Forest Fire Index Research Articles

A Cold Climate Wooden Home and Conflagration Danger Index: Justification and Practicability for Norwegian Conditions

The vast majority of fire-related deaths occur in residential buildings. Until recently, the fire risk for these buildings was only considered through static risk assessments or period-based assessments applying to certain periods of the year, e.g., Christmas holidays. However, for homes with indoor wooden panelling, especially in the ceiling, a dynamic fire danger indicator can be predicted for cold climate regions. Recognising the effect of fuel moisture content (FMC) of indoor wooden panelling on the enclosure fire development allows for the prediction of a wooden home fire danger indicator. In the present study, dry wood fire dynamics are analysed and experimental observations are reported to support in-home wooden panel FMC as a suitable wooden home fire danger indicator. Then, from previous work, the main equation for modelling in-home FMC is considered and a generic enclosure for FMC modelling is justified based on literature data and supported through a sensitivity study for Norwegian wooden homes. Further, ten years of weather data for three selected locations in Norway, i.e., a coastal town, an inland fjord town and a mountain town, were analysed using a three-dimensional risk matrix to assess the usability of the fire risk modelling results. Finally, a cold climate wooden home fire danger index was introduced to demonstrate how the risk concept can be communicated in an intuitive way using similar gradings as the existing national forest fire index. Based on the generic enclosure, the findings support FMC as a fire risk indicator for homes with interior wooden panelling (walls and ceiling). Large differences in the number of days with arid in-home conditions were identified for the selected towns. The number of days with combined strong wind and dry wooden homes appears to depend more on the number of days with strong wind than days of in-home drought. Thus, the coastal town was more susceptible to conflagrations than the drier inland towns. This aligns well with the most significant fire disasters in Norway since 1900. In addition, it was demonstrated that the number of high-risk periods is manageable and can be addressed by local fire departments through proactive measures. In turn, the fire risk modelling and associated index respond well to the recent changes in Norwegian regulations, requiring the fire departments to have systems for detecting increased risk levels. Testing the modelling for a severe winter fire in the USA indicates that the presented approach may be of value elsewhere as well.

Satellite Data for Forest Fire Detection using Deep learning

Wildfires are devastating natural disasters that cause damage in the earth's ecosystem. Many detection and mapping systems which are created use a lot of tools, including artificial intelligence methods and good human observations. One of the most used systems is satellite. Remote sensing imagery is widely used for forest fire detection (FFD) due to their large zone coverage, which uses traditional and deep learning methods. In the last decade deep learning techniques have given promising results in remote sensing problems. This study uses Landsat-8 images dataset and deep convolutional network method for FFD. The network used in this paper has a special characteristic which is using simultaneously multiple kernels with different sizes. In this work, to improve the performance of forest fire detection, we have used several data in the deep learning input layer: bands 2, 6 and 7 of Landsat-8, and Forest Fire Index value, which is powerful index for FFD. Three different scenarios were used in this study with 3 network configurations for each one, resulting in 9 total distinct models, using multiple kernels of 3x3, 5x5 and 7x7. Landsat-8 images dataset and deep neural network model used in this paper have given good results in detecting forest fires of distinct shapes and different sizes in multiple difficult tests

IoT-inspired Framework for Real-time Prediction of Forest Fire

Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniques.

Prediction of Meteorological Parameter Time Series Data for the Forest Fire Early Warning System

A forest fire early warning system must be developed to reduce the impact of greater community losses. One effort to develop an early warning system is to use a forest fire hazard index as a potential assessment guide. The main factor which is a parameter in the fire hazard index calculation method is the meteorological parameter. In general, to know today’s fire hazard index is calculated from today’s weather conditions, but the need for an early warning system is to know the future fire hazard index. Based on a series of meteorological conditions data held for thirty-six months, using the backpropagation algorithm, it is estimated that the meteorological conditions will be several months to come. Several meteorological parameters have their respective roles, the unknown contribution of which is calculated. In this study, each parameter will be measured by predicting time series data and compared with the results of calculations. The method of calculating the forest fire index used is the McArthur Forest Fire Danger Index with the meteorological parameter elements are temperature, relative humidity, wind speed, and drought factor. Each parameter was trained in artificial neural networks and tested its predictions to produce accuracy for data series temperatures of 91.67%, the relative humidity of 83.33%, and wind speed of 50%.

