Forest Fire Risk Zone Research Articles

Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

Forest fire is a primary disaster that destroys forest resources and the ecological environment, and has a serious negative impact on the safety of human life and property. Predicting the probability of forest fires and drawing forest fire risk maps can provide a reference basis for forest fire control management in Hunan Province. This study selected 19 forest fire impact factors based on satellite monitoring hotspot data, meteorological data, topographic data, vegetation data, and social and human data from 2010–2018. It used random forest, support vector machine, and gradient boosting decision tree models to predict the probability of forest fires in Hunan Province and selected the RF algorithm to create a forest fire risk map of Hunan Province to quantify the potential forest fire risk. The results show that the RF algorithm performs best compared to the SVM and GBDT algorithms with 91.68% accuracy, 91.96% precision, 92.78% recall, 92.37% F1, and 97.2% AUC. The most important drivers of forest fires in Hunan Province are meteorology and vegetation. There are obvious differences in the spatial distribution of seasonal forest fire risks in Hunan Province, and winter and spring are the seasons with high forest fire risks. The medium- and high-risk areas are mostly concentrated in the south of Hunan.

Forest Fire Risk Zone Mapping in Mizoram Using RS and GIS

In recent years, India’s Northeastern territory has been plagued by wildfires. Mizoram, as per FSI, has been among the most affected regions, with approximately 20,744-recorded wildfires. Forest fire directly or indirectly affects human health, climate change, and the environment. The forest fire risk zonation is generated utilizing remote sensing data, and Geographic Information System based on specified physical and socioeconomic factors. As part of the current study, a Geographical Information System is employed to determine forest fire risk based on predetermined physical and socioeconomic parameters. This study used three distinct models of fire risk zonation index namely FRI (Fire Risk Index), HFI (Hybrid Fire Index), and SFI (Structural Fire Index) to identify the zones in the study area under the risk of forest fires. The Indian state of Mizoram is categorized into five distinct hazard zones based on the probability of wildfire incidents. Fire alerts generated using risk models, and real-time hotspot datasets (forest fire spots) received from MODIS and USGS have been validated. According to the study’s findings, 18.84 km2 of the study area is at low risk of forest fire, 11.072 km2 is at moderate risk, and 5.38 km2 is at high risk. The probability from each metric varies since the input and weightage of the different parameters vary from one another. SFI, therefore, therefore predicts a lower frequency of high-risk wildfire-prone zones than HFI and FRI. In this research, the coefficient of discrimination (R2) is employed to assess the reliability of the projected fire indices being compared with real-time hot spots. Here, FRI possesses the highest accuracy (R2=0.892), HFI has moderate accuracy (R2=0.676), and FRI has the lowest accuracy (R2=0.629).

Mapping the forest fire risk zones using artificial intelligence with risk factors data.

Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires, and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of 11 human-induced factors, a natural factor, and temperature, which is one of the risk factors that determine the conditions for the occurrence of forest fires. Moreover, k-means clustering algorithm, which is an artificial intelligence method, was employed in preparation of the fire risk zone map. Turkey was selected as the study area because there are social and economic variations among its regions. Thus, the regional forest directorates in Turkey were separated into four clusters as extreme-risk zone, high-risk zone, moderate-risk zone, and low-risk zone. Also, a map presenting these risk zones were provided. The map reveals that, in general, the western and southwestern coastal areas of Turkey are at high risk of forest fires. On the other hand, the fire risk is relatively low in the northern, central, and eastern areas.

