Investigating the Potential of Hyper-Temporal Terrestrial Laser Point Clouds for Monitoring Deciduous Tree Growth
Abstract. Monitoring tree growth processes is relevant for ecological research and understanding the intricate relationship between vegetation and the environment. Time series analyses have revealed a correlation between leaf emergence timing and climate change, with earlier leaf emergence attributed to global warming. While traditional forest inventory methods struggle to quantify growth processes on small scales, terrestrial laser scanning provides a powerful alternative for providing high-resolution 3D information. This study explores the use of high-frequency hyper-temporal terrestrial laser scanning data to quantitatively describe deciduous tree growth, tested on a pedunculate oak (Quercus robur). The research aims to address key questions about detecting leaf growth in hypertemporal terrestrial laser scanning data. Additionally, it explores how 3D tree parameters and point cloud comparisons capture leaf and tree growth throughout the year. Results from M3C2 point cloud analyses indicate that the temporary branch movements correlate with precipitation. Over the year, branch movements were detected to increase with growing distance from the trunk.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Investigating the Potential of Hyper-Temporal Terrestrial Laser Point Clouds for Monitoring Deciduous Tree Growth ; volume:XLVIII-2-2024 ; year:2024 ; pages:33-40 ; extent:8
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-2-2024 (2024), 33-40 (gesamt 8)
- Urheber
- DOI
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10.5194/isprs-archives-XLVIII-2-2024-33-2024
- URN
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urn:nbn:de:101:1-2407311113197.166698368257
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:54 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Bienert, Anne
- Richter, Katja
- Boehme, Sophia
- Maas, Hans-Gerd
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