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Natural Hazards

, Volume 90, Issue 2, pp 623–637 | Cite as

An INSPIRE-compliant open-source GIS for fire-fighting management

  • Nives Grasso
  • Andrea Maria Lingua
  • Maria Angela MusciEmail author
  • Francesca Noardo
  • Marco Piras
Original Paper

Abstract

Every year, there are almost 50,000 forest fires in Europe (127/day), which have burned an area equal to more than 450,000 ha. An effective management of forest fires is therefore fundamental in order to reduce the number of the fires and, especially, the related burned areas, preserving the environment and saving human lives. However, some problems still exist in the structure of information and in the harmonization of data and fire management procedures among different European countries. Pursuing the same interoperability aims, the European Union has invested in the development of the INSPIRE Directive (Infrastructure for Spatial Information in Europe) to support environmental policies. Furthermore, the EU (European Union) is currently working on developing ad hoc infrastructures for the safe management of forests and fires. Moving from this premises and following an analysis of the state of the art of information systems for forest fire-fighting, in the light of the end-user requirements, the paper presents the INSPIRE—compliant design of a geographical information system, implemented using open-source platforms.

Keywords

Forest fire-fighting Decision support system Emergency management INSPIRE data model GIS 

Notes

Acknowledgements

The study was realized on the themes treated in the European project AF3 (Advanced Forest Fire Fighting—www.af3project.eu). The authors would like to thank the CVVFF of Cagliari for their availability and data sharing. Furthermore, they thank Dr. Raffaella Marzano from University of Torino for her help about fuel model and forest type and Dr. Cesti for his availability.

