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Fire risk management starts with an assessment of the most flammable places. In this paper, identification, classification and mapping of forest fire risk is completed with an aim of reducing the ratio and ecological damage caused by the fire. Suggested model is based on the combination of Geographical Information Systems and multi-criteria decisio...
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Fire has an important role in destruction and negative side effects on forest ecosystem. The main aim of this study
was to determine the main factors influencing the fire on set in Zarivar Lake forest area using remote sensing data,
Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) analysis. Vegetation density map
derived f...
Fires are very real problem in Campania, an Italian region in south Italy. A group of companies and bodies with heterogeneous skills integrated a fire-fighting system using different technologies. The most important element is the sharing of the information obtained using shared protocols, terrestrial and satellite technologies. Another important e...
In this paper, the problem related with a fire front propagating laterally on a slope with a nearby canyon is presented. The presence of the canyon can modify the intensity of the fire and create a difficult situation for elements involved in fire suppression. When a fire propagating laterally in a slope enters the canyon, a rapid increase in the f...
On 18th June 2017, an ample amount of smoke plume had been dispersed into the atmosphere during the raging forest fire event which had taken place in Central Portugal, thereby affecting the air quality, vegetation, socioeconomic life of the surrounding regions. This study deals with the vertical profile of aerosols during the forest fire. The verti...
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... software, where they were cropped according to the extent of the study area with the extraction tool and resampled to 90 m with the resample tool of the QGIS bilinear algorithm. This algorithm is commonly used in image and geospatial data processing, as it is a relatively simple method with good image quality [79] and presents advantages over other traditional interpolation methods such as bicubic and nearest neighbor due to its ability to balance the quality of the results, as it has no pixel scaling and no impact on the parameters being evaluated [78], results in a finer and smoother image [80], and has good computational efficiency because it is less complex, using only four neighboring pixels [81]. This resolution (90 m) is optimal for balancing information with data processing efficiency, avoiding the computational complexity and high processing times usually associated with finer resolutions (Figure 2) [82]. ...
Forest fires are the result of poor land management and climate change. Depending on the type of the affected eco-system, they can cause significant biodiversity losses. This study was conducted in the Amazonas department in Peru. Binary data obtained from the MODIS satellite on the occurrence of fires between 2010 and 2022 were used to build the risk models. To avoid multicollinearity, 12 variables that trigger fires were selected (Pearson ≤ 0.90) and grouped into four factors: (i) topographic, (ii) social, (iii) climatic, and (iv) biological. The program Rstudio and three types of machine learning were applied: MaxENT, Support Vector Machine (SVM), and Random Forest (RF). The results show that the RF model has the highest accuracy (AUC = 0.91), followed by MaxENT (AUC = 0.87) and SVM (AUC = 0.84). In the fire risk map elaborated with the RF model, 38.8% of the Amazonas region possesses a very low risk of fire occurrence, and 21.8% represents very high-risk level zones. This research will allow decision-makers to improve forest management in the Amazon region and to prioritize prospective management strategies such as the installation of water reservoirs in areas with a very high-risk level zone. In addition, it can support awareness-raising actions among inhabitants in the areas at greatest risk so that they will be prepared to mitigate and control risk and generate solutions in the event of forest fires occurring under different scenarios.
... Incluyen variables sobre la topografía, vegetación, uso del suelo, población y asentamientos (Dong et al., 2005). En conjunto, estas herramientas se utilizan cada vez más en todos los aspectos relacionados a la gestión de los incendios forestales (Gigović et al., 2018). ...
... La identificación de las zonas prioritarias es fundamental para una asignación eficiente de los recursos y la aplicación de estrategias de conservación más eficaces ante el aumento de los incendios forestales (Gutiérrez López et al., 2019). Este tipo de planteamientos han tenido éxito en otros países, utilizando herramientas tecnológicas como los SIG (Flores Garnica et al., 2018a;Gai;Weng;Yuan, 2011;Gigović et al., 2018;Jaiswal et al., 2002). ...
