Evaluating the efficiency of the Dong model in determining fire vulnerability in Iran’s Zagros forests

Rasooli, Sabri Baqer; Bonyad, Amir Eslam

doi:10.1007/s11676-018-0765-8

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…The weights indicated that the factor of vegetation group has the highest significance and affectation. This statement is in accordance with the results achieved by Valdrevu et al [47] and Rassoli et al [63].…”

Section: Discussionsupporting

confidence: 94%

“…Most fire incidents happen in low elevation areas, due to numerous factors such as human presence, decrease in relative humidity, and the increase in the temperature [63]. Due to this, topography, anthropogenic, and meteorological parameters are fundamental in the study being the meteorological parameter considered the most important of the three with a weight of 0.298 and the topography the lower weight assignment with 0.108.…”

Section: Discussionmentioning

confidence: 99%

“…Once the layer variables were obtained, the next step was the assignment of values from 1 to 5, 1 being the lowest fire risk and 5 the highest one. The weights assignment was carried out to each parameter according their influence on fire risk [20,22,23,40,63,64]. However, historical fires and fuel model types parameters were classified by development of an own methodology.…”

Section: Classificationmentioning

confidence: 99%

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Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

et al. 2020

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The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

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Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

et al. 2020

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“…In this paper, it has a high weight within four topographic factors, this is consistent with the observation in the relevant works [20,20]. Compared with aspect and slope, altitude has a great impact on the probability of forest fire in Laoshan, because low elevation areas have higher human accessibility, which may result in unintended fire by fire sources (for example: cigarette butts thrown by pedestrians) [64]. In our study, area, most of the Laoshan area is flat, and the influence of slope is lower.…”

Section: The Influence Of Factors On Fire Risk Modelsupporting

confidence: 88%

GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

et al. 2021

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Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.

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“…Since forest fires follow a nonlinear and complex process under the effects of various factors, it is difficult to develop highly accurate models for predicting forest fire risks [7]. Various factors have been utilized to identify the potential of forest fire risks, among which one can refer to slope [8], distance from roads [9], aspect [10], altitude [11], rainfall [12], temperature [13], distance from rivers [14], land use [15], vegetation [16], distance from settlements, wind speed [17], curvature [18], and the topographic wetness index [19]. Given that forest fires are caused by many factors, it is necessary to consider various influencing factors as much as possible for high-precision fire risk mapping in specific regions.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran

et al. 2022

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Mapping fire risk accurately is essential for the planning and protection of forests. This study aims to map fire risk (probability of ignition) in Marivan County of Kurdistan province, Iran, using the data mining approaches of the evidential belief function (EBF) and weight of evidence (WOE) models, with an emphasis placed on climatic variables. Firstly, 284 fire incidents in the region were randomly divided into two groups, including the training group (70%, 199 points) and the validation group (30%, 85 points). Given the previous studies and conditions of the region, the variables of slope percentage, slope direction, altitude, distance from rivers, distance from roads, distance from settlements, land use, slope curvature, rainfall, and maximum annual temperature were considered for zoning fire risk. Then, forest fire risk maps were prepared using the EBF and WOE models. The performance of each model was examined using the Relative Operating Characteristic (ROC) curve. The results showed that WOE and EBF are effective tools for mapping forest fire risks in the study area. However, the WOE model shows a slightly higher Area Under the Curve value (0.896) compared to that of the EBF model (0.886), indicating a slightly better performance. The results of this study can provide valuable information for preventing forest fires in the study area.

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Evaluating the efficiency of the Dong model in determining fire vulnerability in Iran’s Zagros forests

Cited by 12 publications

References 16 publications

Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran

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