Articles | Volume 18, issue 3
https://doi.org/10.5194/nhess-18-935-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-935-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Mar 2018
Research article |
| 23 Mar 2018
| 23 Mar 2018
Modeling anthropogenic and natural fire ignitions in an inner-alpine valley
Dipartimento di Scienze Agrarie e Ambientali, Università degli Studi di Milano, Milan, 20123, Italy
Cristiano Foderi
Dipartimento di Gestione dei Sistemi Agrari, Università degli Studi di Firenze, Alimentari e Forestali, Florence, 50145, Italy
Roberta Berretti
Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Grugliasco (TO), 10095, Italy
Enrico Marchi
Dipartimento di Gestione dei Sistemi Agrari, Università degli Studi di Firenze, Alimentari e Forestali, Florence, 50145, Italy
Renzo Motta
Dipartimento di Scienze Agrarie, Forestali e Alimentari, Università degli Studi di Torino, Grugliasco (TO), 10095, Italy
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- An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques O. Mofokeng et al. 10.3390/fire7020061
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- Fire classification in natural ecosystems by physical and environmental characteristics L. Chernogor et al. 10.26565/1992-4259-2023-29-05
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- Study on the Wildfires Occurring Risk Based on Fuzzy FTA C. Chen et al. 10.1088/1755-1315/440/5/052100
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- Lightning-caused fires in the Alps: Identifying the igniting strokes J. Moris et al. 10.1016/j.agrformet.2020.107990
- Recommendations for Ensuring Environmental Safety of Ecosystem Restoration After Fire 10.26565/1992-4259-2020-23-04
- Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level A. Trucchia et al. 10.3390/fire5010030
- Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability A. Jaafari et al. 10.1016/j.jenvman.2019.04.117
- A review of machine learning applications in wildfire science and management P. Jain et al. 10.1139/er-2020-0019
- Wildfire risk modeling S. Oliveira et al. 10.1016/j.coesh.2021.100274
- Analysis of Wildfire Fault Based on F-FTA Method C. Chen et al. 10.1088/1755-1315/300/3/032089
- Natural disturbances risks in European Boreal and Temperate forests and their links to climate change – A review of modelling approaches J. Machado Nunes Romeiro et al. 10.1016/j.foreco.2022.120071
- A deep learning ensemble model for wildfire susceptibility mapping A. Bjånes et al. 10.1016/j.ecoinf.2021.101397
- Wildfire Risk Assessment Considering Seasonal Differences: A Case Study of Nanning, China W. Yue et al. 10.3390/f14081616
- Assessment of the effects of different variable weights on wildfire susceptibility F. Sari 10.1007/s10342-023-01643-z
- Seasonal differences in the spatial patterns of wildfire drivers and susceptibility in the southwest mountains of China W. Wang et al. 10.1016/j.scitotenv.2023.161782
- A Combination of Human Activity and Climate Drives Forest Fire Occurrence in Central Europe: The Case of the Czech Republic R. Berčák et al. 10.3390/fire7040109
- A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy M. Tonini et al. 10.3390/geosciences10030105
- Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey S. Giannakidou et al. 10.1016/j.iot.2024.101171
- Evaluating Seasonal Wildfire Susceptibility and Wildfire Threats to Local Ecosystems in the Largest Forested Area of China X. Tang et al. 10.1029/2021EF002199
- Advancements in Wildfire Detection and Prediction: An In-Depth Review R. SALMAN et al. 10.35940/ijitee.B9774.13020124
- Towards an integrated forest fire danger assessment system for the European Alps M. Müller et al. 10.1016/j.ecoinf.2020.101151
- Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles M. Rybansky 10.3390/app12083939
- Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models F. Sari 10.1007/s11676-022-01502-4
- Environmental drivers and spatial prediction of forest fires in the Western Ghats biodiversity hotspot, India: An ensemble machine learning approach K. Babu et al. 10.1016/j.foreco.2023.121057
Latest update: 18 Apr 2024
Short summary
Here we show that wildland fires in an Italian alpine region are ignited mainly by human negligence. 30 % of fires stars in agricultural areas, 24 % in forests. Lightning plays a role in 10 % of the cases, but its importance has been increasing recently. Areas under hot, dry climate are more prone to fire. Cattle grazing reduces the fuel for winter fires, but increases ignition risk in summer. The maps of fire risk that we produce can help to support fire prevention and ecosystem management.
Here we show that wildland fires in an Italian alpine region are ignited mainly by human...
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