O Parque Estadual do Cocó (PEC) é crucial para a biodiversidade local, mas enfrenta ameaças constantes de incêndios florestais. Este estudo visa identificar áreas prioritárias para a proteção contra incêndios do PEC. Utilizando critérios de perigo (combustíveis), risco (edificações, vias, focos históricos e declividades) e valor ecológico (zoneamento), foram usadas técnicas de geoprocessamento e análise espacial para criar quatro cenários que identificam áreas prioritárias. Foram identificados 749,55 ha com combustíveis vegetais e 39.991 propriedades a 500 metros do PEC, além de 2.630 vias próximas. De 2000 até meados de 2024, foram registrados 35 focos de calor, dos quais 40% ocorreram dentro do PEC. O zoneamento revelou 595,82 ha destinados à preservação, com 551,48 ha de combustíveis em alto perigo e 45,11 ha de edificações próximas em alto risco, enquanto 656,99 ha apresentaram alto valor de conservação. O bairro Edson Queiroz destacou-se com mais de 30% das áreas de alta prioridade identificadas em todos os cenários. As áreas identificadas devem orientar medidas preventivas e de manejo, como vigilância intensificada, técnicas de prevenção de incêndios, promoção de práticas sustentáveis e melhorias nas políticas de conservação, garantindo a integridade ecológica do PEC e mitigando os riscos relacionados à atividade humana nesses ecossistemas sensíveis.
... The utilization of low-and medium-resolution geospatial data from European and global databases (i.e., MODIS, Copernicus, WorldClim, and OpenStreetMap) has been applied across various geographic regions worldwide to derive criteria for modeling wildfire susceptibility, hazard, vulnerability, and risk [19][20][21]. In extensive areas where the creation of distinct groups of natural and anthropogenic criteria is necessary, very high-resolution commercial satellite images from WorldView 2 and 3 satellites are employed [22]. ...
... GIS multi-criteria analysis (GIS-MCDA) utilizing Analytical Hierarchy Process (AHP) [19,23,24,[26][27][28][29][30][31][32] and Fuzzy Analytical Hierarchy Process (F-AHP) [31][32][33][34][35][36][37] approaches are frequently employed in numerous global studies to model wildfire vulnerability, susceptibility, hazard, and risk. In addition to these methodologies, various studies employ different approaches such as Frequency Ratio (FR) [36][37][38][39], Shannon Entropy (SE) [39][40][41], Weight of Evidence (WoE) [42,43], Statistical Index (SI) [43,44], Fuzzy Logic [45,46], Logistic regression (LR) [47,48] and among Machine Learning methods [49][50][51][52][53][54][55] are commonly utilized. ...
... Standards for determining weighting coefficients for various causal criteria, such as vegetative, topographical, climatic, and anthropogenic factors, frequently depend on expert opinions or static methodologies that utilize specific training samples [19,. By combining these approaches, researchers aim to establish a comprehensive and consistent framework that facilitates the understanding and analysis of these diverse causal criteria for differentiation. ...
Wildfires pose a significant ecological, environmental, and socioeconomic challenge in southeastern Europe. The preservation of wildlands is not only essential but also a foremost priority for Montenegro, a country recognized as the world’s first ecological state. Consequently, the development of optimal methodologies and models is of paramount importance to enhance fire protection measures. With this objective in mind, this study strives to create a wildfire susceptibility model on a national scale for Montenegro. The study employed seven natural and anthropogenic causative criteria: vegetation type; aspect; slope; elevation; climate classification; distance from road; and population. The modeling process integrates both natural and anthropogenic causal criteria, employing the Fuzzy Analytic Hierarchy Process (F-AHP) and Frequency Ratio (FR) within geoinformatics environment. The outcomes of the F-AHP model reveal that 72.84% of the total area is categorized as having high to very high susceptibility. Conversely, based on the FR model, only 29.07% of the area falls within these susceptibility levels. In terms of validation, the area under curvature values indicates good performance of the F-AHP model. In contrast, the FR model demonstrates poor performance. These novel findings, pertaining to Montenegro at a national scale, offer valuable insights for preemptive wildfire safeguarding efforts. Moreover, the methodologies employed, with necessary modifications, hold potential for application in geographically diverse regions.
... This study utilised the GISbased AHP to evaluate wildfire risk (Nguyen et al. 2024), considering human, topographic, and climatic factors to address the gap in the current methodology and develop proactive measures for managing ecologically sensitive areas. For instance, Gigović et al. (2018) used AHP to model forest fire hazards across 17,000 hectares in Bosnia and Herzegovina, Southeast Europe, revealing that land use, socioeconomic, topography, and climate had the highest values among the different clusters. Gai et al. (2011) assessed forest fire risk assessment and mapping in Southern China, focusing on hazard, vulnerability, and response capacity. ...
