Fire Hazards: Socio-economic and Regional Issues DOI Creative Commons
Jesús Rodrigo‐Comino, Luca Salvati, Artemi Cerdà

et al.

Springer eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Recent Trends in Fire Regimes and Associated Territorial Features in a Fire-Prone Mediterranean Region DOI Creative Commons
Francisco Moreira, Miguel Leal, Rafaello Bergonse

et al.

Fire, Journal Year: 2023, Volume and Issue: 6(2), P. 60 - 60

Published: Feb. 8, 2023

Fire regimes in Mediterranean countries have been shifting recent decades, including changes wildfire size and frequency. We sought to describe fire across two periods (1975–1995 1996–2018) a fire-prone region of central Portugal, explore the relationships between these territorial features, check whether associations persisted periods. Two independent indicators were determined at parish level: incidence burn concentration. Most parishes presented higher values both second period. Higher associated with lower population densities, proportions farmland areas natural vegetation. levels concentration smaller These differed periods, reflecting contrasting climatic socio-economic contexts. Keeping 40% territory covered by was effective buffer increased risks different management climate The effectiveness densities keeping low decreased last decades. results can improve knowledge on temporal evolution their conditioning factors, providing contributions for spatial planning forest/wildfire policies.

Language: Английский

Citations

20

The 2017 Extreme Wildfires Events in Portugal through the Perceptions of Volunteer and Professional Firefighters DOI Creative Commons
Vittorio Leone, Mario Elia, Raffaella Lovreglio

et al.

Fire, Journal Year: 2023, Volume and Issue: 6(4), P. 133 - 133

Published: March 24, 2023

This study aimed to explore differences in the perceptions of professional and voluntary firefighters regarding extreme wildfire events that occurred Portugal 2017. We collected a sample 185 participants, firefighters, who directly participated suppression activities for Pedrógão Grande October 2017 wildfires Portugal. They were on duty 149 fire stations Central Region A questionnaire was sent via Google Form based mainly close-ended two open-ended questions. It structured into topics concerning characteristics events, problems during activity, emotional response participants lessons learned, consequences. found significant between groups their perception worst fires they had ever experienced. Some discussion phase fires. On contrary, appear be homogeneous when it comes no changes after deadly experience terms fight against rural fires, organization, training, prevention, careers. The results underline inadequacy model vs. also its limits from point view psychological reactions management occurring complex events. There is research gap examples about flow characterize operation Our fills this gap.

Language: Английский

Citations

17

Vegetation fuel characterization using machine learning approach over southern Portugal DOI Creative Commons
Filippe L.M. Santos, Flavio T. Couto, Susana Dias

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2023, Volume and Issue: 32, P. 101017 - 101017

Published: June 21, 2023

Understanding the role of fire in water and carbon cycles is crucial for understanding Earth's system. Remote sensing a valuable tool this purpose as it covers large areas consistently over time. Furthermore, propagation models use vegetation parameters to gather information about wildfire conditions, thus reinforcing need dynamics comprehension. Hence, study aims improve representation fuel load moisture content, through remote in-situ data Southern Portugal. In study, three above-ground biomass (AGB) datasets and, live content (LFMC), biweekly samples two field sites (Herdade da Mitra Serra de Ossa) were collected during period between April October 2022, counting 246 samples. These combined with satellite derived from Sentinel-2 (spectral bands indices) used machine learning approach, Random Forest (RF) classifier, considering 30 variables predict AGB LFMC. Results showed reasonable agreement predicted observed values, r2 RSME values 0.56 (0.69) 17.56 ton ha−1 (6.47%) (LFMC). Finally, RF model generated wall-to-wall dynamic LFMC maps. This allowed product an produce reliable conditions essential risk assessment atmosphere modelling

Language: Английский

Citations

17

Investigation of 2021 wildfire impacts on air quality in southwestern Turkey DOI
Merve Eke, Fulya Cingiroglu, Burçak Kaynak

et al.

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 325, P. 120445 - 120445

Published: March 7, 2024

Language: Английский

Citations

8

Fire Hazards: Socio-economic and Regional Issues DOI Creative Commons
Jesús Rodrigo‐Comino, Luca Salvati, Artemi Cerdà

et al.

Springer eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

8