Territorial Resilience Through Visibility Analysis for Immediate Detection of Wildfires Integrating Fire Susceptibility, Geographical Features, and Optimization Methods

Stavros Sakellariou , George Sfoungaris , Olga Christopoulou

International Journal of Disaster Risk Science ›› 2022, Vol. 13 ›› Issue (4) : 621 -635.

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International Journal of Disaster Risk Science ›› 2022, Vol. 13 ›› Issue (4) : 621 -635. DOI: 10.1007/s13753-022-00433-2
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Territorial Resilience Through Visibility Analysis for Immediate Detection of Wildfires Integrating Fire Susceptibility, Geographical Features, and Optimization Methods

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Abstract

Climate change effects tend to reinforce the frequency and severity of wildfires worldwide, and early detection of wildfire events is considered of crucial importance. The primary aim of this study was the spatial optimization of fire resources (that is, watchtowers) considering the interplay of geographical features (that is, simulated burn probability to delimit fire vulnerability; topography effects; and accessibility to candidate watchtower locations) and geo-optimization techniques (exact programming methods) to find both an effective and financially feasible solution in terms of visibility coverage in Chalkidiki Prefecture of northern Greece. The integration of all geographical features through the Analytical Hierarchy Process indicated the most appropriate territory for the installment of watchtowers. Terrain analysis guaranteed the independence and proximity of location options (applying spatial systematic sampling to avoid first order redundancy) across the ridges. The conjunction of the above processes yielded 654 candidate watchtower positions in 151,890 ha of forests. The algorithm was designed to maximize the joint visible area and simultaneously minimize the number of candidate locations and overlapping effects (avoiding second order redundancy). The results indicate four differentiated location options in the study area: (1) 75 locations can cover 90% of the forests (maximum visible area); (2) 47 locations can cover 85% of the forests; (3) 31 locations can cover 80.2% of the forests; and (4) 16 locations can cover 70.6% of the forests. The last option is an efficient solution because it covers about 71% of the forests with just half the number of watchtowers that would be required for the third option with only about 10% additional forest coverage. However, the final choice of any location scheme is subject to agency priorities and their respective financial flexibility.

Keywords

Burn probability / Greece / Spatial optimization / Topography effects / Viewshed coverage / Watchtowers / Wildfire detection

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Stavros Sakellariou, George Sfoungaris, Olga Christopoulou. Territorial Resilience Through Visibility Analysis for Immediate Detection of Wildfires Integrating Fire Susceptibility, Geographical Features, and Optimization Methods. International Journal of Disaster Risk Science, 2022, 13(4): 621-635 DOI:10.1007/s13753-022-00433-2

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References

[1]

Alkhatib, A.A. 2014. A review on forest fire detection techniques. International Journal of Distributed Sensor Networks 10(3): Article 597368.

[2]

Bao S, Xiao N, Lai Z, Zhang H, Kim C. Optimizing watchtower locations for forest fire monitoring using location models. Fire Safety Journal, 2015, 71: 100-109

[3]

Busico, G., E. Giuditta, N. Kazakis, and N. Colombani. 2019. A hybrid GIS and AHP approach for modelling actual and future forest fire risk under climate change accounting water resources attenuation role. Sustainability 11(24): Article 7166.

[4]

Christopoulou, O.G. 2011. Deforestation/reforestation in Mediterranean Europe: The case of Greece. In Soil erosion studies, ed. D. Godone, and S. Stanchi, 41–58. Rijeka, Croatia: InTech.

[5]

CLMS (Copernicus Land Monitoring Service). 2018. CORINE Land Cover. https://land.copernicus.eu/pan-european/corine-land-cover. Accessed 22 Mar 2018.

[6]

Çolak, E., and F. Sunar. 2020. Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International Journal of Disaster Risk Reduction 45: Article 101479.

