Forest Fire Risk Research Articles

FOREST FIRE HAZARD MAPPING IN BHUTAN

The study intends to assimilate data for assessing fire hazards for the forest areas in Bhutan. Over the past few decades, there has been an increase in fire events and forest fires have made it urgent to research this issue. Bhutan documented 1,403 occurrences of forest fires in less than ten years (Kuensel, 11th Jan. 2020). Many fossil fuels are burned during forest fires, and a significant amount of carbon dioxide is produced, which adds to the most prevalent greenhouse gas and changes for the regional climate. Moreover, studies have indicated that one of the influential elements contributing to forest fires is climate change. The goal of this research is to consolidate all datasets from multiple sources onto a single GIS platform for the creation of maps that take climatic conditions into account while calculating forest fire hazard. To lessen the risk of forest fires, recommendations and adaptation methods are offered in this paper. In particular, the Department of Forest and Park Services in Bhutan can benefit from the findings of this research for the development, planning, and implementation works.

A process-based model for quantifying the effects of canal blocking on water table and CO2emissions in tropical peatlands

Abstract. Drainage in tropical peatlands increases CO2 emissions, the rate of subsidence, and the risk of forest fires. To a certain extent, these effects can be mitigated by raising the water table depth (WTD) using canal or ditch blocks. The performance of canal blocks in raising WTD is, however, poorly understood because the WTD monitoring data are limited and spatially concentrated around canals and canal blocks. This raises the following question: how effective are canal blocks in raising the WTD over large areas? In this work, we composed a process-based hydrological model to assess the peatland restoration performance of 168 canal blocks in a 22 000 ha peatland area in Sumatra, Indonesia. We simulated daily WTD over 1 year using an existing canal block setup and compared it to the situation without blocks. The study was performed across two contrasting weather scenarios representing dry (1997) and wet (2013) years. Our simulations revealed that, while canal blocks had a net positive impact on WTD rise, they lowered WTD in some areas, and the extent of their effect over 1 year was limited to a distance of about 600 m around the canals. We also show that canal blocks are most effective in peatlands with high hydraulic conductivity. Averaging over all modeled scenarios, blocks raised the annual mean WTD by only 1.5 cm. This value was similar in the dry (1.44 cm) and wet (1.57 cm) years, and there was a 2.13 fold difference between the scenarios with large and small hydraulic conductivities (2.05 cm versus 0.96 cm). Using a linear relationship between WTD and CO2 emissions, we estimated that, averaging over peat hydraulic properties, canal blocks prevented the emission of 1.07 Mg ha−1 CO2 in the dry year and 1.17 Mg ha−1 CO2 in the wet year. We believe that the modeling tools developed in this work could be adopted by local stakeholders aiming at a more effective and evidence-based approach to canal-block-based peatland restoration.

Family Ecological Transaction for Disaster Risk Reduction: Case of Anak Dalam Tribe in Bukit Dua Belas National Park, Air Hitam Regency, Jambi, Indonesia

Anak Dalam Tribe (ADT) is an indigenous community living inside the conservation area, protected as cultural heritage. ADT family is highly dependent on the forest, they used to live and fulfill all their needs from the forest. Now with the modernization and regulatory dynamics taking place, ADT family undergoes changes as a form of adaptation, leading to residing categorization i.e. traditional, transition, and bediom. The changes impact on the ecological transactions thus bring a greater potential risk of forest fires, as has happened in recent years. This study aims to discover ADT family ecological transactions based on their residing type so that potential environmental exposure can be predicted and anticipated for disaster risk reduction. The qualitative research was conducted using a cross-sectional method. Data were collected through in-depth interview, observation, and forum group discussion. Mapping was done to understand the transaction pattern. This study found that the pattern of each kampong was different, showing that development has happened following the socio-ecological changes of each kampong, either by nature or intervention from outside parties. However, the livelihood area is still the biggest and outermost area of all six kampongs despite the residing type, covering the forest area. Moreover, the area of exposure is enlarged. It vindicates the escalating disaster risk. It is suggested to create a holistic livelihood ecosystem for ADT near their residing place to minimize the disaster risk. Political identity of ADT also should be reconsidered, with the inevitable global development, how far the heritage should be kept.