Moisture content variation of ground vegetation fuels in boreal mesic and sub-xeric mineral soil forests in Finland

Forest fire risk in Finland is estimated with the Finnish Forest Fire Index (FFI), which predicts the fuel moisture content (FMC) of the forest floor. We studied the FMC variation of four typical ground vegetation fuels, Pleurozium schreberi, Hylocomium splendens, Dicranum spp., and Cladonia spp., and raw humus in mature and recently clear-cut stands. Of these, six were sub-xeric Pinus sylvestris stands, and six mesic Picea abies stands. We analysed the ability of the FFI to predict FMC and compared it with the widely applied Canadian Fire Weather Index (FWI). We found that in addition to stand characteristics, ground layer FMC was highly dependent on the species so that Dicranum was the moistest, and Cladonia the driest. In the humus layer, the differences among species were small. Overall, the FWI was a slightly better predictor of FMC than the FFI. While the FFI generally predicted ground layer FMC well, the shape of the relationship varied among the four species. The use of auxiliary variables thus has potential in improving predictions of ignitions and forest fire risk. Knowledge of FMC variation could also benefit planning and timing of prescribed burns.

Skillful seasonal prediction of key carbon cycle components: NPP and fire risk

We investigate the skill of the GloSea5 seasonal forecasting system for two carbon cycle processes, which are strong contributors to global CO2 variability: the impact of meteorological conditions on CO2 uptake by vegetation (characterised by net primary productivity, NPP), and on fire occurrences (characterised by fire risk indices). Current seasonal forecasts of global CO2 concentrations rely on the relationship with the El Niño–Southern Oscillation (ENSO), combined with estimated anthropogenic emissions. NPP and fire are key processes underlying that global CO2–ENSO relationship: In the tropics, during El Niño events, CO2 uptake by vegetation is reduced and fires occur more frequently, leading to higher global CO2 levels. Our study assesses the skill of these processes in the forecast model for the first time. We use the McArthur forest fire index, calculated from daily data from several meteorological variables. We also assess a simpler fire index, based solely on seasonal mean temperature and relative humidity, to test the need for additional complexity. For NPP, the skill is high in regions that respond strongly to ENSO, such as equatorial South America in boreal winter, and northeast Brazil in boreal summer. There is also skill in some regions without a strong ENSO response. The fire risk indices show significant skill across much of the tropics, including Indonesia, southern and eastern Africa, and parts of the Amazon. We relate this skill to the underlying meteorological variables, finding that fire risk in particular follows similar patterns to relative humidity. On the seasonal-mean timescale, the McArthur index offers no benefits over the simpler fire index: they show the same relationship to burnt area and response to ENSO, and the same levels of skill, in almost all cases. Our results highlight potentially useful prediction skill, as well as important limitations, for seasonal forecasts of land-surface impacts of climate variability.

Comparison of Methods for the Assessment of Fire Danger in the Czech Republic

The performance of fire indices based on weather variables was analyzed with a special focus on the Czech Republic. Three fire weather danger indices that are the basis of fire danger rating systems used in different parts of the world were assessed: the Canadian Fire Weather Index (FWI), Australian Forest Fire Danger Index (FFDI) and Finnish Forest Fire Index (FFI). The performance of the three fire danger indices was investigated at different scales and compared with actual fire events. First, the fire danger indices were analyzed for different land use types during the period 1956–2015. In addition, in the analysis, the three fire danger indices were compared with wildfire events during the period 2001–2015. The fire danger indices were also analyzed for the specific locality of the Bzenec area where a large forest fire event occurred in May 2012. The study also focused on the relationship between fire danger indices and forest fires during 2018 across the area of the Jihomoravský region. Comparison of the index values with real fires showed that the index values corresponded well with the occurrence of forest fires. The analysis of the year 2018 showed that the highest index values were reached on days with the greater fire occurrence. On days with 5 or 7 reported fires per day, the fire danger indices reached values between 3 and 4.

A comparison of forest fire indices for predicting fire risk in contrasting climates in China

The relationships between fire danger indices and fire risk have been extensively studied in many regions of the world. This work uses partial effect analysis in semiparametric logistic regression models to assess the nonlinear relationships among location, day, altitude, fire danger indices, normalized difference vegetation index (NDVI), and fire ignition from 1996 to 2008 in four different climatic regions in China. The four regions are North China (NR), Northeast China (NE), Southeast China (SE), and Southwest China (SW). The three main results are as follows: First, different fire danger indices are selected as significant variables dependent on the region. The inter-regional difference could be partially explained by difference in local weather and vegetation conditions. Second, spatial location exerts highly significant effects in all four regions. NDVI values are selected as explained variable for NR, NE, and SE on fire ignitions. On a daily scale, altitude influences fire ignition for NR, SE, and SW. Third, the robustness of the probability models used in NE, SE, and SW is better than that in NR on a daily scale. The semiparametric logistic regression model used in this study is useful for assessing the ability of fire danger indices to estimate probabilities of fire ignition on a daily scale. This study encourages further research on assessing the predictive ability of fire danger indices developed at other temporal and spatial scales in China.

