Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir

Çolak, E.; Sunar, Filiz

doi:10.1016/j.ijdrr.2020.101479

Cited by 82 publications

(45 citation statements)

References 20 publications

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“…Topographic variables also have a strong influence on RFFO, such as the relief inclination and orientation (MOTA et al, 2019;NADERPOUR et al, 2019;VENKATESH et al, 2020;ÇOLAK et al, 2020). Thus, it is possible to notice from Figure 3 that the study area does not present high slopes, with 30.93% being the highest inclination found.…”

Section: Discussionmentioning

confidence: 87%

Geotechnology applied to predict the risk of occurrence of fire in the Atlantic Forest

Ramalho

Neder

Fiedler

et al. 2020

RICA

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The maintenance of biodiversity is a global concern in economic, social and environmental terms. Thus, conservation units for the protection of natural environments were created. Despite the importance of these areas, forest fires have caused immeasurable and constant damage to Conservation Units. In view of this, the objective of this study was to determine the risk areas of forest fire occurrence through Fuzzy logic modeling in the Córrego Grande Biological Reserve, located in the Mata Atlântica Brazilian biome. In order to prospect for areas at risk of forest fires, the following variables were inserted in the model: land use, road network, slope and relief orientation. Finally, the model was validated by comparing the location of fire occurrences between the years 2008 and 2018, and a layout of the risk classes in the study area. In doing so, it was found that 65.87% of the area is between the 'moderate' and 'very high' range of fire risk classes, and that 70.22% of the fires which occurred in the studied period also occurred in that class range. The study concluded that the most influential variable on the risk level of fire occurrence is the forest road network. In this way, the proposed methodology can be applied to any other areas and types of land cover.

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Section: Discussionmentioning

confidence: 87%

Geotechnology applied to predict the risk of occurrence of fire in the Atlantic Forest

Ramalho

Neder

Fiedler

et al. 2020

RICA

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“…Protecting forest from undesirable fires plays a crucial role in maintaining a sustainable forest ecosystem. Although it is not easy to predict forest fires, the potential damages can be minimized or eliminated using suitable analysis and precautions (Gigović et al, 2018;Colak and Sunar, 2020). The results of the research presented in this paper include important tools for the prevention of forest fires in Manisa, and guidance for all institutions dealing with disaster management and emergency services.…”

Section: Fire Risk Zone Mappingmentioning

confidence: 90%

“…Globally forest fires have many causes of emergence, with climatic drought directly indicating forest fire risk. Therefore, the authors recommend that the FRZI model consider the use climate based variables to further improve reliability (Colak and Sunar, 2020). Indices such as Drought Index introduced by the United Nations Convention to Combat Desertification, the Haines index, the Keetch-Byram drought index, and the Nesterov ignition index have been successful in predicting forest fires.…”

Section: Fire Risk Zone Mappingmentioning

confidence: 99%

Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey

Gülçin¹,

Deniz²

2020

Turkish Journal of Forestry | Türkiye Ormancılık Dergisi

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The aim of this research is to map the potential forest fire risk zones using various landscape analysis techniques in Manisa province, Turkey. Forest fire, which is defined as an ecological disaster caused by natural processes or as a result of human activities, causes environmental degradation and fragmentation of the landscape. Therefore, it is very important to produce a fire risk zone map that can be used to minimize the frequency of fire, to prevent damage, to provide a prediction for the problems that may cause fire and to form a decision mechanism for the solution methods. This research utilized CORINE 2018 produced under the framework of the Copernicus Program which is the European Union's Earth Observation Programme coordinated and managed by the European Commission, ASTER Global DEM digital elevation model data obtained from the website of NASA Earthdata, fire archive records based on MODIS satellite images, digital stand map displaying the spatial distribution of tree species, and the OpenStreetMap (OSM) which were used for mapping the existing road network. Vegetation cover, slope, aspect, elevation, distance to settlement, and distance to road variables were used to determine risk zones. The specific weights were assigned to each thematic map layer according to their capacity on fire ignition. The slope, aspect, and elevation maps were generated from the digital elevation model. The distance to settlement map was generated from the CORINE database while the distance to road map was produced from OSM. The Fire Risk Zone Index (FRZI) was utilized to determine forest fire risk zones. According to the generated final fire risk map, almost 25.8% of the study area was predicted to be under very high and highrisk zones. The final forest fire risk model was validated with past fire incidents data that was acquired from MODIS images as fire points. The result of this research showed that out of 149 fire incidents in Manisa between 2001 and 2018, 97 incidents had occurred in very high and highrisk areas. This finding supports that the presented methodology based on RS and GIS techniques is reliable and can be effectively used in the process of delineation of the forest fire risk zones.

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“…The data required for fire analysis can be obtained by analyzing and extracting the images. Topography, meteorological factor, vegetation, and human activities are recognized as four types of fire ignition factors [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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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Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir

Cited by 82 publications

References 20 publications

Geotechnology applied to predict the risk of occurrence of fire in the Atlantic Forest

Geotechnology applied to predict the risk of occurrence of fire in the Atlantic Forest

Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey

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

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