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Kahramanmaras province is located in an area where Mediterranean, Eastern Anatolia and Southeast Anatolia eco-regions are the nearest to each other. Southern regions of the province, in particular, are dominated by Mediterranean climate and correspond to high susceptible areas in terms of forest fires. In this study, the distribution of forest fire...
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... province is Turkey's 18th biggest province with 14 468 km² area. The highest point of the province is 3084 m, the lowest point is 118 m, and the average altitude is 1324 m 59.7% of the province is covered with mountains, 24% with plateau and 16.3% with plains ( Figure 1). The northern part of the province has a very mountainous structure; the higher parts of these mountainous areas are bare rocks and the lower parts are covered with forests. ...
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... Traditional measurements are very useful for assessing wildfire risk; however, they are expensive, time-consuming, and inaccurate [16] In the last decade, GIS techniques have been integrated with multi-criteria analysis to provide a rapid and effective method for fire risk mapping [17], as they identify various fire risk variables, such as topography, land surface temperature, vegetation types and meteorological conditions, in addition to allowing quick, economical and precise analyzes to generate a fire risk map [9,11,18]. ...
... In the literature, some multi-criteria analysis methods used are analytic hierarchy process (AHP), analytic network process (ANP), artificial neural networks (ANN) and fuzzy logic [11,19]. The analytic hierarchy process (AHP) is one of the most widely used multi-criteria analysis methods for solving spatial problems and can be used as a tool for forest fire planning [10,16,18]. ...
In recent decades, the occurrence of forest fires has increased, causing damage to wild flora and fauna. For this reason, it is necessary to determine the areas susceptible to the occurrence of this phenomenon and thus implement policies for its management. In this study, the AHP and GIS method were used to map areas susceptible to forest fires in the province of Rodrí guez de Mendoza located in the southern Amazon region of Peru, using climatic variables (Temperature, Precipitation and Wind Speed), topographic (altitude, slope and aspect), socioeconomic (proximity to roads and distance to populated centers) and biological (NDVI). The results indicate that 23.65% of the area is in the high-risk class and 19.05% in the very high-risk class. These risk levels are directly related to the topographic, meteorological, social and biological variables, and could trigger large-scale fires, generating losses in biological diversity and economic losses. It is concluded that 42.70% of the study area is classified as high and very high-risk areas, which makes it necessary to take relevant measures to reduce the risk of natural disasters; Furthermore, the methodology used in this research can be used in other provinces that have similar conditions.
... In Turkey, some studies have been done to predict the fire risk in the natural ecosystems of the country at regional scales (Özşahin 2014;Güngöroglu 2017;Esen and Avci 2018;Akay and Şahin 2019;Coban and Erdin 2020;Sivrikaya and Küçük, 2022;Soydan 2022). However, no study aiming at fire susceptibility mapping in the Mediterranean forests of the Kahramanmaras Regional Directorate of Forestry (RDF), which is a valuable ecosystem and a fireprone area in Turkey, has been performed. ...
Forest fires are considered to be among the most destructive factors in forest ecosystems around the world. In recent years, climate change and human activities have led to a notable increase in the occurrence and magnitude of forest fires in the Mediterranean region. In particular, the Mediterranean ecosystems of Turkey provide the proper conditions for fire ignition and propagation in natural areas. Thus, the prediction of future fires is essential to determine the high-susceptibility areas and establish preventive management actions. Therefore, this study aims to identify the fire susceptibility in the Kahramanmaraş Regional Directorate of Forestry (RDF), which is a fire-prone area in Turkey. The methodology consists of combining the analytic hierarchy process (AHP) method, remote sensing (RS) data, and geographic information system (GIS) analysis. First, the fire susceptibility was divided into two components: anthropogenic fire susceptibility and natural fire susceptibility (fuel, climate, and topography factors). Data on different factors were obtained from different sources and were converted into maps. In addition, data on past fires in the study area were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots. Secondly, the weights of the factors in the fire susceptibility were determined by the AHP method. Then, the susceptibility maps (anthropogenic and natural) were overlapped to obtain the fire susceptibility map. The results showed that, on the map, the very high and high classes of fire susceptibility cover 12.32% and 51.67% of the study area, respectively. In addition, the results indicated that 98% of the past fires have occurred in areas with high or very high susceptibility. The fire susceptibility map was evaluated by determining the area under the curve (AUC), which demonstrated a value of 0.858, indicating that the map shows high accuracy. The methodology developed in this research provides a baseline for the protective management of the forests in the study area. In addition, the fire susceptibility map is a valuable tool for forest managers to predict the occurrence of future fires in the Kahramanmaras RDF.
Failure to respond in time to fires causes great disasters. Since the loss of life and property can be significantly reduced with rapid response to fire incidents, fire services should be managed properly. Geographic Information System (GIS) is very effective in determining fire management policies of fire services thanks to its spatial analysis capability. This study focused on waste fires, which constitute the majority of fires. The fires that occurred in Izmir between 2018 and 2021 were analyzed using GIS. First, spatiotemporal analysis of fire incidents was investigated. Fire density maps were created with the Inverse Distance Weighted (IDW) interpolation method by analyzing all fires and waste fires separately. The spatial accessibility of fire stations to neighborhoods was determined by location-allocation analysis. The minimum time required for all neighborhoods to receive service in the study area has been determined as 62 minutes. The accessibility of fire stations to neighborhoods was analyzed for time intervals of 5, 10, 15, 20, 25, 30, and 62 minutes. The results were used to evaluate performance of each fire station based on the amount of population they served.
