Optimal site planning: Fuzzy set membership, multi-criteria evaluation and multi-objective

land allocation

1. Multi-criteria evaluation for optimal site planning:

Decisions about the site planning of industries and urban settlements on certain land areas typically involve the application of multi-criteria algorithm based on logical PAIRWISE comparison. Using GIS-based decision-making processes, the work aimed at designing suitability map depicting the sensitivity of landscape to industrial development and urban settlements. It consisted on landscape evaluation using a decision-support tool developed for the IDRISI geographic information system software package.

The purpose of building-up a multi-criteria evaluation (MCE) typology is to enhance decision-making by

combining a set of criteria to achieve a single composite, which was the basis for the final decision according to the above specific objective.

For doing this, an attempt was made to create a suitability map by inserting interactive effects of several

contributing factors and constraints (delineated in a set of raster and vector maps) that may contribute in enhancing or decreasing the susceptibility of change for each pixel.

Contributing factors and constraints File name Description Factors: 1 2 3 4 5 6 7 8 9 10 11

Urban-dis-fuzzy Industry-dis-fuzzy Roads-dis-fuzzy Water-dis-fuzzy Forest-dis-fuzzy Barren-dis-fuzzy Slope-dis-fuzzy Wetlands-dis-fuzzy Agriculture-dis-fuzzy Rangelands-dis-fuzzy RUI-dis-fuzzy

Proximity to urban settlements Proximity to industrialized zones Proximity to main roads Proximity to water (sea, lakes and rivers) Proximity to forests, shrubs and national parks Proximity to barren lands Proximity to hills and slope (>10%) Proximity to wetlands Proximity to agriculture fields Proximity to rangelands Proximity to rural-urban interface strip

Constraints: 1 2

Water-out Build-up-out

Water (sea, lakes and rivers) Build-up areas (urban, industries, roads, etc)

The factors were raster images containing the target features from which distance was measured thanks to

“Analysis/Distance Operators/DISTANCE” procedure. The target features were vector files such us: roads, rivers, coast, etc. The constraints, on the other hand, were raster images that excluded certain areas from consideration (sea, lagoons, lakes, rivers, urban settlements and industrialized estates). Factors

Constraints

This process, in which several criteria were evaluated in order to meet the specific objective, was also

characterized by some level of assumed risk that strongly influenced the final suitability map. To minimise the risk and reduce the errors of the objectivity of the decision, it is better to have a group of decision-makers rather than a single decision maker. The group could be composed by decision-makers, experts with different backgrounds and local communities. 1.1. Weighted linear combination

Two of the most common procedures for multi-criteria evaluation are weighted linear combination and concordance-discordance analysis (Carver, 1991). Weighted linear combination was used in this case study. Suitability map was derived from:

Where, S: suitability Wi: weight of factor i Xi: criterion score of factor i Ci: constraints Σ: Somme ∏: product Throughout “Analysis/Decision Support/WEIGHT”, weights were developed by providing a series of

“PAIRWISE Comparisons” of the relative importance of factors to the suitability of pixels for the activity being evaluated. Indeed, it is the derivation of weights within the context of the decision objective that provides the major challenge. 1.2. PAIRWISE comparisons

Although a variety of techniques exist for the development of weights, one of the most promising would appear

to be that of PAIRWISE comparison developed by Saaty (1980) in the context of a decision making process known as the Analytical Hierarchy Process (Eastman et al., 1995). In the “PAIRWISE Comparisons” process, the most important of each possible pair effects was selected and subsequently comparison was established in qualitative terms to what extend one effect is more important than the other one to express the differences of importance.

To rate each PAIRWISE comparison and to fill in the matrix cells, column and row variables are rated according

to the following 9-point scale (Eastman et al., 1995): 9: relative to the column variable, the row variable is extremely more important 8 7 6: relative to the column variable, the row variable is strongly more important 5 4: relative to the column variable, the row variable is moderately more important

S = (ΣWiXi) . ∏ Ci

3 2 1: relative to the column variable, the row variable is equally important 1/2 1/3 1/4: relative to the column variable, the row variable is moderately less important 1/5 1/6: relative to the column variable, the row variable is strongly less important 1/7 1/8 1/9: relative to the column variable, the row variable is extremely less important

Weights are then derived from the principal eigenvector of the square reciprocal matrix of PAIRWISE comparison between all contributing factors.

Standardization of contributing factors using Fuzzy set membership functions

All contributing factors and constraints were standardized using Fuzzy set membership functions. Fuzzy set

membership is characterized by a grade (also called a possibility) that ranges from 0.0 to 1.0, indicating a continuous increase from non-membership to complete membership of a pixel in a specific category (Eastman, 2001).

