Classifying and mapping fuels in central European forests

Mapping Intimacies ◽

10.5194/egusphere-egu21-8886 ◽

2021 ◽

Author(s):

Pia Labenski ◽

Michael Ewald ◽

Fabian Ewald Fassnacht

Keyword(s):

Forest Fires ◽

Fire Behavior ◽

Fire Risk ◽

Fire Spread ◽

Fire Frequency ◽

Extreme Weather Events ◽

Central European ◽

European Forests ◽

First Results ◽

Spread Dynamics

<p>In recent years, forest fires have become more frequent in central Europe. As the frequency and magnitude of future extreme weather events such as droughts are projected to increase, also the trend of increasing fire frequency in temperate forests is expected to continue. However, knowledge about fire behavior and spread dynamics in these forests is scarce. One of the key drivers of fire behavior is the availability of flammable vegetation, i.e. fuels. In the project ErWiN, we aim to describe the amount and distribution of fuels in different forest types in Southwestern Germany. Detailed field inventories of fuels in all vertical strata of the stands allow a first classification into different fuel types, which can be used in fire behavior simulations to obtain estimates of fire spread and intensity. In a further step, deep learning algorithms will be trained on recognizing these fuel types on GNSS located photos of forest stand situations to provide an efficient solution for mapping fuels in the field. By coupling field data with detailed remotely sensed information on forest structure obtained from airborne laserscanning, continuous fuel maps will be derived. Such fuel maps in turn allow landscape-scale analysis of fire behavior and can be useful in forest management decisions as well as in developing firefighting strategies. We thus hope to make a contribution to a better understanding of fuel-driven fire risk in central European forests and to facilitate the operational use of fire behavior models. In this contribution we present the concept developed in the ErWiN project and present first results obtained from the field survey of fuel types in Southwestern Germany.</p>

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COMPORTAMENTO DO FOGO EM QUEIMAS CONTROLADAS DE VEGETAÇÃO DE ESTEPE NO MUNICÍPIO DE PALMEIRA, PARANÁ, BRASIL

FLORESTA ◽

10.5380/rf.v43i4.31385 ◽

2013 ◽

Vol 43 (4) ◽

pp. 557

Author(s):

Celso Darci Seger ◽

Antonio Carlos Batista ◽

Alexandre França Tetto ◽

Ronaldo Viana Soares

Keyword(s):

Fire Behavior ◽

Fire Risk ◽

Fire Spread ◽

Flame Height ◽

Fire Intensity ◽

Average Moisture Content ◽

Fuel Loading ◽

Prescribed Burns ◽

Controlled Burning ◽

Heat Released

As queimas controladas constituem práticas de manejo utilizadas em diferentes tipos de vegetação e difundidas em vários países. No entanto, para a realização de tais práticas com segurança e eficiência é fundamental o conhecimento do comportamento do fogo. O objetivo desse trabalho foi caracterizar o comportamento do fogo em queimas controladas de vegetação Estepe Gramíneo-Lenhosa no estado do Paraná. Para isso, foi instalado um experimento no município de Palmeira, onde 20 parcelas foram queimadas, sendo metade a favor e metade contra o vento. A carga de material combustível fino estimada foi de 2,26 kg.m-2, com teor médio de umidade de 50,45%. A quantidade de material consumido pela queima foi de 1,76 kg.m-2, com uma eficiência média de queima de 76,86%. As médias obtidas, a favor e contra o vento, foram respectivamente: velocidade de propagação do fogo de 0,049 e 0,012 m.s-1, altura das chamas de 1,34 e 0,843 m, intensidade do fogo de 210,53 e 50,68 kcal.m-1.s-1 e calor liberado de 4.067,19 e 4.508,92 kcal.m-2. Os resultados permitiram concluir que as queimas controladas em vegetação de campos naturais, realizadas dentro dos critérios estabelecidos de planos de queima, são viáveis e seguras sob o ponto de vista de perigo de incêndios.Palavras chave: Queima prescrita; material combustível; intensidade do fogo; perigo de incêndios. AbstractFire behavior of prescribed burns in grassland on Palmeira county, Paraná, Brazil. The prescribed burns are practices of management used in different types of vegetation and widespread in several countries. However, to carry out such practices safely and effectively is fundamental knowledge of fire behavior. The aim of this study was to characterize the fire behavior in controlled burning of grassland vegetation in Paraná state. For this, an experiment was conducted in Palmeira County, where 20 plots were burned, half in favor and half against the wind. The estimated fine fuel loading was 2.26 kg.m-2, with average moisture content of 50.45%. The fuel consumption by burning was 1.76 kg.m-2 with an average efficiency of burning of 76.86%. The averages, for and against the wind, were: speed of fire spread of 0.049 and 0.012 m.s-1, the flame height of 1.34 m and 0.843, fire intensity of 210.53 and 50.68 kcal.m-1.s-1 and heat released from 4,067.19 and 4,508.92 kcal.m-2. The results show that the controlled burnings of grasslands vegetation, carried out within the established criteria burning plans are feasible and safe from the aspect of fire danger.Keywords: Prescribed burns; fuel loading; fire intensity; fire risk.

