scholarly journals High Resolution Seasonal Forest Fire Danger mapping using WRF forecasts for Greece: a tool for forest fires prevention planning and fire risk management support

2018 ◽  
pp. 1205-1208
Author(s):  
Vassiliki Varela ◽  
et. al.
Keyword(s):  
Risk Management ◽  
High Resolution ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
Fire Danger ◽  
Management Support ◽  
Forest Fire Danger

scholarly journals Temporal variations and change of forest fire danger in Europe in 1960–2012

2013 ◽  
Vol 1 (6) ◽  
pp. 6291-6326
Author(s):  
A. Venäläinen ◽  
N. Korhonen ◽  
N. Koutsias ◽  
F. Xystrakis ◽  
I. R. Urbieta ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
Fire Danger ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Current Season ◽  
Fire Activity ◽  
Forest Fire Danger ◽  
In Fire

Abstract. Understanding how fire-weather danger indices changed in the past, and detecting how changes affected forest fire activity is important in changing climate. We used the Canadian Fire Weather Index (FWI), calculated from two reanalysis datasets, ERA 40 and ERA Interim, to examine the temporal variation of forest fire danger in Europe in 1960–2012. Additionally, we used national forest-fires statistical data from Greece and Spain to relate fire danger and fire activity. There is no obvious trend in fire danger for the time period covered by ERA 40 (1960–1999) whereas for the period 1980–2012 covered by ERA Interim, the mean FWI and the number of high fire risk days shows an increasing trend which is significant at the 99% confidence level for South and East Europe. The cross-correlation calculated at national level in Greece and Spain between mean yearly area burned and mean FWI of the current season is of the order 0.5–0.6, and demonstrates the importance of the fire-season weather on forest fires. Our results show that, fire risk is multifaceted, and factors like changes in fire fighting capacity, ignition patterns, or landscapes might have played a role in forest fires trends. However, weather trends remain as important determinants of forest fires.

scholarly journals Temporal variations and change in forest fire danger in Europe for 1960–2012

2014 ◽  
Vol 14 (6) ◽  
pp. 1477-1490 ◽  
Author(s):  
A. Venäläinen ◽  
N. Korhonen ◽  
O. Hyvärinen ◽  
N. Koutsias ◽  
F. Xystrakis ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
National Level ◽  
Temporal Variations ◽  
Fire Danger ◽  
Fire Season ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Current Season ◽  
Forest Fire Danger

Abstract. Understanding how fire weather danger indices changed in the past and how such changes affected forest fire activity is important in a changing climate. We used the Canadian Fire Weather Index (FWI), calculated from two reanalysis data sets, ERA-40 and ERA Interim, to examine the temporal variation of forest fire danger in Europe in 1960–2012. Additionally, we used national forest fire statistics from Greece, Spain and Finland to examine the relationship between fire danger and fires. There is no obvious trend in fire danger for the time period covered by ERA-40 (1960–1999), whereas for the period 1980–2012 covered by ERA Interim, the mean FWI shows an increasing trend for southern and eastern Europe which is significant at the 99% confidence level. The cross correlations calculated at the national level in Greece, Spain and Finland between total area burned and mean FWI of the current season is of the order of 0.6, demonstrating the extent to which the current fire-season weather can explain forest fires. To summarize, fire risk is multifaceted, and while climate is a major determinant, other factors can contribute to it, either positively or negatively.

scholarly journals Long-term temporal changes in the occurrence of a high forest fire danger in Finland

2012 ◽  
Vol 12 (8) ◽  
pp. 2591-2601 ◽  
Author(s):  
H. M. Mäkelä ◽  
M. Laapas ◽  
A. Venäläinen
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Large Scale ◽  
Weather Conditions ◽  
Temporal Variations ◽  
Fire Danger ◽  
Specific Information ◽  
Climate Data ◽  
Forest Fire Danger

Abstract. Climate variation and change influence several ecosystem components including forest fires. To examine long-term temporal variations of forest fire danger, a fire danger day (FDD) model was developed. Using mean temperature and total precipitation of the Finnish wildfire season (June–August), the model describes the climatological preconditions of fire occurrence and gives the number of fire danger days during the same time period. The performance of the model varied between different regions in Finland being best in south and west. In the study period 1908–2011, the year-to-year variation of FDD was large and no significant increasing or decreasing tendencies could be found. Negative slopes of linear regression lines for FDD could be explained by the simultaneous, mostly not significant increases in precipitation. Years with the largest wildfires did not stand out from the FDD time series. This indicates that intra-seasonal variations of FDD enable occurrence of large-scale fires, despite the whole season's fire danger is on an average level. Based on available monthly climate data, it is possible to estimate the general fire conditions of a summer. However, more detailed input data about weather conditions, land use, prevailing forestry conventions and socio-economical factors would be needed to gain more specific information about a season's fire risk.

