Fire Risk Map Research Articles

The Use of Geographic Information Systems and Multi-Criteria Decision-Making Methods in the Creation of Forest Fire Susceptibility Maps: A Literature Review

Forest fires are natural disasters that cause significant environmental, economic, and social damage worldwide. This study provides a literature review examining how Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods are utilized in the prevention of forest fires and the identification of high-risk areas. GIS, as a system used for the collection and analysis of spatial data, enables the consideration of various factors influencing fire risk, such as climate change, topography, vegetation, and weather conditions. The spatial analysis capabilities offered by GIS play a critical role in identifying regions with high fire susceptibility when generating fire risk maps. Additionally, MCDM methods contribute significantly to the decision-making process by allowing the evaluation of multiple criteria in fire risk analysis. Logistic Regression and Frequency Ratio, which are frequently employed in the literature, are widely used in fire risk analysis and improve the accuracy of susceptibility maps. Furthermore, MCDM methods have been proven effective in estimating the likelihood of forest fire occurrences and identifying fire-prone areas. The integration of GIS and MCDM methods allows for more precise identification of risk zones and supports the development of fire prevention strategies. This literature review highlights the advantages of utilizing GIS and MCDM in the production of forest fire susceptibility maps and suggests that these methods may have broader applications in future research. The effective use of technology in combating forest fires enhances the accuracy of fire risk assessments, contributing significantly to environmental protection efforts.

Forest Fire Risk Assessment and Mapping Using Remote Sensing and GIS Techniques: A Case Study in Nghe An Province, Vietnam

Forest Fire Risk Assessment and Mapping Using Remote Sensing and GIS Techniques: A Case Study in Nghe An Province, Vietnam

A Deep Reinforcement Learning Algorithm for Trajectory Planning of Swarm UAV Fulfilling Wildfire Reconnaissance

Wildfires have long been one of the critical environmental disasters that require a careful monitoring system. An intelligent system has the potential to both prevent/extinguish the fire and deliver urgent requirements postfire. In recent years, unmanned aerial vehicles (UAVs), with the ability to detect missions in high-risk areas, have been gaining increasing interest, particularly in forest fire monitoring. Taking a large-scale area involved in a fire into consideration, a single UAV is often insufficient to accomplish the task of covering the whole disaster zone. This poses the challenge of multi-UAVs optimum path planning with a key focus on limitations such as energy constraints and connectivity. To narrow down this issue, this paper proposes a deep reinforcement learning-based trajectory planning approach for multi-UAVs that permits UAVs to extract the required information within the disaster area on time. A target area is partitioned into several identical subareas in terms of size to enable UAVs to perform their patrol duties over the subareas. This subarea-based arrangement converts the issue of trajectory planning into allowing UAVs to frequently visit each subarea. Each subarea is initiated with a risk level by creating a fire risk map optimizing the UAV patrol route more precisely. Through a set of simulations conducted with a real trace of the dataset, the performance outcomes confirmed the superiority of the proposed idea.

MAPPING THE RISK OF FOREST FIRES IN THE SOUK AHRAS MOUNTAINS

Forests fire is the most devastating factor of degradation of the Algerian forest, despite numerous efforts to limit it. Traditional fire prevention and firefighting methods have shown their limitations due to their slowness… , is it therefore useful to adopt new techniques in order to react quickly and effectively. The main objective of this study is to model the «forest fire» risk, in order to produce a risk map, which is indispensable for preventing and informing on fire-vulnerable areas. To achieve this objective, a forest fire risk map was elaborated, on the basis of the scientific methods using geographical information systems and satellite imagery. The adopted approach is an empirical model, involving three parameters controlling the behavior of fire, the topo-morphology of the ground, the combustibility of the vegetation cover and human activity. The study area chosen is one of the most affected areas by this phenomenon (Souk Ahras Mountains). It belongs to the densest forest in Algeria, known for its sensitivity to fire, particularly in relation to its physical conditions (nature of the vegetation cover, climatic conditions (strong Sirocco wind) and socio-economic conditions (high population density).

Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces

The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets.

