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.

Prescribed burning is a tool that is frequently used for various land management objectives, mainly related to reduction of hazardous forest fuels, habitat management and ecological restoration. Given the crucial role of soil in forest ecosystem processes and functions, assessing the effects of prescribed burning on soil is particularly relevant. This study reviews research on the impacts of repeated prescribed burning on the physical, chemical and biological properties of soil. The available information shows that the effects are highly variable, rather inconsistent and generally minor for most of the soil characteristics studied, while a number of soil properties show contrasting responses. On the other hand, ecosystem characteristics, differences in fire severity, frequency of application and the cumulative effect of treatment repetition over time, have possibly made it more difficult to find a more common response in soil attributes. Our study has also revealed some limitations of previous research that may have contributed to this result, including a limited number of long-term studies, conducted at a few experimental sites, and in a limited number of forest ecosystems. Research issues concerning the effects of prescribed fire on soil are presented. The need to integrate such research into a broader interdisciplinary framework, encompassing the role of the fire regime on ecosystem functions and processes, is also highlighted.

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified.

Bulk density for shrubs and tree crowns is an important variable, useful for many purposes, namely estimations for biomass and carbon sequestration and potential fire behavior prediction. In the latter case, bulk density is required to predict the rate of spread and intensity of crown fires. However, bulk density information is scarce. The estimation of bulk density is crucial to help choosing proper pyrosilviculture options to decrease fire susceptibility. Due to the similar environmental conditions and fuel characteristics in Portugal and Spain, we modelled bulk density for the most common woody species in all the Iberian Peninsula. We used 10 different shrub type formations and a set of tree species or groups common to both countries. Equations for bulk density, in both forest canopy and understory layers, were fitted as a function of biometric variables commonly used in forest inventories for the selected species. Standardized estimates of bulk density can be associated with data from the National Forest Inventories from Portugal and Spain, to estimate biomass of the forest ecosystems and to evaluate potential fire behavior involving tree canopies and shrubs.