Forest fire risk assessment using point process modelling of fire occurrence and Monte Carlo fire simulation

2017 ◽  
Vol 26 (9) ◽  
pp. 789 ◽  
Author(s):  
Hyeyoung Woo ◽  
Woodam Chung ◽  
Jonathan M. Graham ◽  
Byungdoo Lee

Risk assessment of forest fires requires an integrated estimation of fire occurrence probability and burn probability because fire spread is largely influenced by ignition locations as well as fuels, weather, topography and other environmental factors. This study aims to assess forest fire risk over a large forested landscape using both fire occurrence and burn probabilities. First, we use a spatial point processing method to generate a fire occurrence probability surface. We then perform a Monte Carlo fire spread simulation using multiple fire ignition points generated from the fire occurrence surface to compute burn probability across the landscape. Potential loss per land parcel due to forest fire is assessed as the combination of burn probability and government-appraised property values. We applied our methodology to the municipal boundary of Gyeongju in the Republic of Korea. The results show that the density of fire occurrence is positively associated with low elevation, moderate slope, coniferous land cover, distance to roads, high density of tombs and interaction among fire ignition locations. A correlation analysis among fire occurrence probability, burn probability, land property value and potential value loss indicates that fire risk in the study landscape is largely associated with the spatial pattern of burn probability.

2021 ◽  
Author(s):  

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.


2011 ◽  
Vol 22 (4) ◽  
pp. 641-647 ◽  
Author(s):  
Kanchan Puri ◽  
G. Areendran ◽  
Krishna Raj ◽  
Sraboni Mazumdar ◽  
P. K. Joshi

2018 ◽  
Vol 13 (3) ◽  
pp. 307-316 ◽  
Author(s):  
DIVYA MEHTA ◽  
PARMINDER KAUR BAWEJA ◽  
R K AGGARWAL

Forest fires in the mid hills of Himachal Pradesh are mostly related to human activities. More than 90% of fires are originated from either deliberate or involuntary causes. The purpose of study is linked to identification of forest fire risk factors in 19 villages under Nauni and Oachhghat Panchayats. The methodology paradigm applied here is based on knowledge and fuzzy analytic hierarchy process (FAHP) techniques. Knowledge-based criteria involve socio-economic and biophysical themes for risk assessment. The risk factors are identified according to past occurrence of fire. Fuel type scores highest weight (0.3109) followed by aspect (0.2487), agricultural workers (0.1865), nutritional density (0.1244), population density (0.0622), elevation (0.0311), literacy rate (0.0207) and distance from road (0.0155) in descending order. In the study area applying FAHP, 24.96% of total area was classified under high-risk prone area, 21.69% area classified under high-risk, 34.63% area under moderate risk, while 18.61% area under low risk. The results were in accordance with actual fire occurrences in the past years.


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