Influence of wildfires on the conflict (2006–2022) in eastern Ukraine using remote sensing techniques (MODIS and Sentinel-2 images)

Humans primarily alter fire regimes in three ways: changing the distribution and density of ignitions, shifting the seasonality of burning, or altering available fuels. Remote sensing techniques are a highly feasible and effective tool for monitoring wildfires, delineating burn perimeters, describing patterns of fire occurrence, characterizing the degree of intensity, or fire risk mapping. In this study MODIS and Sentinel-2 satellite images were used to analyze the burned areas in the Luhansk and Donetsk Regions of Ukraine (2006–2022), and specifically how the political and military hostilities in 2014 impacted fire activity. The burned areas detected by the MODIS sensor were selected using a size (32 ha) and temporal (May–October) filter. The Relativized Burn Ratio (RBR) index was used to identify burned areas, while the Normalized Difference Water Index (NDWI) was employed to discriminate the water bodies in Sentinel-2 images. We analyzed the burned area data over the 17-year period, eight of them during the years before the conflict and the other nine after the conflict. The results show that burned area has increased from the pre-conflict period (2008–2013) by 689,898 ha to the post-conflict period (2014–2022) around the contact line. In addition, the frequency of fires has increased in the post-war period, especially towards the south of Donetsk. Finally, Sentinel-2 imagery was used to map Large Wildfires (LAWF), which means fires with a burned area >500 ha. Our study concluded that MODIS MCD64A1 underestimates the burned area with a recorded difference of up to 40% of the area in one of the analyzed fires. In addition, an analysis of the burned areas classified according to the main land use has been performed for both sensors. The results obtained show a regression for the total area of R2 = 0,97 and both sensors show high correlation between them for each land use analyzed. Grasslands and agricultural land covers were most affected by LAWFs during the conflict period (2014–2022), noting that fires near urban entities were also more frequent in the war period.

FOREST FIRES RISK IN A SEMI ARID REGION: A CASE STUDY IN WESTERN ALGERIA

Purpose: The objective of this study was to measure the fire risk of forests and to compare areas with high sensitivity to forest fires. Theoretical Framework: Many authors have demonstrated the fragility of Algerian forests in the face of fires (Belhadj, 2003; Souidi et al., 2010 and 2017). In this research paper, the researchers focused on identifying the factors responsible for the risk of forest fires in Algeria using a state-of-the-art tool, the Geographic Information System (GIS). The researchers explained a scientific process for exploring new indicators using GIS, and showed how to develop a forest fire risk map. Design/Methodology/Approach: The elaboration of the fire risk map requires the application of a model set up, and tested on forest areas in the Mediterranean region. The applied model involves the three main factors of forest fire risk assessment: topomorphology, fuel, and human occupation. Findings: According to the results obtained, our study area is dominated by a very high risk index. In addition, more than half of the surface area of our study zone is located in topomorphological conditions that are highly conducive to the multiplication of fires and can increase the risk of forest fires breaking out. This study will help us to better control the risk of fires and to manage fire-fighting operations in the field. Research, Practical & Social Implications: Mapping the risk of forest fires using GIS will help land managers and planners to identify sensitive areas for better preservation and intervention in the event of fire. Originality/Value: The results show that the number of forest fires is increasing over time and that managing them is becoming increasingly difficult. The use of a GIS for all stakeholder and managers will make it possible to combat forest fires.

Integrating meteorological and geospatial data for forest fire risk assessment

Understanding the factors that cause fire is crucial for minimizing the fire risk. In this research, a comprehensive approach was adopted to recognize factors influencing forest fires. Golestan National Park (GNP) was considered as a representative area with a humid climate in this study. Initially, using the Multi-Criteria Evaluation Method, a fire risk map was created by analyzing natural and human factors, and vulnerable areas were identified. Then, the relationship between key elements such as meteorological conditions, Land Surface Temperature (LST), and precipitation, with the occurrence of fire in different years was investigated. CHIRPS and Landsat data were utilized to assess LST changes and precipitation. 23-year changes in fire occurrence areas in GNP were acquired using MODIS products. The results of the data analysis showed that the highest number of fires occurred in forest areas, and in the fire risk prediction map, the extremely high-risk class is completely consistent with the ground truth data. The assigned weights, derived from expert opinions, highlight the substantial significance of elevation, and distance from roads and settlements. Additionally, the effectiveness of the model in providing reliable forecasts for fire risks in GNP is highlighted by the ROC curve with an AUC value of 0.83. Forest fires within GNP exhibit a distinct seasonality, predominantly occurring from July to December. During the warmer months, by coinciding with summer excursions, human activities may contribute to the ignition of fires. In 2013 and 2014, rising fire incidents correlated with elevated temperatures, hinting at a potential connection. GNP fires showed an upward trend with higher monthly LST and a downward trend with increased annual precipitation. The results showed that there is a relationship between LST, precipitation, and the occurrence of fire in GNP. Approximately 176.15 ha of GNP's forest areas have been destroyed by fires over the last two decades. This research demonstrated that there is a dynamic interaction between environmental conditions and fire incidents. By considering these factors, managers and environmental planners can develop effective strategies for managing and preventing forest fire risks.

Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

Forest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices

Fire is one of the most important natural catastrophes threatening the forest ecosystem. The severity and frequency of forest fires are increasing daily due to the increase in population in vulnerable areas and the effects of global climate change. Creating fire risk maps and using them to take the required protective actions to prevent fires will decrease the adverse effects of forest fires. This study focused on producing and comparing fire risk maps based on four vegetation indices, the Normalized Burn Ratio (NBR) index, Normalized Burn Ratio Thermal (NBRT) index, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) and data gathered with the use of remote sensing devices. The Muğla Regional Directorate of Forestry, which is in the Mediterranean climate zone and has experienced mega-fires, was selected as the case study area. Fire risk maps were prepared for the four vegetation indices from Landsat 8 OLI satellite images. Receiver operating characteristic curves and 195 fire ignition points that occurred in 2021 from July 5 to the end of the year were used to assess the accuracy of fire risk maps. Most fire ignition locations (>90%) were in high- and extremely high-risk fire areas on the maps prepared according to the NBR, NDWI, and NDVI. The fact that almost all of the fires occurred in high-risk areas revealed that the study area was sensitive to fire and that the vegetation indices used to draw up the risk maps were highly accurate in predicting where fires might occur. The accuracy results showed that the area under the curve was 0.842 for the NBR, 0.835 for the NDWI, 0.812 for the NBRT, and 0.810 for the NDVI. The NBR approach was more precise than the other models in providing information for fire risk maps. Risk maps created with the NBR could help decision-makers to take precautions and minimize fire damage.

Fire Risk Mapping for Holm Oak Forests in El Hassasna Region as Part of the Ecosystem Restoration Programme

Abstract In the context of the restoration of the holm oak forest ecosystem, which has suffered damage from wildfires, and with the aim of reducing the wildfire risk, the primary threat to these natural habitats, this study seeks to establish a systematic approach to map areas at risk of forest fires. This approach relies on the use of remote sensing data, including Landsat 8 satellite images and digital elevation models using Geographic Information System (GIS). Factors influencing the ignition and spread of forest fires were assessed, including vegetation cover, topography, and human factors. A forest fire risk map has been created for the Holm oak forest in the Al Hassasna region (Saida province), enabling local authorities and forest managers to implement more effective fire prevention and management measures.

Forest Fire Risk Mapping Using GIS Based Analytical Hierarchy Process Approach

Turkiye is located in a region sensitive to forest fires due to the Mediterranean climate, vegetation and topography of the south and west coasts. Every year, forest fires for various reasons cause thousands of hectares of forest area to be burned. Fires damage the ecosystem as well as have economic consequences. While the Mediterranean, Aegean and Marmara coasts, where the Mediterranean climate and fire-sensitive tree species are dominant, are at primary risk against forest fires, the forests located on the Black Sea coasts where the Black Sea climate is dominant are less risky in terms of fire. For an effective fight against forest fires, it is crucial to identify zones with fire risk based on various parameters such as forest structures (tree species, crown closure, stand age), topographic features (slope, aspect), climate, and proximity to certain points (such as roads, settlements, agricultural areas). Fire risk data will shed light on the measures that can be taken against fire. In this study, GIS (Geographic Information Systems) based Analytical Hierarchy Process (AHP) method, one of the well know MCDA (Multi-criteria Decision Making Analysis) methods, was used to develop the fire risk map of Mersin Forestry Regional Directorate (FRD) within the Mediterranean region of Turkiye. Then, the accuracy of the fire risk map was evaluated by taking into account the previous fires in the regional directorate. As a result, it was found that 13.87% of the study area was classified as very high, 25.87% high, 24.68% medium, 22.44% low and 13.14% very low risk areas. The results showed that tree species are the most effective risk factor in forest fires, and followed by stand age factor. The accuracy of the fire risk map was evaluated by using the location information of a total of 562 fires in Mersin FRD between 2003-2022 that damaged forest areas. In order to determine the accuracy of the fire risk map, ROC (Receiver Operating Characteristic) curve method was used in ArcGIS environment. As a result, the AUC (Area Under Curve) value was found to be approximately 74%, and these values showed that the fire risk map developed for Mersin FRD was moderately reliable. With this study, it has been demonstrated that it is possible to produce reliable fire risk maps in a short time using the GIS-based AHP method.