Water level and soil moisture monitoring for peatland fire risk indicator

Peatland forest conversion into cultivated land often creates environmental issues in the form of increasing greenhouse gas (GHG) emissions caused by the decomposition of organic matter that eventually increase the risk of peat forest fires. This paper presents the results of a study of the peatland water table dynamics based on certain peatland characteristics (depth and maturity) and water management techniques (canal blocking), as well as an analysis of water level and soil moisture data for the peatland fire early warning model. This study was conducted in April-December 2018 at a palm oil plantation in Pangkalan Pisang Village, Riau Province. Peatland water table observations were carried out using a water level logger at each of the midpoints of three oil palm blocks measuring 30 ha each. Meanwhile, soil moisture contents were observed using a logger with two humidity sensors at two depths. The recorded observation data showed that the water table level was very dynamic and fluctuating, both temporally and spatially. The temporal observation data indicates that the water table levels after the rain had stopped in the three observed blocks decreased at the rates of 10.3, 10.6, and 13.6 mm/day, respectively. This research shows a significant correlation between soil moisture dry weight percent and the risk level of peatland fires. Soil moisture content was less than 100% dry weight and highly flammable.

솎아베기에 따른 산불연료량과 수관화 위험도 변화 연구

This study was conducted to analyze the effect of forest management on forest fire fuel and forest fire spread. For this purpose, a wildfire prevention forest study site(intensity of 40%, 20%, control) was established in the Goseong, Gangwon province that is vulnerable to wildfires, and regular monitoring investigations were conducted. As a result of this study, the crown bulk density is thinning 40% 0.263kg/m3, thinning 20% 0.417kg/m3, and 0.530kg/m3 in the control, the lower the amount of crown fuel load. In high-strength treatment areas, the amount of shrub fuel composed of fire-resistant tree species increased by more than 17.6% compared to the first year as the amount of light transmission increased due to thinning. It is expected that this study can be used in various research such as forest fire risk assessment by forest management and creation of fire-resistant forest zones.

Susceptibility Modeling for Potential Fire Risk Zone in Semi-Urban Area

Fires inflict major losses in global forest resources as well as human lives and property. Forest fires and their tendencies have increased in recent years in Nepal that, necessity for adequate management actions. The fire risk zone is identified with the origin of the ignitions and the pattern of their occurrence, as well as by shared environmental variables that translate into the same risk potential. To reduce the detrimental impacts of fire, several strategies for preventing and combating fires have been implemented based on identified potential fire risk zone. The work is carried out in Shambhunath Municipality, Saptari. In this study, spatial modeling with integration of MCE, fuzzy and AHP is applied for susceptibility assessment for identifying the potential fire risk zone. In fire risk assessment index map shows that 21.14 percent of municipality extent land occurred in the fire risk in which the distribution of the high risk occupied 0.01 percent, medium risk, 0.49 percent, low risk 20.64 percent and remaining 79 percent land free from fire risk. The high risk of potential fire zone were identified along the east west highway surrounding to the high voltage transmission route and petrol pump location. Likewise, potential high risk of forest fire also found in the dense forest area in elevation greater than 400m.

Understanding Forest Fire Patterns and Risk Assessment to Local Biodiversity of Selected Districts of Mizoram Using GIS and Remote Sensing

Understanding Forest Fire Patterns and Risk Assessment to Local Biodiversity of Selected Districts of Mizoram Using GIS and Remote Sensing

Quality of the Pellets Obtained with Wood and Cutting Residues of Stone Pine (Pinus pinea L.)

