Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India

Human-caused fire occurrence modelling in perspective: a review

“…High evapotranspiration (BadiaPerpinyà and Pallares-Barbera 2006;Xiao et al 2015) and insolation (Heat Load Index, Lozano et al 2007;Yang et al 2015) increase the probability of human-caused ignition. Annual or seasonal temperature and precipitation variables abound (Pew and Larsen 2001;Amatulli et al 2006;Prasad et al 2008;Oliveira et al 2012;Faivre et al 2014;Xiao et al 2015;Ancog et al 2016), with drought estimations (e.g. Palmer Drought Severity Index, Preisler and Westerling 2007;Miranda et al 2012) and climatic classifications in the 127 models analysed.…”

“…Palmer Drought Severity Index, Preisler and Westerling 2007;Miranda et al 2012) and climatic classifications in the 127 models analysed. Summer precipitation has a fire-inhibiting factor (Prasad et al 2008;Parisien and Moritz 2009;Turco et al 2014;Barreal and Loureiro 2015). However, annual precipitation, and especially spring precipitation, is related to an increase in HCFs Krawchuk et al 2009;Avila-Flores et al 2010;Oliveira et al 2012;Hu and Zhou 2014;West et al 2016;Bashari et al 2016).…”

“…Regarding socioeconomic indicators, population density is the most commonly used indicator in relation to the occurrence of HCFs (Prasad et al 2008;Kwak et al 2012;Dondo Bühler et al 2013;Mundo et al 2013;Knorr et al 2014;Alcasena et al 2016;Nunes et al 2016) (96 significant variables in the models analysed). High population densities are related to high wildfire occurrence in general.…”

Integration of Lightning- and Human-Caused Wildfire Occurrence Models

“…Logistic regression analysis has been used widely both to predict and also to explain human-and/or lightning-caused fires by integrating geophysical, environmental, or socioeconomic variables (e.g., related to topography, vegetation, land uses, climate and meteorological conditions, environmental parameters, fire danger indices, human factors) with observed fire occurrence (Martell et al 1987;Vega-García et al 1995;Lin 1999;Pew and Larsen 2001;Vasconcelos et al 2001;Martínez et al 2004;Wotton and Martell 2005;Kalabokidis et al 2007;Prasad et al 2008;Martínez et al 2009;Modugno et al 2008;Vilar et al 2008;Nieto et al 2006). Other statistical methods such as linear regression, classification regression trees, neural networks, generalized additive models, or Bayesian probability have also been used in fire risk mapping to generate risk models (Chuvieco et al 1999;McKenzie et al 2000;Sebastián et al 2001;Chao-Chin 2002;Koutsias et al 2004;Preisler et al 2004;Robin et al 2006;Amatulli et al 2006Amatulli and Camia 2007;Syphard et al 2007;Vega-García 2007;Yang et al 2007;Romero-Calcerrada et al 2008).…”

Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

“…Evaluation of forest fire risk can contribute to reducing the negative impact of fire by improving the level of preparedness of forest managers, and can provide new information to guide planning. Several recent studies have presented models to predict forest fire danger on different spatial and temporal scales (Merril, Alexander 1987;Taylor, Alexander 2006;Prasad et al 2008;Loboda 2009;RomeroRuiz et al 2010;Padilla, Vega-Garcia 2011;Eskandari, Chuvieco 2015), and reports have been published on the occurrence and impact factor of forest fires worldwide. Tian et al (2012) and Hu and Jin (2002) studied the forest fire regime and conducted an analysis of the factors affecting fire distribution.…”