Fuel load sampling of a Cupressus sempervirens hedge in Parc de Cervantes, Barcelona

In this document, data from three destructive samplings performed in a Cupressus sempervirens hedge in Parc de Cervantes (Barcelona) are shown; the methodology applied is also described. Measurements of fuel load have been taken, and moisture content, mass distribution and bulk density have been calculated. Different fuel classes (according to status and diameter) have been taken into account.Postprint (published version

PBD WUI-specific guidelines; initial draft

Postprint (updated version

Technical Note TN 2.2 Fuel load sampling of a Cupressus sempervirens hedge

In this document, data from a destructive sampling of a Cupressus sempervirens hedgerow are shown, as well as the followed methodology. Measures of sizes and weights have been taken. With these data, moisture content, fuel load and bulk density have been calculated. Different fuel classes (state and diameter) have been taken into account.Postprint (updated version

Technical Note TN 2.4. Fuel sampling, tree ignition and burning tests in ADAI facilities

In this document, the followed methodology to characterize several natural fuels (trees of Cupressus arizonica, Cupressocyparis leylandii, Prunus laurocerasus and Thuja occidentalis) is presented. Also, the ignition method and the equipment used to monitor the burning of these fuels in two different configurations are described. These activities were performed in ADAI facilities (Lousa, Portugal).Preprin

Flame filtering and perimeter localization of wildfires using aerial thermal imagery

Airborne thermal infrared (TIR) imaging systems are being increasingly used for wild fire tactical monitoring since they show important advantages over spaceborne platforms and visible sensors while becoming much more affordable and much lighter than multispectral cameras. However, the analysis of aerial TIR images entails a number of difficulties which have thus far prevented monitoring tasks from being totally automated. One of these issues that needs to be addressed is the appearance of flame projections during the geo-correction of off-nadir images. Filtering these flames is essential in order to accurately estimate the geographical location of the fuel burning interface. Therefore, we present a methodology which allows the automatic localisation of the active fire contour free of flame projections. The actively burning area is detected in TIR georeferenced images through a combination of intensity thresholding techniques, morphological processing and active contours. Subsequently, flame projections are filtered out by the temporal frequency analysis of the appropriate contour descriptors. The proposed algorithm was tested on footages acquired during three large-scale field experimental burns. Results suggest this methodology may be suitable to automatise the acquisition of quantitative data about the fire evolution. As future work, a revision of the low-pass filter implemented for the temporal analysis (currently a median filter) was recommended. The availability of up-to-date information about the fire state would improve situational awareness during an emergency response and may be used to calibrate data-driven simulators capable of emitting short-term accurate forecasts of the subsequent fire evolution.Postprint (author's final draft

Short-term fire front spread prediction using inverse modelling and airborne infrared images

A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.Peer ReviewedPostprint (author's final draft