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ORIGINAL RESEARCH article

Front. For. Glob. Change

Sec. Forest Management

This article is part of the Research TopicPrecision Forestry and Advanced Technology in Management of Forest HealthView all articles

#SECURETREE: PURSUING NEW TRAJECTORIES FOR RISK ASSESSMENT MODELS IN PRECISION FORESTRY

Provisionally accepted
Oscar TamburisOscar Tamburis1*Mario MagliuloMario Magliulo2Vincenzo MagliuloVincenzo Magliulo3Giulio PerilloGiulio Perillo4Adriano TramontanoAdriano Tramontano2Eugenio VocaturoEugenio Vocaturo5
  • 1National Research Council (CNR), Roma, Italy
  • 2Institute of Biostructures and Bioimaging, National Research Council (CNR), Roma, Italy
  • 3Institute for Mediterranean Agricultural and Forest Systems, National Research Council (CNR), Roma, Italy
  • 4Department of Electrical Engineering and Information Technology, Universita degli Studi di Napoli Federico II, Naples, Italy
  • 5NANOTEC, National Research Council (CNR), Roma, Italy

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ABSTRACT: 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. This data is processed to generate real-time risk assessment maps, based on the analysis of trees' behavioral progression under varying environmental conditions. The model's primary innovation 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.

Keywords: Environmental modelling, IoT sensors, OPERATION RESEARCH, precision forestry, Risk Assessment

Received: 07 Jan 2026; Accepted: 10 Feb 2026.

Copyright: © 2026 Tamburis, Magliulo, Magliulo, Perillo, Tramontano and Vocaturo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Oscar Tamburis

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material