The use of wood and residual biomass from forestry works is a CO2 emission-neutral source of energy that also contributes to reducing the risk of spreading forest fires, especially under Mediterranean climate. The forest stands of stone pine (Pinus pinea L.) occupies about 0.7 million hectares in the Mediterranean basin. In this study, the commercial quality of the pellets manufactured from different types of cutting residues (needles and thin branches, medium branches and bark), as well as wood from trunks and thick branches, was assessed. It was concluded that with the exclusive use of residual biomass it is not possible to obtain pellets of high commercial quality, useful for residential or industrial use. However, the highest quality pellets could be obtained by combining them with stone pine debarked wood, but in a certain proportion that differs depending on the type of residue (around 15% for bark, 30% for medium branches and less than 15% for needles and thin branches). It is recommended to take advantage of both the thick wood (trunk + thick branches) and a proportion of medium branches and bark, while in the case of needles and thinnest branches it would be more convenient to leave them in the forest for their incorporation into the soil, given their high nutrients concentration and their low quality for energetic use. The results found support a greater valorization of the biomass obtained in the stone pine fellings. In the future it will be necessary to study which is the most appropriate logistics of the silvicultural works to be able to conveniently apply the results of this study.

Utilizing information technology towards reducing forest and peatland fire risk in Riau Province, Indonesia

Forest and peatland fires have become a seasonal challenge experienced in Riau Province. This disaster causes huge economic and environmental losses, accompanied by its alarming impact on human health. The haze as a result of land burning in this province is known to disrupt activities in neighbouring countries causing political and diplomatic tensions. To reduce the risk of forest and peatland fire, the Riau Regional Police developed the Lancang Kuning Nusantara (LKN) digital application. This study examines the general role of the application in reducing forest and peatland fire risks. A qualitative descriptive approach is adopted in analysing the primary data obtained through focus group discussion. Data from secondary sources were also obtained. Results from this study indicate that the use of the LKN digital application in Riau Province has helped immensely in the area of disaster risk communication and logistics support.

The Role of the Government of Riau Provincial in Dealing with Forest and Land Fires

Riau is one of the provinces with a higher risk of forest and land fires in Indonesia. In this study, the researchers wanted to understand the role of the Government of Riau provincial in responding to the problems. The methodology used in this study was a descriptive qualitative research method using data collection techniques in the form of primary data from interviews and FGD as well as secondary data from previous studies and documents supporting this study. The findings of this study are the role of the Government of Riau provincial in reducing forest and land fires in Riau. This’s reflected in a 90% reduction in burned area from 2016 to 2021. Not only does the Riau government work alone, but multiple parties are supporting the Riau government, namely the Indonesian Armed Forces, the Republic of Indonesia Police, the National Research and Innovation Agency, as well as other agencies that help reduce the risk of forest and land fires disasters in Riau.

An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT

Monitoring and early warning technology for forest fires is crucial. An early warning/monitoring system for forest fires was constructed based on deep learning and the internet of things. Forest fire recognition was improved by combining the size, color, and shape characteristics of the flame, smoke, and area. Complex upper-layer fire-image features were extracted, improving the input conversion by building a forest fire risk prediction model based on an improved dynamic convolutional neural network. The proposed back propagation neural network fire (BPNNFire) algorithm calculated the image processing speed and delay rate, and data were preprocessed to remove noise. The model recognized forest fire images, and the classifier classified them to distinguish images with and without fire. Fire images were classified locally for feature extraction. Forest fire images were stored on a remote server. Existing algorithms were compared, and BPNNFire provided real-time accurate forest fire recognition at a low frame rate with 84.37% accuracy, indicating superior recognition. The maximum relative error between the measured and actual values for real-time online monitoring of forest environment indicators, such as air temperature and humidity, was 5.75%. The packet loss rate of the forest fire monitoring network was 5.99% at Longshan Forest Farm and 2.22% at Longyandong Forest Farm.

