Spatial planning of fire-agency stations as a function of wildfire likelihood in Thasos, Greece

Sakellariou, Stavros; Parisien, Marc‐André; Flannigan, Mike D.; Wang, Xianli; Groot, B. de; Tampekis, Stergios; Samara, Fani; Sfougaris, Athanassios; Christopoulou, Olga

doi:10.1016/j.scitotenv.2020.139004

Cited by 23 publications

(10 citation statements)

References 36 publications

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“…Hence, appropriate land management strategies should be adopted, such as the enhancement of spatial planning for greater protection of natural environment from the unregulated urbanization and the rational forest management through the decomposition of dense forests (e.g., thinning, etc.). In addition, more fire-specific measures should be developed, such as the rational planning of firebreaks, the development of efficient watchtowers networks [ 63 ], and spatial decision support systems that would allow the finding of best locations for the fire vehicles, aiming at the minimization of travel time for initial attack [ 64 , 65 ]. It should be noted that significant parts of spatial analysis would be impossible to be conducted without the contribution of remote sensing techniques for the acquirement and manipulation of the necessary information.…”

Section: Discussionmentioning

confidence: 99%

Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard

Sakellariou

Cabral

Caetano

et al. 2020

Sensors

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Forest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of this paper is to perform a spatiotemporal analysis and explore the variability of fire hazard in a small Greek island, Skiathos (a prototype case of fragile environment) where the land uses mixture is very high. First, a comparative assessment of two robust modeling techniques was examined, namely, the Analytical Hierarchy Process (AHP) knowledge-based and the fuzzy logic AHP to estimate the fire hazard in a timeframe of 20 years (1996–2016). The former technique was proven more representative after the comparative assessment with the real fire perimeters recorded on the island (1984–2016). Next, we explored the spatiotemporal dynamics of fire hazard, highlighting the risk changes in space and time through the individual and collective contribution of the most significant factors (topography, vegetation features, anthropogenic influence). The fire hazard changes were not dramatic, however, some changes have been observed in the southwestern and northern part of the island. The geostatistical analysis revealed a significant clustering process of high-risk values in the southwestern and northern part of the study area, whereas some clusters of low-risk values have been located in the northern territory. The degree of spatial autocorrelation tends to be greater for 1996 rather than for 2016, indicating the potential higher transmission of fires at the most susceptible regions in the past. The knowledge of long-term fire hazard dynamics, based on multiple types of remotely sensed data, may provide the fire and land managers with valuable fire prevention and land use planning tools.

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Section: Discussionmentioning

confidence: 99%

Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard

Sakellariou

Cabral

Caetano

et al. 2020

Sensors

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“…Exposure Analysis Simulation modeling [21]; [22]; [30]; [31]; [32]; [33]; [34]; [37]; [69]; [70]; [92]; [93]; [94] Effects Analysis Fire effects [19]; [21]; [35]; [36]; [37]; [38]; [39]; [42] Fire Management Visibility analysis [24]; [43]; [44]; [45]; [46]; [47]; [48]; [49]; [81]; [95]; [96] Fire Management / Emergency Response Network analysis [51]; [52]; [53]; [54]; [55]; [56]; [57]; [58]; [59]; [60]; [61]; [62];…”

Section: Fire Modulementioning

confidence: 99%

“…2. a) Estimation of fire hazard in Skiathos island, Greece (Adapted from [25]), b) Estimation of burn probability in Thasos island, Greece (Adapted from [53]).…”

Section: Fire Modulementioning

confidence: 99%

“…Sakellariou et al [52][53][54] developed Spatial Decision Support Systems for immediate initial attack of forest fires events. The concepts focus on the strategic and operational planning of fire-agency resources to minimize the possibility of expansion of detrimental fires.…”

Section: Network Analysis and Spatial Optimization Techniquesmentioning

confidence: 99%

“…coverage and minimization of required facilities [53], so that the 90% of incidents to be approached within 5 minutes from any optimal position [61].…”

Section: Author Copy • Author Copy • Author Copy • Author Copy • Author Copy • Author Copy • Author Copy • Author Copy • Author Copymentioning

confidence: 99%

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Review of Geoinformatics-Based Forest Fire Management Tools for Integrated Fire Analysis

Sakellariou¹,

Sfougaris²,

Christopoulou³

2021

Pol. J. Environ. Stud.

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Wildfires of high severity can have profound implications on natural and human environment affecting the quality of life; the health of living beings; and the prosperity of any society. Consequently, specific strategies, tactics and techniques should always be adopted for the alleviation of this critical phenomenon. Hence, the aim of the paper is the review of the most common geoinformatics-based techniques contributing to an integrated fire analysis through four pillars. The first one is related with the fire exposure on the ground, primarily analyzing the fire susceptibility in terms of fire risk and burn probability maps; The second one examines the fire effects on the most critical ecological and anthropogenic resources and infrastructures. The third pillar combines two effective geospatial tools supporting the wildfire prevention and suppression, such as the visibility analysis for early detection of fire hotspots and the network analysis for strategic and operational planning of fire events. Last, the Earth-Observation module, through the spatiotemporal monitoring and prediction of land use changes, permits the planners to evaluate the underlying pressures (fires, urbanization) against forests developing the appropriate planning guidelines. In the meantime, new perspectives emerge. Novel machine learning algorithms and remote sensing data techniques are expected to improve the fire risk/probability credibility enhancing the more precise identification of fire effects to any resource. The integration of specific geographic criteria (e.g. topography, accessibility) and programming techniques (e.g. maximizing the visible area) to visibility analysis would empower the immediate fire detection. New technologies such as the adoption of drones would be a cost-effective tool for quick retrieval of vital geo-data. Network analysis could propose financially and environmentally efficient location schemes of fire agencies and resources.

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Fire in Mediterranean Pine Forests: Past, Present and Future

Moreno

Morales‐Molino

Torres

et al. 2021

Pines and Their Mixed Forest Ecosystems in the Mediterranean Basin

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Spatial planning of fire-agency stations as a function of wildfire likelihood in Thasos, Greece

Cited by 23 publications

References 36 publications

Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard

Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard

Review of Geoinformatics-Based Forest Fire Management Tools for Integrated Fire Analysis

Fire in Mediterranean Pine Forests: Past, Present and Future

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