[7]

Cruz MG, Alexander ME. Uncertainty associated with model predictions of surface and crown fire rates of spread. Environmental Modelling & Software, 2013, 47: 16-28

[8]

Di Virgilio G, Evans JP, Blake SA, Armstrong M, Dowdy AJ, Sharples J, McRae R. Climate change increases the potential for extreme wildfires. Geophysical Research Letters, 2019, 46(14): 8517-8526

[9]

Dowdy, A.J., H. Ye, A. Pepler, M. Thatcher, S.L. Osbrough, J.P. Evans, G.D. Virgilio, and N. McCarthy. 2019. Future changes in extreme weather and pyroconvection risk factors for Australian wildfires. Scientific Reports 9(1): Article 10073.

[10]

Esri (Environmental Systems Research Institute). 2019. How to: Identify ridgelines from a DEM. https://support.esri.com/en/technical-article/000011289. Accessed 20 Mar 2019.

[11]

Esri (Environmental Systems Research Institute). 2020. Viewshed. https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/viewshed.htm. Accessed 20 Mar 2020.

[12]

Esri (Environmental Systems Research Institute). 2020. What is ArcPy? https://pro.arcgis.com/en/pro-app/latest/arcpy/get-started/what-is-arcpy-.htm. Accessed 20 Mar 2020.

[13]

Eugenio FC, dos Santos AR, Fiedler NC, Ribeiro GA, da Silva AG, Juvanhol RS, Schettino VR, Marcatti GE GIS applied to location of fires detection towers in domain area of tropical forest. Science of the Total Environment, 2016, 562: 542-549

[14]

Ferreira CR, Andrade MVA, Magalhães SV, Franklin WR, Pena GC. A parallel algorithm for viewshed computation on grid terrains. Journal of Information and Data Management, 2014, 5(2): 171-180.

[15]

Franklin WR. Richardson DE, Oosterom P. Siting observers on terrain. Advances in spatial data handling, 2002, Berlin, Heidelberg: Springer 109-120

[16]

Franklin WR, Ray CK. Waugh TC, Healey RG. Higher isn’t necessarily better: Visibility algorithms and experiments. Advances in GIS research: 6th international symposium on spatial data handling, 1994, London: Taylor & Francis 751-770.

[17]

Franklin WR, Vogt C. Riedl A, Kainz W, Elmes GA. Tradeoffs when multiple observer siting on large terrain cells. Progress in spatial data handling, 2006, Berlin, Heidelberg: Springer 845-861

[18]

Geofabrik GmbH and OpenStreetMap Contributors. 2018. Data for Greece. https://download.geofabrik.de/europe/greece.html. Accessed 20 Dec 2018.

[19]

Göltaş M, Demirel T, Çağlayan İ. Visibility analysis of fire watchtowers using GIS: A case study in Dalaman State Forest Enterprise. European Journal of Forest Engineering, 2017, 3(2): 66-71.

[20]

HFB (Hellenic Fire Brigade). 2018. Hellenic Fire Brigade: Fire events. https://www.fireservice.gr/el_GR/stoicheia-symbanton. Accessed 17 Apr 2018 (in Greek).

[21]

HNMS (Hellenic National Meteorological Service). 2018. Hellenic National Meteorological Service webpage. http://www.hnms.gr/emy/en/index_html?. Accessed 10 Feb 2018.

[22]

Hysa A. Djalante R, Bisri MBF, Shaw R. Classifying the forest surfaces in metropolitan areas by their wildfire ignition probability and spreading capacity in support of forest fire risk reduction. Integrated research on disaster risks, 2021, Cham: Springer 51-70

[23]

Kim YH, Rana S, Wise S. Exploring multiple viewshed analysis using terrain features and optimisation techniques. Computers & Geosciences, 2004, 30(9–10): 1019-1032

[24]

Kucuk O, Topaloglu O, Altunel AO, Cetin M. Visibility analysis of fire lookout towers in the Boyabat State Forest Enterprise in Turkey. Environmental Monitoring and Assessment, 2017, 189(7): 1-18

[25]

Lee J. Digital analysis of viewshed inclusion and topographic features on digital elevation models. Photogrammetric Engineering and Remote Sensing, 1994, 60(4): 451-456.