Forest Fire Dynamics in Nepal’s Mid-hills: Insights from Spatial Analysis and Risk Mapping of Doti District

Forest fires have significantly contributed to forest degradation and pollution all over Nepal. Despite their high severity, limited studies have been conducted on the spatial-temporal analysis and risk mapping of these fires especially in the Mid-hill region. This study aims to fill this gap by analyzing Visible Infrared Imaging Radiometer Suite (VIIRS) data from 2012 to 2022, along with satellite imagery, using remote sensing and geographic information systems (GIS) technology. Nine variables, including land class, distance to roads, proximity to settlements, temperature, wind speed, precipitation, slope, aspect, and elevation, were utilized to identify forest fire patterns and create a fire risk map. A multi-parametric hierarchical weighted index model was employed, with multicollinearity checked (VIF<2) to ensure model accuracy and validated by Area Under Curve (AUC=0.787). The study revealed an increasing trend of forest fires, particularly during March to May, in broad-leaved forests near human interference. Out of the total forested area of 1447.58 km2, 42.9%, 46.03%, and 11.07% were classified under high, moderate, and low-risk zones, respectively. The study emphasizes the recurring forest fire problem in often-neglected Mid-hill districts and underscores the need to prioritize them in future strategies.

Geoinformatics and Machine Learning for Comprehensive Fire Risk Assessment and Management in Peri-Urban Environments: A Building-Block-Level Approach

Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m2) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas.

FOREST FIRE RISK MAPPING FOR THE HIMALAYAN STATE UTTARAKHAND USING GOOGLE EARTH ENGINE

Abstract. Climate change has exacerbated the intensity as well as frequency of forest fire events in the Indian state of Uttarakhand. The present study focusses on undertaking forest fire risk mapping across the state by utilizing geospatial technology along with Google Earth Engine. Ten parameters were identified that have a strong influence in determining fire prone areas. The Analytic Hierarchy Process (AHP) was then implemented for the development of the risk map in which criteria weights were assigned to the parameters based on their ability to influence a forest fire event. The analysis revealed that out of the total forest area, 24.22% is under ‘very high’ risk zone, 29.24% is under ‘high’ risk zone, 18.23% is under ‘moderate’ risk zone, 7.69% is under ‘low’ risk zone and 20.62% is under ‘very low’ risk zone of forest fire. Further study was carried out to determine fire risk levels in populated regions and in some of the most critical nature reserves having high ecological importance which reveals that ‘very high’ and ‘high’ risk zones have greater population density indicating the influence of anthropogenic activities on forest fire occurrence. The results additionally indicate that four national parks and wildlife sanctuaries are particularly vulnerable to forest fires at present which is a source of concern and requires intervention from the stakeholders.

A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm

The subjective and empirical setting of hyperparameters in the random forest (RF) model may lead to decreased model performance. To address this, our study applies the particle swarm optimization (PSO) algorithm to select the optimal parameters of the RF model, with the goal of enhancing model performance. We employ the optimized ensemble model (PSO-RF) to create a fire risk map for Jiushan National Forest Park in Anhui Province, China, thereby filling the research gap in this region’s forest fire studies. Based on collinearity tests and previous research results, we selected eight fire driving factors, including topography, climate, human activities, and vegetation for modeling. Additionally, we compare the logistic regression (LR), support vector machine (SVM), and RF models. Lastly, we select the optimal model to evaluate feature importance and generate the fire risk map. Model evaluation results demonstrate that the PSO-RF model performs best (AUC = 0.908), followed by RF (0.877), SVM (0.876), and LR (0.846). In the fire risk map created by the PSO-RF model, 70.73% of the area belongs to the normal management zone, while 15.23% is classified as a fire alert zone. The feature importance analysis of the PSO-RF model reveals that the NDVI is the key fire driving factor in this study area. Through utilizing the PSO algorithm to optimize the RF model, we have addressed the subjective and empirical problems of the RF model hyperparameter setting, thereby enhancing the model’s accuracy and generalization ability.

