Analysing eucalypt expansion in Portugal as a fire-regime modifier

Fernandes, Paulo M.; Guiomar, Nuno; Rossa, Carlos G.

doi:10.1016/j.scitotenv.2019.02.237

Cited by 72 publications

(69 citation statements)

References 76 publications

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“…Our results concur with works that found that scattered trees with shrubs and treeless shrubs burn at very high severity (e.g., Oliveras et al, 2009;Harris and Taylor, 2015;Estes et al, 2017;Fang et al, 2018;García-Llamas et al, 2019). For example, high severity (RdNBR) areas in the disastrous Portuguese fires of 2017 were mostly constituted by shrublands (Fernandes et al, 2019). Fuel models played a secondary role, mainly because they were overshadowed by other variables related to vegetation composition and structure.…”

Section: What Are the Relative Roles Of Prefire Vegetation And Burninsupporting

confidence: 91%

“…Topographic effects on fire severity are complex and highly variable, and have often given contradictory results, in part due to the complex interactions of topography with fuels (e.g., Broncano and Retana, 2004;Holden et al, 2009;Lentile et al, 2006;Coen et al, 2018) and fire behaviour (e.g., Oliveras et al, 2009;Viedma et al, 2015). Some studies have reported strong control of topography over fire severity even under extreme fire weather conditions (e.g., Holden et al, 2009;Bradstock et al, 2010;Dillon et al, 2011;Viedma et al, 2015; Birch et al, 2015; Harris and Taylor, 2015, 2017; Fang et al, 2015, 2018; Zald and Dunn, 2018), while others have shown that extreme fire weather conditions can override or shift the relationships between topography and fire severity (e.g., Collins et al, 2007;Clarke et al, 2014;Fernández-Alonso et al, 2017;Zald and Dunn, 2018;Fernandes et al, 2019). However, it can be difficult to determine the robustness of some assessments because few studies to date have incorporated fire progression data, which would allow considering the alignment between topographical features and the propagating firefront (see, e,g., Viedma et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Disentangling the role of prefire vegetation vs. burning conditions on fire severity in a large forest fire in SE Spain

Viedma

Chico²,

Fernández³

et al. 2020

Remote Sensing of Environment

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Fire severity is a function of dynamic interactions between vegetation and burning conditions. To understand the factors that control it, accurate methods for estimating prefire vegetation structure and composition as well as fire propagation conditions are required. Here we analyzed the spatial variability of fire severity in a mixedseverity fire (3217 ha) that occurred in southeast Spain (Yeste, Albacete) from 27th July to 1th August 2017, burning mostly a pine woodland, including part of an earlier fire in 1994. Fire severity was estimated using three satellite-based indices derived from the Normalized Burn Ratio (NBR) using Sentinel 2 and Landsat 8 images from the dates before and immediately after fire. The field-based Composite Burn Index (CBI) was used for validation. Prefire vegetation conditions and fuel models were derived from LiDAR metrics and other vegetation data. Fire propagation conditions were estimated based on a fire progression map provided by the Forestry Services of Castilla-La Mancha. In addition, hourly fire weather and aligned (i.e., in the sense of the propagating fire-front) slope and wind speed were calculated for each burning period. Regression models using different spectral fire severity indices and their driving factors were obtained applying Boosted Regression Trees (BRTs). Fire severity was highly predicted by both burning conditions and prefire vegetation (mean adjusted R 2 [Adj.R 2 ]: 86% ± 0.04 and 68% ± 0.05 for training and validation sets, respectively). Alone, burning conditions explained more variance than LiDAR metrics and vegetation separately. The single variables that contributed most to the models were the rate of spread of the fire-front, biomass proxies (i.e., Leaf Area Index [LAI] and fraction of Photosynthetically Active Radiation [fPAR]) and understory vegetation (i.e., density of LiDAR points 1-2 m). Higher fire severity occurred in areas burning uphill, with a high rate of spread driven by high velocity winds and under high maximum temperature. Fire severity was high in wooded stands that were heterogeneous in height, composed by scattered and small Pinus halepensis trees, with high and homogeneous understory cover. In contrast, lower fire severity occurred in mature stands dominated by tall Pinus pinaster and Pinus nigra trees. There were strong interactions between vegetation, weather, fire-aligned topography and rate of spread. Because vegetation variables were important drivers of fire severity, even under extreme fire weather conditions, fuel management treatments to limit fire severity and, potentially, fire size should be implemented.

