2022
DOI: 10.1002/eap.2588
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Wild, tamed, and domesticated: Three fire macroregimes for global pyrogeography in the Anthropocene

Abstract: Climate and natural vegetation dynamics are key drivers of global vegetation fire, but anthropogenic burning now prevails over vast areas of the planet. Fire regime classification and mapping may contribute towards improved understanding of relationships between those fire drivers. We used 15 years of daily active fire data from the MODIS fire product (MCD14ML, collection 6) to create global maps of six fire descriptors (incidence, size inequality, season length, interannual variability, intensity, and fire se… Show more

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Cited by 13 publications
(5 citation statements)
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“…The large number of fire behaviour observations, both at the polygon level (L2) and at the burning-period level (L3), provide enough information for a wide variety of potential applications. Combined with detailed information on the drivers, namely weather and fuel, and effects, it can be used to (i) improve current knowledge on the drivers affecting the behaviour of large wildfires, (ii) calibrate existing or new models which ultimately should help to better predict fire behaviour and support efficient fire management strategies (Alexander and Cruz, 2013), (iii) support the construction of case studies by fire analysts and contribute to better training of fire personnel (Alexander and Thomas, 2003), (iv) contribute to improved operational fire suppression strategies, (v) better understand how fire behaviour is linked to its effects (Collins et al, 2019), (vi) improve fire danger rating (Wotton, 2009), and (vii) better characterize fire regimes (Pereira et al, 2022). In addition, the fire behaviour classes described in Fig.…”
Section: The Pt-firesprd Databasementioning
confidence: 99%
“…The large number of fire behaviour observations, both at the polygon level (L2) and at the burning-period level (L3), provide enough information for a wide variety of potential applications. Combined with detailed information on the drivers, namely weather and fuel, and effects, it can be used to (i) improve current knowledge on the drivers affecting the behaviour of large wildfires, (ii) calibrate existing or new models which ultimately should help to better predict fire behaviour and support efficient fire management strategies (Alexander and Cruz, 2013), (iii) support the construction of case studies by fire analysts and contribute to better training of fire personnel (Alexander and Thomas, 2003), (iv) contribute to improved operational fire suppression strategies, (v) better understand how fire behaviour is linked to its effects (Collins et al, 2019), (vi) improve fire danger rating (Wotton, 2009), and (vii) better characterize fire regimes (Pereira et al, 2022). In addition, the fire behaviour classes described in Fig.…”
Section: The Pt-firesprd Databasementioning
confidence: 99%
“…Human alteration of land cover and climate is reshaping wildfire on Earth (Bowman et al, 2020; Davis, 2021; T. M. Ellis et al, 2022; Pereira et al, 2022). Most terrestrial ecosystems have coevolved with fire over millions of years and many require periodic disturbance to maintain ecosystem structure and function (Bond et al, 2005; Harris et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…While fire is an ancient influence on the Earth system human agency has become increasingly important (Bowman et al, 2020). Today, humans are the primary cause of fire ignitions globally (Bowman et al, 2020;Pereira et al, 2022), and in fire-prone ecosystems including the SDTF and cerrado savannahs of the ARF-Ecotone, fires are more frequent when humans are in the landscape (Power et al, 2016;Le Page et al, 2017;Maezumi et al, 2018b;Brando et al, 2019). Indeed, as the keystone species of fire (Pyne, 2021), humans are the only species able to directly and deliberately manipulate fires through a variety of management practices (Levis et al, 2018;Bowman et al, 2020).…”
Section: Introductionmentioning
confidence: 99%