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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

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    Buchner, Lisa ; Eisen, Anna-Katharina ; Jochner-Oette, Susanne:
    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.
    In: Journal of forestry research. 36 (2025): 137.
    ISSN 1993-0607

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    Verfügbar unter folgender Lizenz: Creative Commons: Attribution 4.0 International (CC BY 4.0) Creative Commons: Namensnennung (CC BY 4.0) .
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    Volltext Link zum Volltext (externe URL):
    https://doi.org/10.1007/s11676-025-01929-5

    Kurzfassung/Abstract

    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.

    Forschungsprojekte

    FraxVir - Detektion, Charakterisierung und Analysen zum Auftreten von Virosen und dem Eschentriebsterben in Sonderbeständen von Fraxinus excelsior–Ergänzungsstudie zu FraxForFuture; Teilvorhaben 1: Bilddatenbasierte Detektion von Schädigungen (Verbundvorhaben)

    Weitere Angaben

    Publikationsform:Artikel
    Schlagwörter:Leaf mass segmentation; Machine learning; Segment anything model; Ash dieback
    Sprache des Eintrags:Englisch
    Institutionen der Universität:Mathematisch-Geographische Fakultät > Geographie > Professur für Physische Geographie/Landschaftsökologie und nachhaltige Ökosystementwicklung
    DOI / URN / ID:10.1007/s11676-025-01929-5
    Open Access: Freie Zugänglichkeit des Volltexts?:Ja
    Peer-Review-Journal:Ja
    Verlag:Univ.
    Die Zeitschrift ist nachgewiesen in:
    Titel an der KU entstanden:Ja
    KU.edoc-ID:35665
    Eingestellt am: 10. Okt 2025 07:16
    Letzte Änderung: 10. Okt 2025 07:16
    URL zu dieser Anzeige: https://edoc.ku.de/id/eprint/35665/
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