#SecureTree: pursuing new trajectories for risk assessment models in precision forestry

The #SecureTree model presents a novel method for assessing tree risk through IoT-based sensors and analytics within a precision forestry context. Unlike conventional techniques that often depend on individual, subjective mechanical assessments, #SecureTree utilizes a network of minimally invasive sensors to continuously monitor key biophysical factors such as temperature, humidity, and branch movement. These data are processed to generate real-time risk assessment maps based on the analysis of trees’ behavioral progression under varying environmental conditions. The primary innovation of the model lies in its capability to track multiple trees over extended periods, providing forest managers with objective, data-driven insights into tree stability and health. These insights make it possible to identify long-term risk patterns, allowing for proactive interventions and improved emergency management. By moving from isolated evaluations to a scalable, sensor-based approach, #SecureTree greatly enhances the accuracy of tree risk assessment and establishes a new benchmark in environmental management. This model allows for significant advancements in precision forestry, enabling more effective, real-time decision-making while promoting sustainable forest management practices aligned with digital innovation.