Logo des Repositoriums
 

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

Kurzanzeige
dc.contributor.authorBracke, Justus
dc.contributor.authorStorch, Marcel
dc.contributor.authorBald, Janis
dc.contributor.authorJarmer, Thomas
dc.date.accessioned2024-04-08T11:56:33Z
dc.date.available2024-04-08T11:56:33Z
dc.date.issued2024
dc.description.abstractTowards 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.en
dc.identifier.isbn978-3-88579-738-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43873
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 344
dc.subjectLiDAR
dc.subjectUAV
dc.subjectmachine-mounted sensors
dc.subjectgrassland
dc.subjectcanopy height estimation
dc.titleComparison of UAV- and mowing machine-mounted LiDAR for grassland canopy height estimationen
dc.typeText/Conference Paper
gi.citation.endPage208
gi.citation.publisherPlaceBonn
gi.citation.startPage203
gi.conference.date27.-28. Februar 2041
gi.conference.locationStuttgart
gi.conference.reviewfull

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
GIL_2024_Bracke_203-208.pdf
Größe:
301.94 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P344 - 44. GIL-Jahrestagung 2023 - Fokus: Biodiversität fördern durch digitale Landwirtschaft