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This paper introduces a novel framework for automated forest inventory and health assessment using a quadrupedal robot equipped with LiDAR and high-resolution cameras. We address the challenges of traditional, labor-intensive forestry surveys by deploying a robotic system to conduct comprehensive dendrometric and floristic analyses in a representative Apennine beech forest. The collected data, which includes 3D point clouds and imagery, were used to perform structural analysis and floristic surveys using a combination of data analysis techniques and machine learning algorithms. Our approach provides an efficient and accurate method for assessing the ecological health of forest ecosystems. This study validates the feasibility of using robotics for efficient and precise data collection in challenging forest environments, offering a valuable tool for sustainable forest management and ecological monitoring.

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Robotics for Forest Status Assessment

  • 章节

  • 发表日期：

    2026年

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  • Simone Tolomei,
  • Giovanni Di Lorenzo,
  • Franco Angelini,
  • Leopoldo de Simone,
  • Emanuele Fanfarillo,
  • Tiberio Fiaschi,
  • Silvia Cannucci,
  • Simona Maccherini,
  • Paolo Remagnino,
  • Clauda Angiolini,
  • Manolo Garabini

摘要

This paper introduces a novel framework for automated forest inventory and health assessment using a quadrupedal robot equipped with LiDAR and high-resolution cameras. We address the challenges of traditional, labor-intensive forestry surveys by deploying a robotic system to conduct comprehensive dendrometric and floristic analyses in a representative Apennine beech forest. The collected data, which includes 3D point clouds and imagery, were used to perform structural analysis and floristic surveys using a combination of data analysis techniques and machine learning algorithms. Our approach provides an efficient and accurate method for assessing the ecological health of forest ecosystems. This study validates the feasibility of using robotics for efficient and precise data collection in challenging forest environments, offering a valuable tool for sustainable forest management and ecological monitoring.