The increasing frequency and intensity of wildfires necessitate effective risk management in biodiversity hotspots to mitigate the potential impacts of wildfire hazards. The study utilised a multi-criteria decision analysis-analytic hierarchy process (MCDA-AHP) model to analyse wildfire risk patterns in the Garden Route District (GRD), focusing on biodiversity hotspots in the Western Cape, South Africa. The study used weight assignment and overlay analysis to evaluate wildfire risk factors, including human, topographic, and climatic factors, using data from Landsat and WorldClim from 1991 to 2021. The wildfire risk model was validated using MODIS historical fire data from the Global Forest Watch database and Confusion Matrix, with the burned area extent identified using differenced Normalized Burn Ratio (dNBR). The results show that despite 53% of the most burned area, only 12% was burned, with the high-risk zone accounting for only 11%, indicating a higher likelihood of wildfires spreading and intensifying. The results reveal a weak positive correlation (r = 0.28) between historical fire occurrences and burned areas and a negative correlation (r = − 0.27) between historical fire occurrences and fire seasons. Human and climatic factors significantly impact wildfire propagation in high-risk zones, while topographic factors have less influence, indicating a lower risk of ignition. The findings show that 26% of high-risk zones in the southwestern region dominated GRD biodiversity hotspots, while 27% were in the low-moderate-risk zone in the northwestern parts. The results of this study can aid in assigning fire risk-based criterion weights to support decision-makers in regional and global wildfire prevention and management.
... The methodology of this study is based on the spatial structure of the Multi-Criteria Decision Analysis (MCDA), and it consists of procedures involving sharing of geographic data and preferences according to specified rules (Malczewski, 1999). The main advantage of the integration of GIS and MCDA is to have unique capabilities that complement each other (Gigović et al., 2018). This multi-criteria decision analysis (MCDA) model integrates the capabilities of geographic information systems (GIS) in data storage, manipulation, management, analysis, and visualization with MCDA procedures, techniques, and algorithms. ...
... Sustainability analysis workflow of this study using Spatial Multi-criteria Decision Analysis (MCDA) [adapted from Gigović et al., 2018]. Year 2023 LULC map of the study area with five boundaries of land use areas: (i) Industrial Tree Plantations (ITP), (ii) forest reserves, (iii) village reserves, (iv) oil palm plantations, and (v) rubber plantations. ...
The assessment of environmental sustainability is of utmost importance for the forests and plantations in Borneo, given the critical need for environmental protection through the identification and mitigation of potential risks. This study was conducted to assess the environmental sustainability of tropical forest and plantations landscape, a case study in northern Sabah, Malaysian Borneo. Applications of the latest high-resolution multi-sensor remote sensing and geospatial MCDA are cost-effective and useful for large-scale environmental sustainability assessment. The land use land cover (LULC) of the study area was mapped with synergistic use of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical and high-resolution PlanetScope satellite imageries, resulting in overall accuracy of 87.24%. Five sustainability indicator layers: slope erosion protection, river buffer, landscape connectivity and quality, high conservation value (HCV), and water turbidity were developed from the LULC map, ancillary datasets of SRTM, and forest operation basemap with reference to standards from the Environment Protection Department (EPD), Roundtable on Sustainable Palm Oil (RSPO), and Forest Management Plan (FMP) for the analysis using multi-criteria decision analysis (MCDA) model. The results revealed that overall, the study areas are in the high sustainability category at 61%, medium at 31%, and low at only 8%. We analyzed the environmental sustainability of five land use boundaries, and the results showed that Industrial Tree Plantations (ITP) and Village Reserve are mostly in the high category. Meanwhile, oil palm plantations, rubber plantations, and forest reserve (FR) are the majority in the medium category. Both oil palm and rubber plantations are a majority in the medium class due to monocropping land use type having low landscape connectivity and quality individual sustainability indicator layer. The study presented the concept of use of multi-sensor remote sensing for LULC mapping with geospatial MCDA for environmental sustainability assessment useful to stakeholders for improving the management plan also contributing toward the progress of achieving UNSDGs and addressing REDD+.
... Several factors are involved in the initiation and development of a forest fire, including the availability of combustible material, climatic conditions, topographic characteristics of the terrain and the ignition source [3,4]. Forest fires can be caused in two ways, the first has to do with natural events such as volcanoes and lightning, the second has to do with anthropogenic events, as humans can trigger forest fires by negligence or carelessness and by their own will to obtain some personal benefit [5], even though forest fires are beneficial for some ecological processes in forests when forest fires are caused by humans the damage increases exponentially as they put people's lives at risk and cause economic damage through the destruction of infrastructure [6], worldwide there are alarming reports on mortality due to this event, Sinha et al. [7] indicate that in the 10 countries most affected by forest fires the number of fatalities due to forest fires between the years 1900 and 2022 amounts to 2851 people. ...
