Forest fires are the result of poor land management and climate change. Depending on the type of the affected eco-system, they can cause significant biodiversity losses. This study was conducted in the Amazonas department in Peru. Binary data obtained from the MODIS satellite on the occurrence of fires between 2010 and 2022 were used to build the risk models. To avoid multicollinearity, 12 variables that trigger fires were selected (Pearson ≤ 0.90) and grouped into four factors: (i) topographic, (ii) social, (iii) climatic, and (iv) biological. The program Rstudio and three types of machine learning were applied: MaxENT, Support Vector Machine (SVM), and Random Forest (RF). The results show that the RF model has the highest accuracy (AUC = 0.91), followed by MaxENT (AUC = 0.87) and SVM (AUC = 0.84). In the fire risk map elaborated with the RF model, 38.8% of the Amazonas region possess...

Dengue, a febrile disease that has caused epidemics and deaths in South America, especially Peru, is vectored by the Aedes aegypti mosquito. Despite the seriousness of dengue fever, and the expanding range of Ae. aegypti, future distributions of the vector and disease in the context of climate change have not yet been clearly determined. Expanding on previous findings, our study employed bioclimatic and topographic variables to model both the present and future distribution of the Ae. aegypti mosquito using the Maximum Entropy algorithm (MaxEnt). The results indicate that 10.23% (132,053.96 km2) and 23.65% (305,253.82 km2) of Peru’s surface area possess regions with high and moderate distribution probabilities, respectively, predominantly located in the departments of San Martín, Piura, Loreto, Lambayeque, Cajamarca, Amazonas, and Cusco. Moreover, based on projected future climate sce...

Background Assessing the severity of forest fires allows us to identify changes that compromise the natural regeneration capacity of vegetation. In this study, we evaluated the severity and recovery of vegetation after a fire using Sentinel-2 satellite images for the Cajamarca department in northeastern Peru. Hot spots were downloaded from the Fire Information for Resource Management System (FIRMS). This allowed us to identify eight groups with an area >100 hectares, heat intensity >100 Fire Radiative Power (FRP), and the presence of trees. By applying the Normalized Burn Ratio (NBR) and the Normalized Difference Vegetation Index (NDVI), the levels of extreme, high, medium, and low severity were determined, as well as the recovery of vegetation before and after the fire events. Results and Conclusions The results indicated that 71.02% of the evaluated territory had low severity, 21.95% h...