Reconstruction of natural fire regimes through ecological modelling

Li, Chao

doi:10.1016/s0304-3800(00)00290-8

Cited by 52 publications

(36 citation statements)

References 28 publications

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“…Modelled fire events are combined, over time, into patterns of fire regime. Four of the models (FIRESCAPE (Cary and Banks 1999;Cary 2002), LAMOS(HS) (Lavorel et al 2000), LANDSUM (Keane et al 2002), SEM-LAND (Li 2000)) are identical to those compared by Cary et al (2006), with the exception of LAMOS(HS), which was modified to use an hourly time-step instead of a daily one. The fifth model, CAFÉ (Bradstock et al 1998a, has not previously been included in experiments involving model comparison .…”

Section: Modelsmentioning

confidence: 99%

Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape - fire - succession models

Cary

Flannigan

Keane

et al. 2009

Int. J. Wildland Fire

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103

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Parsons, R. A. (2009). Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape-firesuccession models. International Journal of Wildland Fire, 18 (2), 147-156.Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape-fire-succession models AbstractThe behaviour of five landscape fire models (CAFE, FIRESCAPE, LAMOS(HS), LANDSUM and SEM-LAND) was compared in a standardised modelling experiment. The importance of fuel management approach, fuel management effort, ignition management effort and weather in determining variation in area burned and number of edge pixels burned (a measure of potential impact on assets adjacent to fire-prone landscapes) was quantified for a standardised modelling landscape. Importance was measured as the proportion of variation in area or edge pixels burned explained by each factor and all interactions among them. Weather and ignition management were consistently more important for explaining variation in area burned than fuel management approach and effort, which were found to be statistically unimportant. For the number of edge pixels burned, weather and ignition management were generally more important than fuel management approach and effort. Increased ignition management effort resulted in decreased area burned in all models and decreased number of edge pixels burned in three models. The findings demonstrate that yearto-year variation in weather and the success of ignition management consistently prevail over the effects of fuel management on area burned in a range of modelled ecosystems. Abstract. The behaviour of five landscape fire models (CAFÉ, FIRESCAPE, LAMOS(HS), LANDSUM and SEM-LAND) was compared in a standardised modelling experiment. The importance of fuel management approach, fuel management effort, ignition management effort and weather in determining variation in area burned and number of edge pixels burned (a measure of potential impact on assets adjacent to fire-prone landscapes) was quantified for a standardised modelling landscape. Importance was measured as the proportion of variation in area or edge pixels burned explained by each factor and all interactions among them. Weather and ignition management were consistently more important for explaining variation in area burned than fuel management approach and effort, which were found to be statistically unimportant. For the number of edge pixels burned, weather and ignition management were generally more important than fuel management approach and effort. Increased ignition management effort resulted in decreased area burned in all models and decreased number of edge pixels burned in three models. The findings demonstrate that year-to-year variation in weather and the success of ignition management consistently prevail over the effects of fuel management on area burned in a range of modelled ecosystems.

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Section: Modelsmentioning

confidence: 99%

Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape - fire - succession models

Cary

Flannigan

Keane

et al. 2009

Int. J. Wildland Fire

Self Cite

103

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“…A fire regime is also dynamic due to the influences of climate conditions and human intervention (Leone et al 2003). A natural fire regime refers to a fire regime without human intervention (Li 2000). Although purely natural fire regimes may not exist in the modern world, understanding of these fire regimes in time and space is still of great interest in both theory and practice.…”