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.
... Taking into consideration its geospatial nature, most fire risk assessment models are focused on computing fire behavior, fire danger, and fire effects to spatially assess a fire hazard and consequently the fire risk using simulation modeling of weather and fuel moistures [31,34]. Multi-criteria decision analysis (MCDA) integrated with a geographical information system (GIS) has proven to be a valuable tool in fire hazard and fire risk assessment and is often used to incorporate and evaluate characteristics of various fire risk components (e.g., [30,[35][36][37][38][39][40]). Apart from the ability to consider various factors affecting the problem at hand, the MCDA approach allows for the efficient control of assigning the preferences to criteria/alternatives considered and works well with both numerical and categorical data. ...
Various factors influence wildfire probability, including land use/land cover (LULC), fuel types, and their moisture content, meteorological conditions, and terrain characteristics. The Adriatic Sea coastal area in Croatia has a long record of devastating wildfires that have caused severe ecological and economic damages as well as the loss of human lives. Assessing the conditions favorable for wildfires and the possible damages are crucial in fire risk management. Adriatic settlements and ecosystems are highly vulnerable, especially during summer, when the pressure from tourist migration is the highest. However, available fire risk models designed to fit the macro-scale level of assessment cannot provide information detailed enough to meet the decision-making conditions at the local level. This paper describes a model designed to assess wildfire risks at the meso-scale, focusing on environmental and anthropogenic descriptors derived from moderate- to high-resolution remote sensing data (Sentinel-2), Copernicus Land Monitoring Service datasets, and other open sources. Risk indices were integrated using the multi-criteria decision analysis method, the analytic hierarchy process (AHP), in a GIS environment. The model was tested in three coastal catchments, each having recently experienced severe fire events. The approach successfully identified zones at risk and the level of risk, depending on the various environmental and anthropogenic conditions.
... With the increasing availability of geospatial data, particularly remotely sensed data, and the incorporation of expert participatory planning to identify relevant criteria, more improved decision-making processes and outcomes have been achieved (Thompson et al., 2020;Jaiswal et al., 2002;Nuthammachot and Stratoulias, 2021). The synergistic use of geographical information systems (GIS) and participatory-based multi-criteria models has demonstrated several strengths in applications related to forest fuel planning (Gigović et al., 2018). This framework enables decision-makers to transparently choose and standardize indicators and criteria by integrating stakeholder preferences into a quantitative spatial-based model (Erden and Coşkun, 2010;Roe, 2012), having high applicability in the strategic prioritisation of areas for effective allocation of Priority Management Zones. ...
... In general, based on the results, distance to roads had the highest weight in fire occurrence, followed by distance to settlements. In addition, many studies have obtained similar results regarding the role of anthropogenic factors in fire occurrence (Sivrikaya et al. 2014, Güngöroglu 2017Gigovic et al. 2018, Novo et al. 2020, which confirms the results of this study. Based on the results of another study, the weight of distance to the roads in fire probability was 0.2615, while the weight of distance to settlements was 0.2299, and the weight of distance to agricultural lands was 0.1788 (Sivrikaya and Küçük 2022), which is similar to the findings of our study. ...
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.
... When the criteria influencing the wildfire risk assessment are evaluated, in almost all papers, the most influential criterion is different. In three studies [25,37,39], risk degree was associated with the criteria of distance to the road or existence of the road in the region. Additionally, the distance to farms criterion is determined as the most critical factor in the two studies. ...
The increasing frequency of wildfires has posed significant challenges to communities worldwide. The effectiveness of all aspects of disaster management depends on a credible estimation of the prevailing risk. Risk, the product of a hazard’s likelihood and its potential consequences, encompasses the probability of hazard occurrence, the exposure of assets to these hazards, existing vulnerabilities that amplify the consequences, and the capacity to manage, mitigate, and recover from their consequences. This paper employs the multiple criteria decision-making (MCDM) framework, which produces reliable results and allows for the customization of the relative importance of factors based on expert opinions. Utilizing the AROMAN algorithm, the study ranks counties in the state of Arizona according to their wildfire risk, drawing upon 25 factors categorized into expected annual loss, community resilience, and social vulnerability. A sensitivity analysis demonstrates the stability of the results when model parameters are altered, reinforcing the robustness of this approach in disaster risk assessment. While the paper primarily focuses on enhancing the safety of human communities in the context of wildfires, it highlights the versatility of the methodology, which can be applied to other natural hazards and accommodate more subjective risk and safety assessments.