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Numerical Prediction of the Fire Spread in Vegetative Fuels Using NISTWFDS Model

International Journal of Engineering Research in Africa ◽

10.4028/www.scientific.net/jera.20.177 ◽

2015 ◽

Vol 20 ◽

pp. 177-192 ◽

Cited By ~ 3

Author(s):

Hadj Miloua

Keyword(s):

Forest Fires ◽

Wildland Fire ◽

Radiation Heat Transfer ◽

Fire Behavior ◽

Reaction Diffusion ◽

Fire Spread ◽

Three Dimensions ◽

Forest Stands ◽

Fire Dynamics ◽

Wind Climate

Current study focuses to the application of an advanced physics-based (reaction–diffusion) fire behavior model to the fires spreading through surface vegetation such as grasslands and elevated vegetation such as trees present in forest stands. This model in three dimensions, called Wildland Fire Dynamics Simulator WFDS, is an extension, to vegetative fuels, of the structural FDS developed at NIST. For simplicity, the vegetation was assumed to be uniformly distributed in a tree crown represented by a well defined geometric shape. This work on will focus on predictions of thermal function such as the radiation heat transfer and and thermal function for diverse cases of spatial distribution of vegetation in forest stands. The influence of wind, climate characteristics and terrain topography will also be used to extend and validate the model. The results obtained provide a basis to carry out a risk analysis for fire spread in the studied vegetative fuels in the Mediterranean forest fires.

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Fire spread across pine needle fuel beds: characterization of temperature and velocity distributions within the fire plume

International Journal of Wildland Fire ◽

10.1071/wf02065 ◽

2004 ◽

Vol 13 (1) ◽

pp. 37 ◽

Cited By ~ 32

Author(s):

Thierry Marcelli ◽

Paul A. Santoni ◽

Albert Simeoni ◽

Eric Leoni ◽

Bernard Porterie

Keyword(s):

Forest Fires ◽

Fire Behavior ◽

Thermal Fluctuation ◽

Fire Spread ◽

Reconstruction Method ◽

Pine Needle ◽

Fire Plume ◽

Two Phase ◽

Experimental Apparatus

The aim of this article is twofold. First, it concerns the improvement of knowledge on the fundamental physical mechanisms that control the propagation of forest fires. To proceed, an experimental apparatus was designed to study, in laboratory conditions, the flame of a fire spreading across a pine needle fuel bed. Characterization of temperature was managed by using a reconstruction method based on a double thermocouple probe technique developed recently. The vertical gas velocity distribution was derived from the previous reconstructed signals by measuring the transit time of a thermal fluctuation between two points of the flow. Second, the experimental data were used for the testing of a physical two-phase model of forest fire behavior in which the decomposition of solid fuel constituting a forest fuel bed as well as the multiple interactions with the gas phase are represented.