Natural Disaster Risk Assessments for Pine Honey Apiaries in Muğla, Turkey

Since Muğla province has 90% of the world's total pine honey production, ensuring efficiency and economic income requires the determination of measures for apiary locations and estimation of risks. However, ensuring development and productivity requires identifying natural disasters susceptibility such as forest fires and floods to maintain productivity. Muğla province has a high forest fire potential due to its dense forest cover and approximately 200 forest fires occur each year. Forest fires are one of the main factors that threaten apiaries, as there are a lot of apiary places (approximately 15,000) in forests for pine honey. On the other hand, due to the mountainous topography and high precipitation rate of Muğla, the province has a high rate of flood formation (20 per year), which threatens the hive sites by destroying the entire colony. In this study, Apiary Locations Risk Index (ALRI) was carried out to guide the insurance process for apiary locations by applying the Forest Fire Risk Index (FFRI) and the Flood Hazard Risk Index (FHRI). Determination of forest fire risk zones and flood hazard maps requires environmental, forestry, topographic, economic and meteorological parameters to be handled within a decision support platform. For this purpose, Analytical Hierarchy Process (AHP) technique supported by Geographic Information System (GIS) was used in the creation of sensitivity maps. As a result, 1533.40 ha (11.82%) of the study area was determined as extremely risky areas for apiary areas. The results were confirmed with 1454 forest fire sites and 20 flood hazard sites where the Eşen, Dalaman, Çine, Sarıçay, Akçay, Kamiişdere and Namnam rivers were stated to be highly susceptible to flood hazard.

Forest Fire Risk Zone Mapping of Aalital Rural Municipality, Dadeldhura District, Nepal

Forest fire is one of the leading causes of forest and wildlife loss. The objective of this study was to use satellite imagery and Geographic Information System techniques to assess the forest fire risk zonation map of the Aalital rural municipality. This rural municipality is a part of the Sudurpaschim province, Nepal; is prone to forest fires. Four fire risk zones were established in the study area i.e. very high, high, medium, and low-risk zone. Thematic layers were derived from topographic maps and satellite imageries. For the delineation of fire risk zones, a multi-parametric weighted index model i.e. the FRI (Fire Risk Index) method was adopted. The fire incidence data provided by MODIS were used to validate the resulting forest fire risk zone map. About 25.17% of the total study area lies under the very high-risk zone followed by 46.51% under high risk, 25.68% under medium risk, and 2.62% under the low-risk zone. It can be inferred that the majority of the area is at high risk of forest fire. This map of fire risk zone can help in disaster and forest management as valuable data to prepare effective measures for appropriate fire risk management in the area.

Demarcation of Forest Fire Risk Zones in Silent Valley National Park and the Effectiveness of Forest Management Regime

Demarcation of Forest Fire Risk Zones in Silent Valley National Park and the Effectiveness of Forest Management Regime

Analyzing the Forest Fire Risk of a Tropical Deciduous Forest in Southern Western Ghats Region in Wayanad Wildlife Sanctuary, Kerala, India

In recent years, the forest fire has increased at an alarming rate in India. It has become a common incident in many areas of Wayanad Wildlife Sanctuary situated along the North-Eastern part of Kerala, lying in the Western Ghats. The forest fire incidents happen mainly in the pre-monsoon period ranging from February to April. Using various forest risk parameters, the study analyses the forest fire risk zone in the Wayanad Wildlife Sanctuary. A weighted index method using GIS and Remote sensing was acquired to obtain and distinguish the forest fire-risk map by including the risk variables such as vegetation, topographic factors, land surface temperature and proximity to the road and settlements. The forest fire risk zone map integrates the observed variable with the help of the GIS platform. Out of the study area's total of 412 sq km, 24.65 % falls in very high and 33.39 % comprises the high fire risk zones. Validation of the forest fire risk zone map with the fire locations illustrates that most fire locations (72 %) fall under high and high-risk zones. The result of the study could help the decision-makers to implement fire management plans and strategies.

