scholarly journals Estimation of wildfire size and risk changes due to fuels treatments

2012 ◽  
Vol 21 (4) ◽  
pp. 357 ◽  
Author(s):  
M. A. Cochrane ◽  
C. J. Moran ◽  
M. C. Wimberly ◽  
A. D. Baer ◽  
M. A. Finney ◽  
...  
Keyword(s):  
Wildland Fire ◽  
Fire Severity ◽  
Fire Risk ◽  
Fire Spread ◽  
Prescribed Fires ◽  
Fuel Treatments ◽  
Human Land Use ◽  
Trade Offs ◽  
Management Policies ◽  
Fuels Treatments

Human land use practices, altered climates, and shifting forest and fire management policies have increased the frequency of large wildfires several-fold. Mitigation of potential fire behaviour and fire severity have increasingly been attempted through pre-fire alteration of wildland fuels using mechanical treatments and prescribed fires. Despite annual treatment of more than a million hectares of land, quantitative assessments of the effectiveness of existing fuel treatments at reducing the size of actual wildfires or how they might alter the risk of burning across landscapes are currently lacking. Here, we present a method for estimating spatial probabilities of burning as a function of extant fuels treatments for any wildland fire-affected landscape. We examined the landscape effects of more than 72 000 ha of wildland fuel treatments involved in 14 large wildfires that burned 314 000 ha of forests in nine US states between 2002 and 2010. Fuels treatments altered the probability of fire occurrence both positively and negatively across landscapes, effectively redistributing fire risk by changing surface fire spread rates and reducing the likelihood of crowning behaviour. Trade offs are created between formation of large areas with low probabilities of increased burning and smaller, well-defined regions with reduced fire risk.

scholarly journals Fitting a stochastic fire spread model to data

2019 ◽  
Vol 5 (1) ◽  
pp. 57-66
Author(s):  
X. Joey Wang ◽  
John R. J. Thompson ◽  
W. John Braun ◽  
Douglas G. Woolford
Keyword(s):  
Wildland Fire ◽  
Data Extraction ◽  
Fire Risk ◽  
Fire Spread ◽  
Small Scale ◽  
Intermediate Step ◽  
Spread Model ◽  
Recorded Data ◽  
Model Variability ◽  
Fire Spread Model

Abstract. As the climate changes, it is important to understand the effects on the environment. Changes in wildland fire risk are an important example. A stochastic lattice-based wildland fire spread model was proposed by Boychuk et al. (2007), followed by a more realistic variant (Braun and Woolford, 2013). Fitting such a model to data from remotely sensed images could be used to provide accurate fire spread risk maps, but an intermediate step on the path to that goal is to verify the model on data collected under experimentally controlled conditions. This paper presents the analysis of data from small-scale experimental fires that were digitally video-recorded. Data extraction and processing methods and issues are discussed, along with an estimation methodology that uses differential equations for the moments of certain statistics that can be derived from a sequential set of photographs from a fire. The interaction between model variability and raster resolution is discussed and an argument for partial validation of the model is provided. Visual diagnostics show that the model is doing well at capturing the distribution of key statistics recorded during observed fires.

scholarly journals Experimental validation in Mediterranean shrub fuels of seven wildland fire rate of spread models

2001 ◽  
Vol 10 (1) ◽  
pp. 15 ◽  
Author(s):  
S. Sauvagnargues-Lesage ◽  
G. Dusserre ◽  
F. Robert ◽  
G. Dray ◽  
D.W. Pearson
Keyword(s):  
Wildland Fire ◽  
Fire Risk ◽  
Mediterranean Area ◽  
Fire Service ◽  
Civil Defense ◽  
Prescribed Fires ◽  
Validation Data ◽  
Rate Of Spread ◽  
Forest Fire Risk ◽  
Validation Experiments

Indexes of forest fire risk are broadcast throughout the Summer months by the French Civil Defense Authority. They are used to guide the deployment of fire prevention resources. However, in some departments, the number of fires during the Winter and Spring months of March–April is equal to or greater than during the Summer months. Some days, conditions are favourable for the propagation of fire (soil moisture content, vegetation in dormancy, relative humidity, ...), but indexes for estimating the risk during this period are not calculated. The objective of this paper is to evaluate various models of fire rate of spread, in order to choose one for Winter and Spring fires. The Fire Service of a department of the French Mediterranean area (the Lozère department) provides the opportunity and the means to conduct validation experiments on prescribed fires. Also, validation data from another department of the French Mediterranean area (Pyrénées Orientales) are presented for the same rate of spread models.