scholarly journals Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

2019 ◽  
Vol 11 (18) ◽  
pp. 2101 ◽  
Author(s):  
M. Ahmed ◽  
Quazi Hassan ◽  
Masoud Abdollahi ◽  
Anil Gupta
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Land Surface ◽  
Vegetation Index ◽  
Significant Risk ◽  
Fire Danger ◽  
Area Of Interest ◽  
Forest Fire Danger ◽  
Very High

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events.

Classification of Territory on Forest Fire Danger Level Using GIS and Remote Sensing

2020 ◽  
pp. 258-267 ◽  
Author(s):  
Elena Petrovna Yankovich ◽  
Ksenia S. Yankovich
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Fire Danger ◽  
Gis And Remote Sensing ◽  
Forest Fuels ◽  
Forest Fire Danger

The vegetation cover is the most important factor in forest fires, because it reflects the presence of forest fuels. The study of the variability of the vegetation cover, as well as observation of its condition, allows estimating the level of fire danger of the forest quarter. The work presents a geo-information system containing a set of tools to determine the level of fire danger of the forest quarter. The system is able to predict (determine the probability) and classify forest quarters according to the level of fire danger. The assessment of forest fire danger of Tomsk forestry of Tomsk region has been carried out. Fire probability maps of forest quarters were created based on remote sensing data and ArcGIS software.

scholarly journals GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6520
Author(s):  
Ivan Novkovic ◽  
Goran B. Markovic ◽  
Djordje Lukic ◽  
Slavoljub Dragicevic ◽  
Marko Milosevic ◽  
...  
Keyword(s):  
Risk Management ◽  
Sensor Network ◽  
Forest Fire ◽  
Forest Fires ◽  
Fuzzy Ahp ◽  
Fire Risk ◽  
Early Warning Systems ◽  
Environmental Data ◽  
Nature Park ◽  
The Republic

The territory of the Republic of Serbia is vulnerable to various natural disasters, among which forest fires stand out. In relation with climate changes, the number of forest fires in Serbia has been increasing from year to year. Protected natural areas are especially endangered by wildfires. For Nature Park Golija, as the second largest in Serbia, with an area of 75,183 ha, and with MaB Reserve Golija-Studenica on part of its territory (53,804 ha), more attention should be paid in terms of forest fire mitigation. GIS and multi-criteria decision analysis are indispensable when it comes to spatial analysis for the purpose of natural disaster risk management. Index-based and fuzzy AHP methods were used, together with TOPSIS method for forest fire susceptibility zonation. Very high and high forest fire susceptibility zone were recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP). The additional support for forest fire prevention is realized through an additional Internet of Thing (IoT)-based sensor network that enables the continuous collection of local meteorological and environmental data, which enables low-cost and reliable real-time fire risk assessment and detection and the improved long-term and short-term forest fire susceptibility assessment. Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and early warning systems in the Republic of Serbia, but are also important for relevant authorities at national, regional, and local level, which will be able to coordinate and intervene in a case of emergency events.

scholarly journals WWLLN Data Cluster Analysis Methods for Lightning-Caused Forest Fires Monitoring

2016 ◽  
Vol 6 (6) ◽  
pp. 3112
Author(s):  
Baranovskiy Nikolay ◽  
Krechetova Svetlana ◽  
Belikova Marina ◽  
Perelygin Anton
Keyword(s):  
Cluster Analysis ◽  
Forest Fire ◽  
Forest Fires ◽  
Research Area ◽  
Fire Danger ◽  
Storm Activity ◽  
Lightning Discharges ◽  
Current Article ◽  
Forest Fire Danger ◽  
New Generation

<p>Storm activity is the main reason for forest fires to occur in remote forested territories. The current article presents the results for cluster analysis of WWLLN data on lightning discharges. It provides the description for clusterization algorithms of lightning discharges over the controlled territory. Research area is Timiryazevskiy forestry of the Tomsk region (Siberia, Russia). We analyzed the applicability of cluster analysis results for monitoring of the forest fire danger caused by storm activity. As a result of the conducted research, we established that the following characteristics of storm activity can be included in deterministic-probabilistic criterion to assess the forest fire danger. The article gives the recommendations how to create new generation information-computer and geoinformation systems for monitoring of the forest fire danger caused by storm activity in the controlled forested territory.</p>

scholarly journals A Probabilistic Method Predicting Forest Fire Occurrence Combining Firebrands and the Weather-Fuel Complex in the Northern Part of the Daxinganling Region, China

