Fire Risk Zones Research Articles

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.

Using GIS Techniques for Assessment of Accessible Forest Lands by Firefighting Teams Considering Fire Risk Degrees

To use the resources allocated for firefighting in an optimum way, it is important to reach fire sites within the critical response time (CRT) and determine regions with high fire risk. This study aimed to evaluate forests that can be reached within CRT using GIS techniques, regarding fire risk levels. The study area is selected from Yeniköy Forest Enterprise Chief, which is in Bursa Regional Directorate of Forestry and located in second-degree fire-sensitive zone. The accessible forests within CRT were determined using the "Network Analyst" method in ArcGIS 10.4 software. Then, a fire risk map was developed using the AHP (Analytic Hierarchy Process) tool (ExtAHP 2.0) in ArcGIS 10.4. The results showed that the firefighting team near the study area can reach 24.25% of forests within CRT for the second-degree fire-sensitive areas (30 minutes). Besides, it was determined that 46.86% of forests can be reached in 40 minutes and 84.31% in one hour. It was found that 85% of forests were in a very high and high fire risk zones. It was determined that 42% of forests that could not be reached within CRT was in a very high fire risk zones while 43.45% was in a high fire risk zone.

Wildfire Risk Zonation of Sudurpaschim Province, Nepal

Wildfire is one of the major destructive hazards which have significant effect on environment, society, and economy. However, limited studies have been carried out on spatial and temporal distribution of wildfire, especially in developing countries like Nepal. The objective of this study was to assess wildfire risk zonation of Sudurpaschim province of Nepal by applying Remote Sensing and GIS. Sudurpaschim province has been divided into four fire risk zones i.e., high, moderate, low and no risk zone. In Sudurpaschim province, about 30.84% area falls under high fire risk zone followed by moderate risk (58.30%), low risk (10.13%) and no risk (0.72%). Among five physiographic regions, Siwalik region is more susceptible to fire due to various factors, such as deciduous forest, topography, terrain, etc. From 2012 to 2019, about 44,342 fire incidences were reported in this province. Approximately 88% wildfire was recorded in spring, the season being dry. Overall, geographically Siwalik region and temporarily spring season should be in high priority for developing and implementing wildfire management activities in Sudurpaschim province.

Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.

Geo-informatics based multi-criteria decision analysis (MCDA) through analytic hierarchy process (AHP) for forest fire risk mapping in Palamau Tiger Reserve, Jharkhand state, India

Forest fires constitute a foremost environmental calamity that distresses the sustainability of the forest. The main source of degradation of Jharkhand forests are forest fires conquered by forest species of Sal and Bamboo. Palamau Tiger Reserve in Jharkhand state, India, is becoming more susceptible to forest fire due to anthropogenic disturbance coupled with speedy upsurge in population. In this study, forest fire risk in PTR was evaluated based on various fire inducing factors, viz., forest fuel, settlements, roads, bare soil index, elevation slope and aspect. Geoinformatics based multi-criteria decision analysis (MCDA) through method of AHP (analytic hierarchy process) used to extract forest fire risk map in five classes: Very low risk, low risk, moderate risk, high risk and very high risk. The results obtained showed that about 180 km2 (14.85%) falls under very low fire risk zone, 234 km2 (19.30%) falls in low fire risk zone, 269.73 km2 (22.16%) falls under moderate fire risk zone, 299.36 km2 (24.59%) falls under high fire risk zone and 232.56 km2 (19.10%) falls in very high fire risk zone. Forest fire risk map was validated from historical fire incidents observed through field data, MODIS and SNPP-VIIRS satellite products. The results showed that the geoinformatics based forest fire risk zones delineated through MCDA-AHP method are in good agreement with historical forest fire occurrences, henceforth may be utilised for fire planning for mitigation in forest areas.

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 New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019

