Determination of fire risk to assist fire management for insular areas: the case of a small Greek island

Sakellariou, Stavros; Tampekis, Stergios; Samara, Fani; Flannigan, Mike D.; Jaeger, Dirk; Christopoulou, Olga; Sfougaris, Athanassios

doi:10.1007/s11676-018-0666-x

Cited by 20 publications

(14 citation statements)

References 29 publications

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“…In a fire, there is a lot of poisonous gas produced, which is the cause of casualties . CO is one of the poisonous gases that can hurt people .…”

Section: Resultsmentioning

confidence: 99%

“…In a fire, there is a lot of poisonous gas produced, which is the cause of casualties. 24 CO is one of the poisonous gases that can hurt people. 25 With increasing demand for environmental protection, intensive attention has been drawn to the development of flame retardant for reducing toxic gases, such as CO. 26 Figure 6A shows the curve of CO released.…”

Section: Release Of Carbon Monoxide (Co) and Carbon Dioxide (Co 2 )mentioning

confidence: 99%

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Flame retardancy and thermal decomposition behavior of TPU/chitosan composites

Jiao

Chen

et al. 2019

Polymers for Advanced Techs

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The application of chitosan (CS) in new materials is a hot research topic. In this paper, CS was used alone as flame retardant to prepare thermoplastic polyurethane elastomer (TPU) composites. Then, the flame retardancy and thermal decomposition behavior of TPU/CS composites were intensively investigated using cone calorimeter test (CCT), scanning electron microscope (SEM), microscale combustion colorimeter (MCC) test, thermogravimetric analysis/infrared spectrometry (TG‐IR), and gas chromatography‐mass spectrometry (GC‐MS). The results showed that CS can reduce the fire risk of TPU; 2.0‐wt% CS could make the peak value of heat release rate (pHRR) decreased to 457.2 kW/m2, reduced by 65.9% compared with TPU. And the peak value of smoke production rate (pSPR) and total smoke release (TSR) of the same sample was decreased by 79.4% and 54.2%, respectively. The TG‐IR and GC‐MS results confirmed that CS could promote TPU decomposition in advance, reacting with the decomposition products of TPU. Therefore, the production of combustible gas was reduced. The GC‐MS results showed that the production of isocyanates and ethers was reduced with the addition of CS. The digital photographs of SEM for the samples after CCT were shown that the char residue layer of the sample containing 2.0‐wt% CS was fibrous in shape. It could be speculated that the thermal decomposition products from TPU could react with CS at low temperature, which reduced the production of flammable gases. So CS had a good prospect in reducing the fire hazard for TPU.

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“…In a fire, there is a lot of poisonous gas produced, which is the cause of casualties . CO is one of the poisonous gases that can hurt people .…”

Section: Resultsmentioning

confidence: 99%

Section: Release Of Carbon Monoxide (Co) and Carbon Dioxide (Co 2 )mentioning

confidence: 99%

Flame retardancy and thermal decomposition behavior of TPU/chitosan composites

Jiao

Chen

et al. 2019

Polymers for Advanced Techs

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“…Forest fires are inherently a complex phenomenon and attract a lot of attention due to the unexpected consequences that they may cause to human and natural environment. Many researchers have estimated the fire hazard/risk integrating natural, anthropogenic [ 11 , 14 ], and meteorological factors [ 13 , 56 , 57 ]. However, the impact of weather is considered of utmost importance, since fire behavior (and the corresponding degree of impacts) is heavily reliant on weather conditions.…”

Section: Discussionmentioning

confidence: 99%

“…The absence of long-term weather data in the study area has been tackled by the adoption of proxy indices, such as the NDVI and NDMI, which may sufficiently and universally describe the state and moisture of vegetation. On the other hand, even though most research is logically based on fire flammability and potential fire behavior of fuels [ 14 , 58 , 59 , 60 ], we connected the fuels’ hazard with the probability of a fire event based on real fire statistics (fire susceptibility) [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…Amalina et al [ 13 ] developed a susceptibility map focusing on the land use types, the meteorological conditions (through vegetation/climatological indices and surface temperature), and the proximity to natural and anthropogenic structures and entities (e.g., water bodies, crop types, road and human settlement network, etc.). Similarly, Sakellariou et al [ 14 ] explored the distinct effect of natural and anthropogenic factors to fire hazard with emphasis on specific measures to alleviate their influence. Analytical Hierarchy Process (AHP) and fuzzy logic modeling have been widely applied to numerous fields in order to reduce the subjectivity and uncertainty of complex phenomena like forest fires vulnerability [ 12 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Assessment of the effects of different variable weights on wildfire susceptibility

Sari

2024

Eur J Forest Res

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In this study, wildfire susceptibility is mapped using various multi-criteria decision analysis techniques (AHP, SAW and VIKOR) and machine learning algorithms (MaxEnt and logistic regression) to reveal the response of models for wildfires. In this study, it is suggested that using natural weights generated by machine learning algorithms instead of artificial weights in MCDA methods can increase the reliability of susceptibility maps because wildfires have very close relationship with climatic, topographic and environmental variables. The contribution rates (natural weights) were obtained using machine learning algorithms and incorporated into MCDA methods to make the spatial relationships between variables more obvious. As a result, eight susceptibility maps were generated using MCDA methods, MaxEnt and logistic regression algorithms. Correlation analysis showed that using natural weights instead of artificial weights increased the correlation between MCDA methods and machine learning algorithms. Each correlation value increased by 10% on average and the highest increase was determined between VIKOR and logistic regression from 0.6286 to 0.7580 when natural weights were used. In addition, 1035 existing wildfire locations were used to evaluate the reliability of generated maps. The results showed that the average risk values of 1035 wildfire locations increased from 6.04 to 7.23 using AHP, from 0.66 to 0.79 using SAW and from 0.35 to 0.25 using the VIKOR method. This indicates a significant increase in the accuracy and reliability of susceptibility maps produced when natural weights determined by machine learning algorithms are used in MCDA methods.

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Determination of fire risk to assist fire management for insular areas: the case of a small Greek island

Cited by 20 publications

References 29 publications

Flame retardancy and thermal decomposition behavior of TPU/chitosan composites

Flame retardancy and thermal decomposition behavior of TPU/chitosan composites

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

Assessment of the effects of different variable weights on wildfire susceptibility

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