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<p>Downed coarse woody debris (CWD), defined here as fallen dead woody material in all stages of decay, is a&#xa0;significant carbon pool in forest ecosystems, accounting for up to 8% of the aboveground forest biomass globally. Historically the quantification of CWD required labour-intensive field measurements, yielding isolated spot measurements of CWD mass and distribution in forests. To improve CWD estimates and increase the sampled area there is considerable interest in remotely sensed measures of CWD, with more recent studies exploring the quantification of CWD with photogrammetry and light detection and ranging (LiDAR) approaches. This study assessed 3330 peer-reviewed articles and dissertations to evaluate photogrammetric and LiDAR-based methods (terrestrial, drone, and airborne) for quantifying CWD, and after careful screening identified 45&#xa0;studies, mainly from North America and Europe, for key learnings for optimizing method application and accuracy.</p><p>Our review found that CWD detection and detection accuracy were mainly influenced by CWD piece size, photo resolution, density of LiDAR point clouds, and occlusion caused by vegetation. Improved accuracy was also associated with data processing methods, and the form and structure of the CWD. Based on the reviewed literature we identified approaches to optimise CWD detection according to image resolution, point cloud density, and data processing workflows to assist researchers in further advancing the field of CWD quantification. CWD with a&#xa0;diameter ≥ 10 cm can be readily detected using high-resolution photogrammetry (1–9 cm/pixel) and airborne LiDAR with a&#xa0;point density ≥ 30 pts/m<sup>2</sup>, whereas terrestrial LiDAR, which produces extremely high-density point clouds (&gt; 10,000 pts/m<sup>2</sup>), can readily detect CWD with a&#xa0;diameter as small as 1 cm where the ground vegetation cover is low. To further improve CWD detection future research should prioritize the testing of multi-sensor approaches, such as the integration of airborne and terrestrial LiDAR, to overcome occlusion issues caused by canopy and understory vegetation.</p>

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A Systematic Review of Remote Sensing Methods for Detecting Coarse Woody Debris—Challenges and Opportunities

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月23日

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  • Minhas Hussain,
  • Liubov Volkova,
  • Christopher J. Weston,
  • Shaun R. Levick

摘要

Downed coarse woody debris (CWD), defined here as fallen dead woody material in all stages of decay, is a significant carbon pool in forest ecosystems, accounting for up to 8% of the aboveground forest biomass globally. Historically the quantification of CWD required labour-intensive field measurements, yielding isolated spot measurements of CWD mass and distribution in forests. To improve CWD estimates and increase the sampled area there is considerable interest in remotely sensed measures of CWD, with more recent studies exploring the quantification of CWD with photogrammetry and light detection and ranging (LiDAR) approaches. This study assessed 3330 peer-reviewed articles and dissertations to evaluate photogrammetric and LiDAR-based methods (terrestrial, drone, and airborne) for quantifying CWD, and after careful screening identified 45 studies, mainly from North America and Europe, for key learnings for optimizing method application and accuracy.

Our review found that CWD detection and detection accuracy were mainly influenced by CWD piece size, photo resolution, density of LiDAR point clouds, and occlusion caused by vegetation. Improved accuracy was also associated with data processing methods, and the form and structure of the CWD. Based on the reviewed literature we identified approaches to optimise CWD detection according to image resolution, point cloud density, and data processing workflows to assist researchers in further advancing the field of CWD quantification. CWD with a diameter ≥ 10 cm can be readily detected using high-resolution photogrammetry (1–9 cm/pixel) and airborne LiDAR with a point density ≥ 30 pts/m2, whereas terrestrial LiDAR, which produces extremely high-density point clouds (> 10,000 pts/m2), can readily detect CWD with a diameter as small as 1 cm where the ground vegetation cover is low. To further improve CWD detection future research should prioritize the testing of multi-sensor approaches, such as the integration of airborne and terrestrial LiDAR, to overcome occlusion issues caused by canopy and understory vegetation.