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.

An Analysis to Predict Forest Fire Danger and Fire Spread

2003 ◽  
Author(s):  
Kohyu Satoh ◽  
Shiro Kitamura ◽  
Kunio Kuwahara ◽  
K. T. Yang
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Intelligent System ◽  
Fire Spread ◽  
Fire Danger ◽  
Weather Data ◽  
Fire Weather ◽  
The Us ◽  
Weather Stations ◽  
Forest Fire Danger

Forest fires are of common occurrence all over the world, causing severe damages to valuable natural environment and loss of human lives. In order to reduce the damages by forest fires, it is useful to utilize a system, which can predict the occurrence of forest fires and the spread of fires. Well known is a system in USA, called NFDRS to predict forest fire occurrence and FARSITE to predict fire growth, based on the fire weather information taken from a network, combined with forest fuel conditions and land topography data, and processed by an algorithm to generate the various fire danger indices. In Japan the number of forest fires is roughly 3,000 per year, which is 1/30 times compared with USA, and there are very few fires exceeding 1000 ha burnt area, hence there has existed scant demand for this type of intelligent system. Although recently there is an increasing demand for such a system in Japan, the US system for forest-fire prediction is however not applicable to Japan, since the forest topology and weather conditions between Japan and USA are far different. Moreover, many fire weather stations have been installed in the US forests, but in Japan no such fire weather stations are installed in forests. Thus, as a first step to develop an intelligent system for Japan, we have analyzed the fundamentals of forest fire danger rating and the fire spread, based on the weather data and other information on forest fires. The objective of this study is to examine how the fundamentals, based on analyzing the past fire occurrences and CFD simulations particularly on “Katunuma Fire”, can predict the occurrence of forest fires and the spread 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 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>

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>

Effects of meteorology on forest fires in India: A modeling study

2020 ◽  
Author(s):  
Anasuya Barik ◽  
Somnath Baidya Roy
Keyword(s):  
Relative Humidity ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Hazard ◽  
Fire Behavior ◽  
Fire Danger ◽  
Fire Season ◽  
Strong Impact ◽  
Fire Weather Index ◽  
The Relationship

&lt;p&gt;Meteorology has a strong impact on forest fires. Meteorological parameters such as temperature, relative humidity, wind speed and precipitation alter the fuel loading in forests, control the changes in spatial distribution, intensity and frequency of forest fires and changes in forest fire season. Hence, it is important to understand the relationship between forest fires and meteorological factors and build models that can simulate these relationships.&lt;/p&gt;&lt;p&gt;The Canadian Forest Fire Danger Rating System (CFFDRS) has been used globally to assess and predict the fire behavior in various forest ecosystems. The Fire Weather Index (FWI) of CFFDRS models the relationship between meteorology and forest fires. In this study we calibrate the FWI over Indian forests using percentile analysis and logistic regression technique and test the performance using satellite-derived (MODIS daily fire data from 2003-2018) fire count and Fire Radiative Power (FRP). As the Indian forest landscape is highly heterogeneous, we calibrate the FWI over 4 FWI zones namely Himalayan, Deciduous, Western Ghats and Thorn forests based on IGBP forest classification and Koppen climatic zones.&amp;#160; Five fire danger classes having thresholds of 99&lt;sup&gt;th&lt;/sup&gt;, 95&lt;sup&gt;th&lt;/sup&gt;, 90&lt;sup&gt;th&lt;/sup&gt;, 80&lt;sup&gt;th&lt;/sup&gt; and 70&lt;sup&gt;th&lt;/sup&gt;of FWI percentiles have been defined with decreasing severity. Results show that the calibrated FWI is capable of simulating the forest fire behavior over India. Sensitivity studies show that temperature and relative humidity are the key controlling factors of forest fires over India.&lt;/p&gt;&lt;p&gt;This study is one of the first attempts to use fire models to simulate fire behavior over India. It can serve as a launchpad for further work on fire hazard prediction and effects of climate change on fire hazard in India.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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