Development of a Spatial Decision Support System (SDSS) for the active forest-urban fires management through location planning of mobile fire units

Sakellariou, Stavros; Samara, Fani; Tampekis, Stergios; Sfougaris, Athanassios; Christopoulou, Olga

doi:10.1080/17477891.2019.1628696

Cited by 16 publications

(11 citation statements)

References 28 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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“…motorway; service road etc.). Every road category has been assigned an adjusted maximum travel speed respecting the institutional limits [12]. At the last stage, we calculated the travel time of each road segment, since the fire vehicles will be moving through these linear entities.…”

Section: Fig 1 Study Area -Districts Of Operational Responsibility mentioning

confidence: 99%

“…To this end, Dijkstra algorithm [9] has been widely used for the estimation of shortest route between given locations. The location planning for emergencies [10][11][12] is an issue of utmost importance, since it may prevent destructive events with multiple environmental and social repercussions.…”

Section: Introductionmentioning

confidence: 99%

Location Planning of Fire Service Fleet Based on Forest Fire Susceptibility

Sakellariou

Sfougaris

Christopoulou

2020

Wseas Transactions on Environment and Development

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Forest fires constitute one of the greatest risks for certain ecosystems services. Hence, the location planning of firefighting units based on forest fire hazard is a key point for the immediate containment of fire incidents before they become uncontrollable. The primary aim of the paper is the estimation and finding of the required number and positions (i.e. the exact geographical locations and the respective type of installation – fire hydrant etc.) of fire fleet to fully cover (in terms of travel time) the districts of fire services in Chalkidiki (Greece), taking into account the scalable fire susceptibility. The proposed location plans revealed the fact that most of the regions may adequately respond to the fire vulnerability with the current forces, whereas a few regions clearly need a reinforcement in order to sufficiently cover their territories. A future perspective may be related with the exploration of best locations through the entire study area, merging the current administrative boundaries. One more asset of the project constitutes the flexibility and applicability of the sub-modules to other geographic regions after the necessary adjustments to local conditions.

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“…Roads strongly influence both forest fire activity and control tactics. Forest roads are associated with spatial patterns of ignitions and fire perimeters, function as fuelbreaks and firebreaks, and can support safer and more effective wildfire management [1][2][3][4][5][6][7][8]. Addressing fire management needs with forest road network analysis and planning is therefore an important area of research, especially as climate and other factors contribute to increased fire activity in many areas around the globe [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Forest Roads and Operational Wildfire Response Planning

Thompson

Gannon

Caggiano

2021

Forests

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Supporting wildfire management activities is frequently identified as a benefit of forest roads. As such, there is a growing body of research into forest road planning, construction, and maintenance to improve fire surveillance, prevention, access, and control operations. Of interest here is how road networks directly support fire control operations, and how managers incorporate that information into pre-season assessment and planning. In this communication we briefly review and illustrate how forest roads relate to recent advances in operationally focused wildfire decision support. We focus on two interrelated products used on the National Forest System and adjacent lands throughout the western USA: potential wildland fire operational delineations (PODs) and potential control locations (PCLs). We use real-world examples from the Arapaho-Roosevelt National Forest in Colorado, USA to contextualize these concepts and illustrate how fire analytics and local fire managers both identified roads as primary control features. Specifically, distance to road was identified as the most important predictor variable in the PCL boosted regression model, and 82% of manager-identified POD boundaries aligned with roads. Lastly, we discuss recommendations for future research, emphasizing roles for enhanced decision support and empirical analysis.

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Development of a Spatial Decision Support System (SDSS) for the active forest-urban fires management through location planning of mobile fire units

Cited by 16 publications

References 28 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

Location Planning of Fire Service Fleet Based on Forest Fire Susceptibility

Forest Roads and Operational Wildfire Response Planning

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