the rural environment, it is necessary to apply beforehand a land evaluation method in which the land uses to evaluate are very specific, and defined with great accuracy. This condition excludes the capability systems, whose aim is land evaluation for a general use, defined in a wide sense, that, in the case of the methods described in this work, corresponds to a traditional agricultural use. In addition, in the framework of land use planning, land evaluation should not be confined to assessing the physical characteristics, but should consist of the analysis of physical suitability, economic viability, social consequences, and environmental impact produced. To this end, the FAO framework provides a flexible system in which the different assessments can be integrated. However, the FAO methodology obtains a result that is barely quantified (land is classified into five categories) and cannot be used by most land use allocation techniques as input information to select the optimum use. This limitation is overcome in evaluations based on fuzzy methodology, which provide continuous land suitability maps, but consider only biophysical variables as evaluation factors. Moreover, the application of this theory requires accurate information about soil and crop properties. The same drawback is met in the case of dynamic simulation models. Due to the lack of this type of information for Galicia, and to the need to consider many socioeconomic factors, the FAO framework stands as the most adequate evaluation method. To enable this method to provide continuous land suitability maps, it is necessary to use a matching procedure based on fuzzy logic, or on a continuous suitability function. The results of the land evaluation and the objectives of the land use plan condition the choice of the land use allocation method. Multi-criteria evaluation and linear programming are the most widely used techniques. The selection of one technique or the other is based on the type of results sought. Mathematical programming generally provides the optimum area for each land use, but does not indicate the geographic location of the area within the evaluated unit, with the exception of the integer programming models. These latter models provide as a result an optimum land use allocation map, but the computation time required restricts its application to small areas. The multi-criteria evaluation techniques integrated in GIS allow the mapping of the optimum land uses, and require, in the case of MOLA or ideal point analysis, the introduction of the area desired for each use as input data. Therefore, in Galicia—where there are no studies about the area required for each land use—both techniques could be applied complementarily, using the results of one mathematical programming model as input variables of a multi-criteria evaluation The flexibility and design possibilities of spatial simulation models and expert systems allows their application to very diverse conditions and problems. However, complex development and programming is demanded to apply these models to a specific region, in this case, Galicia, where there are no previous experiences in these types of models. Specific computer programs have been developed to make land use planning processes easier. Within the context of land evaluation, the most frequently used software was the ALES software, which enables the design of models based on the FAO framework. Land use allocation can be based on very diverse methodologies, for which different computer programs have been developed. Some examples are: LADSS, based on two genetic algorithms, AEZWIN, which implements the ARBDS, or GOAL-QUASI, which uses IMGLP. These programs are usually intended to solve or facilitate a specific phase in the planning process. Although there are some Planning Support Systems which include a larger number of phases, they usually focus on urban land use. IDRISI is the only commercial GIS that has specific tools for land use planning. Consequently, the design of a rural land use planning model involves the integration of different computer tools or the development of customized software that includes all the steps of the land use planning process.

5 Conclusions The analysis of the methods has allowed us to draw several conclusions for the implementation of a rural land use plan in Galicia:

1. The land evaluation method must consider all the land uses present in the rural environment, this excludes the ‘capability systems’.

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2. The land evaluation system should comprise not only the analysis of physical suitability but also the socioeconomic viability and the environmental impact, that is why the FAO framework is one of the most adequate methods.

3. The land evaluation system must provide continuous land suitability maps, that are necessary for the subsequent land use allocation. So, the matching procedure used in the FAO methodology must be based on fuzzy logic, or on a continuous suitability function.

4. Multi-criteria evaluation techniques also provide continuous land suitability maps and allow the consideration of socioeconomic factors.

5. The multi-criteria evaluation integrated in GIS has been successfully applied in many situations for optimal land use allocation and it is easily implemented, the application of integer linear programming models is less common and computing time demanding.

6. The flexibility of spatial simulation models and expert systems allows them to be applied to diverse conditions and problems, but complex development is demanded for them to be adapted to a specific region where there are not any previous experiences in this type of models, like Galicia.

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Additional Information Acknowledgements: This work was carried out in the framework of the project “Design of a GIS methodology for rural land use planning”, funded by the Galician Regional Government under Contract PGIDIT02RAG29103PR.