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New Trends in Forest Fire Research Incorporating Big Data and Climate Change Modeling

25-27 September 2017, Chania, GREECE

Topics


Satellite remote sensing systems and technologies have been long considered as basic components in fire research and management due to the extensive use of Earth Observation data over the past decades, which has significantly contributed towards more integrated methodological schemes in fire related studies and operational applications at local, regional and global scales. In recent years, new trends and shifts in fire research have been mainly driven by sensor availability as well as data distribution policies which have provided free access to large archives of satellite data.

Indeed, continuity and future missions of various satellite systems (e.g. SUOMI-VIIRS, Landsat-8, Sentinels, PROBA-V) ensure constant provision of coarse to high spatial resolution datasets, thus facilitating the systematic monitoring of fire disturbance at various spatio-temporal scales. In addition, the rapid progress in computer technology enables the processing and multi-sensor fusion of large volumes of datasets. Consequently, new approaches that have been proposed by the scientific community focus on the development of automated and semi-automated techniques, especially for active fire detection and mapping of burned areas.

The growing need for extraction of valuable information from large volumes of spatio-temporal data regarding the long-term evolution of fire regimes resulted in a shift the last few years from multi-temporal to hyper-temporal approaches. Exploitation of dense satellite time-series and efficient management of large data volumes, derived from multiple observation systems, are expected to contribute towards a more comprehensive understanding of the response of ecosystems and biomes under different fire frequency and severity scenarios.

Such scenarios are often triggered by climatic influences on fire regimes. Undoubtedly, the effects of climate change on fire occurrence are evident on a global scale, resulting in a substantial increase of extreme fire incidents, which in turn contribute to an increase in greenhouse gas emissions. Modeling tools and data assimilation schemes, exploiting available EO data among others, are expected to significantly support future projections of the intensity of the climate change phenomenon, taking into account the contribution of forest fires.

The EARSeL Special Interest Group on Forest Fires (FF-SIG) was created in 1995, following the initiative of several researchers studying fires in Mediterranean Europe. The FF-SIG, which currently represents one of the most active groups within EARSeL, promotes the integration of advanced technologies and the production of satellite-derived products for the benefit of forest managers, researchers, local governments and global organizations. Previous workshops of the SIG have been held in Alcalá de Henares (1995), Luso (1998), Paris (2001), Ghent (2003), Zaragoza (2005), Thessaloniki (2007), Matera (2009), Stresa (2011) Coombe Abbey (2013) and Limassol (2015).

The 11th EARSeL Forest Fire SIG workshop is co-organised by the School of Forestry and Natural Environment, Aristotle University of Thessaloniki, the Mediterranean Agronomic Institute of Chania, of the International Centre for Advanced Mediterranean Agronomic Studies, and the National Aeronautics and Space Administration. The workshop and proposed Special Issue will be focused on global systems for monitoring wildfires, as well as the missions providing data for this purpose, and the modeling endeavours with regards to climate change, considering the contribution of forest fires.

We invite you to submit articles on the following topics:

  1. Studies on the impact of climate change on forest fires occurrence and severity;
  2. Contribution of the current and upcoming Sentinel missions on forest fire research;
  3. Exploitation of Big Data and dense satellite time-series for fire disturbance monitoring;
  4. Improved methods of modelling post-fire vegetation trends;
  5. Improved capabilities for sharing / understanding / modelling large-volume fire data sets;
  6. Methods of forest fire detection and monitoring on multiple scales.