Section: Introductionmentioning

confidence: 99%

Spatial patterns in vegetation fires in the Indian region

Vadrevu

Badarinath

Eaturu

2007

Environ Monit Assess

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In this study, we used fire count datasets derived from Along Track Scanning Radiometer (ATSR) satellite to characterize spatial patterns in fire occurrences across highly diverse geographical, vegetation and topographic gradients in the Indian region. For characterizing the spatial patterns of fire occurrences, observed fire point patterns were tested against the hypothesis of a complete spatial random (CSR) pattern using three different techniques, the quadrat analysis, nearest neighbor analysis and Ripley's K function. Hierarchical nearest neighboring technique was used to depict the 'hotspots' of fire incidents. Of the different states, highest fire counts were recorded in Madhya Pradesh (14.77%) followed by Gujarat (10.86%), Maharastra (9.92%), Mizoram (7.66%), Jharkhand (6.41%), etc. With respect to the vegetation categories, highest number of fires were recorded in agricultural regions (40.26%) followed by tropical moist deciduous vegetation (12.72), dry deciduous vegetation (11.40%), abandoned slash and burn secondary forests (9.04%), tropical montane forests (8.07%) followed by others. Analysis of fire counts based on elevation and slope range suggested that maximum number of fires occurred in low and medium elevation types and in very low to low-slope categories. Results from three different spatial techniques for spatial pattern suggested clustered pattern in fire events compared to CSR. Most importantly, results from Ripley's K statistic suggested that fire events are highly clustered at a lag-distance of 125 miles. Hierarchical nearest neighboring clustering technique identified significant clusters of fire 'hotspots' in different states in northeast and central India. The implications of these results in fire management and mitigation were discussed. Also, this study highlights the potential of spatial point pattern statistics in environmental monitoring and assessment studies with special reference to fire events in the Indian region.

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“…Therefore, the disturbance plays a very important role in the FLM. A large number of research results about the fire disturbance have been published, including the prediction for fire frequency and fire period (Ratz, 1995;Li, 2002), and improvement for historical and natural fire policy (Li, 2000;Wimberly, 2002;Keane et al, 2003;Nonaka et al, 2005;Thompson et al, 2006). Keane et al (1996) used the FIRE-BGC model to simulate the long-term dynamic of coniferous forest landscape in the northern part of Rocky Mountains, USA, and compared it with the dynamic of the U.S. white bark pine under different fire policies in the past 200 years.…”

Section: Research For Forest Landscape Patternmentioning

confidence: 99%

“…Hardy et al (2001) used the FRCC model with the historical fire data and vegetation data to simulate the forest risks under different fire policies, and then serve the local forest management. The fire expectation SEM-LAND model invented by Li (2000) was used to simulate different fire policies' influence on the forest landscape structure in the western part of Alberta in Canada. Meanwhile, many research results of the climate change, wind, and harvest interference on forest structure and process were published as well.…”

Section: Research For Forest Landscape Patternmentioning

confidence: 99%

Progress and prospect of research on forest landscape model

Dai

Wang

et al. 2014

J. Geogr. Sci.

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Abstract:The Forest Landscape Model (FLM) is an efficiency tool of quantified expression of forest ecosystem's structure and function. This paper, on the basis of identifying FLM, according to the stage of development, summarizes the development characteristics of the model, which includes the theoretical foundation of mathematical model, FLM of stand-scale, primary development of spatial landscape model, rapid development of ecosystem process model as the priority, and developing period of structure and process driven by multi-factor. According to the characteristics of different FLMs, this paper classifies the existing FLM in terms of mechanism, property and application, and elaborates the identifications, advantages and disadvantages of different types of models. It summarizes and evaluates the main application fields of existing models from two aspects which are the changes of spatial pattern and ecological process. Eventually, this paper presents FLM's challenges and directions of development in the future, including: (1) more prominent service on the practical strategy of forest management's objectives; (2) construction of multi-modules and multi-plugin to satisfy landscape research demand in various conditions; (3) adoption of high resolution's spatial-temporal data; (4) structural construction of multi-version module; (5) improving the spatial suitability of model application.

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Reconstruction of natural fire regimes through ecological modelling

Cited by 52 publications

References 28 publications

Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape - fire - succession models

Relative importance of fuel management, ignition management and weather for area burned: evidence from five landscape - fire - succession models

Spatial patterns in vegetation fires in the Indian region

Progress and prospect of research on forest landscape model

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