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<p>Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of <i>Fraxinus excelsior</i> L. trees affected by ash dieback, a major threat to common ash populations across Europe. In this study, both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery. The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR–NDVI image, whereas the coarse segmentation relied on the segment anything model (SAM). Both methods successfully delineated tree crown outlines, however, only the fine segmentation accurately captured internal canopy gaps. Despite these structural differences, mean NDVI values calculated per tree crown revealed no significant differences between the two approaches, indicating that coarse segmentation is sufficient for mean vegetation index assessments. Nevertheless, the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees, underscoring its value for detailed structural and health analyses. Furthermore, the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys. For applications focused on structural integrity and spatial variation in canopy health, the fine segmentation approach is recommended.</p>

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How precise is precise enough? Tree crown segmentation using high resolution close-up multispectral UAV images and its effect on NDVI accuracy in Fraxinus excelsior L. trees

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月9日

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  • Lisa Buchner,
  • Anna-Katharina Eisen,
  • Susanne Jochner-Oette

摘要

Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L. trees affected by ash dieback, a major threat to common ash populations across Europe. In this study, both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery. The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR–NDVI image, whereas the coarse segmentation relied on the segment anything model (SAM). Both methods successfully delineated tree crown outlines, however, only the fine segmentation accurately captured internal canopy gaps. Despite these structural differences, mean NDVI values calculated per tree crown revealed no significant differences between the two approaches, indicating that coarse segmentation is sufficient for mean vegetation index assessments. Nevertheless, the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees, underscoring its value for detailed structural and health analyses. Furthermore, the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys. For applications focused on structural integrity and spatial variation in canopy health, the fine segmentation approach is recommended.