Linking fire ignitions hotspots and fuel phenology: The importance of being seasonal

A crucial factor for prevention and immediate confrontation of destructive fires and their socioeconomic and environmental consequences constitutes the early detection and spatial localization of fire ignitions, so that the firefighting forces to be activated and act within the critical time of response. Thus, principal objective of the paper constitutes the spatial optimization of the most effective locations of watchtowers developing a constructive network for the immediate and early detection of forest fires. This optimization involves the exploration of the fewest locations for watchtowers with the maximum visible area and reduced degree of overlapping. The results highlighted 4 groups of watchtowers (20 observers in total) determining the optimum locations. The total visibility amounted to 70% of the island, while the visibility percentages per land cover are variable, since they are depended on the spatial structure of them. Definitely, the final selection of the final number and the spatial structure of the watchtowers purely constitute decisions of political nature and will.

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Predicting spatial patterns of fire on a southern California landscape

International Journal of Wildland Fire ◽

10.1071/wf07087 ◽

2008 ◽

Vol 17 (5) ◽

pp. 602 ◽

Cited By ~ 150

Author(s):

Alexandra D. Syphard ◽

Volker C. Radeloff ◽

Nicholas S. Keuler ◽

Robert S. Taylor ◽

Todd J. Hawbaker ◽

...

Keyword(s):

Spatial Patterns ◽

Southern California ◽

Vegetation Type ◽

Fire Hazard ◽

Fire Regimes ◽

Fire Frequency ◽

Fuel Loading ◽

Landscape Characteristics ◽

Management Actions ◽

Fire Ignitions

Humans influence the frequency and spatial pattern of fire and contribute to altered fire regimes, but fuel loading is often the only factor considered when planning management activities to reduce fire hazard. Understanding both the human and biophysical landscape characteristics that explain how fire patterns vary should help to identify where fire is most likely to threaten values at risk. We used human and biophysical explanatory variables to model and map the spatial patterns of both fire ignitions and fire frequency in the Santa Monica Mountains, a human-dominated southern California landscape. Most fires in the study area are caused by humans, and our results showed that fire ignition patterns were strongly influenced by human variables. In particular, ignitions were most likely to occur close to roads, trails, and housing development but were also related to vegetation type. In contrast, biophysical variables related to climate and terrain (January temperature, transformed aspect, elevation, and slope) explained most of the variation in fire frequency. Although most ignitions occur close to human infrastructure, fires were more likely to spread when located farther from urban development. How far fires spread was ultimately related to biophysical variables, and the largest fires in southern California occurred as a function of wind speed, topography, and vegetation type. Overlaying predictive maps of fire ignitions and fire frequency may be useful for identifying high-risk areas that can be targeted for fire management actions.

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A note on estimation of intensities of fire ignitions with incomplete data

Fire Safety Journal ◽

10.1016/j.firesaf.2006.02.006 ◽

2006 ◽

Vol 41 (5) ◽

pp. 399-405 ◽

Cited By ~ 2

Author(s):

Jesper Rydén ◽

Igor Rychlik

Keyword(s):

Incomplete Data ◽

Fire Ignitions

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Different Factors for Different Causes: Analysis of the Spatial Aggregations of Fire Ignitions in Catalonia (Spain)

Risk Analysis ◽

10.1111/risa.12339 ◽

2015 ◽

Vol 35 (7) ◽

pp. 1197-1209 ◽

Cited By ~ 19

Author(s):

José Ramón González-Olabarria ◽

Blas Mola-Yudego ◽

Lluis Coll

Keyword(s):

Fire Ignitions

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Identifying location and causality of fire ignition hotspots in a Mediterranean region

International Journal of Wildland Fire ◽

10.1071/wf11039 ◽

2012 ◽

Vol 21 (7) ◽

pp. 905 ◽

Cited By ~ 43

Author(s):

José Ramón Gonzalez-Olabarria ◽

Lluis Brotons ◽

David Gritten ◽

Antoni Tudela ◽

José Angel Teres

Keyword(s):

Law Enforcement ◽

Kernel Methods ◽

Mediterranean Basin ◽

Mediterranean Region ◽

Spatial Clustering ◽

Historical Records ◽

Fire Occurrence ◽

History Of ◽

Fire Ignitions ◽

Fire Ignition

Fire ignitions tend to be spatially aggregated depending on their causality. In highly populated regions, such as the northern Mediterranean basin, human activities are the main cause of ignitions. The ability to locate zones with an intense and recurrent history of fire occurrence and identify their specific cause can be helpful in the implementation of measures to reduce the problem. In the present study, kernel methods, non-parametric statistical methods for estimating the spatial distribution of probabilities of point-based data, are used to define ignition hotspots based on historical records of fire ignitions in Catalonia for the period 1995–2006. Comparison of the cause of the ignitions within the area of the hotspots enabled analysis of the relation between the cause of the ignitions and the occurrence of hotspots. The results obtained highlighted that the activity of arsonists showed strong spatial clustering, with the share of intentionally caused ignitions within the hotspot areas accounting for 60.1% of the fires, whereas for the whole of Catalonia they only represented 24.3%. The findings of the study provide an opportunity to optimally allocate law-enforcement and educational resources within hotspot areas.

