Journal of Coastal Conservation

, Volume 17, Issue 4, pp 805–811 | Cite as

Dynamic modeling of urban areas for supporting integrated coastal zone management in the South Coast of São Miguel Island, Azores (Portugal)

  • Raquel Medeiros
  • Pedro Cabral


Coastal areas are complex systems subject to significant erosion processes resulting from both physical and anthropogenic factors. This context introduces the importance of quantifying the impacts resulting from land artificialization increase, the assessment of coastal erosion and the development of strategies for achieving an Integrated Coastal Zone Management (ICZM). In this study we employed the Land Transformation Model (LTM) to forecast urban growth to year 2014 in the South Coast of São Miguel Island, Azores (Portugal). Two different scenarios for modeling urban growth were tested: (1) one that considered an urban sprawl trend equivalent to the one measured between years 1998 and 2005, and (2) another that considered the restrictions included in the intervention area of South Coast Management Plan (POOC Costa Sul). The objective was to evaluate the impact of the POOC Costa Sul in the urban growth of the studied area. Results show that the POOC Costa Sul is not effective in containing urban growth quantities which are equivalent to the non-restricted scenario. However, it was possible to observe that it is effective in deciding where the urban expansion is likely to happen, preventing, for instances, the occurrence of urban growth near water lines or in the maritime public domain. We conclude that this type of models can be very relevant to manage and monitor coastal management plans.


Artificial neural network Coastal zone Geographic information system Integrated coastal zone management Land transformation model Urban growth 