Design & Application of Forest Fire Monitoring System by Remote Sensing for Beijing, Tianjin & Hebei

In order to monitor the forest fire effectively and to reduce the loss caused by forest fire in North China areas, a forest fire monitoring system was designed. Based on satellite remote sensing data, GIS data and meteorological information, the system has functions such as processing of data, adjusting of forest fire index for judging hot spots, intelligent judging of hot spots, and analyzing of geographical information and weather condition of hot spots. Also fire information is issued on web and by short message service. The system makes the remote sensing monitoring of forest fire automatically.

Assessment of forest fire danger in a boreal forest environment: description and evaluation of the operational system applied in Finland

ABSTRACTForest fires constitute the second largest form of disturbance to the Finnish boreal forest environment, playing a significant role in vegetation succession and landscape transformation. Warning systems aiming to minimize the risk of fires are based on fire danger estimation methods using weather information. In this study, the operational fire danger rating method used in Finland, the Finnish Forest Fire Index, is introduced and its performance evaluated by analysing the relationship between the predicted daily fire danger and observed fire activity. The Finnish Forest Fire Index is a physically‐based soil surface moisture estimation method employing as input traditional surface observations, numerical weather forecast model fields and weather radar measurements. The fire danger index is found to predict high fire danger conditions well, with some decline in performance northward where the observations network is sparser than in the southern part of the country. The model indicated higher hit rates in the prediction of multiple fire (54%) and large fire days (54.3%), which are less dependent on a human‐dominated fire environment than are single fire days. Highlighted future development priorities include the direct application of numerical analyses of meteorological data in the operational computation procedure. In addition, inclusion of the impact of land cover, such as vegetation type, on the fire danger index is under development.

Evaluation of the performance of meteorological forest fire indices for German federal states

Meteorological forest fire risk indices have been developed to forecast the risk of fire occurrence and aid forest managers to take suitable preventive measures. We evaluate five meteorological fire risk indices and relevant meteorological variables for their predictive capacity against monthly fire statistics for 13 German states between 1993 and 2010. Mean relative humidity stands out as the best overall predictor (for 9 out of 13 states) for the recorded number of fires with a median correlation coefficient for Germany of −0.7. The indices with best explanatory power were, in decreasing order, the German modified M-68, the Canadian Fire Weather Index and Angström. The correlations of fire data with relative humidity and fire indices were stronger for states particularly prone to fire occurrence. At the monthly scale, correlations of relative humidity and fire indices with area burnt are in average weaker than with the number of fires. For the same time period, we investigated the performance on a daily scale for the state of Brandenburg. In this case, the performance of fire indices and relative humidity were more similar than at the monthly level. In addition, the number of fires could be explained equally well as the area burnt. Climate projections under different temperature and moisture conditions consistently indicate a monthly decrease in relative humidity until 2060, particularly in the summer months. Future monthly values of M-68 also denote a considerable increase of fire risk in summer. The increase in fire risk at the beginning and end of the fire season points to a possible extension of the current fire season.Our results reveal that mean relative humidity is sufficient to describe observed fire occurrences in Germany at both monthly and daily scales. Correlation coefficients were robust in state, country, monthly and daily analysis. Due to its predictive power and simplicity of calculation, relative humidity is a valid or better alternative in Germany as a proxy for monthly forest fire risk.

Comparison of fire danger indices in the Mediterranean for present day conditions

This paper assesses the ability of two forest fire danger indices to estimate fire danger and to define the beginning and the end of fire season. Two indices are studied: the Canadian Fire Weather Index (FWI) and the Finnish Forest Fire Index (FFI). The indices are designed to meet the needs of particular regions but they have also proved to be suitable for the Mediterranean region. The aim of this study is to assess the skill performance of both indices and identify their strengths and weaknesses. The Canadian FWI depends on temperature, precipitation, relative humidity and wind measurements, while the Finnish FFI relies on potential evaporation and precipitation. Results indicate that, in general, FWI and FFI determine a fairly similar fire danger for a set of weather conditions. Higher correlations are found especially for locations under significant fire danger. The results improve for the lower values of fire danger if a spin up period is used in the computation of FFI. Both indices show similar features especially during summer, but some deviations are typical during early spring and autumn, as FWI probably overestimates the fire danger.

