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Por: Mariam Valladares

Ingeniera en Ambiente y Desarrollo Socioeconómico de Zamorano

Maestría en Manejo de Recursos Forestales enfocada en Teledetección de Purdue University

Fire prediction systems rely on meteorological descriptors and fuel characteristics to determine fire risk at national scales. However, at a regional scale, anthropogenic dynamics play an important role in determining fire ignition. Under an increasing fire activity scenario projected for the next century, there is an imminent necessity to improve fire management worldwide particularly within fragile ecosystems.

At a global or local scale, fire activity is constantly interacting with socioeconomic, landscape, and climate factors. Historically, several countries have experienced fire frequency fluctuations outside of the average fire intervals, where climate dynamics affects the fluctuation intensity but political, economic, and social characteristics affect the fire ignition. Nowadays, more than 90% of the uncontrolled fire events registered worldwide are related directly or indirectly to human activities. These increasing number of fire events has exceeded the capacity of the current predictions and management systems to prevent and assess fire variability. Therefore, the inclusion of anthropogenic indicators on fire prediction systems is key to developing accurate predictions and effective fire management efforts.

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As the relationship between human dynamics and fire is complex, one of the biggest challenges in trying to understand the human component is that it could be evaluated either as spatial or non-spatially explained patterns. Therefore, first, it is important to understand which specific anthropogenic indicators have the most significant effects on fire in order to include them in the fire predictions systems.

Under this context, as a master student at Purdue University, I conducted a study to identify the particular anthropogenic variables that are affecting fire ignition under specific socioeconomic, and landscape scenarios in two case studies. The first case study focuses on understanding and selecting specific spatial patterns that have a statistical significant effect on fire variability using satellite images and remote sensing tools. The second case study focuses on selecting the most significant socioeconomic variables that affect fire and integrates all the significant anthropogenic descriptors into a fire prediction model and comparing the output predictions against a climate-based model to evaluate the fire prediction accuracy at a regional scale.

As general insights, we found particular importance of land-use change occurring within the urban-wildlife interface also identifies as the peripheral areas of cities. The changes within peripheral areas mainly associated with agriculture and tree establishment were triggering most of the fire activity increases. We found a higher impact on fire ignition in the urban-wildlife interface when combining those particular land use changes with higher poverty level. Finally as a fire prediction model, we found a higher fire prediction accuracy for the urban-wildlife interface areas using a model that include variables related to the landscape and socioeconomic patterns previously described when compared with the climate based models. This is a key finding to focus the fire-prevention efforts but also for land use regulation and population interventions within those areas.

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