Understanding fire patterns and fire drivers for setting a sustainable management policy of the New-Caledonian biodiversity hotspot

Curt, Thomas; Borgniet, L.; Ibanez, Thomas; Moron, Vincent; Hély, Christelle

doi:10.1016/j.foreco.2014.10.032

Cited by 31 publications

(17 citation statements)

References 80 publications

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“…Another approach may be the inclusion of agricultural areas within vegetation variables as a separate land cover class in order to understand the fire regimes of anthropogenic and natural origin in a region (e.g. Curt et al 2015). However, such an approach will leave unanswered the question of what the drivers of natural fire regimes are, because this cannot be inferred from models including both agricultural lands and vegetation-covered areas.…”

Section: Discussionmentioning

confidence: 99%

“…However, cropland fires have often been ignored in studies determining the drivers of fire activity and regimes on a regional or global basis, especially if satellite fire activity data are used (Murphy et al 2013;Pausas and Ribeiro 2013;Curt et al 2015). Consequently, in most cases, cropland and wildland fires have been evaluated jointly.…”

Section: Introductionmentioning

confidence: 99%

“…Using this approach, for example, Chuvieco et al (2008) were able to reveal the relative importance of cropland fires in different biomes and ecoregions. It is also possible to monitor and detect cropland fires by coupling satellite fire activity data and a land cover dataset (Korontzi et al 2006;Le Page et al 2010;van der Werf et al 2010;Tulbure et al 2011;Magi et al 2012;Murphy et al 2013;Curt et al 2015;Rabin et al 2015;Hall et al 2016;Xie et al 2016). The latter approach allows us to detect cropland fires from satellite data by considering fire activity on croplands.…”

Section: Introductionmentioning

confidence: 99%

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Modelling the drivers of natural fire activity: the bias created by cropland fires

Bekar

Tavşanoğlu

2017

Int. J. Wildland Fire

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Abstract. Wildland and cropland fires, which differ considerably in fire regime characteristics, have often been evaluated jointly to estimate regional or global fire regimes using satellite-based fire activity data. We hypothesised that excluding cropland fires will change the output of the models regarding the drivers of natural fire activity. We modelled MODIS fire activity data of western and southern Turkey for the years 2000-2015 using binomial generalised linear models in which many climatic, anthropogenic and geographic factors were included as predictor variables. For modelling, we used different datasets created by the exclusion of various cropland and vegetation land cover classes. More fire activity was observed as the number of cropland-dominated cells increased in a dataset. The explained deviance (%) of the binomial GLM differed substantially in the separate datasets for most of the variables. Moreover, excluding croplands gradually from the overall dataset resulted in a substantial decrease in the explained deviance (%) in the models for all variables. The results suggest that cropland fires have a significant effect on the output of fire regime models. Therefore, a clear distinction should be drawn between wildland and cropland fires in such models for a better understanding of natural fire activity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling the drivers of natural fire activity: the bias created by cropland fires

Bekar

Tavşanoğlu

2017

Int. J. Wildland Fire

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“…There are complex interrelationships among vegetation, fuels and flammability that can lead to a fire trap, whereby vegetation can be moved into and maintained in an alternative state (Bowman, Perry, & Marston, 2015;Curt, Borgniet, Ibanez, Moron, & Hely, 2015;Lindenmayer, Hobbs, Likens, Krebs, & Banks, 2011). Fuel type strongly affects fire characteristics and can lead to fire-vegetation feedbacks (Bowman et al, 2015;D'Antonio & Vitousek, 1992;Vila, Lloret, Ogheri, & Terradas, 2001;Wood & Bowman, 2012) and influence the spatial structure of vegetation (Hochberg, Menaut, & Gignoux, 1994).…”

Section: Introductionmentioning

confidence: 99%

Differential demographic filtering by surface fires: How fuel type and fuel load affect sapling mortality of an obligate seeder savanna tree

