Soft Computing Modeling of Wild Fire Risk Indices

2012 ◽  
pp. 220-234 ◽  
Author(s):  
L. Iliadis ◽  
T. Betsidou
Keyword(s):  
Soft Computing ◽  
Forest Fire ◽  
Forest Fires ◽  
Fuzzy Inference ◽  
Research Effort ◽  
Fire Risk ◽  
Geographic Area ◽  
Burned Area ◽  
Wild Fire ◽  
Risk Indices

It is essential to find ways that can reduce the risk of devastating forest fires which have multiple negative ecological and financial consequences. This preliminary research effort focuses on the implementation of an intelligent rule based fuzzy inference system evaluating wild fire risk in the forest departments of Greece. The system uses soft computing techniques and was built in the Matlab integrated environment. The whole research is related to the wild fires in Greece during the period 1983-1997 with data coming from the general forest management service. It classifies all Greek forest departments (by assigning three labels) according to their forest fire risk due to distinct parameters. The estimation of the risk indices was done by using fuzzy triangular membership functions and Einstein fuzzy conjunction T-Norms. Moreover the system produces the profile of the forest departments located in the geographic area of “Peloponnesus.” This is a region located in the southern part of the country and it has a vast number of annual forest fire breakouts. Meteorological, topographic, and historical (total burned area and intervention time) features were considered for the determination of the risk indices. The system has shown a good performance which can be improved further if more data is gathered and used. Its main advantage is that it offers an innovative and reliable model that can be employed in any part of the world as a basis for natural disasters’ risk estimation.

Soft Computing Modeling of Wild Fire Risk Indices

2013 ◽  
pp. 1073-1087
Author(s):  
L. Iliadis ◽  
T. Betsidou
Keyword(s):  
Soft Computing ◽  
Forest Fire ◽  
Forest Fires ◽  
Fuzzy Inference ◽  
Research Effort ◽  
Fire Risk ◽  
Geographic Area ◽  
Burned Area ◽  
Wild Fire ◽  
Risk Indices

It is essential to find ways that can reduce the risk of devastating forest fires which have multiple negative ecological and financial consequences. This preliminary research effort focuses on the implementation of an intelligent rule based fuzzy inference system evaluating wild fire risk in the forest departments of Greece. The system uses soft computing techniques and was built in the Matlab integrated environment. The whole research is related to the wild fires in Greece during the period 1983-1997 with data coming from the general forest management service. It classifies all Greek forest departments (by assigning three labels) according to their forest fire risk due to distinct parameters. The estimation of the risk indices was done by using fuzzy triangular membership functions and Einstein fuzzy conjunction T-Norms. Moreover the system produces the profile of the forest departments located in the geographic area of “Peloponnesus.” This is a region located in the southern part of the country and it has a vast number of annual forest fire breakouts. Meteorological, topographic, and historical (total burned area and intervention time) features were considered for the determination of the risk indices. The system has shown a good performance which can be improved further if more data is gathered and used. Its main advantage is that it offers an innovative and reliable model that can be employed in any part of the world as a basis for natural disasters’ risk estimation.

scholarly journals Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

2021 ◽  
Vol 13 (1) ◽  
pp. 432
Author(s):  
Aru Han ◽  
Song Qing ◽  
Yongbin Bao ◽  
Li Na ◽  
Yuhai Bao ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Change ◽  
Fire Severity ◽  
Dynamic Change ◽  
Burned Area ◽  
Fire Event ◽  
Vegetation Dynamic ◽  
Area Index ◽  
Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

scholarly journals MULTICRITERIA ANALYSIS FOR IDENTIFYING FOREST FIRE RISK ZONES IN THE BIOLOGICAL RESERVE OF THE SAMA CORDILLERA, BOLIVIA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 113-118
Author(s):  
S. Mariscal ◽  
M. Ríos ◽  
F. Soria
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Risk Model ◽  
Multicriteria Analysis ◽  
Fire Risk ◽  
Temperature And Precipitation ◽  
Spatial Risk ◽  
Forest Fire Risk ◽  
Very High ◽  
Biological Reserve

Abstract. Forest fires have negative effects on biodiversity, the atmosphere and human health. The paper presents a spatial risk model as a tool to assess them. Risk areas refer to sectors prone to the spread of fire, in addition to the influence of human activity through remote sensing and multi-criteria analysis. The analysis includes information on land cover, land use, topography (aspect, slope and elevation), climate (temperature and precipitation) and socio-economic factors (proximity to settlements and roads). Weights were assigned to each in order to generate the forest fire risk map. The investigation was carried for a Biological Reserve in Bolivia because of the continuous occurrence of forest fires. Five risk categories for forest fires were derived: very high, high, moderate, low and very low. In summary, results suggest that approximately 67% of the protected area presents a moderate to very high risk; in the latter, populated areas are not dense which reduces the actual risk to the type of events analyzed.

