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

Abstract

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.

GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria

“…Similar results were reported in related studies [78]. However, the forest fire susceptibility literature shows a high objective reliability in deriving maps using FR and AHP, such as in Turkey [79], Ethiopia [80], and Brazil [81]. In the context of explaining the higher accuracy of the FR method than that of the AHP method, it needs to be stated that the FR method took into account the true spatial distribution of forest fire events in relation to sub-classifications of the driving factors, thus determining the effectiveness of each sub-classification's influence in enhancing the probability of forest fires.…”

Modeling fires based on the results of correlation analysis

“…et al, 2011) used two weather parameters to effectively predict the occurrence of forest fires in developing countries. Pradeep et al (Xufeng Lin et al, 2023;Pradeep G. S. et al, 2021) used GIS tools to classify forest fire hazard zones, and Gulchin et al (Xufeng Lin et al, 2023;Gülçin D. et al, 2020) built a forest fire risk map using GIS. To predict the characteristics of forest fires, Maffei et al (Xufeng Lin et al, 2023;Maffei, C. et al, 2021) used a combination of multispectral and thermal remote sensing data to predict forest fires.…”

NBR Index-Based Fire Detection Using Sentinel-2 Images and GIS: A Case Study in Mosul Park, Iraq

“…With the advent of geographic information systems (GIS) and Remote Sensing (RS) [3] and [7], fire risk forecast has become popular in sophisticated fire-controlling scenarios [8]. GIS offers massive benefits in evaluating many geographical features and analyzing the events that develop according to these geographical features [9]. GIS identifies and prioritizes the geographical site of fire events and hotspots and enables map development to visualize the scenario results [6].…”