Current and future land fire risk mapping in the southern region of Sumatra, Indonesia, using CMIP6 data and GIS analysis

Land fires lead to a deterioration of environmental quality and have perilous social and economic aspects. One method for mitigating this hazard is to determine the range of fire risk areas. Thus, this study aimed to analyze the current and potential future fire risk in the Ogan Komering Ilir, southern Sumatra region of Indonesia, using CMIP6 data and geospatial technology. In order to analyze fire risk areas, the Landsat 8 satellite image from 2021 (as a baseline period) was used. Variables like the normalized difference moisture index (NDMI), the normalized difference vegetation index (NDVI), and the land surface temperature (LST) were extracted from the satellite image. Then, the standard precipitation index (SPI) was determined using rainfall data from meteorological stations. For future period prediction (periods 2021–2040 and 2041–2060), this study used future climate data from the CMIP6. By overlaying these variables, current and future fire risk zone maps were produced. The results obtained showed that in the current period, around 726.4 km2 (4.2%) of the area was at high fire risk, then 5,621.3 km2 (32.7%) and 10,835.3 km2 (63.1%) were at moderate and low fire risk zones, respectively. Furthermore, for the future period, this study estimated there was an increase in high fire risk areas of about 14% during 2021–2040 and 30% during 2041–2060. This was recorded in the southern and northern parts of the study area. Fire risk areas were strongly associated with NDMI, NDVI, and LST. The study's findings may be useful for land fire management in fire-prone areas.

WILDFIRE RISKS IN THE SOUTHWEST OF KY SON DISTRICT, NGHE AN PROVINCE – A MULTI-CRITICAL MODEL

Wildfires are considered one of the most common hazards in mountainous areas, posing a serious threat to flora, fauna, and humans. This article focuses on building a forest fire risk map based on GIS techniques in the southwest area of Ky Son district, Nghe An province. The selected factors to create the forest fire risk map include 9 indices: NDDI drought index, surface temperature, slope, elevation, distance to roads, distance to rivers and streams, distance to upland fields, distance to residential areas, and aspect. The results of determining the weights of these factors using the AHP hierarchical analysis technique show that drought and surface temperature are the two factors that have the greatest influence on the forest fire risk index. The limit values that lead to forest fire risks for the drought criterion are 0.2 - 0.4 mm and for the surface temperature criterion are 25 - 30°C.The area with high fire risk accounts for the largest proportion, 38%, covering an area of 24,519.78 ha, followed by the average fire risk with 21,012.48 ha, accounting for 33% of the entire study area. The area with extremely high fire risk occupies the smallest area, 1,283.76 ha (2%). While controlling wildfires is challenging, forest fire risk maps can detect areas with higher risk and mitigate them. Therefore, creating forest fire risk maps for the identified areas is necessary.

Assessment of two methods on zoning wildfire propagation in Itacolomi State Park, Minas Gerais State, Brazil

This study aimed to assess the wild fire propagation risk to wildfires in the Itacolomi State Park, in Minas Gerais State, Brazil, using GIS and to compare the efficiency of the incident solar radiation over the aspect variable. The following variables were used: land cover/use (LCU), slope (SLP), slope curvature (CUR), aspect (ASP) and incident solar radiation (SOL). The weights of each variable were calculated from the ratio between the total area and the burned area of each class in order to generate the fire propagation risk maps. Fire data from 2016 to 2019 were used for validation. When the moderate risk class was considered susceptible, inadequate precision was observed for both methods (ASP and SOL). On the other hand, when the moderate class was considered non-susceptible to fire, the results presented moderate accuracy. Furthermore, the methods using SOL and ASP showed similar results. The results can guide fire mitigation actions on the park.

Mapping South Florida Daily Fire Risk for Decision Support Using Fuel Type, Water Levels, and Burn History

Mapping fire risk in South Florida depends on spatially varying water levels, fuel characteristics, and topography. When surface water levels recede below the lowest topographic features (cypress strands, marshes, etc.), the ecosystem loses its natural, wetted fire breaks, and landscape-level fire risk increases. We developed a geospatial method to generate daily, categorical fire risk maps; the maps visualize low-to-high risk areas using a newly developed 100 m DEM, modeled water levels, fuel types, and fire management units. We assigned fire risk by creating a water level distribution for each unique combination of fuel type and fire management unit; fire risk was then assigned for each pixel based on risk percentiles commonly used by fire management agencies. Assigning risk based on unique fuel types and management units helped avoid over- or under-assigning fire risk that may occur when applying landscape-level “average” risk relationships. Daily maps also incorporated (1) energy release component data to better estimate fuel moisture and (2) historical burn footprints to reduce risk in recently burned areas. Our data-driven approach generated at management-relevant spatial scales may enable more informed prescribed burn planning and may increase the efficiency of staff and resource allocation across the landscape on high-wildfire-risk days.