A. Kanavos¹, M. Chalaris², D. Anastasiadou³, E. Housos¹ and E. Adamides³
¹Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece
²Department of Chemistry, International Hellenic University, Kavala, Greece
³Department of Electrical & Computer Engineering, University of Patras, Patras, Greece

Part of the book: The Challenges of Disaster Planning, Management, and Resilience

Abstract

Even the most advanced societies are faced with a considerable amount of challenges when it comes to forest protection against wildfires. Authorities and societies seek to address this major environmental issue through improved stakeholders’ readiness and effectiveness in forest protection. The main objective of this chapter is to inform the local authorities that the impact of technological systems introduction in disaster management is depended on the adopted organizational context and the implemented strategy. The research question of this study is to explore the role of 17 technological systems that were established in specific areas around Greece, after the mega-fires of 2007 and how reacted to the effectiveness of local communities against forest fires. The research was conducted by a mixed methodology. The material was obtained from operational officers in crisis management authorities and oversight bodies by open interviews, focus groups, participatory observations, and public databases. The outcome confirms that the adoption of an effective policy of technological systems in the context of forest protection against fires is in fact valuable but also an unexploited approach. Findings indicated that the highest benefits cannot be drawn if forest fire protection technological systems are not designed centrally and are not distributed for concurrent use by different collaborating bodies with diverse responsibilities and jurisdiction levels. It is argued that such systems should provide a unified effective administration of incidents and support the efficient coordination of resources, provided that key users actually operate properly those systems. Inefficiencies in the utilization and underperformance of technological systems often come about the lack of proper integration in terms of organizational or operational aspects.

Keywords: technological systems, forest fires, disaster management, readiness and effectiveness, investment project, evaluation process, operational plan

