Scaling up Environmental Governance in Precision Forestry

Area of Science:

• Environmental Science
• Forestry Technology
• Digital Agriculture

Background:

• Traditional tree risk assessment methods are often subjective and mechanical.
• There is a need for objective, non-invasive approaches to environmental management.
• Digital technologies are transforming decision-making in forestry.

Purpose of the Study:

• To introduce the #SecureTree model as an innovative alternative for tree risk assessment.
• To demonstrate the utility of sensor-based biophysical parameter monitoring.
• To develop data-driven strategies for environmental and emergency management.

Main Methods:

• Deployment of sensors to measure biophysical parameters (temperature, humidity, acceleration).
• Processing sensor data to map tree risk based on behavioral progression.
• #SecureTree model development for non-invasive and objective risk evaluation.

Main Results:

• The #SecureTree model provides an objective and non-invasive method for tree risk assessment.
• Field tests validated the model's accuracy in assessing tree behavior.
• The model can identify long-term risk trends, aiding future planning.

Conclusions:

• The #SecureTree model offers a significant advancement over traditional tree risk assessment.
• This approach enables enhanced planning for disruptive events and digital emergency management strategies.
• Precision Forestry principles are advanced through sensor-based, data-driven environmental monitoring.