Logo des Repositoriums
 
Konferenzbeitrag
Full Review

Comparison of UAV- and mowing machine-mounted LiDAR for grassland canopy height estimation

Lade...
Vorschaubild

Volltext URI

Dokumententyp

Text/Conference Paper

Zusatzinformation

Datum

2024

Zeitschriftentitel

ISSN der Zeitschrift

Bandtitel

Verlag

Gesellschaft für Informatik e.V.

Zusammenfassung

Towards autonomous process monitoring, canopy height estimation in grassland based on data from a mowing machine-mounted LiDAR and a UAV-LiDAR system is compared to manually measured ground truth heights. In a field trial, a LiDAR mounted on the cabin roof of the mowing machine recorded data during the mowing process, while two recording flights before and after the mowing were conducted with a UAV-LiDAR. The data from both systems were processed similarly and parameters such as height estimation method, spatial resolution and percentile filters were systematically varied to investigate their influence on height estimation accuracy. Statistical evaluation showed that canopy height estimates based on the UAV-LiDAR (R² = 0.89, RMSE = 0.05 m) were more accurate and precise than those based on the mowing machine-mounted LiDAR (R² = 0.51, RMSE = 0.08 m). The influence of the different investigated parameters varied.

Beschreibung

Bracke, Justus; Storch, Marcel; Bald, Janis; Jarmer, Thomas (2024): Comparison of UAV- and mowing machine-mounted LiDAR for grassland canopy height estimation. 44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft. DOI: 10.18420/giljt2024_19. Bonn: Gesellschaft für Informatik e.V.. ISSN: 2944-7682. PISSN: 1617-5468. ISBN: 978-3-88579-738-8. pp. 203-208. Stuttgart. 27.-28. Februar 2024

Zitierform

Tags