Artificial Neural Network


American Standard Code for Information Interchange


Maritime Public Domain


Geographic Information System


Integrated Coastal Zone Management


Land Transformation Model


Land Use Cover Change


Percent Correct Match Metric


Coastal Zone Management Plan


Special Areas of Conservation


Sites of Community Importance


Universal Transverse Mercator


World Geodetic System


  1. Abul-Azm A, Abdel-Gelil I, Trumbic I (2003) Integrated Coastal Zone Management in Egypt: the Fuka-Matrouh project. J Coast Conserv 9(1):5–12. doi: 10.1652/1400-0350(2003)009[0005:ICZMIE]2.0.CO;2 CrossRefGoogle Scholar
  2. Agarwal C, Green G, Grove J, Evans T, Schweik C (2000) A review and assessment of land-use change models: dynamics of space, time, and human choice. United States Department of Agriculture. NE-297Google Scholar
  3. Alveirinho-Dias J (2005) Evolução da zona costeira portuguesa: Forçamentos antrópicos e naturais. Encontros Científicos - Tour Manag Stud Faro 1:7–27Google Scholar
  4. Antonidze E (2009) ICZM in the Black Sea region: experience and perspectives. J Coast Conserv. doi: 10.1007/s11852-009-0067-6 Google Scholar
  5. Batty M, Shie Y, Sun Z (1999) Modeling urban dynamics through GIS-based cellular automata. Comput Environ Urban 23:205–233. doi: 10.1016/S0198-9715(99)00015-0 CrossRefGoogle Scholar
  6. Batty M, Desyllas J, Duxbury E (2003) The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades. Int J Geo Inform Sci 17:673–697. doi: 10.1080/1365881031000135474 CrossRefGoogle Scholar
  7. Baker W (1989) A review of models of landscape change. Landscape Ecol 2(2):111–133. doi: 10.1007/BF00137155 CrossRefGoogle Scholar
  8. Benati S (1997) A cellular automaton for the simulation of competitive location. Environ Plann B 24:205–218CrossRefGoogle Scholar
  9. Cechini A (1996) Urban modelling by means of cellular automata: generalised urban automata with the help on-line (AUGH) model. Environ Plann B 23:721–732CrossRefGoogle Scholar
  10. Clarke K, Gaydos L (1998) Loose-coupling a cellular automaton model and GIS: longterm urban growth prediction for San Francisco and Washington/Baltimore. Int J Geo Inform Sci 12(7):699–714. doi: 10.1080/136588198241617 CrossRefGoogle Scholar
  11. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  12. DROTRH (2008) O Ordenamento do Território nos Açores, Política e Instrumentos. Ed. Secretaria Regional do Ambiente e do Mar, Direcção Regional do Ordenamento do Território e dos Recursos Hídricos, Ponta Delgada, Portugal, ISBN 978-989-95723-4-8Google Scholar
  13. EPA (2000) Projecting land-use change: a summary of models for assessing the effects of community growth and change on land-use patterns. Environmental Protection Agency. EPA/600/R-00/098Google Scholar
  14. Hagen A (2003) Fuzzy set approach to assessing similarity of categorical maps. Int J Geo Inform Sci 17:235–249. doi: 10.1080/13658810210157822 CrossRefGoogle Scholar
  15. Herold M, Goldstein N, Clarke K (2003) The spatio-temporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 85:95–105. doi: 10.1016/S0034-4257(03)00075-0 Google Scholar
  16. Li X, Yeh A (2000) Modelling sustainable urban development by the integration of constrained cellular automata and GIS. Int J Geo Inform Sci 14(2):131–152. doi: 10.1080/136588100240886 CrossRefGoogle Scholar
  17. Maithani S (2009) A neural network based urban growth model of an Indian city. J Indian Soc Remote Sens 37(3):363–376. doi: 10.1007/s12524-009-0041-7 CrossRefGoogle Scholar
  18. Markandya A, Arnold S, Cassinelli M, Taylor T (2008) Protecting coastal zones in the Mediterranean: an economic and regulatory analysis. J Coast Conserv 12(3):145–159. doi: 10.1007/s11852-008-0038-3 CrossRefGoogle Scholar
  19. Parker D, Berger T, Manson S (2001) Agent-based models of land-use and land-cover change. LUCC International Project Office. 6Google Scholar
  20. Partidário M, Vicente G, Lobos V (2009) Strategic environmental assessment of the national strategy for integrated coastal zone management in Portugal. J Coastal Res. SI 56 (Proceedings of the 10th International Coastal Symposium), 1271–1275. Lisbon, Portugal, ISSN 0749–0258;Google Scholar
  21. Pijanowski B, Shellito B, Bauer M, Sawaya K (2001) Using GIS, Artificial Neural Networks and Remote Sensing to model urban change in the Minneapolis-St. Paul and Detroit metropolitan areas. ASPRS Proceedings 2001, St. Louis, Mo, April, 21–26, 2001, 13 ppGoogle Scholar
  22. Pijanowski B, Brown G, Manik G, Shellito B (2002a) Using neural nets and GIS to forecast land use changes: a land transformation model. Comput Environ Urban 26(6):553–575. doi: 10.1016/S0198-9715(01)00015-1 CrossRefGoogle Scholar
