Building national people-centered early warning systems (EWS) is strongly recommended by the United Nations International Strategy for Disaster Reduction (UNISDR). Most of the scientific literature is critical of the conventional view of EWS as a linear model with a top-down approach, in which technological features are given more attention than human factors. It is argued that EWS should be people-centered, and used for risk prevention, with an emphasis on resilience, rather than only being triggered when a hazard occurs. However, both the UNISDR and the literature fail to say how a people-centered EWS should be built, and what steps are needed to put EWS into effect. This article examines the obstacles and measures required to promote people-centered EWS, with a focus on the situation in Brazil. After assessing the institutional vulnerability of EWS, we analyze some measures that can be taken to reduce institutional vulnerability, based on experiences with a participatory citizen science educational project that involved high school students. Some guidelines are developed for adopting a bottom-up approach towards achieving the four elements of EWS—risk knowledge, monitoring, communication of warnings, and response capability—with the help of school curricula.

The economic and environmental impacts of wildfires have leveraged the development of new technologies to prevent and reduce the occurrence of these devastating events. Indeed, identifying and mapping fire-susceptible areas arise as critical tasks, not only to pave the way for rapid responses to attenuate the fire spreading, but also to support emergency evacuation plans for the families affected by fire-related tragedies. Aiming at simultaneously mapping and measuring the risk of fires in the forest areas of Brazil’s Amazon, in this paper we combine multitemporal remote sensing, derivative spectral indices, and anomaly detection into a fully unsupervised methodology. We focus our analysis on recent forest fire events that occurred in the Brazilian Amazon by exploring multitemporal images acquired by both Landsat-8 Operational Land Imager and Modis sensors. We experimentally confirm that the current methodology is capable of predicting fire outbreaks immediately at posterior instants, which attests to the operational performance and applicability of our approach to preventing and mitigating the impact of fires in Brazilian forest regions.

The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August–October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product.