The purpose of this paper is to present a new method for early detection of forest fires, especially in forest zones prone to fires using microwave remote sensing and information-modeling tools. A decision-making system is developed as a tool for operational coupled analysis of modeling results and remote sensing data. The main operating structure of this system has blocks that calculate the moisture of forest canopy, the soil-litter layer, and the forest physical temperature using the observed brightness temperature provided by the flying platform IL-18 equipped with passive microwave radiometers of 1.43, 13.3 and 37.5 GHz frequencies. The hydrological parameters of the forest are assessed with both a developed regional hydrological model and remote sensing observations. The hydrological model allows for the detection of fire-prone zones that are subject to remote sensing when modeling results are corrected and thermal temperatures are evaluated. An approach for the real time forest fires classification via daytime remote sensing observations is proposed. The relative theoretical and experimental results presented here have allowed us to use a new approach to forests monitoring during periods of potential fire. A decision-making algorithm is presented that aims at analyzing data flows from radiometers located on the remote sensing platform to calculate the probability of forest fire occurring in geographical pixels. As case study, the state of forest fires that occurred in Siberia in 2019 using microwave remote sensing measurements conducted by a flying IL-18 laboratory is presented. This remote sensing platform is equipped with optical and microwave tools that allow the optical and microwave images of the observed forest areas. The main operating frequencies of microwave radiometers are 1.43, 13.3 and 37.5 GHz. Microwave radiometers provide data on water content in the forest canopy and on litter and physical temperatures. Based on the long-term measurements made in Siberia, the possible improvement of the proposed decision-making system for future relevant studies is discussed in detail. The basic idea of cost-effective monitoring of forested areas consists of a two-stage exploration of fire risk zones. The first monitoring stage is performed using the hydrological model of the study area to identify low moisture areas of the forest canopy and litter. The second stage of monitoring is conducted using the remote sensing platform only in the local fire-dangerous areas in order to more precisely identify the areas prone to fire and to detect and diagnose real burning zones. The developed algorithm allows the calculation of physical temperatures and the detection of temperature anomalies based on measured brightness temperatures. Finally, the spatial distribution of the probability of forest fire occurrence is given as an example of the decision-making system along with a comparison of this distribution with the satellite images provided by the EOSDIS Land data.

Engaging Palm Oil and Hot Spot Area to Mitigate Forest Fires

Forest fire is one of environmental problem happens in Central Kalimantan. The fire does not only damage the forest ecosystem and biodiversity but also threaten the health and socio-economic of local people. Forest fire in Central Kalimantan is widely known as human-made, such as the process of shifting cultivation and land clearing. The expansion of forest into palm oil plantation is often blamed as the cause of forest fire since the forest clearing involves a massive amount of fires. Therefore, this study aims to explore whether the existence of palm oil cultivation contributes to the occurrence of forest fires. We used satellite imagery of hotspot, and overlay it with the land use data to generate the fire risk zone map using geographic information system (GIS) method. Through the map, the risk of fire can be monitored in advance to help the fire authority provide the act of mitigation. The result of this study suggested that risk mapping is vital for forest fire management to mitigate the spread of forest fire. The region to be fire-prone within the palm oil cultivation is suggested to form a preventive act through active forest-fires monitoring. In sum, this study is expected to provide a map of forest fires' risk around the cultivation area, mainly palm oil plantation, and help the fire authorities as well as stakeholders to identify the risk zone for fires prevention in the future.

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.

Forest Fire Risk Mapping by using GIS Techniques and AHP Method: A Case Study in Bodrum (Turkey)

In Turkey, forest areas located along the coastline of the Marmara, the Aegean and the Mediterranean regions are very sensitive to fire. As a result of forest fires, about 10000 hectares of forest area is damaged annually. One of the key elements in firefighting is early detection and quick intervention. In order to achieve this goal, first of all, the forest areas with fire risk should be determined especially for fire sensitive forest areas. The forest fire risk can be evaluated considering various risk factors such as stand structures, topographic factors, proximity to some features (roads, settlements, and water resources), and climatic factors. In this study, GIS techniques and Analytic Hierarchy Process (AHP) method was used to produce forest fire risk map for the first degree fire sensitive forest land located in Bodrum province of Muğla in Turkey. The results indicated that 11.83% and 21.98% of the forest area was categorized as very high and high fire risk, respectively, while 22.28% and 25.93% was moderate and low fire risk, respectively. The fire risk was found to be very low at the rest of the study area (17.98%). To compare the fire risk map with actual forest fire occurrences in the study area, it was overlapped with the fire map indicating forest components where previous forest fires (>1.0 ha) occurred in the study area in last five years. It was found that 38.32% of the areas damaged by the previous fires were categorized as high and very high fire risks zones in fire risk map, while 28.44% was moderate fire risk zones. The result showed that tree species was the most effective risk factor, followed by tree stages and proximity to water resources. This study revealed that the combination of GIS techniques and AHP method is very advantageous approach to map forest areas with fire risk in short time.

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.