Standardized factor

Calculate weights

2. Multi-objective land allocation

Multi-objective land allocation map showing suitable areas for (1) industrial development and urban expansion and (2) forestation and protecting agriculture lands Conclusions

Suitability maps resulting from multi-criteria evaluation (MCE) and multi-objective land allocation have shown different classes for which the degree of susceptibility to accept new industrial estates and urban settlements vary from extremely prone areas to weakly prone. Areas with high suitability are concentrated in the surroundings of main industrial zones such as “Echarguia”, “Ariana”, “Ettadhamen” and “Mannouba”, encompassing the built-up areas, spreading along the coastal areas, substituting agriculture lands and encompassing forests. This is due to a relative saturation of big metropolitans that usually have several numbers of industrial zones and huge urban settlements. In fact, an existing sub-urban area at 30 Km far away from Tunis-capital is at present a target to rural exodus and consequent abusive urban settlements.

The main reasons for which suitable areas are having more weight and in consequence prone to be more

suitable is because of its proximity to existing industrial zone in which the probability of finding existing infrastructure and organized industrial system is high, its proximity to residential sites, shopping centres and commercial areas that represent a first necessity for any social insert and economic activity, its proximity to main roads and its good connection to the market and access to transportation is thus an important consideration, and its geographic location which can represent an environmental constraint. Some areas were also allocated to highly prioritized areas for forestation and protection actions.

MCE made available a flexible way of dealing with qualitative multi-dimensional environmental effects,

factors and constraints. It has involved multiple criteria according to several, often conflicting-objectives. Decision making was also subject to risk from three perspectives including spatial data sources, data structures and evaluation methods.

References Abrahammson, K.V. (1997) Paradigms of sustainability; In S. Sörlin, ed. The road towards sustainability, A historical perspective, A sustainable Baltic Region, The Baltic University programme, Uppsala University, pp. 30-35. Altman, D. (1994) Fuzzy set theoretic approaches for handing imprecision in spatial analysis, International Journal of Geographical Information Systems 8(3); 271-289. Altobelli, A., Feoli, E., Iadarola F. and Milesi C. (1996) Industrial Ecology: An application of GIS and quantitative community methods for the analysis of industrial patterns. Coenoses 11:109-114. ANPE (1995) National Report on “State of the Environment in Tunisia”, Agence Nationale de Protection de l'Environnement (ANPE), 1995, pp. 66-67 Bailey, T.C. and Gatrell, A.C. (1995) Interactive spatial data analysis. Harlow, Essex, England: Longman Scientific and Technical. Ball, G.H. and Hall, D.J. (1965) ISODATA: a novel method of data analysis and pattern classification; Technical report, Stanford Research Institute, Menlo Park, 1965. Bounfour, A. and Lambin, E. (1999) How valuable is remotely sensed information? The case of tropical deforestation modelling, Space Policy, 15(3), 149-158 Burrough, P.A. (1991) Principles of GIS for Land Resources Assessment, Clarendon Press, Oxford, pp. 194. Campbell, N. and Wallace, J. (2001) Vegetation monitoring using LANDSAT TM data, Audit themes and projects, National Land and Water Resources Audit, Australia, 2001. Carver, S.J. (1991) Integrating Multi-Criteria Evaluation with Geographical Information Systems, International Journal of Geographical Information Systems, 5(3): 321-339. CCSP (2003) Strategic Plan for the U.S. Climate Change Science Program (CCSP): A Report by the Climate Change Science Program and the Subcommittee on Global Change Research, July 2003. Cicone, R.C. and Crist, E.P. (1984) “Application of the tasselled cap concept to simulated thematic Mapper data”, Photogrammetric Engineering and Remote Sensing, vol. 50, pp. 343-352 Colwell, R.N. (1997) “History and Place of Photographic Interpretation”, p. 3-47. In Manual of Photographic Interpretation, Second Edition, American Society for Photogrammetry and Remote Sensing Congalton R.G. and Mead R.A. (1983) A quantitative method to test consistency and correctness in photo interpretation- Photogrammetric Engineering and Remote Sensing, vol. 49, pp. 69-74 Covey, R.J. (1999) Remote Sensing in Precision Agriculture, Educational Primer, Iowa State University, March-1999 Cowen, J. (1993) A proposed method for calculating the LS factor for use with the USLE in a grid-based environment: Proceedings of the thirteenth annual ESRI user conference, pp. 65 - 74. Duncan, D. (1997) Analysis and comparison of two AVHRR derived NDVI through time series analysis, ASE389P term project, May 1997 Eastman, J.R. (2001) IDRISI for Windows, version IDRISI32 Release 2, Worcester MA: Clark school of Geography, Clark University. Eastman, J.R. (1997) A grid based geographic analysis system; IDRISI for windows, version 2.0, tutorial of exercises, Worcester MA: Graduate school of Geography, Clark University. Eastman, J.R., Jin, W., Kyem, P.A.K. and Toledano, J. (1995) Raster Procedures for Multi-Criteria/Multi-Objective Decisions; “Photogrammetric Engineering & Remote Sensing”, Vol. 61, No. 5, May 1995, pp. 539-547. EGIS (1994) L'importance des systèmes d'information géographique pour la gestion et la planification de l'espace urbain du grand Tunis, Fondation EGIS, Tunisie Eurimage (2001) ERS chapter from “Eurimage Products and Services”; Guide on first missions acquiring commercially available microwave radar data of the European Space Agency (ESA). Eurimage (1997) LANDSAT Data User's Handbook: Guide on commercially available data of the European Space Agency (ESA). FAO (2002) Terrestrial Carbon Observation: The Rio de Janeiro Recommendations for Terrestrial and Atmospheric Measurements, Environment and Natural Resources Series No. 2, United Nations Food and Agriculture Organization (FAO), Rome, Italy, 42 pp, 2002. FAO (1998) Land Cover and Land Use Classification as Tools for Change Detection; published by the Sustainable Development Department (SD), United Nations Food and Agriculture Organization (FAO), Rome, Italy, October 1998.