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Regional Level Data Server for Fire Hazard Evaluation and Fuel Treatments Planning

Remote Sensing ◽

10.3390/rs12244124 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4124

Author(s):

Goran Krsnik ◽

Eduard Busquets Olivé ◽

Míriam Piqué Nicolau ◽

Asier Larrañaga ◽

Adrián Cardil ◽

...

Keyword(s):

Forest Fires ◽

Fire Hazard ◽

Fire Behavior ◽

Remote Sensing Data ◽

Fire Risk ◽

Regional Level ◽

Hazard Evaluation ◽

Level Data ◽

Risk Assessment And Management ◽

Geospatial Interaction

Both fire risk assessment and management of wildfire prevention strategies require different sources of data to represent the complex geospatial interaction that exists between environmental variables in the most accurate way possible. In this sense, geospatial analysis tools and remote sensing data offer new opportunities for estimating fire risk and optimizing wildfire prevention planning. Herein, we presented a conceptual design of a server that contained most variables required for predicting fire behavior at a regional level. For that purpose, an innovative and elaborated fuel modelling process and parameterization of all needed environmental and climatic variables were implemented in order to enable to more precisely define fuel characteristics and potential fire behaviors under different meteorological scenarios. The server, open to be used by scientists and technicians, is expected to be the steppingstone for an integrated tool to support decision-making regarding prevention and management of forest fires in Catalonia.

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Understorey fire frequency and the fate of burned forests in southern Amazonia

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2012.0163 ◽

2013 ◽

Vol 368 (1619) ◽

pp. 20120163 ◽

Cited By ~ 114

Author(s):

D. C. Morton ◽

Y. Le Page ◽

R. DeFries ◽

G. J. Collatz ◽

G. C. Hurtt

Keyword(s):

Forest Fires ◽

Temporal Dynamics ◽

Fire Risk ◽

Fire Frequency ◽

Climate Control ◽

Night Time ◽

Mato Grosso ◽

Fire Activity ◽

Southern Amazonia ◽

Long Term Impact

Recent drought events underscore the vulnerability of Amazon forests to understorey fires. The long-term impact of fires on biodiversity and forest carbon stocks depends on the frequency of fire damages and deforestation rates of burned forests. Here, we characterized the spatial and temporal dynamics of understorey fires (1999–2010) and deforestation (2001–2010) in southern Amazonia using new satellite-based estimates of annual fire activity (greater than 50 ha) and deforestation (greater than 10 ha). Understorey forest fires burned more than 85 500 km 2 between 1999 and 2010 (2.8% of all forests). Forests that burned more than once accounted for 16 per cent of all understorey fires. Repeated fire activity was concentrated in Mato Grosso and eastern Pará, whereas single fires were widespread across the arc of deforestation. Routine fire activity in Mato Grosso coincided with annual periods of low night-time relative humidity, suggesting a strong climate control on both single and repeated fires. Understorey fires occurred in regions with active deforestation, yet the interannual variability of fire and deforestation were uncorrelated, and only 2.6 per cent of forests that burned between 1999 and 2008 were deforested for agricultural use by 2010. Evidence from the past decade suggests that future projections of frontier landscapes in Amazonia should separately consider economic drivers to project future deforestation and climate to project fire risk.

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A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽

10.3390/f12030294 ◽

2021 ◽

Vol 12 (3) ◽

pp. 294

Author(s):

Nicholas F. McCarthy ◽

Ali Tohidi ◽

Yawar Aziz ◽

Matt Dennie ◽

Mario Miguel Valero ◽

...

Keyword(s):

Deep Learning ◽

Decision Support ◽

Real Time ◽

Forest Fires ◽

Wildland Fire ◽

Fire Spread ◽

Low Earth Orbit ◽

Practical Implementation ◽

Spatiotemporal Resolution ◽

Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

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Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA

Fire ◽

10.3390/fire4020026 ◽

2021 ◽

Vol 4 (2) ◽

pp. 26

Author(s):

Casey Teske ◽

Melanie K. Vanderhoof ◽

Todd J. Hawbaker ◽

Joe Noble ◽

John Kevin Hiers

Keyword(s):

Prescribed Fire ◽

Spatial Data ◽

Fire History ◽

Fire Management ◽

Fire Behavior ◽

Fire Risk ◽

Behavior Modeling ◽

Burned Area ◽

Burned Areas ◽

Burn Probability

Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.

