Ultra-Reduced-Impact-Encased-Logging (URIEL): A New Method for Selective Sustainable Logging and Post-Harvest Silvicultural Treatment in Tropical Forests Using Airborne Robotics Systems

Ultra-Reduced-Impact-Encased-Logging (URIEL)：一种利用空中机器人系统进行热带森林选择性可持续采伐及采伐后造林处理的新方法

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Abstract: Tropical forests worldwide are under intense deforestation pressure driven by economic and political interests, and scientific evidence suggests this deforestation contributes to climate change. This paper proposes a novel logging method for tropical forests, Ultra-Reduced-Impact-Encased-Logging (URIEL).

摘要： 全球热带森林正面临由经济和政治利益驱动的巨大毁林压力，科学证据表明，这种毁林行为加剧了气候变化。本文提出了一种针对热带森林的新型采伐方法——超低影响封装采伐（URIEL）。

This new method is based on heli-logging techniques combined with intensive use of robotics and AI integrated with post-harvest silvicultural treatments performed by drones. The concept of appropriate equipment for this method was developed, dimensions were determined, details were completed in a digital proof of concept, and an effective digital simulation and economic feasibility analysis were carried out for various helicopter-timber-distance combinations.

该方法基于直升机采伐技术，结合了机器人与人工智能的深度应用，并整合了由无人机执行的采伐后造林处理。研究开发了该方法所需的设备概念，确定了相关尺寸，完成了数字概念验证的细节，并针对各种“直升机-木材-距离”组合进行了有效的数字模拟和经济可行性分析。

The results demonstrated that a URIEL method has high economic viability and makes it possible to virtually eliminate collateral damage to forests while maintaining ecosystem services. The main conclusion of this paper is that, despite the satisfactory scientific and technological results, the feasibility of a Uriel method depends on the integration of stakeholders intrinsic to the context: high-tech industry; political governments; certified logging companies; and native populations.

结果表明，URIEL 方法具有很高的经济可行性，能够在维持生态系统服务的同时，几乎消除对森林的附带损害。本文的主要结论是，尽管科学和技术成果令人满意，但 URIEL 方法的可行性取决于该背景下各利益相关方的整合：高科技产业、政府部门、认证采伐公司以及原住民群体。

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Paper Details:

• Authors: Daniel Albiero, Gelton Fernando de Morais, Daniela Han, Flávio Roberto de Freitas Gonçalves, Artur Vitório Andrade Santos, Wesllen Lins de Araújo, Alessandra Maia Freire, Cláudio Kiyoshi Umezu, Mateus Peressin, Francesco Toscano, Admilson Írio Ribeiro, Alfeu J. Sguarezi Filho, Américo Ferraz Dias Neto, Angel Pontin Garcia
• arXiv ID: 2605.28883
• Submitted: 26 May 2026
• Subjects: Artificial Intelligence (cs.AI); Robotics (cs.RO)

论文详情：

• 作者： Daniel Albiero 等 14 人
• arXiv 编号： 2605.28883
• 提交日期： 2026 年 5 月 26 日
• 学科分类： 人工智能 (cs.AI)；机器人学 (cs.RO)