Urbanization of Forest Fires: An Evaluation on Metropolitan Forest Fires in The Mediterranean Ecosystem

The main purpose of the study is to examine the impact of drastic and proactive forest fire interventions, which are applied to avoid the risk of loss of life and property close to urban areas, on the continuity of plant species with fire-adapted characters and the potential to cause species loss. In this context, the basic assumption of the study is that the reduced frequency of fires caused by suppressed fires in natural areas in order to avoid the risk of forest fires that may affect residential areas, will lead to the reduction of various plant species that sustain their lives thanks to their fire-adaptive characters. As a qualitative data analysis method, the rates of urban and forest areas, the number of forest fires, the amount of burned area and plant species diversity data were used to deal with the study data with document analysis. In the selection of the 10 metropolitan cities that constitute the sample area of the study, the criteria of being in the Mediterranean ecosystem, containing plant species adapted to fire, and being metropolitan (where natural and built environment elements are intertwined) were taken as basis. The study will create an ecological perspective in fire prevention policies and strategies to be developed through the determination of plant species characteristics in large cities located in the fire-prone Mediterranean ecosystem.

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).

산불예방교육 실태와 학습지도 방안

Worldwidely most of the causes of forest fires are caused by human unintentional mistakes and negligence. As the forest fire prevention education, interest in forest fire risk prevention activities can be strengthened, and social costs related to forest fire risks can be reduced by the regular education and learning. In this study, the actual state of forest fire prevention education in domestic elementary and secondary schools was investigated and a learning guide plan for education that could be implemented. As the result, it is necessary to carry out regular forest fire prevention education in regular curriculum to raise awareness about the importance of forests and to protect them from forest fires. In addition, by preventing the causative behavior that threatens forest fires in advance, it is considered that it will contribute to reducing the risk of forest fires caused by unintentional mistakes and carelessness in future generations.

Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data

This work focuses on the automatic identification of forest fire risk areas along high-voltage power lines through the development of a tool and its validation on a real forest area. The tool allows one to automate the whole process, which includes the classification of the point cloud, the computation of the catenary of the wires using different calculation methods, the estimation of the vegetation growth and the identification of the risk areas. To this end, a coarse-to-fine approach is proposed, so that a preliminary analysis is performed with public airborne LiDAR data, and then a more detailed inspection is provided with drone LiDAR data over those areas classified as high risk. The tool and the methodology developed were validated along a high-voltage power line of 53 km in a real forest area. The results show that although the preliminary analysis based on public airborne LiDAR data is more conservative, it is very useful for selecting those areas of higher risk for further analysis with drone LiDAR data.

Ongoing climatic change increases the risk of wildfires. Case study: Carpathian spruce forests

The challenge to the sustainable development of forestry in the Eurasian temperate - boreal zone is the increase in the frequency and severity of natural disturbances due to global climate change. In this study, a mathematical model for predicting the risk of wildfires in spruce stands growing in the territory of Slovak Paradise National Park under climate change has been proposed and tested. Wildfire risk is described in terms of the observed probabilities of the destruction of spruce stands in relation to their age for a period of 10 years. As the indicators of assumed climate change, the time series of daily values of four fire weather indices (Angstrőm, Nesterov, Baumgartner, and the Meteorological Forest Fire Risk Index) for the period 1951–2019 have been analysed. The results obtained indicated the significant dependence of the observed increasing annual population proportions of burnt areas on the gradually increasing annual population proportions of risky days recorded and evaluated by using the common scales of risk classification. We found that ongoing climate change has a significant impact on increasing the risk of fires. The Meteorological Forest Fire Risk Index has proven to be the most suitable measure for predicting the probability of fire occurrence under the climate conditions in the experimental territory. The indicated risk of fire occurrence in spruce stands under the assumption of a climatic change is substantially higher than in the case when this assumption is neglected. This information can also serve as a basis for the formulation of efficient landscape fire protection measures focused on building the infrastructure to support the efficient retardation of propagation, including the quick suppression of this detrimental natural hazard.

Risk of forest fires occurrence on a transition island Amazon-Cerrado: Where to act?

