GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain)

Calcerrada, Raúl Romero; Novillo, Carlos J.; Millington, James D.A.; Gomez-Jimenez, I.

doi:10.1007/s10980-008-9190-2

Cited by 152 publications

(121 citation statements)

References 26 publications

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“…Livestock density was not considered because it has a dual influence. It tend to increase NF through burnings for pasture renewal (Koutsias et al, 2010;Martínez et al, 2009) and to decrease both NF and BA by the reduction of the fine fuel load/accumulation through pasture (Oliveira et al, 2012;Romero-Calcerrada et al, 2008;Sebastián-López et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Negligent and intentional fires in Portugal: Spatial distribution characterization

Parente

Pereira

Amraoui

et al. 2018

Science of The Total Environment

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In the European context, Portugal is the country with the highest number of wildfires and the second with more burnt area. The vast majority of these events are of human origin, whether caused by accident, negligence or arson, reason why it is particularly important to know the regime of these wildfires for forest and wildfire management activities. The study focuses on the most recent years of 2001-2014, when wildfire ignition's coordinates are known, and aims to identify and characterize the wildfire incidence spatial patterns of variability as well as their main drivers. After grouping wildfires with human cause into negligent and intentional, we studied their spatial distribution in terms of normalized number of wildfires (NNF) and burnt area (NBA) in the five Nomenclature of Territorial Units for Statistics II regions of mainland Portugal. Results disclose an uneven spatial distribution of the fire incidence, characterized by a south-north gradient, much higher values in Norte region and more evident for intentional than for negligent wildfires. Human and biophysical drivers strongly influence NNF and NBA, at regional and national scales. Distribution patterns at regional scale, for negligent and intentional wildfires are quite different from entire mainland and all wildfires. Drivers' influence is higher for intentional than for negligent wildfires, for southern than for northern regions and for NBA than for NNF. The leading drivers of NNF are distance to roads (d) population density (pd) and altitude (h) while of NBA are h, d, slope and pd, and this influence is higher for intentional than for negligent wildfires.

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Section: Discussionmentioning

confidence: 99%

Negligent and intentional fires in Portugal: Spatial distribution characterization

Parente

Pereira

Amraoui

et al. 2018

Science of The Total Environment

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“…The location of human activities is highly dependent on site-related variables that determine the number and distribution of human sources of ignition. Human presence can be analysed from explicit spatial factors such as proximity to, or density of, infrastructure such as roads (Dickson et al 2006;Yang et al 2008Yang et al , 2015Gralewicz et al 2012b;Hegeman et al 2014;Syphard and Keeley 2015;Zhang et al 2016;Mhawej et al 2016;Vilar et al 2016b), tracks (Pew and Larsen 2001;Romero-Calcerrada et al 2008, trails (Syphard et al 2008;Vilar del Hoyo et al 2011;Arndt et al 2013) and railways (Sturtevant and Cleland 2007;Guo et al 2015;Alcasena et al 2016), all of which are associated with an increase in fire occurrence. For example, in Spain (MAGRAMA 2015), the United States (Morrison 2007) and south-eastern Australia (Penman et al 2013), more than half of HCFs start along road systems.…”

Section: Predictors For Long-term Studiesmentioning

confidence: 99%

Human-caused fire occurrence modelling in perspective: a review

Costafreda-Aumedes

Comas

Vega‐García

2017

Int. J. Wildland Fire

132

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Abstract. The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.

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“…Although the results were acceptable, human effects were ignored due to study areas were located in rural places mainly. Oppositely, some papers were evaluated only human effects and study results were useful too because of study area was located nearby the big city (Calcerrada et al 2008). Many researches showed that forest fire probability approaches and data must be match with landscape structure for a reasonable result.…”

Section: Discussionmentioning

confidence: 99%

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Şatır

Berberoğlu

Dönmez

2015

Geomatics, Natural Hazards and Risk

174

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Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R D ¡0.43), tree cover (R D 0.93) and temperature (R D 0.42) were strongly correlated with forest fire probability in the USB region. KEYWORDSForest fire probability and hazard; landscape feature; weighting; artificial neural network; Mediterranean region; fire weather index

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GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain)

Cited by 152 publications

References 26 publications

Negligent and intentional fires in Portugal: Spatial distribution characterization

Negligent and intentional fires in Portugal: Spatial distribution characterization

Human-caused fire occurrence modelling in perspective: a review

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

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