Download Training Programme. Download Book Abstract. Dynamic modelling fire occurrence, fuel structure and fuel moisture models Application of image processing techniques and machine learning to support fire management Integration of satellite, airborne, and field sensor for wildfire management Fire detection and monitoring on multiple scales Fire behaviour and fire impacts Burned area, severity estimation and ecological impacts, Fuel consumption and fuel load estimation.
Laboratory and field studies of fire and post-fire residues Fire emissions estimation and air quality monitoring wildfire impacts and post-fire treatments. Exploitation of Big Earth Data and satellite time-series for fire disturbance monitoring Studies on the impact of climate change on forest fires occurrence and severity; Contribution of Sentinel missions on forest fire research; Improved methods of modelling post-fire vegetation trends; Modelling and Monitoring post fire vegetation recovery. Welcome Letter.
Chuvieco Emilio University of Alcala, Spain. Mitri George University of Balamand, Lebanon. Juan de la Riva University of Zaragoza, Spain. Roy David University of S.
Lara Vilar - Google Scholar Citations
Dakota, USA. Tanase Mihai University of Melbourne, Australia. Stephen Plummer ESA. Susanne Mecklenburg ESA. Olivier Arino ESA. Michael Rast ESA. Andrea Vajda FMI. George Petropulos ESA. Francesco Sarti ESA. Mattia Crespi La Sapienza. Nikos Koutsias University of Patras. Eufemia Tarantino Politecnico di Bari. Luciana Ghermandi Conicet.
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Ioannis Z. Carmen Fattore. Early registration with reduced fees is mandatory for authors of oral and poster contributions September 6, Otherwise their contributions will be removed from the programme. Registration Fees. EARLY before Early registration with reduced fees is mandatory for authors of oral and poster contributions Otherwise their contributions will be removed from the programme. Remote sensing for forest fire. Early registration with reduced fees is mandatory for authors of oral and poster contributions September 6, Special Issue.
Organized by. Segui un collegamento aggiunto manuale. Costs The training is free of charge for all participants. CNR headquarter Location. Besides the ecological effects, there also are social consequences related to loss of property value, burned houses and even direct health effects due to degradation of air quality. Portugal is a case study for several reasons, but mainly because of its atypical characteristics when compared to other Southern European countries. Additionally, the property area per owner is very small on average i.
This causes severe difficulties for an efficient management. Wildfires affect the production of environmental goods and services through impacts on biodiversity, soils, water resources and air quality. Detailed spatio-temporal monitoring of post-fire vegetation recovery is important to help forecast harmful environmental impacts, such as landslides and floods, and to assist burned area rehabilitation activities. Extended post-fire monitoring of vegetation re-growth is also useful for characterizing fuel hazard dynamics, an important contributor to wildfire risk assessment.
In Portugal, the fire season of was the worst on record, with , ha more than 1 million acres of burned area, about four times the annual average and 1.
Using Machine Learning to Predict and Map Likelihood of Fires
Between July 30 and Aug. Our research goals are to understand post-fire vegetation dynamics in Portugal and evaluate if the recent fire history is influencing vegetation re-growth. The fire season in Portugal provides a good case study due its severity and due to existence of a fire perimeter dataset with information since That year, wildfires burned more than ha 1, acres each.
For this study, we analyze the two largest fires Figure 1 occurring in mainland Portugal in that year. Fire 93 is located in the central part of the country and the burned area was dominated by pine forest Figure 2a. This area is managed for wood extraction and sometimes resin.
After the fires, these pine trees grow back from the existing seed bank on the soil. Fire 78 is located in the South and the area is characterized by a mix of hardwood, broadleaf, and shrubs Figure 2b. After a fire, natural regeneration occurs with the seeds that survived the fire.
Characteristics and controls of extremely large wildfires in the western Mediterranean Basin
The approach is based on the use of a vegetation index derived from satellite imagery to characterize the evolution of vegetation greenness and response to fires in each of the study areas, in the analyzed period. Using a product provided by NASA, the MODIS vegetation index , a time series was created spanning the years from until for the selected burned areas of the fire season. The abrupt decrease of EVI index values EVI, Enhanced Vegetation Index allows the identification of the moment of fire occurrence and how the greenness values change after that.
Interpolation was performed for each pixel, using cubic splines. The starting date of February allows the characterization of undisturbed recently unaffected by fire vegetation dynamics in the fire-affected areas. The post-fire length of the time-series should allow for detecting substantial vegetation recovery, up to fuel loads capable of sustaining new fires. Effectively, the model provides a type of fire score that shows areas where fires are more likely where the model uses past patterns of fire events.
Analyzing the Resilience of Mediterranean Forest Systems to Wildfire Using Satellite Imagery
For example, it is not just how dry a place is but also the fact that wind speeds may have picked up that could affect one given area. Combing the different factors that lead to fires is how the tool provides a better regional understanding of fire occurrence. In this case, topography and land cover were seen as among the most important factors.
There are, however, other approaches that have been developed that utilize both human and natural factors in assessing fire risk. For instance, using MODIS data, a learning algorithm using expectation-maximum methods was used to evaluate a series of inputs from different regions.
Bayesian networks and GIS were used to evaluate regional factors of different inputs influencing fires from historical data. The satellite data can be used to train the model which can then use the previous data to forecast future conditions that might be similar to past occurrences. Other research has applied Landsat data , as these provide current and even long-term dataset for which forecasting models can be build.
Using burn history with fire events, data on slope, aspect, and weather conditions can be applied with Landsat vegetation data to create forecasting models that determine the likelihood of fire. While current imagery does provide better resolution and better band coverage, older data could also be useful for creating long-term models or models with deeper datasets that can potentially better predict variations in fires that may differ from more recent events.
Other methods have tried to combine GIS, remote sensing and interviews with local experts to better understand fires.