Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Ascoli, Davide; Vacchiano, Giorgio; Scarpa, Carla; Arca, Bachisio; Barbati, Anna; Battipaglia, Giovanna; Elia, Mario; Esposito, Assunta; Garfì, Vittorio; Lovreglio, Raffaella; Mairota, Paola; Marchetti, Marco; Marchi, Enrico; Meytre, S.; Ottaviano, Marco; Pellizzaro, Grazia; Rizzolo, Rolando; Sallustio, Lorenzo; Salis, Michele; Sîrca, C.; Valese, E.; Ventura, Andrea; Bacciu, Valentina

doi:10.3832/ifor3587-013

Cited by 19 publications

(12 citation statements)

References 43 publications

(49 reference statements)

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“…Examples of some studies that used destructive sampling to estimate fuel load are found in Supplementary Table S1. According to the literature, litter and ground fuels (duff and humus) comprise the largest portion of fuel loads (for instance, Ascoli et al 2020), which is in line with the results of this study (excluding humus and duff layers). Thanks to country-wide inventories of the Austrian forest floor (Englisch et al 1991;Mutsch et al 2013), we could infer that the entire organic layer above the mineral soil (L, F and H-layers; Zanella et al 2011) has a much higher load than litter alone (Table 2, Supplementary Table S1).…”

Section: International Contextsupporting

confidence: 92%

“…Although canopy fuel load can be now modelled by remote sensing with promising accuracy (González-Ferreiro et al 2014Skowronski et al 2016), the estimation of surface fuels requires ground observations. For southern Europe and the boreal region, more data are available than for Central Europe (see, for instance, Dimitrakopoulos 2002;Curt et al 2013;Elia et al 2015;Piqué and Domènech 2018;Ascoli et al 2020;Ivanova et al 2020). Fuel loads are recognised as the foundation for reliable fire modelling (Pugnet et al 2013) and can be linked with fuel type mapping (Ascoli et al 2020;Aragoneses and Chuvieco 2021).…”

Section: Introductionmentioning

confidence: 99%

“…For southern Europe and the boreal region, more data are available than for Central Europe (see, for instance, Dimitrakopoulos 2002;Curt et al 2013;Elia et al 2015;Piqué and Domènech 2018;Ascoli et al 2020;Ivanova et al 2020). Fuel loads are recognised as the foundation for reliable fire modelling (Pugnet et al 2013) and can be linked with fuel type mapping (Ascoli et al 2020;Aragoneses and Chuvieco 2021).…”

Section: Introductionmentioning

confidence: 99%

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Fuel loads and fuel structure in Austrian coniferous forests

Neumann

Vilà-Vilardell²,

Müller

et al. 2022

Int. J. Wildland Fire

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Understanding fires in temperate European coniferous forests is hindered by a lack of reliable field observations on fuel load and structure. Fuel load influences the spread, intensity and spotting distance of a surface fire, torching likelihood and potential carbon emissions. We quantified fuel load and structure for Austrian coniferous forests using 93 sample plots across Austria. We compared Austrian fuel types with fuels collected in other regions and biomes. We found significant differences among regions and forest types. Fuel load was more dependent on region and forest type than on age class. Highest fuel load was found in Picea abies stands, lowest in Pinus nigra forests. Dead fuel loads were positively correlated with basal area, while live fuels were negatively correlated, suggesting that basal area drives accumulation of dead fuels and suppresses growth of understorey vegetation. Fuel loads in Austria are similar to published data for other temperate forests. The large variation in observed fuel loads and lack of previous studies highlight the need to further develop fuel models for mixed conifer-broadleaf forests. This pilot study underpins that consistent terminology and fuel classification are important to interpret differences between regions and forest types.

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Section: International Contextsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuel loads and fuel structure in Austrian coniferous forests

Neumann

Vilà-Vilardell²,

Müller

et al. 2022

Int. J. Wildland Fire

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“…Such unbalanced stratification of the sample in favor of the most educated and youngest could leave aside important considerations on wildfire impact and management in at-risk communities, such as those with poor education, low income, and the elderly [44][45][46]. Future research aimed at evaluating the interplay between the characteristics of vulnerable groups and fire-related environmental variables, such as climate variability, forest fuel distribution, and topographic features is warranted [47,48]. Lastly, we acknowledge the need to use validated scales for assessing wildfire risk perception.…”

Section: Study Strengths and Limitationsmentioning

confidence: 99%

Is Experience the Best Teacher? Knowledge, Perceptions, and Awareness of Wildfire Risk

Spano

Elia

Cappelluti

et al. 2021

IJERPH

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Wildfires represent a natural phenomenon with detrimental effects on natural resources and human health. A better knowledge, perception, and awareness of wildfire risk may help communities at risk of exposure to prevent future events and safeguard their own lives. The aim of this study is to explore differences between individuals with and without previous wildfire experience, in terms of (1) subjective and advanced wildfire knowledge, (2) self-reported perceptions, (3) level of information, (4) self-protection measures, and (5) importance of community involvement. As a second step, we investigated differences in the same variables, focusing more deeply on a group of individuals with previous wildfire experience, classifying them according to fire-related employment (fire-related workers vs. non-workers) and wildland–urban interface (WUI) proximity (WUI residents vs. non-WUI residents). The Kruskal–Wallis test was applied to establish differences between the pairs of subsamples. Our results partially confirmed our hypothesis, that direct experience leads individuals to have a greater preparedness on the topic of wildfires. Perception of knowledge is reflected only at a shallow level of expertise, and, therefore, no relevant within-group differences related to fire-related employment or to WUI proximity were detected. Moreover, available information was perceived to be insufficient, thus we report a strong need for developing effective communication to high-risk groups, such as homeowners and fire-related workers.

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“…Dead fuels, live herbaceous loads, and bulk density are essential input data layers for simulating wildfire behavior and spread. Currently, to obtain these data two procedural methods are mainly employed: one is the adoption of standard fuel parameters, such as the fuel model developed by Ottmar et al [12] or Rothermel's models [13], and its related studies [14][15][16][17], and the second is the alternative use of site-specific fuel parameters estimated ad hoc for the landscape where we aimed to simulate wildfire [18]. While general consensus affirms that the first method represents the most economic and rapid method for obtaining fuel parameters, it has been equally well demonstrated that fire simulation could be affected by a great variety of biases given the non-site-specific input data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data

et al. 2021

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Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1-h fuel load using standard fuel parameters or site-specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi-source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1-h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1-h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel-1), optical (Sentinel-2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R2) and Pearson’s correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R2: 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R2: 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R2: 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1-h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1-h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management.

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Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Cited by 19 publications

References 43 publications

Fuel loads and fuel structure in Austrian coniferous forests

Fuel loads and fuel structure in Austrian coniferous forests

Is Experience the Best Teacher? Knowledge, Perceptions, and Awareness of Wildfire Risk

Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data

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