Forest Fire Risk Research Articles

Ensemble algorithms for modeling forest live fuel loads and multivariate probability proportional to size sampling in Kunming, Yunnan, China

Forest live fuel load (LFL) models of tree, shrub, and herbaceous layers were established using linear function (LF), power function (PF), K-nearest neighbor (KNN), gradient boosting regression tree (GBRT), and random forest (RF) algorithms to improve the fit and prediction accuracy and reduce the bias of forest LFL models. Multivariate probability proportional to size sampling (MPPS), probability proportional to size sampling (PPS), and probability proportional to frequency sampling (PPF) were used to obtain mean estimates of the forest LFLs, and the sampling accuracies were assessed. We utilized forest stock and biomass survey data (forest fire risk data and forest resource planning and design survey data) from Kunming, Yunnan, China. The results indicated that the RF model had the highest fit and prediction accuracy. The R2 values of the fitted models for the LFLs of Pinus yunnanensis, Quercus spp., Pinus armandi, Eucalyptus spp., other soft broadleaf species, other hard broadleaf species, other conifer species, shrubs, and herbaceous species were 0.79, 0.89, 0.86, 0.88, 0.91, 0.83, 0.88 0.88, and 0.92, respectively. The F-values were 4.652, 2.582, 2.565, 2.551, 2.738, 3.158, 16.156, 3.479, and 8.468, respectively. The sampling results of forest LFLs were much better for MPPS than for PPF and PPS. Moreover, the two-stage MPPS was the most efficient method for estimating tree, shrub, and herbaceous LFLs. The mean estimates of tree fuel loads, shrub fuel loads, and herbaceous fuel loads were 144.7760, 3.8094, and 0.6442 t/hm2, respectively. There were 555 overlapping subunits for the three types of forest LFLs. The proposed method can significantly reduce the cost of LFL surveys and improve estimation accuracy.

Determining The Soil Loss Susceptibility To Forest Fires By Using RUSLE And A New Approach In Çınarpınar Forestry Enterprise, Turkey

Soil erosion by water (WSE) is an environmental, economic, and sociological problem in the world. Nowadays, forest fires have triggered more WSE, especially in the Mediterranean basin. Therefore, the present study aims to determine the effect of forest fires on soil loss susceptibility in the Çınarpınar Forestry Enterprise, Turkey. The RUSLE model was used to determine soil loss. Two soil loss maps were generated for the actual situation (base scenario) and forest fire scenario. For the forest fire scenario, R, K, and LS factors in the RUSLE model were modified based on the forest fire severity index. Finally, two maps representing base and forest fire scenarios were compared. The actual mean soil loss was found as 5.34 t ha-1 year-1 in the Çınarpınar Forestry Enterprise while the mean soil loss was determined as 12.44 t ha-1 year-1 for the forest fire scenario. It was found that forest fires would increase soil loss by more than 2 times in the study area. Areas with very low soil loss susceptibility to forest fires constitute 41.97% of productive forests, while areas with very high, high, medium, and low soil loss susceptibility constitute 3.64%, 9.28%, 27.50%, and 17.61% of productive forests, respectively. It was also revealed that there is not always a linear relationship between fire severity and soil loss susceptibility under natural conditions. Consequently, it is hoped that this study will help decision-makers in the implementation of the multi-purpose approach, which aims to reduce the risk of both forest fire and soil loss.

Forest fire in Thailand: Spatio-temporal distribution and future risk assessment

Understanding the spatiotemporal distribution of forest fires and future predictions is very important for management strategies. To identify the present status of forest fires in the Kingdom of Thailand and their risk in the future, ten-year forest fire data were used, and a forest fire hotspot was prepared. A geospatial technique was used in the study to characterize the parameters of forest fires in the country and identify future forest fire risk areas. Most of the forest fires in the country were found to be seasonal. Deciduous forests in higher elevations and on moderate slopes were most vulnerable to forest fire. The level of aridity, soil moisture, temperature, precipitation, vegetation status, and topography influenced the spatiotemporal distribution of forest fires in the country. Greater than 50% of fire risks were observed in 22 administrative divisions, and 17 of the 209 protected areas are also in the high-risk category. The final forest fire hotspot map can be used in policy development and successful management strategies. A better monitoring strategy should be used in the fire hotspot areas as a precautionary measure to minimize the anthropogenic causes of forest fires.

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.

Creation of Prototypes of Case-Based Knowledge Bases Using Transformations of Decision Tables to Predict the Risk of Forest Fires

Creation of Prototypes of Case-Based Knowledge Bases Using Transformations of Decision Tables to Predict the Risk of Forest Fires

Prediction model of water content in surface dead fuel based on convolution neural network and meteoro-logical factors regression.