Forests ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 428 ◽  
Author(s):  
Ping Sun ◽  
Yunlin Zhang
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Probabilistic Method ◽  
Fire Risk ◽  
The United States ◽  
Fire Danger ◽  
Fire Occurrence ◽  
Weather Variables ◽  
Rating Method ◽  
Actual Fire

The fire danger rating method currently used in the northern part of the Daxinganling Region with the most severe forest fires in China only uses weather variables without considering firebrands. The discrepancy between fire occurrence and fire risk by FFDWR (Forest Fire-Danger Weather Rating, a method issued by the National Meteorological Bureau, that is used to predict forest fire probability through links between forest fire occurrence and weather variables) in the northern part is more obvious than that in the southern part. Great discrepancy has emerged between fire danger predicted by the method and actual fire occurrence in recent years since a strict firebrand prohibition policy has significantly reduced firebrands in the region. A probabilistic method predicting fire probability by introducing an Ignition Component (IC) in the National Fire Danger Rating System (NFDRS) adopted in the United States to depict effects of both firebrand and weather-fuel complex on fire occurrence is developed to solve the problem. The suitability and accuracy of the new method in the region were assessed. Results show that the method is suitable in the region. IC or the modified IC can be adopted to depict the effect of the weather-fuel complex on fire occurrence and to rate fire danger for periods with fewer firebrands. Fire risk classes and corresponding preparedness level can be determined from IC in the region. Methods of the same principle could be established to diminish similar discrepancy between actual fire occurrence and fire danger in other countries.

WWLLN Data Cluster Analysis Methods for Lightning-Caused Forest Fires Monitoring

2016 ◽  
Vol 6 (6) ◽  
pp. 3112 ◽  
Author(s):  
Baranovskiy Nikolay ◽  
Krechetova Svetlana ◽  
Belikova Marina ◽  
Perelygin Anton
Keyword(s):  
Cluster Analysis ◽  
Forest Fire ◽  
Forest Fires ◽  
Research Area ◽  
Fire Danger ◽  
Storm Activity ◽  
Lightning Discharges ◽  
Current Article ◽  
Forest Fire Danger ◽  
New Generation

<p>Storm activity is the main reason for forest fires to occur in remote forested territories. The current article presents the results for cluster analysis of WWLLN data on lightning discharges. It provides the description for clusterization algorithms of lightning discharges over the controlled territory. Research area is Timiryazevskiy forestry of the Tomsk region (Siberia, Russia). We analyzed the applicability of cluster analysis results for monitoring of the forest fire danger caused by storm activity. As a result of the conducted research, we established that the following characteristics of storm activity can be included in deterministic-probabilistic criterion to assess the forest fire danger. The article gives the recommendations how to create new generation information-computer and geoinformation systems for monitoring of the forest fire danger caused by storm activity in the controlled forested territory.</p>

scholarly journals Efficiency evaluation of the weighted mean calculation of the forest fire hazard class

2021 ◽  
Vol 875 (1) ◽  
pp. 012064
Author(s):  
R V Kotelnikov ◽  
A N Chugaev
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Hazard ◽  
Fire Danger ◽  
Weighted Mean ◽  
Calculation Algorithm ◽  
Forest Protection ◽  
The Mean ◽  
Weather Stations ◽  
Forest Fire Danger

Abstract Nowadays, cost-optimization of aerial patrolling plays a key role in the context of limited aerial forest protection funding. Forest Fire Danger Class is the main indicator that regulates the work of forest fire services. Usually, it’s calculated by the nearest weather station data. Some information systems use the mean of several nearby weather stations to estimate large areas, such as the surveyed area of aerial forest protection. The idea of using the mean weighted index with the weather stations weighting factor is not new. Even though, this idea isn’t widespread due to the calculation complexity and questionable efficiency in practice, this study proposes a scientifically substantiated method of quantitative comparison of two approaches and the direct calculation method of the economic impact when transition to using the mean weighted Forest Fire Danger Class calculation algorithm. The first time such an indicator was used to obtain derivatives of analytical information products. A long-term analysis of forest fire rate showed that the weighted mean of the Forest Fire Danger Class value is 6.7% greater in correlation with the number of forest fires than the usual mean value. The use logarithmic transformation of the forest fire occurrence frequency and population density allows statistical criteria to be reasonably used.

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