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 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 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.

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
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Forest Fire ◽  
Fire Risk ◽  
Fire Spread ◽  
Fire Occurrence ◽  
Occurrence Probability ◽  
Burn Probability ◽  
Forest Fire Risk ◽  
Fire Ignition

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.

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.

Evolution of the legal prevention measures concerning forest fire risk in a context of climate change

2017 ◽  
Vol 97 ◽  
pp. 73-80
Author(s):  
Valérie Sanseverino-Godfrin ◽  
Emmanuel Garbolino ◽  
Guillermo Hinojos-Mendoza
Keyword(s):  
Climate Change ◽  
Forest Fire ◽  
Fire Risk ◽  
Prevention Measures ◽  
Forest Fire Risk

scholarly journals Impacts of climate change on forest fire risk in Paraná State-Brazil

2014 ◽  
pp. 1182-1192
Author(s):  
Antonio Carlos Batista ◽  
Alexandre França Tetto ◽  
Flavio Deppe ◽  
Leocádio Grodzki
Keyword(s):  
Climate Change ◽  
Forest Fire ◽  
Fire Risk ◽  
Forest Fire Risk ◽  
Impacts Of Climate Change

scholarly journals GISCIENCE FOR FOREST FIRE MODELING: NEW ADVANCES IN WILDLAND FIRES WITH MANAGEMENT

2021 ◽  
Vol 21 (no 1) ◽  
Author(s):  
Jagpal Singh Tomar ◽  
Shruti Kanga ◽  
Suraj Kumar Singh
Keyword(s):  
Climate Change ◽  
Forest Fire ◽  
Spatial Data ◽  
Forest Fires ◽  
Fire Risk ◽  
Fire Regimes ◽  
Spatial Data Analysis ◽  
Mitigation And Adaptation ◽  
The World ◽  
Urban Rural

Wildfire is one of the complex and damaging natural phenomena in the world. Wildfires pose an enormous challenge to predict and monitor complicated integration chemistry with the physical aspects of solid-gas stage combustion and heat transmission spatially diverse vegetations, topography, and detailed time and space conditions at various spatial and time scales. The research community has greatly enhanced its efforts in the last 25 years to better understand wildfires by improving observation, measurement, analysis and modelling. The fast development of spatial data analysis and computer technology has been facilitated. This combination allowed new decision promotion systems, information collection, analysis methods, growth, and existing fire management instruments. In several countries, despite this activity, forest fires remain a serious problem. Factors that raise the world risk of wildfires are climate change, urban-rural migration and the creation of the interface between urban and wildlands. These events demonstrate the tremendous destructive force of wildfires of great magnitude, sometimes well beyond our concrete containment and control capability. In addition to firefighters, foresters and other organised systems, the scientific community is key to addressing the problems of fire recognition in the countryside. Advances in our understanding of fire-fighting mechanisms and the relationship between fire activity and the natural and constructed environment can lead to successful fire risk decision support systems, the predictions for fire propagation and the reduction of fire risk. The convergence of forest ecosystems and forest fires has become the growing threat posed by human influences and other factors to ecosystems, resources and even human lives. Climate change will change forest fire regimes to enhance forest fire understanding and to build strategies for mitigation and adaptation. The study highlights broad aspects of forest fire in combination with

scholarly journals Mapping the Forest Fire Risk Zones Using Artificial Intelligence with Risk Factors Data

2022 ◽  
Author(s):  
Volkan Sevinc
Keyword(s):  
Artificial Intelligence ◽  
High Risk ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
Geographical Information ◽  
Data Set ◽  
Risk Zone ◽  
Risk Zones ◽  
Forest Fire Risk

Abstract Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of nine human-induced factors, three natural factors, and a temperature factor causing forest fires. Moreover, an artificial intelligence method, k-means, clustering algorithm was employed in preparation of the fire risk zone map. Turkey was selected as the study area as there are social and economic varieties among its zones. Therefore, the forestry zones in Turkey were separated into three groups as low, moderate, and high-risk categories and a map was provided for these risk zones. The map reveals that the forestry zones on the west coast of Turkey are under high risk of forest fire while the moderate risk zones mostly exist in the southeastern zones. The zones located in the interior parts, in the east, and on the north coast of Turkey have comparatively lower forest fire risks.

scholarly journals MAPEAMENTO DE RISCO DE DESASTRE POR INCÊNDIO FLORESTAL NA AMAZÔNIA: uma abordagem multifatorial no município de Moju (PA)

2020 ◽  
Vol 5 (19) ◽  
pp. 202009
Author(s):  
Tarsis Esaú Gomes Almeida ◽  
Maria do Socorro Almeida Flores ◽  
Mário Vasconcellos Sobrinho
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
Disaster Risk ◽  
Risk Map ◽  
Economic Activities ◽  
Socioeconomic Aspects ◽  
Fire Disaster ◽  
Risk Maps ◽  
Forest Fire Risk