Water content of surface fuels is an important indicator of forest fire risk level and fire behavior, and the prediction model of which has a significant effect on fire risk prediction and management. Based on field meteorological factors of Quercus mongolica and Pinus sylvestris var. mongolica forests and water content data of dead fuels on the ground, we conducted the relative importance ranking of meteorological factors random forest and Pearson correlation analysis, and predicted water content of fuels using deeply learned convolutional neural network and meteorological factors regression. The results showed that water content of Q. mongolica fuels in the wild was significantly higher than that of P. sylvestris var. mongolica. The results of random forest showed that the factors that had significant effect on water content of fuel were humidity, temperature, rainfall, wind speed, and solar radiation, with the importance ranking from the largest to the smallest. Results of correlation analysis showed that temperature, humidity, and rainfall of current day had a significant impact on water content of fuels, and certain correlations were observed between meteorological factors. The prediction R2 of the convolutional neural network model for the surface fuel water content of Q. mongolica and P. sylvestris var. mongolica forest was 0.928 and 0.905, the mean absolute error (MAE) was 6.1% and 8.1%, and the mean relative error (MRE) was 8.9% and 4.2%, respectively. However, the R2, MAE, MRE of meteorological factors regression were 0.495 and 0.525, 30.5% and 39.5%, 52.1% and 32.6%, respectively. The precision of convolution neural network model was significantly higher than that of meteorological factors regression. Our results showed that the deeply learned convolutional neural network could provide some reference for the prediction of fuel water content in the future, and effectively support higher level forest fire management.

Prediction of forest fire risk according to climate change in Bhutan using a shared socioeconomic pathways (SSP) scenario and random forest

Prediction of forest fire risk according to climate change in Bhutan using a shared socioeconomic pathways (SSP) scenario and random forest

Forest Fire Driving Factors and Fire Risk Zoning Based on an Optimal Parameter Logistic Regression Model: A Case Study of the Liangshan Yi Autonomous Prefecture, China

Planning the analyses of the spatial distribution and driving factors of forest fires and regionalizing fire risks is an important part of forest fire management. Based on the Landsat-8 active fire dataset of the Liangshan Yi Autonomous Prefecture from 2014 to 2021, this paper proposes an optimal parameter logistic regression (OPLR) model, conducts forest fire risk zoning research under the optimal spatial analysis scale and model parameters, and establishes a forest fire risk prediction model. The results showed that the spatial unit of the optimal spatial analysis scale in the study area was 5 km and that the prediction accuracy of the OPLR was about 81%. The climate was the main driving factor of forest fires, while temperature had the greatest influence on the probability of forest fires. According to the forest fire prediction model, mapping the fire risk zoning, in which the medium- and high-risk area was 6021.13 km2, accounted for 9.99% of the study area. The results contribute to a better understanding of forest fire management based on the local environmental characteristics of the Liangshan Yi Autonomous Prefecture and provide a reference for related forest fire prevention and control management.

Tropical peat surface oscillations are a function of peat condition at North Selangor peat swamp forest, Malaysia

Tropical peatland condition across southeast Asia is deteriorating as a result of conversion to agriculture and urban zones. Conversion begins by lowering the water table, which leads to peat decomposition, subsidence and increased risk of large-scale forest fires. Associated changes in mechanical peat properties impact the magnitude and timing of changes in peatland surface motion, making them a potential proxy for peatland condition. However, such a relationship is yet to be observed in a tropical peatland setting. This study aimed to establish whether patterns of tropical peatland surface motion were a function of peat condition at North Selangor Peat Swamp Forest in Selangor, Malaysia. Results showed that subsidence was greatest at fire-affected scrubland sites, whilst the lowest mean water table levels were found at smallholder oil palm sites. Peat condition and magnitude of tropical peat surface oscillation were significantly different between peat condition classes, whilst peat condition differed with depth. More degraded tropical peats with high bulk density throughout the peat profile due to high surface loading and low mean water table levels showed greater surface oscillation magnitudes. The dominant peat surface oscillation mechanisms present at all sites were compression and shrinkage from changes in water table level. Mean water table level and subsidence rate were related to surface oscillation magnitude. However further work towards measuring surface and within-water table range bulk densities and surface loading is required to better understand the controls on surface oscillation magnitudes.

Time Series Forest Fire Prediction Based on Improved Transformer

Forest fires, severe natural disasters causing substantial damage, necessitate accurate predictive modeling to guide preventative measures effectively. This study introduces an enhanced window-based Transformer time series forecasting model aimed at improving the precision of forest fire predictions. Leveraging time series data from 2020 to 2021 in Chongli, a myriad of forest fire influencing factors were ascertained using remote sensing satellite and GIS technologies, with their interrelationships estimated through a multicollinearity test. Given the intricate nature of real-world forest fire prediction tasks, we propose a novel window-based Transformer architecture complemented by a dual time series input strategy premised on 13 influential factors. Subsequently, time series data were incorporated into the model to generate a forest fire risk prediction map in Chongli District. The model’s effectiveness was then evaluated using various metrics, including accuracy (ACC), root mean square error (RMSE), and mean absolute error (MAE), and compared with traditional deep learning methods. Our model demonstrated superior predictive performance (ACC = 91.56%, RMSE = 0.37, MAE = 0.05), harnessing spatial background information efficiently and effectively utilizing the periodicity of forest fire factors. Consequently, the study proves this method to be a novel and potent approach for time series fire prediction.

