Biophysical controls over fire regime properties in Central Portugal

Bergonse, Rafaello; Oliveira, Sandra; Zêzere, José Luís; Moreira, Francisco; Ribeiro, Paulo Flores; Leal, Miguel; Santos, Jorge

doi:10.1016/j.scitotenv.2021.152314

Cited by 17 publications

(52 citation statements)

References 52 publications

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“…This dataset is not available for the general public and therefore we just use the data made available by GNR. Nonetheless, fires occurrences in mainland Portugal are positively autocorrelated, showing higher prevalence in the north territory of the Tagus River [40]. Therefore, using the municipalities of Cantanhede, Figueiró dos Vinhos, Ílhavo, Miranda do Corvo, and Penela, as validation areas seems adequate.…”

Section: Methodsmentioning

confidence: 99%

Uncovering Vegetation Changes in the Urban–Rural Interface through Semi-Automatic Methods

et al. 2022

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Forest fires are considered by Portuguese civil protection as one of the most serious natural disasters due to their frequency and extent. To address the problem, the Fire Forest Defense System establishes the implementation of fuel management bands to aid firefighting. The aim of this study was to develop a model capable of identifying vegetation removal in the urban–rural interface defined by law for fuel management actions. The model uses normalised difference vegetation index (NDVI) of Sentinel-2 images time series and is based on the Welch t-test to find statistically significant differences between (i) the value of the NDVI in the pixel; (ii) the mean of the NDVI in the pixels of the same land cover type in a radius of 500 m; and (iii) their difference. The model identifies a change when the t-test points for a significant difference of the NDVI value in the ‘pixel’ as comparted to the ‘difference’ but not the ‘mean’. We use a moving window limited to 60 days before and after the analysed date to reduce the phenological variations of vegetation. The model was applied in five municipalities of Portugal and the results are promising to identify the places where the management of fuel bands was not carried out. This indicates which model could be used to assist in the verification of the annual management of the fuel bands defined in the law.

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Section: Methodsmentioning

confidence: 99%

Uncovering Vegetation Changes in the Urban–Rural Interface through Semi-Automatic Methods

et al. 2022

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“…These data were all obtained from the burned area vector maps produced annually by the National Forests Service (ICNF). Following prior research developed by Bergonse et al (2022), the cumulative percentage of parish area burned (CPAB) was used to measure the propensity of each parish to burn extensively over time. Area-weighted wildfire frequency (AWWF) was calculated as the total number of wildfires recorded within the parish over the study period, divided by the parish area (in km 2 ) in order to avoid scale effects because of contrasting parish sizes.…”

Section: Study Areamentioning

confidence: 99%

“…Prior research developed for the study area indicated an association between fire regime parameters and particular biophysical conditions (Bergonse et al, 2022). A set of 12 biophysical variables was adopted (Table 1), which were found to be significantly associated with the three fire regime parameters under analysis (Bergonse et al, 2022), although no differentiation 110 has been tested regarding their spatial distribution and aggregation.…”

Section: Potential Fire Regime Driversmentioning

confidence: 99%

“…LULC patch fragmentation has a well-known influence over the capacity of wildfire to propagate efficiently (Curt et al, 2013;Fernandes et al, 2016;Gralewicz et al, 2012;Turner & Romme, 1994). Following Bergonse et al (2022), we calculated the fragmentation of forest patches by merging all forest patches into a single polygon, dividing them into individual unconnected polygons, and generating the centroid for each of these. The number of centroids contained within each parish was quantified, 155 and then divided by the forest area of the parish (in ha).…”

Section: Potential Fire Regime Driversmentioning

confidence: 99%

“…Recently, Bergonse et al (2022) described fire regime over a 44-year study period in Central Portugal using three complementary variables: cumulative percentage of parish area burned, area-weighted total number of wildfires, and the Gini concentration index of burned area over time. They then quantified the influence of a set of 12 biophysical variables over each of these fire regime descriptors using ordinal logistic regression.…”

Section: Introductionmentioning

confidence: 99%

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Differentiating fire regimes and their biophysical drivers in Central Portugal

Bergonse

Oliveira

Zêzere³

et al. 2022

Preprint

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Abstract. The spatial and temporal patterns of wildfires and their effects over a given area can be described using the concept of fire regime. Here, we characterize fire regimes Central Portugal and investigate the degree to which the differences between regimes are influenced by a set of biophysical drivers. Using civil parishes as units of analysis, we employ three complementary parameters to describe the fire regime over a reference period of 44 years (1975–2018): cumulative percentage of parish area burned, Gini concentration index of burned area over time, and area-weighted total number of wildfires. Cluster analysis is used to aggregate parishes into groups with similar fire regime based on these parameters. A classification tree model is then used to assess the capacity of a set of potential biophysical drivers to discriminate between the different parish groups. Drivers included slope, summer temperature and spring rainfall, land use/land cover (LULC) type and patch fragmentation, and net primary productivity. Results allowed to distinguish four types of fire regime and show that these can be significantly differentiated using the biophysical drivers, of which LULC, slope and spring rainfall are the most important. Among LULC classes, shrubland and herbaceous vegetation play the foremost role, followed by agriculture. Our results highlight the importance of vegetation type, availability, and rate of regeneration, as well as that of topography, in influencing fire regimes in the study area, while suggesting that these regimes should be subject to specific wildfire prevention and mitigation policies.

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