Forest Fire Risk Zone Mapping of Eravikulam National Park in India

Forest fire is one of the most common natural hazards occurring in the Western Ghats region of Kerala and is one of the reasons for forest degradation. This natural disaster causes considerable damage to the biodiversity of this region during the dry fire season. The area selected for the present study, Eravikulam National Park, which is predominantly of grassland vegetation, is also prone to forest fires. This study aims to delineate the forest fire risk zones in Eravikulam National Park using remote sensing (RS) data and geographic information system (GIS) techniques. In the present study, methods such as Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) were used to derive the weights, and the results were compared. We have used seven factors, i.e. land cover types, normalized difference vegetation index, normalized difference water index, slope angle, slope aspect, distance from the settlement, and distance from the road to prepare the fire risk zone map. The area of the prepared risk zone maps is divided into three zones, namely low, moderate, and high. From the study, it was found that the fire occurring in this area is due to natural as well as anthropogenic factors. The prepared forest fire risk zone maps are validated using the fire incidence data for the period from January 2003 to June 2019 collected from the records of the Forest Survey of India. The investigation revealed that 72% and 24% of the fire incidences occurred in the high risk zone of the maps prepared using the AHP and FR methods, respectively, which ascertained the superiority of the AHP method over the FR method for forest fire risk zone mapping. The receiver operating characteristic (ROC) curve analysis gives an area under the ROC curve (AUC) value of 0.767 and 0.567 for the AHP and FR methods, respectively. The risk zone maps will be useful for staff of the forest department, planners, and officials of the disaster management department to take effective preventive and mitigation measures.

Identification and modelling of forest fire severity and risk zones in the Cross – Niger transition forest with remotely sensed satellite data

Forest fires are a serious environmental hazard within the forest ecosystem, which can be studied with Remote Sensing and GIS. The aim of the study is to identify and model forest fires severity and risk zones within the Cross – Niger transition forest. To achieve this aim, remotely sensed data, such as Landsat – 8 OLI (2020) and ASTER DEM were used to produce land cover maps and topography parameters such as aspect, elevation and slope. The topographic maps and the Google Earth imagery were used to extract human settlements and road networks. The final forest fire risk zone (FFRZ) map was prepared by integrating the different parameters such as Land cover, aspect, elevation, slope, proximities to roads and settlements in the ArcGIS environment. The FFRZ was categorized into three categories as low, moderate and high risk zones, based on their fire susceptibility. The category of low, moderate and high FFRZ were represented as 2731.7 km2 (12.5%), 17997.69 km2 (82.59 %) and 1061.63 km2 (4.87%) respectively. The study shows that Remote Sensing and GIS are excellent tools for modelling forest fire risk zones, hence proving that fires are anthropogenic in origin.

Analysis and interpretation of forest fire data of Sikkim

Forest ecosystems are depleting and heading towards degradation which would adversely affect the world's socio-economic harmony. Various disasters disturb the cordial relationship of the flora and fauna and impose imbalance in the ecology as a whole; forest fire is one of its kind. India has witnessed a 125% rise in forest fire occurrences between the years 2015 and 2017. This paper presents a study of various factors and the analysis of forest fire in Sikkim. The period of 10 years, forest fire incidences, i.e., from the year 2004 to the year 2014 have been considered for the study. The forest fire data was collected from Forest and Environment Department, Government of Sikkim, and preliminary processing was performed to check for anomalies. The study observed that there has been an increased forest fire incidence over the years and highest being in the year 2009. These fire incidences have damaged a total area of 5,047.16 ha of land damaging various flora and fauna. It was observed that the maximum forest fire cases are below an altitude of 1500m, during winter months (December to February extending to March) and in sub-tropical Sal (Shorea robusta) forest. West district of Sikkim recorded the highest number of forest fire incidences and area covered followed by south and east districts; the north district was least affected. As per the visual interpretation of forest fire incidence data and literature review, the main factors responsible for forest fire in Sikkim are low rainfall, dry winter season, and type of vegetation. Also, a linear regression was performed between weather factors like average temperature (°C), relative humidity (%), and wind velocity (Km/h) on incidences of forest fire between the year 2009-2014 (n=389). It was found that the average temperature (r=0.37, Slope=9.59 and SD= ±12.00) and relative humidity (r=-0.6, Slope=-4.52, and SD=±2.68) plays a moderate linear relationship in influencing the incidences of forest fires. However, wind velocity showed almost a flat curve indicating its minimal role in influencing forest fire incidences. Parameter modelling and preparation of forest fire risk zone map would be an effective tool in preventing and managing forest fire in Sikkim.