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Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather

International Journal of Wildland Fire ◽

10.1071/wf08133 ◽

2009 ◽

Vol 18 (8) ◽

pp. 932 ◽

Cited By ~ 73

Author(s):

R. A. Bradstock ◽

J. S. Cohn ◽

A. M. Gill ◽

M. Bedward ◽

C. Lucas

Keyword(s):

Threshold Effect ◽

Large Fire ◽

Central Coast ◽

Ignition Probability ◽

Blue Mountains ◽

Eastern Australia ◽

Large Fires ◽

Forest Fire Danger ◽

Fire Ignitions ◽

Fire Ignition

The probability of large-fire (≥1000 ha) ignition days, in the Sydney region, was examined using historical records. Relative influences of the ambient and drought components of the Forest Fire Danger Index (FFDI) on large fire ignition probability were explored using Bayesian logistic regression. The preferred models for two areas (Blue Mountains and Central Coast) were composed of the sum of FFDI (Drought Factor, DF = 1) (ambient component) and DF as predictors. Both drought and ambient weather positively affected the chance of large fire ignitions, with large fires more probable on the Central Coast than in the Blue Mountains. The preferred, additive combination of drought and ambient weather had a marked threshold effect on large-fire ignition and total area burned in both localities. This may be due to a landscape-scale increase in the connectivity of available fuel at high values of the index. Higher probability of large fires on the Central Coast may be due to more subdued terrain or higher population density and ignitions. Climate scenarios for 2050 yielded predictions of a 20–84% increase in potential large-fire ignitions days, using the preferred model.

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Modeling anthropogenic and natural fire ignitions in an inner-alpine valley

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-935-2018 ◽

2018 ◽

Vol 18 (3) ◽

pp. 935-948 ◽

Cited By ~ 8

Author(s):

Giorgio Vacchiano ◽

Cristiano Foderi ◽

Roberta Berretti ◽

Enrico Marchi ◽

Renzo Motta

Keyword(s):

Forest Fires ◽

Goodness Of Fit ◽

Fire Management ◽

Fire Regime ◽

Principal Component ◽

Late Winter ◽

Natural Fire ◽

Alpine Valley ◽

Anthropogenic Component ◽

Fire Ignitions

Abstract. Modeling and assessing the factors that drive forest fire ignitions is critical for fire prevention and sustainable ecosystem management. In southern Europe, the anthropogenic component of wildland fire ignitions is especially relevant. In the Alps, however, the role of fire as a component of disturbance regimes in forest and grassland ecosystems is poorly known. The aim of this work is to model the probability of fire ignition for an Alpine region in Italy using a regional wildfire archive (1995–2009) and MaxEnt modeling. We analyzed separately (i) winter forest fires, (ii) winter fires on grasslands and fallow land, and (iii) summer fires. Predictors were related to morphology, climate, and land use; distance from infrastructures, number of farms, and number of grazing animals were used as proxies for the anthropogenic component. Collinearity among predictors was reduced by a principal component analysis. Regarding ignitions, 30 % occurred in agricultural areas and 24 % in forests. Ignitions peaked in the late winter–early spring. Negligence from agrosilvicultural activities was the main cause of ignition (64 %); lightning accounted for 9 % of causes across the study time frame, but increased from 6 to 10 % between the first and second period of analysis. Models for all groups of fire had a high goodness of fit (AUC 0.90–0.95). Temperature was proportional to the probability of ignition, and precipitation was inversely proportional. Proximity from infrastructures had an effect only on winter fires, while the density of grazing animals had a remarkably different effect on summer (positive correlation) and winter (negative) fires. Implications are discussed regarding climate change, fire regime changes, and silvicultural prevention. Such a spatially explicit approach allows us to carry out spatially targeted fire management strategies and may assist in developing better fire management plans.

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Modeling and mapping wildfire ignition risk in Portugal

International Journal of Wildland Fire ◽

10.1071/wf07123 ◽

2009 ◽

Vol 18 (8) ◽

pp. 921 ◽

Cited By ~ 171

Author(s):

Filipe X. Catry ◽

Francisco C. Rego ◽

Fernando L. Bação ◽

Francisco Moreira

Keyword(s):

Spatial Patterns ◽

Regression Models ◽

Good Accuracy ◽

National Level ◽

Risk Map ◽

Wildfire Management ◽

Logistic Regression Models ◽

Explanatory Variables ◽

Fire Ignitions ◽

Southern European Countries

Portugal has the highest density of wildfire ignitions among southern European countries. The ability to predict the spatial patterns of ignitions constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. In this study, we analyzed 127 490 ignitions that occurred in Portugal during a 5-year period. We used logistic regression models to predict the likelihood of ignition occurrence, using a set of potentially explanatory variables, and produced an ignition risk map for the Portuguese mainland. Results show that population density, human accessibility, land cover and elevation are important determinants of spatial distribution of fire ignitions. In this paper, we demonstrate that it is possible to predict the spatial patterns of ignitions at the national level with good accuracy and using a small number of easily obtainable variables, which can be useful in decision-making for wildfire management.

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