Scaling up Environmental Governance in Precision Forestry

Precision Forestry is an emerging approach that uses digital technologies for data-driven decision-making in environmental management. Traditional methods for assessing tree risk are often subjective and focus on individual trees using mechanical approaches. The #SecureTree model offers an innovative alternative by deploying sensors to measure biophysical parameters like temperature, humidity, and acceleration. Data from these sensors is processed to create a risk assessment map based on the progression of trees' behaviors. This model is non-invasive and objective, addressing risk more effectively than current methods. Field tests validated the model's accuracy and highlighted its potential to identify long-term risk trends, enabling better planning for disruptive events and the development of digital strategies for emergency management.

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Publication Details

Journal
Studies in health technology and informatics
Published
2024-11-22
DOI
https://doi.org/10.3233/shti241055
Citations
1
Primary Topic
Forest Management and Policy
Type
article
Field-Weighted Citation Impact
1.70
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article

Scaling up Environmental Governance in Precision Forestry

Mario Magliulo, Oscar Tamburis, Adriano Tramontano, Giulio Perillo
1 citations
Studies in health technology and informatics
Forest Management and Policy
1.70
article

Scaling up Environmental Governance in Precision Forestry

Mario Magliulo, Oscar Tamburis, Adriano Tramontano, Giulio Perillo
article en
1 citations

Abstract

Precision Forestry is an emerging approach that uses digital technologies for data-driven decision-making in environmental management. Traditional methods for assessing tree risk are often subjective and focus on individual trees using mechanical approaches. The #SecureTree model offers an innovative alternative by deploying sensors to measure biophysical parameters like temperature, humidity, and acceleration. Data from these sensors is processed to create a risk assessment map based on the progression of trees' behaviors. This model is non-invasive and objective, addressing risk more effectively than current methods. Field tests validated the model's accuracy and highlighted its potential to identify long-term risk trends, enabling better planning for disruptive events and the development of digital strategies for emergency management.

Studies in health technology and informaticsVol. 321
Institute of Biostructure and Bioimaging (IT), National Research Council (IT)
Life in Land
Openalex Percentile: Top 12%
Forest Management and Policy
1.70
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