References

[1] Ahsan, K., & Gunawan, I. (2010). Analysis of cost and schedule performance of international
development projects. International Journal of Project Management, 28(1), pp. 68-78.
[2] Andrews, P. L., & Queen, L. P. (2001). Fire modeling and information system technology. International
Journal of Wildland Fire, 10(4), pp. 343-352.
[3] Brynjolfsson, E. (1993, Dec). The productivity paradox of information technology. Communications of
the ACM, 36(12), pp. 66-77.
[4] Chowdary, V. & Gupta, M. K. (2018). Automatic Forest Fire Detection and Monitoring Techniques: A
Survey, in Intelligent Communication, Control and Devices. Springer. p. 1111-1117.
[5] Comfort, L. K., Dunn, M., Johnson, D., Skertich, R., & Zagorecki, A. (2004). Coordination in complex
systems: increasing efficiency in disaster mitigation and response. International Journal of Emergency
Management, 2(1), pp. 62-80.
[6] Devaraj, S., & Kohli, R. (2003). Performance impacts of information technology: is actual usage the
missing link? Management Science, 49(3), pp. 273-289.
[7] Di Biase, V., & Laneve, G. (2018). Geostationary sensor based forest fire detection and monitoring: An
improved version of the SFIDE algorithm. Remote Sens. 2018, 10, 741
[8] EFFMIS. (2013). EFFMIS: European Forest Fires Monitoring using Information Systems. Retrieved
from www.effmis.eu/ProjectDeliverables/ActionsPlans.aspx
[9] Faas, A. J., Velez, Anne-Lise K., Nowell, Branda L., & Steelman, Toddi A. (2019). Methodological
considerations in pre- and post-emergency network identification and data collection for disaster risk
reduction: Lessons from wildfire response networks in the American Northwest. International Journal
of Disaster Risk Reduction, 40 (2019-11), pp.101260
[10] Gunasekaran, A., Ngai, E. W., & McGaugheyc, R. E. (2006). Information technology and systems
justification: A review for research and applications. European Journal of Operational Research,
173(3), pp. 957-983.
[11] Halikias, I., (2017). Statistical Methods of Analysis for Business Decisions, Ed. Rosili, Athens.
[12] Hellenic fire brigade (2013). Retrieved May 21, 2013, from fireservice: http://www.fireservice.gr/pyr/site/home.csp
[13] Hémond, Y., & Benoît, R. (2012). Preparedness: the state of the art and future prospects. Disaster
Prevention and Management 21.4, 21(4), pp. 404-417.
[14] Irani, Z. (2002). Information systems evaluation: navigating through the problem domain. Information
& Management, 40(1), pp. 11-24.
[15] Jain, S., & McLean, C. (2003). Simulation for emergency response: a framework for modeling and
simulation for emergency response. In Proceedings of the 35th Conference on Winter Simulation:
Driving Innovation (pp. 1068-1076). Winter Simulation Conference 2003.
[16] Jain, S., & McLean, C. R. (2003). Modeling and simulation for emergency response. Retrieved from
Workshop Report, Relevant Standards and Tools, National Institute of Standards and Technology
Internal Report, NISTIR-7071: www. nist. gov/msidlibrary/doc/nistir7071. pdf
[17] Jennex, M. E. (2007). Modeling emergency response systems. System Sciences. 40th Annual Hawaii
International Conference on IEEE.
[18] Karma, S., Zorba, E., Pallis, G. C., Statheropoulos, G., Balta, I., Mikedi, K., Vamvakari, J., Pappa, A.,
Chalaris, M., Xanthopoulos, G., & Statheropoulos, M. (2015). Use of unmanned vehicles in search and
rescue operations in forest fires: Advantages and limitations observed in a field trial International
Journal of Disaster Risk Reduction, 13 (2015-09), pp.307-312.
[19] Lederer, A. L., & Sethi, V. (1988, Sep). The Implementation of Strategic Information Systems Planning
Methodologies. MIS Quarterly, 12(3), pp. 445-461.
[20] Lentile, L. B., Holden, Z. A., Smith, A. M., Falkowski, M. J., Hudak, A. T., Morgan, P., & Benson, N.
C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects.
International Journal of Wildland Fire, 15(3), pp. 319–345.
[21] Mansor, S., Shariah, M. A., Billa, L., Setiawan, I., & Jabar, F. (2004). Spatial technology for natural
risk management. Disaster Prevention and Management, 13(5), pp. 364-373.
[22] Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Review: Information technology and organizational
performance: An integrative model of IT business value. MIS Quarterly, 28(2), pp. 283-322.
[23] Molina, J. (2010). Mixed Methods Research in Strategic Management: Impact and Applications.
Organizational Research Methods 000(00) 1-24.
[24] Naderpour, M., Rizeei, H. M., Khakzad, N. & Pradhan, B. (2019). Forest fire induced Natech risk
assessment: A survey of geospatial technologies. Reliability Engineering & System Safety, 191,
p.106558.
[25] Palen, L., & Vieweg, S. (2008). The emergence of online widescale interaction in unexpected events:
Assistance, alliance and retreat. In Proceedings of the ACM Conference on Computer Supported
Cooperative Work (CSCW), (pp. 117-126).
[26] Palen, L., Anderson, K. M., Mark, G., Martin, J., Sicker, D., Palmer, M., & Grunwald, D. (2010, April).
A vision for technology-mediated support for public participation & assistance in mass emergencies &
disasters. In Proceedings of the 2010 ACM-BCS Visions of Computer Science Conference, British
Computer Society, p. 8.
[27] Pearson, C. M., & Mitroff, I. I. (1993). From crisis prone to crisis prepared: A framework for crisis
management. The Academy of Management Executive, 7(1), pp. 48-59.
[28] Pradhan, B., Suliman, M. D., & Awang, M. A. (2007). Forest fire susceptibility and risk mapping using
remote sensing and geographical information systems (GIS). Disaster Prevention and Management,
16(3), pp. 344-352.
[29] Rai, A., Patnayakuni, R., & Patnayakuni, N. (1997, July). Technology investment and business
performance. Communications of the ACM, 40(7), pp. 89-97.
[30] Reddick, C. (2011). Information technology and emergency management: preparedness and planning in
US states. Disasters, 35(1).
[31] Setiawan, I., Mahmud, A., Mansor, S., Shariff, A. M., & Nuruddin, A. (2004). GIS-grid-based and multi criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia.
Disaster Prevention and Management, 13(5), pp. 379-386.
[32] Sharma, L. K., Kanga, S., Nathawa, M. S., Sinha, S., & Pandey, P. C. (2012). Fuzzy AHP for forest fire
risk modeling. Disaster Prevention and Management, 21(2), pp. 160-171.
[33] Suarez-Alvarez, M. M., Pham, D. T., Prostov, M. Y., & Prostov, Y. I. (2012). “Statistical Approach to
Normalization of Feature Vectors and Clustering of Mixed Datasets.” Proceedings of the Royal Society
A: Mathematical, Physical and Engineering Sciences, 468 (2145), 2630–2651.
[34] Toledo, Tomer., Marom, Ido., Grimberg, Einat., Bekhor, Shlomo. (2018). Analysis of evacuation
behavior in a wildfire event. International Journal of Disaster Risk Reduction, 31 (2018-10), pp. 1366-1373.
[35] Wade, M., & Hulland, J. (2004). Review: The resource-based view and information systems research:
Review, extension, and suggestions for future research. MIS Quarterly, 28(1), pp. 107-142.
[36] WWF Greece, (2007). Ecological report of the catastrophic fires of August 2007 in the Peloponnese,
Retrieved November 28, 2021, from: http://www.env-edu.gr/Documents/FIRE_report_Peloponnisos.pdf
[37] Xanthopoulos, G. (2009, November 18-19). Lessons learned from the dramatic fires of 2007 and 2009
in Greece. Retrieved from Xanthopoulos, 2009: http://www.jornadesbombers.ctfc.cat/ang/documentacio.htm
[38] Xanthopoulos, G., & Varela, V. (1999). Forest fire risk distribution in Greece based on the data for the
1983-93 period. Geotechnical Scientific Issues, 10(2), pp. 178-190.