MODELING OF PARAMETERS FOR FOREST FIRE RISK ZONE MAPPING

Abstract. Forest fire has been regarded as one of the major reasons for the loss of biodiversity and dreadful conditions of environment. Global warming is also increasing the incidence of forest fire at an alarming rate. That’s why, one need to understand the complex biophysical parameters, which are responsible for this disaster. As it is difficult to predict forest fire, fire risk zone map can be useful for combating the forest fire. So the main aim of this study is to generate a Fire risk model to map fire risk zone using Remote Sensing & GIS technique. Pauri Garhwal District, located in Uttarakhand, India, has been selected for this study as it continually faces the problem of forest fire. Landsat-8 data of 18th April, 2016 have been used for land use land cover mapping. Slope and other information have been derived from topographic maps and field information. For thematic and topographic information analysis ArcGIS and ERDAS Imagine software have been used. Forest fire risk model was generated by using AHP method, where each category was assigned subjective weight according to their sensitivity to fire. Three categories of forest fire risk ranging from very high to low were derived. The generated forest fire risk model was found to be in strong agreement with actual fire-affected sites.

Significant decline of forest fires in Nilgiri Biosphere Reserve, India

Yet there is considerable uncertainty over management effectiveness of many protected areas around the world. In this regard, long-term monitoring and management of disturbances in the protected areas need to be properly addressed and documented. The present study is aimed to analyze spatial extent, frequency and distribution of forest fires in Nilgiri Biosphere Reserve, Western Ghats, India over four decades (1973–2014). Multi-temporal Landsat and Indian Remote Sensing satellite data were used to quantify forest burnt area. The results showed that the spatial extent of burnt area greatly varied between 1318 km2 (in 1989) to 15.7 km2 (in 2013). The monitoring of fires during the recent decade (2005–2014) was observed to have a gradual decline over the reserve. High frequency of fires has been found in dry deciduous forest followed by moist deciduous forest, scrub, grassland and semi-evergreen forest. Among six protected areas, Bandipur National Park had represented more fire incidents followed by Mudumalai Wildlife Sanctuary, Nagarhole and Mukurthi National Parks. Grid analysis shows that 67% of the grids were affected by forest fires. Large burnt patches have steadily decreased in their size while the other patch classes showed a significant reduction in their size over the study period. This study had revealed the role of climate and accessibility in relation to fire occurrences. Based on historical burnt area mapping outputs, fire hotspots have been modeled to address fire risk zones. Indeed, the Mann-Kendall trend test has indicated that the overall trend of fire incidents have been declining and this indicates the management effectiveness in Nilgiri Biosphere Reserve.

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 analysis of potential forest fires in a wildlife sanctuary in the western ghats (Southwest Indian Peninsula) using geospatial techniques

Introduction: Forest fires are potential threats to most of the wildlife sanctuaries in the world. Such areas for conservation of precious wildlife in parts of the Western Ghats in India are no exception. The present study area representing the Periyar Tiger Reserve forms a part of the Western Ghats, where during the past decade more than 200 wildfires have been recorded. Objective: The present study is designed to demarcate the fire risk zones applying principles of geospatial techniques. Materials and Methods: The technique integrates Remote Sensing and Geographic Information System. Parameters such as land cover type, surface slope, aspect, proximity to settlements, closeness to roads, and elevation have been considered. A Modified Fire Risk Index method has been used for preparing the risk zone map. Results: The prepared map shows five fire risk zones such as very high, high, moderate, low, and very low. About 27.38% of the study area, according to this study falls within the high and very high risk zones. The final result of the study is validated with the MODIS active fire (hotspot) data, which shows very good reliability. Conclusion: This study based on geospatial technology is very useful to the local authorities for identifying in advance the fire risk zones for prevention and management of forest fires in future.

Spatial distribution of grassland fires at the regional scale based on the MODIS active fire products

Grassland fires are major disturbances to ecosystems and economies around the world. Therefore, research on the spatial patterns of grassland fires is important for understanding the dynamics of fire occurrence and providing evidence for fire prevention and management. One of the problems in grassland fire risk analysis is that historically observed fire data are generally in the point format, with imprecise positions, whereas other influencing factors are often expressed in continuous areal units. To minimise the influences of inaccurate locations and grid size, density estimates can be produced using kernel density estimation (KDE) – a nonparametric statistical method for estimating probability densities. This method has been widely used to convert historical fire data into continuous surfaces. In this study, KDE was applied to grassland fire events in the eastern Inner Mongolia of China, based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily active fire data from 2001 to 2014. The bandwidth choice was based on the mean random distance method. Annual and seasonal kernel density maps were produced, showing that the spatial patterns of grassland fire events remained temporally consistent. These results were used to create grassland fire risk zones on the basis of the mean density values in the study area. Grassland fire prevention and planning may focus on high-risk areas identified using this method.