MAPPING DISASTER RISK BY FOREST FIRE IN THE AMAZON: a multifactorial approach in the municipality of Moju (PA)MAPEO DEL RIESGO DE DESASTRE POR INCENDIO FLORESTAL EN LA AMAZONÍA: un enfoque multifactorial en el municipio de Moju (PA)RESUMONo estado do Pará o município de Moju é um dos que apresentam a maior quantidade de focos de calor conforme dados oficiais. Note-se que a base de suas atividades econômicas são a agricultura familiar e as plantações de dendê e coco-da-baía, diante disso propôs-se questionar sobre o risco não apenas da existência de incêndios florestais, mas da magnitude das consequências socioeconômicas deles. A pesquisa bibliográfica e documental em artigos acadêmicos e científicos, dissertações e teses possibilitou a compreensão do significado de mapeamento de áreas de risco de incêndio florestal identificadas no mapa de risco, bem como a possibilidade de desenvolver com base teórica e metodológica a criação de um mapeamento e ponderação de aspectos socioeconômicos expressado no mapa de vulnerabilidade, a fim de refinar um produto final na elaboração do mapa de risco de desastre. Assim, objetivo deste artigo é mostrar e discutir a incorporação de fatores sociais e econômicos na formulação dos mapas de risco de incêndio florestal. Mais precisamente, um Mapa de Risco de Desastre por Incêndio Florestal (MRDIF), que consiste na fusão entre Mapas de Risco de Incêndio Florestal e um Mapa Avaliativo Socioeconômico. Como resultado imediato da formação do MRDIF é o planejamento de ações preventivas. Percebeu-se que houve variação nas áreas de risco dos mapas com e sem a inclusão dos aspectos socioeconômicos, o que pode indicar quais sejam as áreas principais para ações a fim de diminuir os riscos ou as consequências dos possíveis desastres causados por incêndios florestais. Palavras-chave: Gestão de Risco; Incêndios Florestais; Uso do Solo na Amazônia; Cartografia.ABSTRACTIn the state of Pará, the municipality of Moju is one of those with the highest number of hot spots according to official data. It should be noted that the basis of its economic activities are family farming and oil palm and coconut plantations. In view of this, it was proposed to ask about the risk not only of the existence of forest fires, but of the magnitude of their socioeconomic consequences. Bibliographic and documentary research in academic and scientific articles, dissertations and theses made it possible to understand the meaning of mapping areas of forest fire risk identified in the risk map, as well as the possibility of developing a mapping with theoretical and methodological basis. and weighting of socioeconomic aspects expressed in the Vulnerability Map, in order to refine a final product in the preparation of the disaster risk map. Thus, the objective of this article is to show and discuss the incorporation of social and economic factors in the formulation of forest fire risk maps. More precisely, a Forest Fire Disaster Risk Map (FFDRP), which consists of the merger between Forest Fire Risk Maps and a Socioeconomic Assessment Map. As an immediate result of the formation of FFDRP is the planning of preventive actions. It was noticed that there was variation in the risk areas of the maps with and without the inclusion of socioeconomic aspects, which may indicate what are the main areas for actions in order to reduce the risks or the consequences of possible disasters caused by forest fires.Keywords: Risk Management; Fire Forest; Land Use in the Amazon; Cartography.RESUMENEn el estado de Pará, el municipio de Moju es una de las regiones con el mayor número de focos de calor según datos oficiales. Cabe señalar que la base de sus actividades económicas son la agricultura familiar y las plantaciones de palma aceitera y coco, en vista de esto, se propuso preguntar sobre el riesgo no solo de la existencia de incendios forestales, sino de la magnitud de sus consecuencias socioeconómicas. La investigación bibliográfica y documental en artículos académicos y científicos, disertaciones y tesis permitió comprender el significado de las áreas de mapeo de riesgo de incendio forestal identificadas en el mapa de riesgo, así como la posibilidad de desarrollar un mapeo con base teórica y metodológica. y ponderación de los aspectos socioeconómicos expresados en el mapa de vulnerabilidad, con el fin de refinar un producto final en la preparación del mapa de riesgo de desastres. Por lo tanto, el objetivo de este artículo es mostrar y discutir la incorporación de factores sociales y económicos en la formulación de mapas de riesgo de incendios forestales. Más precisamente, un Mapa de Riesgo de Desastres por Incendios Forestales (MRDIF), que consiste en la fusión entre Mapas de riesgo de incendios forestales y un Mapa de evaluación socioeconómica. Como resultado inmediato de la formación de MRDIF es la planificación de acciones preventivas. Se observó que hubo variación en las áreas de riesgo de los mapas con y sin la inclusión de aspectos socioeconómicos, lo que puede indicar cuáles son las principales áreas de acción para reducir los riesgos o las consecuencias de posibles desastres causados por incendios forestales.Palabras clave: Gestión de Riesgos; Incendios Florestales; Uso del Suelo en la Amazonia; Cartografía.

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