Fedra, K. and Feoli, E. (1998) GIS Technology and Spatial Analysis in Coastal Zone Management, EEZ Technology, pp. 171-179. Feoli, E. and Orlóci, L. (1991) Properties and interpretation of observations in vegetation study, COENOSES 6 (2): 61-70, Edizioni LINT Trieste, Italy, 1991. Feoli, E. and Zuccarello, V. (1994) Naiveté of Fuzzy system spaces in vegetation dynamics? COENOSES 9 (1): 25-32, C.E.T.A, Gorizia, Italy, 1991. Gomarasca, M.A., Brivio, P.A., Pagnoni, F., and Galli, A. (1993) One century of land-use changes in the metropolitan area of Milan, International Journal of Remote Sensing, 14(12), 2403-2409 Gong, P. (1994) Integrated analysis of spatial data from multiple sources, an overview, Canadian Journal of Remote Sensing, 20 (4): 349-359. Griffith, D. A. and Amrhein, C. G. (1991) “Statistical Analysis for Geographers”, Upper Saddle River, NJ: Prentice Hall. Halbwachs, G. (1983) Effects of air pollution on vegetation. In: Holzner, W., Werger M.J.A., and Ikusima I. (Eds), Man’s impact on vegetation, The Hague, 55-67. Hoffer, R.M. (1978) Biological and physical considerations in applying compute-aided analysis techniques to remote sensor data, in: Swain P. H. and Davis S.M. (Eds), Remote Sensing: The quantitative approach, McGraw-Hill Book Company, 227-289. Huete, A.R. (1988) “A Soil-Adjusted Vegetation Index (SAVI)”, Remote Sensing of Environment, vol. 25, pp. 295-309. IAEA (1996) Manual for the classification and prioritization of risks due to major accidents in process and related industries, International Atomic Energy Agency (IAEA), IAEA-TECDOC-727 (Rev. 1), November 1996, Vienna, Austria INAT (1997) Annual report: Meteorological station of the National Agronomic Institute of Tunis (INAT), Tunisia, 1997. INGREF (1997) Annual report: Indicators of industrial damage on the forest, Institut National de Recherche en Génie Rural, Eau et Forêt, 1997, Tunis, Tunisie. IPCC (2001) Intergovernmental Panel on Climate Change 2001: The Scientific Basis. A Contribution of Working Groups to the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, United Kingdom and New York, USA, 881 pp. Jensen, J.R. (1996) “Introductory digital image processing”: a remote sensing perspective. Upper Saddle River, NJ: prentice Hall, 1996. Jensen, J.R. and Troll, G. (1982) In: Shoshany, M., and Wechsler, E., (1995) Relationships between distributions of satellite driven indices and Urban floor area densities, Department of Geography, Bar-Ilan University, Ramat-Gan, 52900, Israel. Mas, J.F. and Ramirez, I. (1996) Comparison of land-use classifications obtained by visual interpretation and digital processing, ITC Journal n: 1996-3/4, pp. 278-283. MCIIE (1998) Annual report; Cabinet of the Ministry of International Cooperation and External Investment, Ministère de la Coopération Internationale et de l’investissement Extérieur (MCIIE), 1998. MICFI (1994) Investment climate in Tunisia, Ministry of International Cooperation and Foreign Investment (MICFI), May 1994 Morgan, R.P.C. (1995) Soil Erosion and Conservation; RUSLE guide tables, Addison-Wesley Longman, Edinburgh Mtimet, A. (1992) Démographie et espace agricole: agriculture en Tunisie, ministère de l’agriculture, Tunis, 1992. Mtimet, A. and Mizouri, M. (1995) Etude de la pression agricole sur les terres agricoles, ministère de l’agriculture, Tunis, 1995 NASA (1996) Remote Sensing and Photo Interpretation: Tutorial, Goddard Space Flight Center, NASA, USA, 1996. Netler, M. (1998) Introduction to GRASS-GIS; Institute of Physical Geography and Landscape Ecology, University of Hanover, Hanover, Germany, 1998 Peddle, D. R. (1993) An empirical comparison of Evidential reasoning, Linear Discriminant Analysis and Maximum Likelihood Algorithms for Alpine land-cover classification, Canadian Journal of Remote Sensing, Vol. 19, pp. 31-44. Plan Bleu (2003) Les menaces sur les sols dans les pays méditerranéens; étude bibliographique, Plan Bleu, Mai 2003. Podani, J. (1990) Supplementary programs to SYNTAX IV, Data analysis in ecology and systematic for the IBM-PC and Macintosh computers, User’s Manual.

