<p>The central South American forest is one of the area most affected by wildfires in the world. Because of climate changes and land use management, these events are becoming more frequent and extended in the last years. For example, in 2019 Bolivia faced an extremely extensive wildfire event that had a serious ecological impact in the department of Santa Cruz. This region, called Chiquitania and characterized by a mosaic where wet tropical forests, dry tropical forests and savannas alternate, accounts for more than two-thirds of the total wildfires in the country. Despite Bolivia is between the top-ten countries with the highest expected risk in terms of annual burned forest area, the literature on wildfires here is quite limited, also because of the scarcity of available data and resources. To fill this gap, as part of the present study, we implemented an accurate dataset of burned areas, based on MODIS wildfire product, occurred in the entire Santa Cruz region in the period 2010-2019. Predisposing factors, such as topography, land use and ecoregions, were also collected in the form of digital spatial data. This information allowed assessing the susceptibility to wildfires on the entire region, with a special focus on the municipality of San Ignacio de Velasco. The analysis was performed using Random Forest (RF), an ensemble-learning algorithm based on decision trees, capable of learning from and make predictions on data by modeling the hidden relationships between a set of input and output variables. The goodness of fit was estimated by the area under the ROC (receiver operating characteristic) curve (AUC), selecting the validation dataset by using a 5-folds cross validation procedure. In addition, the last three years of observed burned areas were kept out during the medialization stage and used to test if the implemented model gives good predictions on new data. As result, we obtained a probabilistic output from RF indicating the probability for an area to burn in the future, which allowed elaborating the susceptibility maps. For San Ignacio de Velasco it resulted an AUC of 0.8, while for the entire Santa Cruz the AUC was of 0.73. Likewise, the predictive capabilities of the model gave quite good results, better at municipality that at regional level. The detailed investigation of the relative importance of each categorical class belonging to the variables ecoregions and land use reveals that &#8220;Flooded savanna&#8221; and &#8220;Shrub or herbaceous cover, flooded, fresh/saline/brakish water&#8221; are respectively the classes most related with wildfires. This important outcome confirms recent findings, that seasonally wet and dry climate, coupled with hydrologic controls on the vegetation, create in this ecoregion favorable conditions to the ignition and spreading of large wildfires during the driest period, when the biomass is abundant. The occurrence of large fires, initiated by slash-and-burn practice getting out of control, is predicted to increase in the near future and the development of new tools for fire risk assessment and reduction is thus needed.&#160;</p>
Joint Symbol Rate-Modulation Format Identification and OSNR Estimation using Random Forest Based Ensemble Learning for Intermediate Nodes