Correction to: Application of GIS and AHP Method in Forest Fire Risk Zone Mapping: a Study of the Parambikulam Tiger Reserve, Kerala, India

Correction to: Application of GIS and AHP Method in Forest Fire Risk Zone Mapping: a Study of the Parambikulam Tiger Reserve, Kerala, India

Application of GIS and AHP Method in Forest Fire Risk Zone Mapping: a Study of the Parambikulam Tiger Reserve, Kerala, India

Application of GIS and AHP Method in Forest Fire Risk Zone Mapping: a Study of the Parambikulam Tiger Reserve, Kerala, India

Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: a case study in the mountainous terrain of Northeast India

This study presents a geospatial approach in conjunction with a multi-criteria decision-making (MCDM) tool for mapping forest fire risk zones in the district of Ri-Bhoi, Meghalaya, India which is very rich in biodiversity. Analytical hierarchy process (AHP)-based pair-wise comparison matrix was constructed to compare the selected parameters against each other based on their impact/influence (equal, moderate, strong, very strong, and extremely strong) on a forest fire. The final output delineated fire risk zones in the study area in four categories that include very high-risk, high-risk, moderate-risk, and low-risk zones. The delineated fire risk zones were found to be in close agreement with actual fire points obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) fire data for the study area. Results indicated that Ri-Bhoi’s 804.31 sq. km. (32.86%) the area was under ‘very high’ fire susceptibility. This was followed by 583.10 sq. km. (23.82%), 670.47 sq. km. (27.39%), and 390.12 sq. km. (15.93%) the area under high, moderate, and low fire risk categories, respectively. These results can be used effectively to plan fire control measures in advance and the methodology suggested in this study can be adopted in other areas too for delineating potential fire risk zones.

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

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.

A Comparative Analysis of Forest Fire Risk Zone Mapping Methods with Expert Knowledge

Despite repeated occurrences of forest fire, very less scientific studies have been reported in the Indian context especially in Kudremukh region to mitigate and suppress the fire. The objective of this article was to pool the expert knowledge on forest fire triggering factors from officials working in wildlife division in the Western Ghats of India through a questionnaire and to validate the risk zones obtained from three popular fire risk zone mapping methods namely logistic regression, multi-criteria decision analysis, and weighted overlay. Based on the earlier studies and expert knowledge, fire ignition parameters considered are elevation, slope, and aspect, proximity to roads, water bodies and area of human activities, normalized difference vegetation index (NDVI), land surface temperature (LST), and vegetation type. The regression model was based on previous fire occurrences and the other two based on expert’s opinion. The three models were validated and compared using past fire occurrence events. The logistic regression model gave 88.89% of accuracy and that of multi-criteria decision analysis with 74.6% accuracy, and that of weighted overlay method with an accuracy of 68.24% for the specific study area. The logistic regression model is useful in the presence of historical data, whereas expert knowledge is helpful for mapping risk zones using multi-criteria decision analysis and weighted overlay analysis when historical data are scarce or not available for mapping risk zones. The obtained risk maps can be used for deciding watchtower locations, installation of sensors, cameras, etc. In every forest division, it is recommended to prepare a standard questionnaire form and document their experiences on forest fire in the region under their supervision before they are getting transferred to another location.

Assessment of Above Ground Biomass and Fire Risk Zonation in Selected Forest Areas of LudhiKhola Watershed, Gorkha Nepal

The drive for robust, accurate and cost-effective methods for biomass estimation over large areas is ever great with the launch of carbon crediting mechanisms in the developing countries such as UN-REDD [United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation] and climate change mitigation program. Traditional ground based measurement requires abundant manpower, resources, cost and time. Remote sensing based technologies pertinently answer the need of time in enhancing the successful implementation of such programs. The region growing and valley following algorithm used to delineate individual tree crowns produced a segmentation accuracy of 59.35% and 54.83%, respectively. Both algorithms have similar approaches for delineation. Above ground biomass was calculated using allometric equation form and height, diameter measured from the field. Linear regression models were applied to derive the relation of biomass with crown projection area, field measured height with biomass. All models were significant at 95% confidence level and the lowest Root Mean Square Error (RMSE %) of 27.45 % (Shorea robusta) and 33.33% (others species). The total amount of biomass stocks was approximately 30620 Kg/ha-1. For forest fire hazard zonation an Analytic Hierarchy Process (AHP) method was used .The result show that 11% of the study area falls under very low fire risk zone, 55 % falls under low fire risk zone and 30 % falls under moderate fire potential zone while 4% of area falls under high forest fire risk zone. The map is also validated through major past fire incidents. The results show that the predicted fire zones are found to be in good agreement with past fire incidents, and hence, the map can be used for future forest resources management.

