Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India

Renard, Quentin; Pélissier, Raphaël; Ramesh, B.R.; Kodandapani, Narendran

doi:10.1071/wf10109

Cited by 112 publications

(80 citation statements)

References 26 publications

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“…This technique is well-suited to model fire probability in a presence-only framework, i.e., when fire records only exist for a limited number of years (Parisien et al 2012, Renard et al 2012. MaxEnt belongs to a class of models used extensively for predicting habitat suitability from observed species occurrence data.…”

Section: Statistical Modeling Of Fire Probabilitymentioning

confidence: 99%

Climate change and disruptions to global fire activity

et al. 2012

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Abstract. Future disruptions to fire activity will threaten ecosystems and human well-being throughout the world, yet there are few fire projections at global scales and almost none from a broad range of global climate models (GCMs). Here we integrate global fire datasets and environmental covariates to build spatial statistical models of fire probability at a 0.58 resolution and examine environmental controls on fire activity. Fire models are driven by climate norms from 16 GCMs (A2 emissions scenario) to assess the magnitude and direction of change over two time periods, 2010-2039 and 2070-2099. From the ensemble results, we identify areas of consensus for increases or decreases in fire activity, as well as areas where GCMs disagree. Although certain biomes are sensitive to constraints on biomass productivity and others to atmospheric conditions promoting combustion, substantial and rapid shifts are projected for future fire activity across vast portions of the globe. In the near term, the most consistent increases in fire activity occur in biomes with already somewhat warm climates; decreases are less pronounced and concentrated primarily in a few tropical and subtropical biomes. However, models do not agree on the direction of nearterm changes across more than 50% of terrestrial lands, highlighting major uncertainties in the next few decades. By the end of the century, the magnitude and the agreement in direction of change are projected to increase substantially. Most far-term model agreement on increasing fire probabilities (;62%) occurs at mid-to high-latitudes, while agreement on decreasing probabilities (;20%) is mainly in the tropics.Although our global models demonstrate that long-term environmental norms are very successful at capturing chronic fire probability patterns, future work is necessary to assess how much more explanatory power would be added through interannual variation in climate variables. This study provides a first examination of global disruptions to fire activity using an empirically based statistical framework and a multi-model ensemble of GCM projections, an important step toward assessing fire-related vulnerabilities to humans and the ecosystems upon which they depend.

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Section: Statistical Modeling Of Fire Probabilitymentioning

confidence: 99%

Climate change and disruptions to global fire activity

et al. 2012

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“…Employing fire occurrence records within empirical models is essential to quantify the characteristics of fire activities to support planning and decision-making (Andrews & Finney 2007). These models can be used to identify fireprone areas and help forest managers target suppression efforts (Pew & Larsen 2001;Syphard et al 2008;Romero-Calcerrada et al 2010;Wang & Anderson 2010;Renard et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Studies on fire occurrence (e.g. Renard et al 2012;Hawbaker et al 2013;Yulianti et al 2013) have been undertaken using spatial and temporal wildfire occurrence information provided by active fire products (Giglio et al 2003) generated from MODIS data. However, none of these studies have explored the relationship between MODIS-based active fire locations and their determinants in Australia.…”

Section: Introductionmentioning

confidence: 99%

Modelling spatial patterns of wildfire occurrence in South-Eastern Australia

Zhou

Lim

Sharples

2016

Geomatics, Natural Hazards and Risk

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This paper describes the development and validation of spatial models for wildfire occurrence at a broad landscape scale. The hotspots databases from the Moderate Resolution Imaging Spectroradiometer (MODIS) and logistic regression models are investigated for the comprehensive understanding of environmental and socioeconomic determinants regulating the spatial distribution of wildfires over the 11-year period 2003À2013. The probability of occurrence of at least one fire on a 1 km 2 grid cell in a 1,030,000 km 2 region located in South-Eastern Australia is studied for the prediction of future fire occurrence. Our research shows that wildfires are most likely to occur in mountainous areas, forests, savannas and lands with high vegetation coverage, and are less likely to occur on grasslands and shrublands. Wildfires also tend to occur in areas near human infrastructures. Environmental variables are strong individual predictors of fire occurrence while socioeconomic variables contribute more to the final model. The influence of environmental and socioeconomic conditions on wildfire occurrence and the spatial patterns of wildfires identified in this study can assist fire managers in implementing appropriate management actions in South-Eastern Australia. This paper also demonstrates the potential of applying the MODIS active fire product in wildfire occurrence studies.

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“…Other authors have proposed regression trees (Amatulli et al, 2006), neural networks (Vega-García et al, 2007), Bayesian probability techniques (Romero-Calcerrada et al, 2008), and generalized additive models (Vilar et al, 2010). Compared to such algorithms, most of which are parametric, the machine-learning approach adopted by MaxEnt has been performing equally, if not better (Bar Massada et al, 2013).…”

Section: Analytical Approachmentioning

confidence: 99%

“…Considerable research has been carried out to quantify the influence of natural (climate, vegetation, topography, and landscape connectivity) and anthropogenic drivers of wildland fire ignitions (Cardille and Ventura, 2001;Yang et al, 2007;Martínez et al, 2009;Renard et al, 2012), but the relative importance of each factor is still debated. The prevailing paradigm at the planetary scale is that climate (Carcaillet et al, 2001;Whitlock et al, 2003), fire weather (Schoennagel et al, 2004), or fuel (Krawchuk et al, 2006) is the most important factor.…”

Section: Introductionmentioning

confidence: 99%

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

Vacchiano

Foderi

Berretti

et al. 2018

Nat. Hazards Earth Syst. Sci.

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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)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(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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Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India

Cited by 112 publications

References 26 publications

Climate change and disruptions to global fire activity

Climate change and disruptions to global fire activity

Modelling spatial patterns of wildfire occurrence in South-Eastern Australia

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

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