  23. Pijanowski B, Shellito B, Pithadia S, Konstantinos A (2002b) Forecasting and assessing the impact of urban sprawl in coastal watersheds along eastern Lake Michigan. Lakes Reservoirs: Res Manage 7:271–285. doi: 10.1046/j.1440-1770.2002.00203.x CrossRefGoogle Scholar
  24. Pijanowski B, Pithadia S, Shellito B, Alexandridis K (2005) Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. Int J Geogr Inf Sci 19(2):197–215. doi: 10.1080/13658810410001713416 CrossRefGoogle Scholar
  25. Pijanowski B, Alexandridis K, Mueller D (2006) Modeling urbanization in two diverse regions of the world. J Land Use Sci 1(2):83–108. doi: 10.1080/17474230601058310 CrossRefGoogle Scholar
  26. Pontius R, Huffaker D, Denman K (2004) Useful techniques of validation for spatially explicit land-change models. Ecol Model 179:445–461. doi: 10.1016/j.ecolmodel.2004.05.010 CrossRefGoogle Scholar
  27. Pontius R, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19(2):243–265. doi: 10.1080/13658810410001713434 CrossRefGoogle Scholar
  28. Pontius R, Boersma W, Castella J, Clarke K, Nijs T, Dietzel C, Zengqiang D, Fotsing E, Goldstein N, Kok K, Koomen E, Lippitt C, McConnell W, Pijanowski B, Pithadia S, Sood A, Sweeney S, Trung T, Veldkamp A, Verburg P (2008) Comparing the input, output, and validation maps for several models of land change. Ann Reg Sci 42(1):11–37. doi: 10.1007/s00168-007-0138-2 CrossRefGoogle Scholar
  29. Ray D, Pijanowski B (2010) A backcast land use change model to generate past land use maps: application and validation at the Muskegon River watershed of Michigan, USA. J Land Use Sci 5(1):1–29. doi: 10.1080/17474230903150799 CrossRefGoogle Scholar
  30. Sanders L, Pumain D, Mathian H, Guérin-Pace F, Bura S (1997) A multiagent system for the study of urbanism. Environ Plann B 24:287–305CrossRefGoogle Scholar
  31. Sousa S, Caeiro S, Painho M (2002) Assessment of map similarity of categorical maps using kappa statistics: The case of Sado Estuary. ESIG2002 – VII Encontro de Utilizadores de Informação Geográfica, Tagus Park, November, 13–15, 2002Google Scholar
  32. Szlafsztein C, Sterr H (2007) A GIS-based vulnerability assessment of coastal natural hazards, state of Pará, Brazil. J Coast Conserv 11:53–66. doi: 10.1007/s11852-007-0003-6 CrossRefGoogle Scholar
  33. Szpiro G (1997) A search for hidden relationships: data mining with genetic algorithms. Comput Econ 10(3):267–277. doi: 10.1023/A:1008673309609 CrossRefGoogle Scholar
  34. SRA (2001) Plano Regional da Água: Relatório Técnico – Versão para Consulta Pública. Secretaria Regional do Ambiente. Direcção Regional do Ordenamento do Território e dos Recursos Hídricos. Instituto da ÁguaGoogle Scholar
  35. Tang Z, Engel A, Pijanowski B, Lim J (2005) Forecasting land use change and its environmental impact at a watershed scale. J Environ Manage 76:35–45. doi: 10.1016/j.jenvman.2005.01.006 CrossRefGoogle Scholar
  36. Veldkamp A, Fresco L (1996) CLUE: a conceptual model to study the conversion of land use and its effects. Ecol Model 85:253–270. doi: 10.1016/0304-3800(94)00151-0 CrossRefGoogle Scholar
  37. Veloso-Gomes F, Taveira-Pinto F, Neves L, Barbosa J (2006) Pilot site of River Douro: Cape Mondego and case studies of Estela, Aveiro, Caparica, Vale do Lobo and Azores. EUrosion, A European Initiative for Sustainable Coastal Erosion Management, EUROSION-PORTUGAL, 316p. + vol. anexos: 22pGoogle Scholar
  38. Veloso-Gomes F, Barroco A, Pereira A, Reis C, Calado H, Ferreira J, Freitas M, Biscoito M (2008) Basis for a national strategy for integrated coastal zone management - in Portugal. J Coast Conserv 12(1):3–9. doi: 10.1007/s11852-008-0017-8 CrossRefGoogle Scholar
  39. White R, Engelen G (1993) Cellular-automata and fractal urban form - a cellular modeling approach to the evolution of urban land-use patterns. Environ Plann A 25:1175–1199CrossRefGoogle Scholar
  40. White R, Engelen G (1997) Cellular automata as the basis of integrated dynamic regional modelling. Environ Plann B 24:235–246CrossRefGoogle Scholar
  41. Wetzel LB, Polette M (2002) ICZM and integration of coastal management and protected area policies in Brazil. Littoral 2002, The Changing Coast. EUROCOAST/EUCC, Porto, Portugal. Ed. EUROCOAST, Portugal, ISBN 972-8558-09-0Google Scholar
  42. Wu F (1998) An emprirical model of intra-metropolitan land-use changes in a Chinese city. Environ Plann B 25:245–263CrossRefGoogle Scholar
  43. Wu F, Webster C (2000) Simulating artificial cities in a GIS environment: urban growth under alternative regulation regimes. Int J Geogr Inf Sci 14:625–648. doi: 10.1080/136588100424945 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Direção Regional das Obras Públicas, Tecnologia e Comunicações, DROPTCSecretaria Regional do Turismo e TransportesPonta DelgadaPortugal
  2. 2.Instituto Superior de Estatística e Gestão de Informação, ISEGIUniversidade Nova de LisboaLisboaPortugal

Personalised recommendations