Integrated Vulnerability of Forest Systems to Wildfire: Implications on Forest Management Tools. VIS4FIRE Project
A special issue of Fire (ISSN 2571-6255).
Deadline for manuscript submissions: 10 July 2024 | Viewed by 12488
Special Issue Editors
Interests: forest fires; integrated fire management
Interests: fire vulnerability; fire management; fire prevention; fire behavior; fire risk and danger assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Wildfires are major threats in many forested areas with important socioeconomic and environmental implications. Climatic change predictions, forest management and land use changes (global change) will exacerbate the problem, generating additional threats and challenges in civil protection protocols and forest management.
The VIS4FIRE project is based on the concept of vulnerability of forest systems to fire and its implications for management strategies oriented to integrate wildfire management practices. Therefore, the main aim of VIS4FIRE is the characterization and analysis of the main components of vulnerability of forest systems to wildfire as the basis of an efficient normalized system. The evaluation of integral vulnerability to wildfires in order to carry out a more efficient integrated forest management protection of forests and landscapes is in the frame of sustainable forest management and adaptive silviculture. Consequently, to achieve this goal, we will develop methodologies, tools and technologies, applicable before, during and after the fire, through the following main topics:
- Prediction of forest fuel characteristics as a key vulnerability factor to wildfire;
- Determining the effects of fuel treatments (including prescribed fires) on vulnerability components;
- Assessing the influence of fire severity and fire recurrence on forest systems’ resilience and vulnerability;
- Improving wildfire behaviour predictions to reduce vulnerability in forest stands;
- Econometric analysis of vulnerability: production and suppression capacity, decision support under uncertain environments and fire management;
- Evaluation of post-fire restoration treatments’ efficacy and their influence on burned area vulnerability;
- Approach to integral vulnerability analysis of forest areas including the development of new indices and tools based on modelling, remote sensing and computation of large and big data.
We invite additional authors to contribute with scientific and technological knowledge related to these topics
Dr. Javier Madrigal
Dr. Juan Ramón Molina Martínez
Dr. Eva Marino
Guest Editors
Manuscript Submission Information
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Keywords
- decision support systems
- econometrics
- fire impacts
- fire resilience
- fire risk
- fire vulnerability indices
- flammability
- forest fuels modelling
- forest fire behaviour
- remote sensing
- resilience
- post-fire restoration
- software
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Transferability of empirical models derived from satellite imagery for live fuel moisture content estimation and fire risk prediction Eva Marino1, Lucía Yañez1, Mercedes Guijarro2, Francisco Senra3, Sergio Rogríguez3, José Luis Tomé1 Estimation of live fuel moisture content (LFMC) is critical for vegetation flammability assessment and potential fire behaviour prediction, providing relevant information in wildfire prevention and management. Previous research demonstrates that empirical modelling based on spectral data derived from remote sensing is useful to retrieve LFMC. However, these types of models are often very site-specific and generally considered difficult to extrapolate. In the present work, we analysed the performance of empirical models based on spectral data derived from Sentinel-2 for LFMC estimation in fire-prone shrubland dominated by Citus ladanifer. For this purpose, we used field data of LFMC monitored between June 2021 and September 2022 in 27 plots in Andalusia region (South Spain). The specific objectives of the study included: i) testing of previous models fitted for the same shrubland species in a different study area in Madrid region (Central Spain); ii) calibration of empirical models with the field data from Andalusia region, comparing the results with the performance of previous existing models; iii) testing the performance of the best empirical models to predict LFMC decrease to critical threshold values in historical wildfire events. Our results showed that: i) empirical models derived from Sentinel-2 provided a good accuracy in LFMC monitoring, with a mean absolute error (MAE) of 15% in the estimation of LFMC variability along the year, and with an error decreasing to MAE of 10% for the critical lower LFMC values (< 100%); ii) previous models could be easily recalibrated for the extrapolation to a different geographical area, achieving similar errors to the specific empirical models fitted in the study area in independent validation; iii) LFMC decrease in historic wildfire events were accurately predicted by the empirical models, occurring under LFMC values < 80% for this fire-prone shrubland species.