Scaling up Environmental Governance in Precision Forestry.

Adriano Tramontano, Giulio Perillo, Mario Magliulo, Oscar Tamburis
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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.

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扩大精准林业的环境治理。
精准林业是一种新兴的方法,在环境管理中利用数字技术进行数据驱动决策。评估树木风险的传统方法往往是主观的,而且侧重于使用机械方法对单棵树木进行评估。#SecureTree 模型通过部署传感器来测量温度、湿度和加速度等生物物理参数,提供了一种创新的替代方法。对这些传感器的数据进行处理后,就能根据树木行为的发展绘制出风险评估图。该模型具有非侵入性和客观性,能比现有方法更有效地解决风险问题。实地测试验证了该模型的准确性,并强调了其识别长期风险趋势的潜力,从而能够更好地规划破坏性事件,并为应急管理制定数字战略。
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