Probability Models of Fire Risk Based on Forest Fire Indices in Contrasting Climates over China

Abstract: Fire weather indices have been widely applied to predict fire risk in many regions of the world. The objectives of this study were to establish fire risk probability models based on fire indices over different climatic regions in China. We linked the indices adopted in Canadian, US, and Australia with location, time, altitude, vegetation and fire characteristics during 1998–2007 in four regions using semi— parametric logistic (SPL) regression models. Different combinations of fire risk indices were selected as explanatory variables for specific regional probability model. SPL regression models of probability of fire ignition and large fire events were established to describe the non—linear relationship between fire risk indices and fire risk probabilities in the four regions. Graphs of observed versus estimated probabilities, fire risk maps, graphs of numbers of large fire events were produced from the probability models to assess the skill of these models. Fire ignition in all regions showed a ...

Climate change impacts on forest fire potential in boreal conditions in Finland

The aim of this work was to study the forest fire potential and frequency of forest fires under the projected climate change in Finland (N 60°–N 70°). Forest fire index, generally utilized in Finland, was used as an indicator for forest fire potential due to climatological parameters. Climatic scenarios were based on the A2 emission scenario. According to the results, the forest fire potential will have increased by the end of this century; as a result of increased evaporative demand, which will increase more than the rise in precipitation and especially in southern Finland. The annual number of forest fire alarm days is expected to increase in southern Finland to 96–160 days by the end of this century, compared to the current 60–100 days. In the north, the corresponding increase was from 30 to 36 days. The expected increase in the annual frequency of forest fires over the whole country was about 20% by the end of this century compared to the present day. The greatest increase in the frequency of fires, per 1,000 km2, was in the southernmost part of the country, with six to nine fires expected annually per 1,000 km2 at the end of this century, meaning a 24–29% increase compared to the present day frequencies.

Tree mortality after low-intensity prescribed fires in managed Pinus sylvestris stands in southern Finland

Tree mortality, its causes, and the input of dead charred wood were studied in 11 managed 30–45-year-old Scots pine (Pinus sylvestris L.) stands 1 year after experimental low-intensity prescribed burnings in southern Finland. First, the relationship between fire-induced tree damage and several external variables, e.g. stand density, within-stand wind speed, open-air wind speed, the Finnish Forest Fire Index (FFI) and flame height, was studied. Secondly, the study examined which damage and morphological characteristics best predicted tree mortality. Tree mortality was very variable in the experimental plots, ranging from 0% to 48% on the basis of stem number and from 0% to 41% in terms of wood volume. The input of dead and charred wood decreased with stand age, being 19.4 m3 ha−1 in 30–35-year-old stands, but only 1.7 m3 ha−1 in 45-year-old stands. The input of dead wood was on average 10 m3 ha−1, representing less than 5% of the mean volume before the prescribed fire. The external variables that best explained fire-induced damage were within-stand wind speed, flame height and FFI. Tree mortality was best predicted by charred stem ratio with bark thickness, and by charred stem ratio with tree diameter. The results indicate that prescribed burning that is conducted downwind increases tree mortality and changes subsequent stand structure with increasing within-stand wind speed.

The activities of the German Weather Service (DWD) in the field of agroclimatology

AbstractAgroclimatology represents the link between several agrometeorological fields and climatological applications. The German Weather Service (DWD) uses data from many meteorological stations for 40‐year calculations by agrometeorological models. The topics extend from microclimatic elements over the wide field of soil conditions and water content, agricultural operations and product quality to forest fires and plant pests. Analyses of agroclimatic output allow for better planning, risk assessment, objective evaluation of the current situation and even for a test of the models used. Current applications concern crop microclimate, including soil, evapotranspiration and volatilisation, plant protection, phenology and product quality, with examples such as the frequency of extreme soil frost, optimum harvest conditions, plant water stress on different soils, and the frequency of high pest incidence. Not only singular places, but regions too can be evaluated. Results from combinations of elements, such as soil frozen with high water content, are considered valuable for recommendations to agricultural users. Agroclimatic model information concerning, for example, removal of plastic cover from spring crops, soil conditions for plant water use, tractability, herbicide effectiveness, germination, nutrient washout, and harvest, is directly usable by farmers. The information about forest fire index, extreme season assessment (for compensation payments), and trend analyses is of practical use for administration. The use of risk levels for plant pathogens is important both for farmers and extension services. The future importance of agroclimatic applications is considered high. Copyright © 2006 Royal Meteorological Society