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Section: What Are the Relative Roles Of Prefire Vegetation And Burninsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Disentangling the role of prefire vegetation vs. burning conditions on fire severity in a large forest fire in SE Spain

Viedma

Chico²,

Fernández³

et al. 2020

Remote Sensing of Environment

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“…The fact that wildfires affected large areas of Portugal during the period of study is well known, and its effect on the overall reduction of forest area has already been pointed out [20]. Forest plantations, particularly of fast-growing species like maritime pine (Pinus pinaster) and eucalyptus (mainly Eucalyptus globulus) [40] and spontaneous encroachment (more frequent in the northeastern mountainous areas of the country) [41] have not been enough to counterbalance the effect of wildfires, although this is debated by some sources [16]. On the contrary, afforestation is one of the main processes in Spain, particularly in the more rugged, mountainous areas located at higher altitude [42], but also in areas where forest plantations coexist with high livestock density (Galicia, Basque Country, Catalonia).…”

Section: Discussionmentioning

confidence: 96%

Determinants of Land Use/Cover Change in the Iberian Peninsula (1990–2012) at Municipal Level

Nogueira

Rico

2019

Land

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This work analyzes the determinants associated with main land use/cover changes in the Iberian Peninsula during the 1990–2012 period using a decision tree model. Our main objective is to identify broad-scale patterns that associate the characteristics of geographic areas with the dominant land use/cover change process based on CORINE Land Cover (Coordination of Information on the Environment) and defined in a previously published work. Biophysical, structural and socioeconomic variables were considered as potentially explanatory of the dominant change process at municipal scale. The resulting model allowed identification of a common pattern in Portugal and Spain (urbanization being highly associated to areas already densely populated in the previous period), but also some diverging ones. In particular, dominant trends in Portuguese territory appear to be highly determined by wildfire occurrence. In contrast, Spanish municipalities showed more diverse patterns, usually associated to biophysical determinants like average forest productivity or average terrain slope.

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“…Production of natural fibres usually leads to the cultivation of a monoculture on large areas. This can lead to such environmental impacts as loss of biodiversity [68] or an increase of wild fires, e.g., in the case of eucalyptus forests, which are often used as a raw material for the production of cellulose fibres [69]. All these aspects are underrepresented in the requirements of the labels.…”

Section: Limitations Of Resultsmentioning

confidence: 99%

Life Cycle Based Comparison of Textile Ecolabels

et al. 2021

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Environmental impacts of textile production increased over the last decades. This also led to an increasing demand for sustainable textiles and ecolabels, which intend to provide information on environmental aspects of textiles for the consumer. The goal of the paper is to assess selected labels with regard to their strengths and weaknesses, as well as their coverage of relevant environmental aspects over the life cycle of textiles. We applied a characterization scheme to analyse seven selected labels (Blue Angel Textiles, bluesign®, Cotton made in Africa (CMiA), Cradle to Cradle CertifiedTM, Global Organic Textile Standard (GOTS), Global Recycled Standard (GRS), VAUDE Green Shape), and compared their focus to the environmental hotpots identified in the product environmental footprint case study of t-shirts. Most labels focus on the environmental aspects toxicity, water use, and air emissions predominantly in the upstream life cycle phases of textiles (mainly garment production), whereas some relevant impacts and life cycle phases like water in textile use phase remain neglected. We found significant differences between the ecolabels, and none of them cover all relevant aspects and impacts over the life cycle. Consumers need to be aware of these limitations when making purchase decisions.

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Analysing eucalypt expansion in Portugal as a fire-regime modifier

Cited by 72 publications

References 76 publications

Disentangling the role of prefire vegetation vs. burning conditions on fire severity in a large forest fire in SE Spain

Disentangling the role of prefire vegetation vs. burning conditions on fire severity in a large forest fire in SE Spain

Determinants of Land Use/Cover Change in the Iberian Peninsula (1990–2012) at Municipal Level

Life Cycle Based Comparison of Textile Ecolabels

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