A coupled modelling approach to assess the effect of fuel treatments on post-wildfire runoff and erosion

2016 ◽  
Vol 25 (3) ◽  
pp. 351 ◽  
Author(s):  
Gabriel Sidman ◽  
D. Phillip Guertin ◽  
David C. Goodrich ◽  
David Thoma ◽  
Donald Falk ◽  
...  
Keyword(s):  
National Park ◽  
Assessment Tool ◽  
Wildland Fire ◽  
Fire Severity ◽  
Coupled Model ◽  
Spatial Modelling ◽  
Hydrological Response ◽  
Fuel Treatments ◽  
Coupled Modelling ◽  
Modelling Approach

The hydrological consequences of wildfires are among their most significant and long-lasting effects. As wildfire severity affects post-fire hydrological response, fuel treatments can be a useful tool for land managers to moderate this response. However, current models focus on only one aspect of the fire–watershed linkage (fuel treatments, fire behaviour, fire severity, watershed responses). This study outlines a spatial modelling approach that couples three models used sequentially to allow managers to model the effects of fuel treatments on post-fire hydrological responses. Case studies involving a planned prescribed fire at Zion National Park and a planned mechanical thinning at Bryce Canyon National Park were used to demonstrate the approach. Fuel treatments were modelled using FuelCalc and FlamMap within the Wildland Fire Assessment Tool (WFAT). The First Order Fire Effects Model (FOFEM) within WFAT was then used to evaluate the effectiveness of the fuel treatments by modelling wildfires on both treated and untreated landscapes. Post-wildfire hydrological response was then modelled using KINEROS2 within the Automated Geospatial Watershed Assessment tool (AGWA). This coupled model approach could help managers estimate the effect of planned fuel treatments on wildfire severity and post-wildfire runoff or erosion, and compare various fuel treatment scenarios to optimise resources and maximise mitigation results.

Current approaches to modelling the spread of wildland fire: a review

1998 ◽  
Vol 22 (2) ◽  
pp. 222-245 ◽  
Author(s):  
G. L.W. Perry
Keyword(s):  
Information Technologies ◽  
Spatial Information ◽  
Wildland Fire ◽  
Fire Risk ◽  
Fire Spread ◽  
Physical Models ◽  
Accurate Estimation ◽  
Fire Event ◽  
Fire Modelling ◽  
Modelling Techniques

This review considers the development of some of the models and modelling approaches designed to predict the spread and spatial behaviour of wildland fire events. Such events and their accurate prediction are of great importance to those seeking to understand and manage fire-prone ecosystems. The key problem which fire modelling seeks to address is outlined. Models predicting the rate of fire spread may be classified as physical, semi-physical or empirical according to the nature of their construction. The benefits and shortcomings of each type of model are considered with reference to specific examples of each type. It is shown that there are problems with current operational models which restrict their effective use. However, the development of rigorous physical models as replacements is impeded by conceptual and practical difficulties. Accurate estimation of the rate of spread and the intensity of a fire allows prediction of the final shape and area of a fire event. The modelling techniques used to estimate the shape and area of a fire are considered including the development of sophisticated computer-based simulations of fire spread. Spatial information technologies such as remote sensing and geographic information systems (GIS) offer great potential for the effective modelling of wildland fire behaviour. While such spatial information technologies have been frequently used in the evaluation of fire danger risk, their use for the simulation of the spatiotemporal behaviour of wildland fire is not common. The way in which spatial information technologies and decision-support systems are used for fire risk evaluation and fire spread simulation is discussed. Two research areas of great importance if fire modelling techniques are to improve are a better understanding of fire-dependent phenomena and the development of a ‘new generation’ of fire spread models; current trends in these areas of research are evaluated.

scholarly journals Patchy Fires Promote Regeneration of Longleaf Pine (Pinus palustris Mill.) in Pine Savannas

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 367 ◽  
Author(s):  
Kevin M. Robertson ◽  
William J. Platt ◽  
Charles E. Faires
Keyword(s):  
Fire Severity ◽  
Longleaf Pine ◽  
Pinus Palustris ◽  
Fire Spread ◽  
Tree Regeneration ◽  
Prescribed Fires ◽  
Pine Needle ◽  
Canopy Tree ◽  
Canopy Trees ◽  
Pine Savannas

Research Highlights: Spatial patterns of fire spread and severity influence survival of juvenile pines in longleaf pine savannas. Small areas that do not burn during frequent fires facilitate formation of patches of even-aged longleaf pine juveniles. These regeneration patches are especially associated with inner portions of openings (gaps) and where canopy trees have died in recent decades. Patterns of prescribed fire can thus have an important influence on stand dynamics of the dominant tree in pine savannas. Background and Objectives: Savannas are characterized by bottlenecks to tree regeneration. In pine savannas, longleaf pine is noted for recruitment in discrete clusters located within gaps away from canopy trees. Various mechanisms promoting this pattern have been hypothesized: light limitations, soil moisture, soil nutrients, pine needle mulching, competition with canopy tree roots, and fire severity associated with pine needle litter. We tested the hypothesis that regeneration patches are associated with areas that remain unburned during some prescribed fires, as mediated by gaps in the canopy, especially inner portions of gaps, and areas re-opened by death of canopy trees. Materials and Methods: We mapped areas that were unburned during prescribed fires applied at 1–2 year intervals from 2005–2018 in an old-growth pine savanna in Georgia, USA. We compared the maps to locations of longleaf pine juveniles (<1.5 m height) measured in 2018 and canopy cover and canopy tree deaths using a long-term (40 year) tree census. Results: Logistic regression analysis showed juveniles to be associated with unburned areas, gaps, inner gaps, and areas where canopy trees died. Conclusions: Patterns of fire spread and severity limit survival of longleaf pine juveniles to patches away from canopy trees, especially where canopy trees have died in recent decades. These processes contribute to a buffering mechanism that maintains the savanna structure and prevents transition to closed canopy forest or open grassland communities.