[26]

Magalhaes, S.V., M.V. Andrade, and W.R. Franklin. 2010. An optimization heuristic for siting observers in huge terrains stored in external memory. In Proceedings of the 10th International Conference on Hybrid Intelligent Systems, 23–25 August, Atlanta, USA, 135–140.

[27]

Milanović, S., N. Marković, D. Pamučar, L. Gigović, P. Kostić, and S.D. Milanović. 2021. Forest fire probability mapping in Eastern Serbia: Logistic regression versus random forest method. Forests 12(1): Article 5.

[28]

Mota, P.H.S., S.J.S.S. da Rocha, N.L.M. de Castro, G.E. Marcatti, L.C. de Jesus França, B.L.S. Schettini, P.H. Villanova, H.T. dos Santos, et al. 2019. Forest fire hazard zoning in Mato Grosso State, Brazil. Land Use Policy 88: Article 104206.

[29]

NCMA (National Cadastre & Mapping Agency S.A.). 2012. Hellenic Cadastre. https://www.ktimatologio.gr/. Accessed 15 Jun 2018.

[30]

Parisien, M.-A., V. Kafka, K.G. Hirsch, J.B. Todd, S.G. Lavoie, and P.D. Maczek. 2005. Mapping fire susceptibility with the Burn-P3 simulation model. Information Report NOR-X-405. Edmonton, Alberta: Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre.

[31]

Parisien MA, Walker GR, Little JM, Simpson BN, Wang X, Perrakis DD. Considerations for modeling burn probability across landscapes with steep environmental gradients: An example from the Columbia Mountains Canada. Natural Hazards, 2013, 66(2): 439-462

[32]

Preparedness Advice. 2022. Understanding the uses of the military crest of a hill or ridge. https://preparednessadvice.com/understanding-uses-military-crest-hill-ridge/. Accessed 5 Aug 2022.

[33]

Python. 2020. Numeric and mathematical modules. https://docs.python.org/3/library/numeric.html. Accessed 20 Mar 2020.

[34]

Rana S. Fast approximation of visibility dominance using topographic features as targets and the associated uncertainty. Photogrammetric Engineering and Remote Sensing, 2003, 69(8): 881-888

[35]

Ruffault J, Curt T, Martin-StPaul NK, Moron V, Trigo RM. Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean. Natural Hazards and Earth System Sciences, 2018, 18(3): 847-856

[36]

Saaty TL. How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 1990, 48(1): 9-26

[37]

Sakellariou, S., P. Cabral, M. Caetano, F. Pla, M. Painho, O. Christopoulou, A. Sfougaris, N. Dalezios, et al. 2020. Remotely sensed data fusion for spatiotemporal geostatistical analysis of forest fire hazard. Sensors 20(17): Article 5014.

[38]

Sakellariou, S., M.A. Parisien, M. Flannigan, X. Wang, B. de Groot, S. Tampekis, F. Samara, A. Sfougaris, et al. 2020. Spatial planning of fire-agency stations as a function of wildfire likelihood in Thasos, Greece. Science of the Total Environment 729: Article 139004.

[39]

Sakellariou S, Samara F, Tampekis S, Christopoulou O, Sfougaris A. Optimal number and location of watchtowers for immediate detection of forest fires in a small island. International Journal of Agricultural and Environmental Information Systems, 2017, 8(4): 1-19

[40]

Sakellariou S, Samara F, Tampekis S, Sfougaris A, Christopoulou O. Development of a Spatial Decision Support System (SDSS) for the active forest-urban fires management through location planning of mobile fire units. Environmental Hazards, 2020, 19(2): 131-151

[41]

Sakellariou, S., A. Sfougaris, O. Christopoulou, and S. Tampekis. 2022. Integrated wildfire risk assessment of natural and anthropogenic ecosystems based on simulation modeling and remotely sensed data fusion. International Journal of Disaster Risk Reduction 78: Article 103129.