scholarly journals Climate change and forest management affect forest fire risk in Fennoscandia

2021 ◽  
Author(s):  
Keyword(s):  
Climate Change ◽  
Forest Management ◽  
Black Carbon ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
The Arctic ◽  
Large Fires ◽  
Forest Fire Risk ◽  
Fire Ignition

Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.

scholarly journals Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea

2020 ◽  
Vol 10 (22) ◽  
pp. 8213
Author(s):  
Yoojin Kang ◽  
Eunna Jang ◽  
Jungho Im ◽  
Chungeun Kwon ◽  
Sungyong Kim
Keyword(s):  
South Korea ◽  
Forest Fire ◽  
Forest Fires ◽  
Risk Index ◽  
Fire Risk ◽  
Ensemble Model ◽  
Weather Factors ◽  
Meteorological Model ◽  
Forest Fire Risk ◽  
Fire Risk Index

Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea’s current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined.

scholarly journals Mathematical Modeling of Heat Transfer in an Element of Combustible Plant Material When Exposed to Radiation from a Forest Fire

Safety ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 56 ◽  
Author(s):  
Nikolay Baranovskiy ◽  
Alena Demikhova
Keyword(s):  
Heat Transfer ◽  
Forest Fire ◽  
Forest Fires ◽  
Plant Material ◽  
Mathematical Physics ◽  
Burned Area ◽  
Herbaceous Plant ◽  
One Dimensional ◽  
Impact Prediction ◽  
The Impact

The last few decades have been characterized by an increase in the frequency and burned area of forest fires in many countries of the world. Needles, foliage, branches, and herbaceous plants are involved in burning during forest fires. Most forest fires are surface ones. The purpose of this study was to develop a mathematical model of heat transfer in an element of combustible plant material, namely, in the stem of a herbaceous plant, when exposed to radiation from a surface forest fire. Mathematically, the process of heat transfer in an element of combustible plant material was described by a system of non-stationary partial differential equations with corresponding initial and boundary conditions. The finite difference method was used to solve this system of equations in combination with a locally one-dimensional method for solving multidimensional tasks of mathematical physics. Temperature distributions were obtained as a result of modeling in a structurally inhomogeneous stem of a herbaceous plant for various scenarios of the impact of a forest fire. The results can be used to develop new systems for forest fire forecasting and their environmental impact prediction.

The spatio-temporal characteristics of forest fires in China: observations from hybrid remote sensing data

2020 ◽  
Author(s):  
Lei Fang ◽  
Zeyu Qiao ◽  
Jian Yang
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
High Frequency ◽  
Fire Management ◽  
Hot Spot ◽  
Landsat Imagery ◽  
Low Frequency ◽  
Burned Area ◽  
Prevention Policy ◽  
The Past

<p>Forest fire is a natural disaster threatening global human well-beings as well as a crucial disturbance agent driving forest landscape changes. The remotely sensed burned area (BA) products can provide spatially and temporally continuous monitoring of global fires, but the accuracies remain to be improved. We firstly developed a hybrid burned area mapping approach, which integrated the advantages of a 250 m global BA product (CCI_Fire) and a 30 m global forest change (GFC) product, to generate an improved 250 m BA product (so-called CCI_GFC product). Based on 248 fire patches derived from Landsat imagery, the results showed that the CCI_GFC product improved the CCI_Fire product substantially, which are significantly better than MCD64A1 product. According to the CCI_GFC, we found the total BA in the past 17 years was about 12.1 million ha in China, which approximately covered 6.1% of the total forested areas with a significantly decreased trend through Mann-Kendall test (Tau= -0.47, P<0.05) . We conducted a grid analysis (0.05°×0.05°) to determine the hot spots of forest fire from 2001 to 2017. We also quantified fire characteristics on frequency, spatial distribution, and seasonality in terms of Burned Forest Rate (BFR), hot spot areas, and fire seasons, respectively. We found that low frequency burns with a 0<BFR≤20% in 17 years covered 64% of total grids; the medium-low frequency burns (20%<BFR≤40%), the medium frequency burns (40%<BFR≤60%), the medium-high frequency burns (60%< BFR≤80%) accounted for 15%, 7%, 4% respectively; the high frequency burns (80%<BFR≤100%) and extremely high burns (100%<BFR≤120%) together occupy 10% of total grids which mainly distributed in Xiao Hinggan mountains, south China, and southwest China. The seasonality of forest fires differed substantially among eco-regions. The fire seasons of two temperate forest eco-regions are spring and autumn. The two peak fire months are May and October, in which about 22% and 37% of the total burned area were founded respectively. As a comparison, fire seasons in tropical and subtropical eco-regions are spring and winter (i.e., November to March of the next year), which accounted 88% of the total burned area. Our study clearly illustrated the characteristics of forest fire patterns in the past 17 years, which highlighted the remarkable achievements due to a nationwide implementation of fire prevention policy. At the same time, we emphasized that it is critically important to regard the long-term forest fire dynamics to design scientific and reasonable strategies or methods for fire management and controlling, which will be of sound significance to optimize the allocation of financial resources on fire management, and to achieve sustainable management of forests.</p>

