Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques

Adab, Hamed; Kanniah, Kasturi Devi; Solaimani, Karim

doi:10.1007/s11069-012-0450-8

Cited by 223 publications

(134 citation statements)

References 51 publications

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“…Various indices have been proposed in literature to estimate fire risks using natural (vegetation, humidity, elevation, slope, aspect) and anthropogenic (proximity to settlements and roads) parameters (Jaiswal et al 2002;Filippidis and Mitsopoulos 2004;Saglam et al 2008;Cáceres 2011;Adab et al 2013;Mohammadi et al 2014;Sivrikaya et al 2014;Zhang et al 2014). These indices are grouped into three categories: structural and long-term indices; dynamic and shortterm indices; and combined indices (Adab et al 2013). Chuvieco and Congalton (1989) introduced a structural fire index (SFI) using vegetation, elevation, slope, aspect and proximity to roads to estimate fire hazard.…”

Section: Analysing Forest Fire Risk Zonesmentioning

confidence: 99%

“…Forest fire due to natural and anthropogenic factors (Adab et al 2013) causes economic losses to people in this region and increases the emission of carbon that influences climate change (Chowdhury and Hassan 2015). Catastrophic forest fire causes the destruction of large areas in many countries in Europe, America and Australia (Bonazountas et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Geospatial models have been used in some parts of the world to map fire risk indices (Jaiswal et al 2002;Saglam et al 2008;Adab et al 2013;Mohammadi et al 2014;Sivrikaya et al 2014). These models integrate various factors related to vegetation, topography and proximity of forests to human activities to estimate risk.…”

Section: Introductionmentioning

confidence: 99%

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Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data

Matin

Chitale

Murthy

et al. 2017

Int. J. Wildland Fire

142

100

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Abstract. Forest fire is one of the key drivers of forest degradation in Nepal. Most of the forest fires are human-induced and occur during the dry season, with ,89% occurring in March, April and May. The inaccessible mountainous terrain and narrow time window of occurrence complicate suppression efforts. In this paper, forest fire patterns are analysed based on historical fire incidence data to explore the spatial and temporal patterns of forest fires in Nepal. Three main factors are involved in the ignition and spread of forest fires, namely fuel availability, temperature and ignition potential. Using these factors a spatially distributed fire risk index was calculated for Nepal based on a linear model using weights and ratings. The input parameters for the risk assessment model were generated using remote sensing based land cover, temperature and active fire data, and topographic data. A relative risk ranking was also calculated for districts and village development committees (VDCs). In total, 18 out of 75 districts were found with high risk of forest fires. The district and VDC level fire risk ranking could be utilised by the Department of Forest for prioritisation, preparedness and resource allocation for fire control and mitigation.Additional keywords: fire behaviour, fire danger, fire management, fire risk.

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Section: Analysing Forest Fire Risk Zonesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data

Matin

Chitale

Murthy

et al. 2017

Int. J. Wildland Fire

142

100

View full text Add to dashboard Cite

show abstract

“…Several approaches were discussed in the literature on forest fire hazard mapping. Most of them were applied multilayer evaluation system provided from different data sources such as remote sensing, climate stations and pre-designed maps (road, settlement, land use) (Jaiswal et al 2002;Adab et al 2013). In a multilayer assessment system, weighting the each layer is the most important issue.…”

Section: Discussionmentioning

confidence: 99%

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Şatır

Berberoğlu

Dönmez

2015

Geomatics, Natural Hazards and Risk

175

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Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R D ¡0.43), tree cover (R D 0.93) and temperature (R D 0.42) were strongly correlated with forest fire probability in the USB region. KEYWORDSForest fire probability and hazard; landscape feature; weighting; artificial neural network; Mediterranean region; fire weather index

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“…The phenomenon of forest fires is one of the most significant natural disasters that occur in the country. Even though Iranian forest fire statistics are not very reliable, some estimates indicate that more than 5,000 ha of land are affected annually (Adab et al 2013). Forest ecosystems are important natural resources with a role in maintaining environmental balance, and their health is a good indicator of the ecological conditions prevailing in a region.…”

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confidence: 99%

Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

Jafarzadeh¹,

Mahdavi²,

Jafarzadeh³

2017

J. For. Sci.

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Jafarzadeh A.A., Mahdavi A., Jafarzadeh H. (2017): Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering. J. For. Sci., In this study we evaluated forest fire risk in the west of Iran using the Apriori algorithm and fuzzy c-means (FCM) clustering. We used twelve different input parameters to model fire risk in Ilam Province. Our results with minimum support and minimum confidence show strong relationships between wildfire occurrence and eight variables (distance from settlement, population density, distance from road, slope, standing dead oak trees, temperature, land cover and distance from farm land). In this study, we defined three clusters for each variable: low, middle and high. The data regarding the factors affecting forest fire risk were distributed in these three clusters with different degrees of membership and the final map of all factors was classified by FCM clustering. Each layer was then created in a geographic information system. Finally, wildfire risks in the area obtained from overlaying these layers were classified into five categories, from very low to very high according to the degree of danger.

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Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques

Cited by 223 publications

References 51 publications

Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data

Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

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