et al. 2017

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We studied the effects of fuel type, fuel load and their associated flammability attributes on growth and survival of Callitris intratropica saplings. Callitris intratropica is a fire‐sensitive conifer that is widespread across northern Australia, but its range is contracting because of frequent and intense fires. A small‐scale field experiment was used to compare the effect of three fuel types (grass, eucalypt litter and C. intratropica litter), and a mix of grass and eucalypt litter by varying fuel loads within their naturally occurring bounds, and measuring multiple flammability attributes. Fuel type had the strongest influence on flammability attributes and hence sapling survival. Grass burnt rapidly, producing high temperatures, while duration of flaming was longer for eucalypt litter. Grass–eucalypt litter mixtures had flammability attributes more like grass, while C. intratropica litter was difficult to burn. Fuel load had a secondary effect, with strong interactions between fuel type and load. Differences in sapling survival could be attributed to temperatures at 5 cm height; there was no additional effect of fuel type or canopy temperature. Sapling size variables were also important, and strongly correlated with bark thickness, so we could not identify the protective mechanism. Synthesis. Mortality of Callitris intratropica saplings was consistent with damage to the lower stem, because of the direct relationship with temperatures at 5 cm height. Our results demonstrate the existence of a grass–fire cycle in C. intratropica stands, whereby hot fires damage the stands and allow grass to invade, increasing the stand ignitability and combustibility and promoting further fires. Interrupting this cycle by reducing grass fuel loads and hence the frequency of hot fires should therefore be a management priority to safeguard C. intratropica populations. Our findings also highlight that, under a common climate, vegetation type can shape fire regimes, because fuel type strongly influences flammability attributes, which in turn act as a powerful filter of plant populations.

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“…Fire ignition datasets can be obtained from satellite observations [43]. Using the moderate-resolution imaging spectroradiometer (MODIS) active fire products MOD14A1 (Terra) and MYD14A1 (Aqua) and related climate data, the model of fire climate index is established based on the logistic regression method for HulunBuir.…”

Section: Introductionmentioning

confidence: 99%

Study on Climate and Grassland Fire in HulunBuir, Inner Mongolia Autonomous Region, China

Liu

Zhao

Guo

et al. 2017

Sensors

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Grassland fire is one of the most important disturbance factors of the natural ecosystem. Climate factors influence the occurrence and development of grassland fire. An analysis of the climate conditions of fire occurrence can form the basis for a study of the temporal and spatial variability of grassland fire. The purpose of this paper is to study the effects of monthly time scale climate factors on the occurrence of grassland fire in HulunBuir, located in the northeast of the Inner Mongolia Autonomous Region in China. Based on the logistic regression method, we used the moderate-resolution imaging spectroradiometer (MODIS) active fire data products named thermal anomalies/fire daily L3 Global 1km (MOD14A1 (Terra) and MYD14A1 (Aqua)) and associated climate data for HulunBuir from 2000 to 2010, and established the model of grassland fire climate index. The results showed that monthly maximum temperature, monthly sunshine hours and monthly average wind speed were all positively correlated with the fire climate index; monthly precipitation, monthly average temperature, monthly average relative humidity, monthly minimum relative humidity and the number of days with monthly precipitation greater than or equal to 5 mm were all negatively correlated with the fire climate index. We used the active fire data from 2011 to 2014 to validate the fire climate index during this time period, and the validation result was good (Pearson’s correlation coefficient was 0.578), which showed that the fire climate index model was suitable for analyzing the occurrence of grassland fire in HulunBuir. Analyses were conducted on the temporal and spatial distribution of the fire climate index from January to December in the years 2011–2014; it could be seen that from March to May and from September to October, the fire climate index was higher, and that the fire climate index of the other months is relatively low. The zones with higher fire climate index are mainly distributed in Xin Barag Youqi, Xin Barag Zuoqi, Zalantun Shi, Oroqen Zizhiqi, and Molidawa Zizhiqi; the zones with medium fire climate index are mainly distributed in Chen Barag Qi, Ewenkizu Zizhiqi, Manzhouli Shi, and Arun Qi; and the zones with lower fire climate index are mainly distributed in Genhe Shi, Ergun city, Yakeshi Shi, and Hailar Shi. The results of this study will contribute to the quantitative assessment and management of early warning and forecasting for mid-to long-term grassland fire risk in HulunBuir.

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Understanding fire patterns and fire drivers for setting a sustainable management policy of the New-Caledonian biodiversity hotspot

Cited by 31 publications

References 80 publications

Modelling the drivers of natural fire activity: the bias created by cropland fires

Modelling the drivers of natural fire activity: the bias created by cropland fires

Differential demographic filtering by surface fires: How fuel type and fuel load affect sapling mortality of an obligate seeder savanna tree

Study on Climate and Grassland Fire in HulunBuir, Inner Mongolia Autonomous Region, China

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