Assessing mitigation of wildfire severity by fuel treatments - an example from the Coastal Plain of Mississippi

2008 ◽  
Vol 17 (3) ◽  
pp. 415 ◽  
Author(s):  
Erik J. Martinson ◽  
Philip N. Omi
Keyword(s):  
Prescribed Fire ◽  
Treatment Effectiveness ◽  
Fire Severity ◽  
Prescribed Fires ◽  
Fuel Treatments ◽  
Treatment Area ◽  
Wildfire Management ◽  
Treated Area ◽  
Previously Treated ◽  
Fuel Profile

Fuel treatments such as prescribed fire are a controversial tenet of wildfire management. Despite a well-established theoretical basis for their use, scant empirical evidence currently exists on fuel treatment effectiveness for mitigating the behaviour and effects of extreme wildfire events. We report the results of a fire severity evaluation of an escaped prescribed fire that burned into an area previously treated with repeated prescribed fires. We observed significantly lower scorch heights, crown damage, and ground char in the treated area. We attribute the moderated fire severity in the treated area to a significantly altered fuel profile created by the repeated prescribed fires. Though our results represent just one treatment area in a single wildfire, they add to a depauperate database and bring us a step closer to defining the conditions under which fuel treatments are an effective pre-suppression strategy.

Optimal spatial patterns of fuel management and timber harvest with fire risk

2010 ◽  
Vol 40 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Masashi Konoshima ◽  
Heidi J. Albers ◽  
Claire A. Montgomery ◽  
Jeffrey L. Arthur
Keyword(s):  
Spatial Configuration ◽  
Fire Risk ◽  
Timber Harvest ◽  
Fire Spread ◽  
Fuel Management ◽  
Fuel Treatment ◽  
Trade Offs ◽  
Dynamic Optimization Model ◽  
Cost Efficient ◽  
The Cost

The stochastic and spatial nature of fire poses challenges for the cost-efficient allocation of fuel treatment over the landscape. A model that addresses complex but important components of fuel management decisions, spatial and dynamic aspects of fire risk, and a carefully designed framework that allows us to draw general insight into the optimal spatial pattern of management are necessary to provide a basis for developing efficient fuel treatment plans. For this purpose, we combine a physical fire model and a spatial-dynamic optimization model to explore harvest and fuel treatment across a hypothetical landscape under risk of a moving fire over a range of physical and economic conditions. Our model is able to describe spatial trade-offs involved in decision process, namely trade-offs between protection of on-site values and protection against fire spread. We found that the spatial configuration of management units can lead to heterogeneity in management across seemingly homogeneous units.

Allocating fuel breaks to optimally protect structures in the wildland–urban interface

2011 ◽  
Vol 20 (1) ◽  
pp. 59 ◽  
Author(s):  
Avi Bar Massada ◽  
Volker C. Radeloff ◽  
Susan I. Stewart
Keyword(s):  
Treatment Approach ◽  
Wildland Fire ◽  
Fire Risk ◽  
Fuel Treatments ◽  
Treatment Area ◽  
Housing Density ◽  
Wildland Urban Interface ◽  
Or Groups ◽  
Total Treatment ◽  
A Minor

Wildland fire is a major concern in the wildland–urban interface (WUI), where human structures intermingle with wildland vegetation. Reducing wildfire risk in the WUI is more complicated than in wildland areas, owing to interactions between spatial patterns of housing and wildland fuels. Fuel treatments are commonly applied in wildlands surrounding WUI communities. Protecting the immediate surroundings of structures and building with fire-resistant materials might be more effective, but limited resources and uncooperative homeowners often make these impractical. Our question was how to allocate fuel treatments in the WUI under these constraints. We developed an approach to allocate fuel breaks around individual or groups of structures to minimise total treatment area. Treatment units were ranked according to their housing density and fire risk. We tested this method in a Wisconsin landscape containing 3768 structures, and found that our treatment approach required considerably less area than alternatives (588 v. 1050 ha required to protect every structure independently). Our method may serve as a baseline for planning fuel treatments in WUI areas where it is impractical to protect every single house, or when fire-proofing is unfeasible. This approach is especially suitable in regions where spotting is a minor cause of home ignitions.

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

Forests ◽  
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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