A lack of proper planning in terms of fire prevention in protected areas can have several ecological and socioeconomic implications. This study aimed to investigate the necessity and feasibility of implementing fire management efforts in fire-prone Brazilian conservation unit on Bananal Island. Specifically, this study entailed a comprehensive study of pattens underlying the occurrence of forest fires (FFs) on Bananal Island using geotechnical tools, meteorological variables, and occurrence records. Fuzzy logic was applied to develop a forest fire risk zoning based on the following variables: land use and occupation, altitude, slope, relief orientation, air and land surface temperatures, precipitation, and proximity to heat zones. To analyze the patterns of FFs, fire occurrence records and meteorological variables between 2010 and 2020 were used. The validation of the model was conducted based on fire risk classes and burned areas between 2010 and 2019. The results showed that 3.61%, 16.41%, 33.65%, 30.17%, and 16.17% of the area was at very low risk, low risk, moderate risk, high risk, and very high risk, respectively, with respect to fire occurrence. Of the fire occurrences, 92% occurred in clear fields (native pastures); furthermore, 75.9% of these fires were arsons. The results of this study will allow managers of Ilha do Bananal to develop fire management plans that involve the well-directed use of resources, specifically the appropriate channeling of resources to the neediest areas. This means acting directly in areas with the highest risks and in surrounding sensitive regions, such as gallery forests. Additionally, our results justify the investment in education campaigns, as most fires result from vandalism. Finally, we conclude that the approach outlined in this study will not only improve fire prevention efforts on Bananal Island but can also be applied in other areas.

Modeling Historical and Future Forest Fires in South Korea: The FLAM Optimization Approach

Climate change-induced heat waves increase the global risk of forest fires, intensifying biomass burning and accelerating climate change in a vicious cycle. This presents a challenge to the response system in heavily forested South Korea, increasing the risk of more frequent and large-scale fire outbreaks. This study aims to optimize IIASA’s wildFire cLimate impacts and Adaptation Model (FLAM)—a processed-based model integrating biophysical and human impacts—to South Korea for projecting the pattern and scale of future forest fires. The developments performed in this study include: (1) the optimization of probability algorithms in FLAM based on the national GIS data downscaled to 1 km2 with additional factors introduced for national specific modeling; (2) the improvement of soil moisture computation by adjusting the Fine Fuel Moisture Code (FFMC) to represent vegetation feedbacks by fitting soil moisture to daily remote sensing data; and (3) projection of future forest fire frequency and burned area. Our results show that optimization has considerably improved the modeling of seasonal patterns of forest fire frequency. Pearson’s correlation coefficient between monthly predictions and observations from national statistics over 2016–2022 was improved from 0.171 in the non-optimized to 0.893 in the optimized FLAM. These findings imply that FLAM’s main algorithms for interpreting biophysical and human impacts on forest fire at a global scale are only applicable to South Korea after the optimization of all modules, and climate change is the main driver of the recent increases in forest fires. Projections for forest fire were produced for four periods until 2100 based on the forest management plan, which included three management scenarios (current, ideal, and overprotection). Ideal management led to a reduction of 60–70% of both fire frequency and burned area compared to the overprotection scenario. This study should be followed by research for developing adaptation strategies corresponding to the projected risks of future forest fires.

Areas of the Terai Arc landscape in Nepal at risk of forest fire identified by fuzzy analytic hierarchy process

Forest fire frequency has increased in South Asia in recent decades, with a growing impact on forest ecosystems. Nepal's Terai Arc Landscape (TAL) is one of the most ecologically important landscapes in Asia, hosting a great diversity of endangered flora and fauna. To better predict the threat of fire to forest ecosystems in the TAL, we identified fire-prone areas using fuzzy AHP methods. We produced a fire risk map by applying the weighted linear combination method using topographic (slope, aspect, and elevation), climatic (temperature, precipitation, and wind speed), biophysical (normalised difference vegetation index and landcover classes), and anthropogenic variables (distance to road and proximity to settlements). We then validated the map with records of past fires by applying a confusion matrix. The accuracy of our technique of fire location prediction was above 95%, with a kappa coefficient of 0.93 and AUC value of 0.83. Locations of medium to very high forest fire risk were found in around 51% of the study area, usually in the vicinity of areas affected by anthropogenic factors. This landscape-level study shows the potential of multicriteria prediction models to inform the preparation of national and regional forest fire management strategies and plans.