No abstract

Global fire monitoring systems are crucial to study fire behaviour, fire regimes and their impact at the global scale. Although global fire products based on the use of Earth Observation satellites exist, most remote sensing products only partially cover the requirements for these analyses. These data do not provide information like fire size, fire spread speed, how fires may evolve and joint into single event, or the number of fire events for a given area. This high level of abstraction is very valuable; it makes it possible to characterize fires by types (either size, spread, behaviour, etc.). Here, we present and test a data mining work flow to create a global database of single fires that allows for the characterization of fire types and fire regimes worldwide. This work describes the data produced by a data mining process using MODIS burnt area product Collection 6 (MCD64A1). The entire product has been computed until the present and is available under the umbrella of the Global Wildfire Information System (GWIS).

Forest fires are an integral part of the natural Earth system dynamics, however they are becoming more devastating and less predictable as anthropogenic climate change exacerbates their impacts. In order to advance fire science, fire danger reanalysis products can be used as proxy for fire weather observations with the advantage of being homogeneously distributed both in space and time. This manuscript describes a reanalysis dataset of fire danger indices based on the Canadian Fire Weather Index system and the ECMWF ERA5 reanalysis dataset, which supersedes the previous dataset based on ERA-Interim. The new fire danger reanalysis dataset provides a number of benefits compared to the one based on ERA-Interim: it relies on better estimates of precipitation, evaporation and soil moisture, it is available in a deterministic form as well as a probabilistic ensemble and it is characterised by a considerably higher spatial resolution. It is a valuable resource for forestry agencies and scientists in the field of wildfire danger modeling and beyond. The global dataset is produced by ECMWF, as the computational centre of the European Forest Fire information System (EFFIS) of the Copernicus Emergency Management Service, and it is made available free of charge through the Climate Data Store.

A global fire danger rating system driven by atmospheric model forcing has been developed with the aim of providing early warning information to civil protection authorities. The daily predictions of fire danger conditions are based on the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. Weather forcings are provided in real time by the European Centre for Medium-Range Weather Forecasts forecasting system at 25-km resolution. The global system’s potential predictability is assessed using reanalysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 yr of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire, and low values correspond to nonobserved events. A more quantitative skill evaluation was performed using the extremal dependency index, which is a skill score specifically designed for rare events. It revealed that the three indices were more skillful than the random forecast to detect large fires on a global scale. The performance peaks in the boreal forests, the Mediterranean region, the Amazon rain forests, and Southeast Asia. The skill scores were then aggregated at the country level to reveal which nations could potentially benefit from the system information to aid decision-making and fire control support. Overall it was found that fire danger modeling based on weather forecasts can provide reasonable predictability over large parts of the global landmass.

No abstract

To improve environmental monitoring, the availability of great coverage of spatiotemporal data in an interoperable way is crucial for its integration into environmental models, for example, to compute fire danger models. To produce up-to-date and accurate results those models need the availability of data with high temporal and spatial resolution. Thus, it is promising to consider the increasing number of in-situ sensors providing observations of our environment in real-time. Today, interoperable access to such spatio-temporal data is achieved by Geospatial Information Infrastructures (GIIs). From a technical point of view GIIs provide this data through standards-based Web service interfaces. While those Web service interfaces already enable the interoperable discovery and retrieval of sensor observations, the functionality to publish sensor observations is still an arduous task. Hence, in this paper, we present an approach to improve the registration of sensors and the publication of their observations via standards-based Web service interfaces. We evaluate our approach by extending a standards-based GII and by applying the developed approach to the example of integrating in-situ weather observations into the European Forest Fire Information System for assessing fire danger in Spain.

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a nonsupervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

Wildfire, as a global phenomenon, is an integral part of the Earth system that affects different regions in diverse ways resulting in variable levels of long-lasting impacts to environmental, social, and economic systems. In a context where extreme and high severity events are becoming more frequent, it is crucial to respond with a more robust preparedness and planning, identifying the risks posed by wildland fires, fostering better fire management policy tools, and developing mitigation strategies accordingly. However, scope and methods for wildfire risk assessment vary widely among countries leading to different regional/national approaches not always comparable, although wildfires are often transborder events and may affect several countries simultaneously. The elaborateness of these assessments is often related to the impact of fires in the corresponding regions, with countries more often confronted with wildfires being more prepared by having more elaborated and detailed wildfire risk maps at country/regional level, although based on the specificities of each country. To integrate currently incompatible approaches, harmonised procedures for wildfire risk assessment are needed at the pan-European scale, enhancing planning and coordination of prevention, preparedness, and cross-border firefighting actions to mitigate the damaging effects of wildfires. The development of a pan-European approach follows from a series of European Union (EU) regulations requiring the European Commission (EC) to have a wide overview of the wildfire risk in Europe, to support the actions of its Member States and to ensure compliance in the implementation of EU regulations related to wildfires. The conceptualization of the European Wildfire Risk Assessment (WRA) as the combined impact of wildfire hazard on people, ecosystems, and goods exposed in vulnerable areas, explicitly accounts for the multiplicity of risk dimensions and sources of uncertainty. Already serving as an integrated framework for gathering the European countries’ experience on fire management and risk, it will support the inter-comparison of WRA among countries, with the aim to complement existing national WRA with a simpler, but harmonised, methodology. A semi-quantitative approach, designed to be robust to uncertainty and flexible in ingesting new components, is currently under development in close cooperation with the EC Joint Research Center (JRC), other Commission services, and the Commission Expert Group on Forest Fires which is now composed of fire management representatives from 43 countries in the region. Additionally, the harmonised framework can serve as a first approach to assess wildfire risk in those countries that have not yet performed a national WRA, and as a guideline for extending the approach to larger areas, where data coverage may be scarcer and more uncertain.