Quaramby and Cushnie (1989) In: Shoshany, M., and Wechsler, E., (1995) Relationships between distributions of satellite driven indices and Urban floor area densities, Department of Geography, Bar-Ilan University, Ramat-Gan, Israel. Renard, K.G., McCool, D.K. and Yoder, D.C. (1997) Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE), Agric. Handb., No.703, US Department of Agriculture, Washington, DC. Rivas-Martínez, S. et al. (2002) Worldwide Bioclimatic Classification System, Review of Definitions: Phytosociologic, bioclimatic and biogeographic terms, notions and units, Phytosociological Research Center, Spain, 2002. Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. (1973) “Monitoring vegetation systems in the great plains with ERTS”, Third ERTS Symposium, NASA SP-351, vol. 1, pp.309-317. Saaty, T.L. (1980) The Analytical Hierarchy Process, McGraw Hill, New York Scapini et al. (2002) Baseline for the integrated sustainable management of Mediterranean sensitive coastal ecosystems; A manual for managers, scientists and all those studying coastal processes and management in the Mediterranean, Felicita Scapini (Editor) and all partners of the MECO project, IAO, 2002. Shoshany, M., and Wechsler, E. (1995) Relationships between distributions of satellite driven indices and Urban floor area densities, Department of Geography, Bar-Ilan University, Ramat-Gan, Israel. SMTM (1999) Station météorologhique de Tunis-Mannouba (SMTM), climatic data record reports, 1999 Steven, M.D. and Rondeaux, G. (1997) Comparison of vegetation indices to retrieve vegetation cover from remotely sensed data: a simulation study for the ATSR-2 channels, Geography Department, University of Nottingham, Nottingham NG7 2RD, UK. Terrill, W.R. (1994) Vegetation index, Div. of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA. Troeh, F.R., Hobbs, J.A. and Donahue, R.L. (1991) Predicting Soil Loss, Soil and Water Conservation, 2nd ed., Chapter 6, Prentice-Hall, Englewood Cliffs, New Jersey, USA. UNDP (2003) Arab Human Development Report 2002 and 2003, United Nations Development Programme (UNDP), United Nations Publications, 2003, New York, USA. UNDP (2002) Enhancing business community relations; National Research Report, United Nations Development Programme (UNDP), Beirut, Lebanon, March 2002 UNEP (1993) GIS-Based Soil Erosion Model; Use of GIS methods to assess the soil erosion risk, UNEP-GRID Arendal, 1993, Norway. UNESCO (1993) Environment and Development BRIEFS; United Nations Educational, Scientific and Cultural Organization, UNESCO, Paris, 1993. URAM (1997) “Etude du schéma Directeur d’aménagement du Grand Tunis”, Rapport Final de 1ere phase, GROUPEMENT URBACONSULT-URAM, December 1997, Tunis. Van der Meulen F. and Janssen M. (1992) Towards a monitoring programme for European coastal environments. In: Carter RWG et al., Coastal dunes, geomorphology, ecology and management for conservation, AA Balkema, Rotterdam. Wang, Y. and Moskovits, D.K. (2001) Tracking Fragmentation of Natural Communities and Changes in Land Cover: Applications of LANDSAT Data for Conservation in an Urban Landscape (Chicago Wilderness), Conservation Biology, Volume 15, Issue 4, Page 835, August 2001. Weber, C. and Hirsch, J. (1992) Some urban measurements from SPOT data: urban life quality indices, International Journal of Remote Sensing, 13, 3251-3261. Wischmeier, W.H. and Smith, D.D. (1978) Predicting rainfall erosion losses; a guide to conservation planning, Agric. Handb., No. 537, US Department of Agriculture, Washington, DC WTO (1995) What tourism managers need to know: a practical guide to the development and use of indicators of sustainable tourism, World Tourism Organisation (WTO), Madrid.