[42]

Sakellariou S, Tampekis S, Samara F, Flannigan M, Jaeger D, Christopoulou O, Sfougaris A. Determination of fire risk to assist fire management for insular areas: The case of a small Greek island. Journal of Forestry Research, 2019, 30(2): 589-601

[43]

Shi X, Xue B. Deriving a minimum set of viewpoints for maximum coverage over any given digital elevation model data. International Journal of Digital Earth, 2016, 9(12): 1153-1167

[44]

Sivrikaya F, Sağlam B, Akay AE, Bozali N. Evaluation of forest fire risk with GIS. Polish Journal of Environmental Studies, 2014, 23(1): 187-194.

[45]

Sousa, M.J., A. Moutinho, and M. Almeida. 2020. Wildfire detection using transfer learning on augmented datasets. Expert Systems with Applications 142: Article 112975.

[46]

Tian X, Cui W, Shu L. Evaluating fire management effectiveness with a burn probability model in Daxing’anling China. Canadian Journal of Forest Research, 2020, 50(7): 670-679

[47]

Tymstra, C., R.W. Bryce, B.M. Wotton, S.W. Taylor, and O.B. Armitage. 2010. Development and structure of Prometheus: the Canadian Wildland Fire Growth Simulation Model. Information Report NOR-X-417. Edmonton, Alberta: Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre.

[48]

USDA (U.S. Department of Agriculture). 2018. Fire, fuel and smoke science program. Rocky mountain research station. http://www.firelab.org/project/windninja. Accessed 13 Sept 2018.

[49]

van Kreveld, M. 1996. Variations on sweep algorithms: Efficient computation of extended viewsheds and class intervals. Department of Computer Science, Utrecht University, The Netherlands. http://www.cs.uu.nl/research/techreps/repo/CS-1996/1996-22.pdf. Accessed 15 Jun 2020.

[50]

van Leeuwen TT, van der Werf GR, Hoffmann AA, Detmers RG, Rücker G, French NH, Archibald S, Carvalho J Biomass burning fuel consumption rates: A field measurement database. Biogeosciences, 2014, 11(24): 7305-7329

[51]

van Wagner, C.E. 1987. Development and structure of the Canadian Forest Fire Weather Index System. Forestry Technical Report 35. Ottawa: Canadian Forestry Service.

[52]

Wang Y, Dou W. A fast candidate viewpoints filtering algorithm for multiple viewshed site planning. International Journal of Geographical Information Science, 2020, 34(3): 448-463

[53]

Wotton, B.M., M.D. Flannigan, and G.A. Marshall. 2017. Potential climate change impacts on fire intensity and key wildfire suppression thresholds in Canada. Environmental Research Letters 12(9): Article 095003.

[54]

Wu B, Wang Z, Zhang Q, Shen N. Distinguishing transport-limited and detachment-limited processes of interrill erosion on steep slopes in the Chinese loessial region. Soil and Tillage Research, 2018, 177: 88-96

[55]

Xu G, Zhong X. Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8. Remote Sensing Letters, 2017, 8(11): 1052-1061

[56]

Zhang F, Zhao P, Thiyagalingam J, Kirubarajan T. Terrain-influenced incremental watchtower expansion for wildfire detection. Science of the Total Environment, 2019, 654: 164-176

[57]

Zhang, F., P. Zhao, S. Xu, Y. Wu, X. Yang, and Y. Zhang. 2020. Integrating multiple factors to optimize watchtower deployment for wildfire detection. Science of the Total Environment 737: Article 139561.

[58]

Zhao, Y., J. Ma, X. Li, and J. Zhang. 2018. Saliency detection and deep learning-based wildfire identification in UAV imagery. Sensors 18(3): Article 712.

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