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Forest fires in Galicia have become a serious environmental problem over the years. This is especially the case in the Pontevedra region, where in October 2017 large fires (>500 hectares) burned more than 15,000 Ha. In addition to the area burned being of relevance, it is also very important to know quickly and accurately the different severity degrees that soil has suffered in order to carry out an optimal restoration campaign. In this sense, the use of remote sensing with the Sentinel-2 and Landsat-8 satellites becomes a very useful resource due to the variations that appear in soil after a forest fire (changes in soil cover are noticeably appreciated with spectral information). To calculate these variations, the spectral indices NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) are used, both before and after the fire and their differences (dNBR and dNDVI, respectively). In addition, as a reference for a correct discrimination between severity degrees, severity data measured in situ after the fire are used to classified at 5 levels of severity and 6 levels of severity. Therefore, this study aims to establish a methodology, which relates remote-sensing data (spectral indices) and severity degrees measured in situ. The R2 statistic and the pixel classification accuracy results show the existing synergy of the Sentinel-2 dNBR index with the 5 severity degrees classification (R2 = 0.74 and 81% of global accuracy) and, for this case, the good applicability of remote sensing in the forest fire field.

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Adequate quantification of canopy fuel load and canopy bulk density is required for assessment of the susceptibility of forest stands to crown fire and evaluation of silvicultural treatments aimed at reducing the risk of crowning. The use of tree biomass equations and vertical profile distributions of crown fuels provide the most accurate estimates of the canopy fuel characteristics. In this study, 100 pole-size maritime pine (Pinus pinaster Aiton) trees were destructively sampled in five different sites, covering a wide range of its geographical distribution in the Iberian Peninsula. To estimate crown fuel mass, allometric equations were fitted separately for needles, twigs, and fuel available for crown fire. Models were also fitted to characterize the vertical fuel distributions as a function of tree height. All models were fitted simultaneously to guarantee additivity among tree biomass components, and corrections were also made for heterocedasticity and autocorrelation. Diameter at breast height was the best explanatory variable for all the allometric models. The vertical distribution of crown biomass fractions along tree height depended on the crown size and tree dominance. The system of equations provides a good balance between accurate predictions and low data requirements, allowing quantification of canopy fuel characteristics at stand level.

The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.