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Determination of Critical Separation Distance Between Dwellings in Informal Settlements Fire

Fire Technology ◽

10.1007/s10694-020-01075-w ◽

2021 ◽

Author(s):

Yu Wang ◽

Lesley Gibson ◽

Mohamed Beshir ◽

David Rush

Keyword(s):

Heat Flux ◽

Informal Settlement ◽

Informal Settlements ◽

Fire Risk ◽

Fire Spread ◽

Separation Distance ◽

Flame Length ◽

Critical Distance ◽

Fire Disaster ◽

Critical Separation

AbstractApproximately one billion people across the globe are living in informal settlements with a large potential fire risk. Due to the high dwelling density, a single informal settlement dwelling fire may result in a very serious fire disaster leaving thousands of people homeless. In this work, a simple physics-based theoretical model was employed to assess the critical fire separation distance between dwellings. The heat flux and ejected flame length were obtained from a full-scale dwelling tests with ISO 9705 dimension (3.6 m × 2.4 m × 2.4 m) to estimate the radiation decay coefficient of the radiation heat flux away from the open door. The ignition potential of combustible materials in adjacent dwellings are analyzed based on the critical heat flux from cone calorimeter tests. To verify the critical distance in real informal settlement fire, a parallel method using aerial photography within geographic information systems (GIS), was employed to determine the critical separation distances in four real informal settlement fires of 2014–2015 in Masiphumelele, Cape Town, South Africa. The fire-spread distances were obtained as well through the real fires. The probabilistic analysis was conducted by Weibull distribution and logistic regression, and the corresponding separation distances were given with different fire spread probabilities. From the experiments with the assumption of no interventions and open doors and windows, it was established that the heat flux would decay from around 36 kW/m2 within a distance of 1.0 m to a value smaller than 5 kW/m2 at a distance of 4.0 m. Both experiments and GIS results agree well and suggest the ignition probabilities at distances of 1.0 m, 2.0 m and 3.0 m are 97%, 52% and 5% respectively. While wind is not explicitly considered in the work, it is implicit within the GIS analyses of fire spread risk, therefore, it is reasonable to say that there is a relatively low fire spread risk at distances greater than 3 m. The distance of 1.0 m in GIS is verified to well and conservatively predict the fire spread risk in the informal settlements.

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Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data

Remote Sensing ◽

10.3390/rs13122386 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2386

Author(s):

Aqil Tariq ◽

Hong Shu ◽

Qingting Li ◽

Orhan Altan ◽

Mobushir Riaz Khan ◽

...

Keyword(s):

Forest Fires ◽

Prescribed Burning ◽

Fire Risk ◽

Fire Season ◽

Prescribed Burn ◽

Backscatter Coefficient ◽

Prescribed Burns ◽

Southeastern Australia ◽

Sar Data ◽

Sar Imagery

Prescribed burning is a common strategy for minimizing forest fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity, and rate of spread. Such conditions deviate from those encountered during the fire season. Prescribed burns mostly affect surface fuels and understory vegetation, an outcome markedly different when compared to wildfires. Data on prescribed burning are crucial for evaluating whether land management targets have been reached. This research developed a methodology to quantify the effects of prescribed burns using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery in the forests of southeastern Australia. C-band SAR datasets were specifically used to statistically explore changes in radar backscatter coefficients with the intensity of prescribed burns. Two modeling approaches based on pre- and post-fire ratios were applied for evaluating prescribed burn impacts. The effects of prescribed burns were documented with an overall accuracy of 82.3% using cross-polarized backscatter (VH) SAR data under dry conditions. The VV polarization indicated some potential to detect burned areas under wet conditions. The findings in this study indicate that the C-band SAR backscatter coefficient has the potential to evaluate the effectiveness of prescribed burns due to its sensitivity to changes in vegetation structure.

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