[en] Unmanned Aircraft Systems (UAS) are being used more often in agriculture to provide estimations of important metrics such as biomass because of the potential for improved temporal and spatial resolutions. More recently LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Being an active sensor, LiDAR signals are capable of penetrating through the vegetation canopy providing more information on plant structure. Commonly, LiDAR data is used to derive only height information. However, newer studies have shown the retrieval of additional information from the spatial distribution and intensity of LiDAR signals. This study takes a unique look at combining these types of informative products, that are particular to LiDAR, for making biomass estimation with winter wheat.
Bates, Jordan ; Université de Liège - ULiège > Département de géographie > Earth Observation and Ecosystem Modelling (EOSystM Lab)
Jonard, François ; Université de Liège - ULiège > Département de géographie ; Institute of Bio-and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Bajracharya, Rajina; Institute of Bio-and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Vereecken, Harry; Institute of Bio-and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Montzka, Carsten; Institute of Bio-and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Language :
English
Title :
UAS Lidar Derived Metrics for Winter Wheat Biomass Estimations using Multiple Linear Regression
Publication date :
2022
Event name :
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Event place :
Kuala Lumpur, Mys
Event date :
17-07-2022 => 22-07-2022
Audience :
International
Main work title :
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Publisher :
Institute of Electrical and Electronics Engineers Inc.
The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS)
Funding text :
Research partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy-EXC 2070 –390732324.
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