Wildfire Hotspots Forecasting and Mapping for Environmental Monitoring Based on the Long Short-Term Memory Networks Deep Learning Algorithm

Global warming is raising the earth’s temperature, and resulting in increased forest fire events, especially in tropical regions with locations that are at high risk of wild and forest fires. Indonesia is a country in Southeast Asia that has experienced a severe number of wildfires, which have dangerous impacts on neighboring countries due to the emission of carbon and haze to the free air. The objective of this research is to map and plot the locations that consist of a significant number of fire hotspots and forecast the possible forest fire disasters in Indonesia based on the collected data of forest fires. The results of forecasting data are beneficial for the government and its policymakers to take preventive action and countermeasures regarding this wildfire issue. The Long Short-Term Memory (LSTM) algorithm, a deep learning method, was applied to analyze and then forecast the number of wildfire hotspots. The wildfire hotspot dataset from the year 2010 to 2022 is derived from the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS). The total number of collected observations is more than 700,000 wildfire data in Indonesia. The distribution of wildfire hotspots as shown in the results is concentrated mainly on two big islands, Kalimantan and Sumatra, Indonesia. The main issue is the peat type of land that is prone to spreading fire. Forecasting the number of hotspots for 2023 has achieved good results with an average error of 7%. Additionally, to prove that the proposed algorithm is working well, a simulation has been conducted using training data from 2018 to 2022 and testing data from 2021 to 2022. The forecasting result achieved a similar pattern of the number of fire hotspots compared to the available data in 2021 and 2022.

The True Cost of Climate Change

This Editor's Corner has frequently brought to the forefront the social and monetary costs associated with the impacts of a changing climate. More frequent and volatile hurricanes and tornadoes, massive flooding, extreme heat conditions impacting farming, and reduced availability of potable water—leading to premature deaths—are undeniable. Alongside these issues, the increasing risk of forest fires due to severe, long‐term drought conditions and human behavior (ignoring fire bans), fire hazards from downed power lines, and more lighting strikes from increasingly volatile storms place our communities and our health in danger.

Spatial layout siting method for fire stations based on comprehensive forest fire risk distribution

In order to shorten the response time of forest fire (FF) rescue and improve the efficiency of forest fire prevention and control, this paper proposes a fire station spatial layout location method based on the spatial distribution of FF comprehensive risk. A comprehensive forest fire risk assessment model has been developed by three perspectives, namely the risk factors of forest fires, exposure degree of the disaster-bearing body, and vulnerability of the disaster-bearing body. The location-assignment model was introduced to scientifically plan the spatial layout of FF stations from different perspectives. Zhuhai in Guangdong Province, China, a typical “city surrounded by mountains,” was chosen as the research site for case analysis. The results show that the high-risk areas of Zhuhai are mainly concentrated in the forests of Xiangzhou District and Gaoxin District; the medium-risk areas mainly lie in the forests in the north of Gaoxin District, the forests of Hengqin Zone and some forests of Doumen District. Through a combination of the minimized facility point model and the maximized coverage model, it is calculated that 9 FF stations are the optimal choice, which can meet Zhuhai's demand for FF prevention and control with an acceptable construction cost and good coverage.

How students perceive natural and human‐made risks on the island of Madeira (Portugal)

AbstractIn this study the spatial perceptions of students about the likelihood of natural and environmental risks were examined, considering both mainland Portugal and island of Madeira. It intends to understand how students perceived the risks, taking into account the causal attributions, future tendency, and the support from public entities, as well as the willingness of attitudinal changes with respect to mitigating and reducing risks. The results suggest that students have a relatively low perception of analysed risks, considering the risk of forest fires, heatwaves, air and water pollution, and floods the most likely to occur, mainly as a consequence of climate change intensification. Gender proved to be the variable with the greatest influence on perception, particularly in terms of risk occurrence and personal perception of risk. These results could be important for the improvement of strategies and resources to be applied in the educational context in order to reduce disaster risk and strengthen the resilience of the community at large.

The application of gap planting technology to rehabilitate degraded natural forest

In 2020 degraded forests in Indonesia reached 33.4 million ha. These forests must be rehabilitated. The purpose of this research was to show the application of gap planting technology to rehabilitate degraded natural forests. This improvement of forest regeneration methods by planting local intolerant tree species in gaps area was used. Artificial gaps area was designed and it’s location was adjusted to the topography and forest cover. The size of each gap area is 2,000 m2. The ratio of total gaps area to total natural forests area was about four to six. Open areas, soil density, forest fire risk, working time and cost of gap planting, and tree growth were studied. The results showed that after the gap planting: forest cover consists of 37.43% gap planting area and 62.57% natural forest area; forest soil is not compacted; the fire hazard rating in the gap and natural forests is low. Moreover, it is cost-effective and has positive impacts such as job creation for local communities and a productivity increase of rehabilitated forests. It is concluded that the gap planting technology was environment friendly technology, and it is recommended to be used for rehabilitation of degraded forests in Indonesia.

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.

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.