Assessment of forest fire vulnerability zones in Missouri, United States of America

ABSTRACTForest fire is one of the major disasters that distresses the terrestrial environment and causes economic disruptions for people and communities in areas prone to forest fire. Information on forest fire risk zones is therefore essential for effective and sound decision-making in forest management. Forest fire risk assessment is a critical part and the most important step in forest management because it enables us to know where the risk is higher in order to minimize threats to life, property and natural resources. This study used a hazard assessment model to assess forest fire risk in Missouri based on several measurable environmental parameters influencing forest fire risk vulnerability. Using the four ecological zones in Missouri as the basis of analysis, three forest risk zones were identified. These were high forest fire risk zones, moderate forest fire risk zone and low forest fire risk zone. Strategies for the mitigation of the hazard of forest fire in the state were also recommended.

Geographical information system-based forest fire risk assessment integrating national forest inventory data and analysis of its spatiotemporal variability

Improvements in forest fire risk estimation and mapping fire risk zones are vital to reduce the negative impacts of fire and to facilitate planning for the protection of forested areas. This is especially important for places with little previous data on fire history. This paper presents an improved conceptual scheme for the assessment and mapping of fire risk using a Forest Resource Inventory Database, based on four aspects of topographical, human activity, climate, and forest characteristics factors. We selected 12 variables based on our defined conceptual scheme to generate a synthetic forest fire risk index (FRI) to quantify potential forest fire risk and map risk zones in the Wuyishan Scenery District (WSD), a world heritage site that located in the northwest of Fujian province, People's Republic of China. Spatial statistics were used to examine the spatio-temporal variation of FRI. The results showed the main fire risk zones in the WSD were in the low or moderate categories (accounting for 76.7% of the total area of the WSD in 1997 and 79.2% in 2009). The spatial heterogeneity of FRI showed anisotropic variability characteristics which changed over time. From 1997 to 2009, there was an increasing influence from both autocorrelation factors and random factors. Moreover, these factors played almost equally important roles in forest fire processes in the WSD. The fire risk map was applied to assess the vulnerability of cultural heritage resources in the WSD. Most were located in low- or moderate-risk areas, and therefore would be at low risk from potential fire damage.

The Risk Assessment Study of Potential Forest Fire in Idukki Wildlife Sanctuary using RS and GIS Techniques

In recent years, forest fires have become a major disaster in many countries of the world because of their impact on biodiversity, landscape, health, environment, ecology and economy. Forest fires are one of the major natural hazards occurring in the forests of the Western Ghats, a biodiversity hotspot in India. The present study aims to demarcate the risk zones of potential forest fire in Idukki Wildlife Sanctuary using integrated Remote Sensing (RS) and Geographic Information System (GIS) techniques. Every year, during March-May months, the forests in this area are affected by severe fires. In order to generate the fire risk zone map of the study area, a thematic study was carried out on each of the influencing factors viz. land cover type, slope, distance from settlement, distance from road, and elevation. A Fire Risk Index method was used to prepare the risk zone map. The area of the final map was grouped into five different categories of risk zones, viz. very low, low, moderate, high, and very high. Finally the risk zone map was validated with the fire incidence data for the last 10 years. Results of this study show that the most important factors which contribute to fire ignition are the human induced factors, also known as the anthropogenic factors. The resulting map of forest fire risk zone can be of great benefit for understanding the fire problem and will offer a more effective database for the preparation and control of forest fires.