References

  1. AF3—Advanced Forest Fire Fighting. http://af3project.eu/. Accessed 27 Sept 2017
  2. Ager AA, Vaillant NM, Finney MA (2011) Integrating fire behavior models and geospatial analysis for wildland fire risk assessment and fuel management planning. J Combust 2011:1–19CrossRefGoogle Scholar
  3. Andrews PL, Rothermel RC (1982) Charts for interpreting wildland fire behavior characteristics. USDA, Forest Service, Ogden, pp 1–21CrossRefGoogle Scholar
  4. Arco E, Boccardo P, Gandino F, Lingua AM, Noardo F, Rebaudengo M (2016) An integrated approach for pollution monitoring: smart acquirement and smart information. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3:67–74CrossRefGoogle Scholar
  5. Bonazountas M, Kallidromitou D, Astyakopoulos A (2012) ArcFIRE™/ArcFUEL™: forest fire management geoplatform and fuel maps. In: Brebbia CA, Perona G (eds) Modelling, monitoring and management of forest fires III. Witt Press, Buffalo, pp 67–78CrossRefGoogle Scholar
  6. Burgan RE, Klaver RW, Klaver JM (1998) Fuel models and fire potential from satellite and surface observations. Int J Wildland Fire 8:159–170CrossRefGoogle Scholar
  7. Cephas Consulting Corp (2006) The fast guide to model driven architecture. The basics of model driven architecture. http://www.omg.org/mda/mda_files/Cephas_MDA_Fast_Guide.pdf. Accessed 27 Sept 2017
  8. Cesti G (2002) Tipologie e comportamenti particolari del fuoco: risvolti nelle operazioni di estinzione. Il fuoco in foresta: ecologia e controllo. In: Atti del XXXIX Corso di Cultura in Ecologia. Centro Studi per l’Ambiente Alpino, S. Vito di Cadore, 2–6 settembre 2002. Università di Padova, Padova, pp 77–116Google Scholar
  9. Chuvieco E, Salas J (1996) Mapping the spatial distribution of forest fire danger using GIS. Int J Geogr Inf Syst 10:333–345CrossRefGoogle Scholar
  10. Chuvieco E et al (2010) Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecol Model 221(1):46–58CrossRefGoogle Scholar
  11. European Commission (2004) IDABC programme—Interoperable Delivery of European eGovernment Services to public Administrations, Business and Citizens. http://ec.europa.eu/idabc/en/document/5707/3.html. Accessed 27 Sept 2017
  12. European Commission—Joint Research Centre (2000) EFFIS—European forest fire information system. European Commission, Luxembourg. http://forest.jrc.ec.europa.eu/effis/. Accessed 27 Sept 2017
  13. European Commission—Joint Research Centre (2012) Forest fires in Europe, middle east and north Africa 2012. European Commission, LuxembourgGoogle Scholar
  14. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architecture elements and future directions. Future Gener Comput Syst 29:1645–1660CrossRefGoogle Scholar
  15. Guha-Sapir D, Hoyois P, Below R (2015) Annual disaster statistical review 2014: the numbers and trends. CRED, Brussels. http://www.cred.be/sites/default/files/ADSR_2014.pdf. Accessed 27 Sept 2017
  16. Han S et al (1992) The method for calculating forest fire behaviour index. Fire Saf Sci 1:77–82Google Scholar
  17. INSPIRE Directive (2007) Luxembourg: Official Journal of the European Union. http://inspire.ec.europa.eu/. Accessed 27 Sept 2017
  18. IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part B: regional aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, New YorkGoogle Scholar
  19. ISO/TC211—geographic information/geomatics. https://committee.iso.org/home/tc211. Accessed 27 Sept 2017
  20. Kutzner T, Donaubauer A (2012) Critical remarks on the use of conceptual schemas in geospatial data modelling—a schema translation perspective. In: Gensel J et al (ed) Bridging the geographic information sciences: international AGILE’2012 conference, Avignon (France), April 24–27, 2012. Springer, Heidelberg, pp 43–60Google Scholar
  21. Laurini R, Thompson D (1992) Fundamentals of spatial information systems, 1st edn. Academic, LondonGoogle Scholar
  22. Lisboa-Filho J et al (2013) Domain and model driven geographic database design. In: Reinhartz-Berger I, Sturm A, Clark T, Cohen S, Bettin J (eds) Domain engineering. Product lines, languages, and conceptual models. Springer, Heidelberg, pp 375–399. http://www.dpi.ufv.br/~jugurta/papers/DE%20book.pdf. Accessed 27 Sept 2017
  23. Losso A et al (2012) SIRIO: an integrated forest fire monitoring, detection and decision support system—performance and results of the installation in Sanremo (Italy). In: Brebbia CA, Perona G (eds) Modelling, monitoring and management of forest fires III. Witt Press, Buffalo, pp 79–90CrossRefGoogle Scholar
  24. Mitsopoulos ID et al (2014) Accuracy assessment of a mediterranean fuel-type map for wildland fire management at national scale: the cases of Greece and Portugal. In: Viegas DX (ed) Advances in forest fire research. Imprensa da Universidade de Coimbra, pp 1615–1622Google Scholar
  25. Moreno A et al (2012) Introducing GIS-based simulation tools to support rapid response in wildland fire fighting. In: Brebbia CA, Perona G (eds) Modelling, monitoring and management of forest fires III. Witt Press, Buffalo, pp 163–174CrossRefGoogle Scholar
  26. Neal D (1997) Reconsidering the phases of disaster. Int J Mass Emerg Disasters 15(2); 239–264. http://www.ijmed.org/articles/335/download/. Accessed 27 Sept 2017
  27. Pausas JG, Fernández-Muñoz S (2012) Fire regime changes in the Western Mediterranean Basin: from fuel-limited to drought-driven fire regime. Clim Change 110(1):215–226CrossRefGoogle Scholar
  28. Perry DG (1990) Wildland firefighting: fire behavior, tactics, and command. Fire Publications, BellflowerGoogle Scholar
  29. Pyne SJ (2007) Megaburning: the meaning of megafires and the means of the management. In: 4th international wildland fire conference proceedings, 13–17 May 2007, Sevilla, Spain [CD-ROM]. Madrid: Organismo Autónomo de Parques Nacionales, Ministerio de Medio AmbienteGoogle Scholar
  30. Sendai Framework: Sendai Framework for Disaster Risk Reduction 2015–2030. http://www.preventionweb.net/. Accessed 27 Sept 2017
  31. Sriparasa SS (2013) JavaScript and JSON essentials. Packt Publishing Ltd, BirminghamGoogle Scholar
  32. TRAGSA EMERCARTO webGIS. http://visores.tragsatec.es/. Accessed 27 Sept 2017
  33. The PostgreSQL Global Development Group (2016) PostgreSQL 9.6.1 Documentation. https://www.postgresql.org. Accessed 27 Sept 2017
  34. Van den Brink L, Stoter JE, Zlatanova S (2012) Modelling an application domain extension of CityGML in UML. In: ISPRS conference 7th international conference on 3D geoinformation, the international archives on the photogrammetry, remote sensing and spatial information sciences, 16–17 May 2012, Québec, Canada, ISPRSGoogle Scholar
  35. Vivalda C, Verda V, Carpignano A, Dell’Erba C, Ca-gliero D, Guelpa E (2017a) Forest fire risk analysis methods and simulation tools. ESREL 2017 international conference, June 18–22, 2017. Portoroz, SloveniaGoogle Scholar
  36. Vivalda C, Musci MA, Grasso N, Guelpa E, Piras M, Verda V (2017b) Forest wildfire risk mapping and the influence of the weather and geo-morphological input data. In: European safety and reliability conference ESREL 2017, Portoroz, Slovenia, 18–22 June, 2017, pp 171–178Google Scholar
  37. Zlatanova S et al (2010) Models of dynamic data for emergency response: a comparative study. In: Proceedings of the Gi4DM conference—geomatics for disaster management, February 2–4, 2010, TorinoGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria Dell’ambiente, Del Territorio E Delle Infrastrutture (DIATI)Politecnico di TorinoTurinItaly

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