scholarly journals FOREST FIRE RISK ZONING FOR THE VILA VELHA STATE PARK AND ITS SURROUNDINGS (PONTA GROSSA, PARANÁ)

FLORESTA ◽  
2020 ◽  
Vol 50 (4) ◽  
pp. 1818
Author(s):  
Bruna Kovalsyki ◽  
Alexandre França Tetto ◽  
Antonio Carlos Batista ◽  
Nilton José Sousa ◽  
Marta Regina Barrotto do Carmo ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Hazard ◽  
Fire Risk ◽  
Risk Mapping ◽  
Ignition Probability ◽  
Risk Zoning ◽  
Extreme Risk ◽  
Forest Fire Hazard ◽  
Forest Fire Risk

Forest fire hazard and risk mapping is an essential tool for planning and decision making regarding the prevention and suppression of forest fires,as well as fire management in general, as it allows the spatial visualization of areas with higher and lower ignition probability. This study aimed to develop a forest fire risk zoning map for the Vila Velha State Park and its surroundings (Ponta Grossa, Paraná State, Brazil), for the period of higher incidence of forest fires (from April to September) and for the period of lower incidence (from October to March). The following risk and hazard variables were identified: human presence, usage zones, topographical features, soil coverage and land use and meteorological conditions. Coefficients (0 to 5) reflecting the fire risk or hazard degree were allocated to each variable in order to construct the maps. The integration of these maps, through a weighting model, resulted in the final risk mapping. The very high and extreme risk classes represented about 38% of the area for both periods. The forest fire risk mapping spatially represented the levels of fire risk in the area, allowing the managers to identify the priority sectors for preventive actions in both fire seasons.

Monitoring Forest Fire using Geo-Spatial Information Techniques and Spatial Statistics: One Case Study of Forest fire in Margalla Hills, Islamabad, Pakistan

2020 ◽  
Author(s):  
Aqil Tariq ◽  
Hong Shu ◽  
Saima Siddiqui
Keyword(s):  
Spatial Patterns ◽  
Forest Fire ◽  
Forest Fires ◽  
Human Activities ◽  
Fire Risk ◽  
Maximum Temperature ◽  
Forest Density ◽  
Burned Areas ◽  
Severity Rating

Abstract Background Understanding the spatial patterns of forest fires is of key importance for fire risk management with ecological implications. Fire occurrence, which may result from the presence of an ignition source and the conditions necessary for a fire to spread, is an essential component of fire risk assessment. Methods The aim of this research was to develop a methodology for analyzing spatial patterns of forest fire danger with a case study of tropical forest fire at Margalla Hills, Islamabad, Pakistan. A geospatial technique was applied to explore influencing factors including climate, vegetation, topography, human activities, and 299 fire locations. We investigated the spatial extent of burned areas using Landsat data and determined how these factors influenced the severity rating of fires in these forests. The importance of these factors on forest fires was analyzed and assessed using logistic and stepwise regression methods. Results The findings showed that as the number of total days since the start of fire has increased, the burned areas increased at a rate of 25.848 ha / day (R 2 = 0.98). The average quarterly mean wind speed, forest density, distance to roads and average quarterly maximum temperature were highly correlated to the daily severity rating of forest fires. Only the average quarterly maximum temperature and forest density affected the size of the burnt areas. Fire maps indicate that 22% of forests are at the high and very high level (> 0.65), 25% at the low level (0.45-0.65), and 53% at the very low level (0.25 – 0.45). Conclusion Through spatial analysis, it is found that most forest fires happened in less populated areas and at a long distance from roads, but some climatic and human activities could have influenced fire growth. Furthermore, it is demonstrated that geospatial information technique is useful for exploring forest fire and their spatial distribution.

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