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Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations
Bates, Jordan; Jonard, François; Bajracharya, Rajina et al.
2022In Proceedings of the 25th AGILE Conference on Geographic Information Science, 2022
Peer reviewed
 

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Keywords :
Biomass; UAS; LiDAR; ANN; Machine Learning
Abstract :
[en] Abstract. Biomass is an important indicator in the ecological and management process that can now be estimated at higher temporal and spatial resolutions because of unmanned aircraft systems (UAS). LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Its signals are capable of penetrating through vegetation canopies enabling the capture of more information along the plant structure. Separate studies have used LiDAR for crop height, rate of canopy penetrations as related to leaf area index (LAI), and signal intensity as an indicator of plant chlorophyll status or green area index (GAI). These LiDAR products are combined within a machine learning method such as an artificial neural network (ANN) to assess the potential in making accurate biomass estimations for winter wheat.
Disciplines :
Environmental sciences & ecology
Earth sciences & physical geography
Author, co-author :
Bates, Jordan 
Jonard, François  ;  Université de Liège - ULiège > Département de géographie
Bajracharya, Rajina
Vereecken, Harry 
Montzka, Carsten 
Language :
English
Title :
Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations
Publication date :
10 June 2022
Event name :
AGILE: GIScience Series
Event place :
Vilnius, Lithuania
Event date :
June 14-17, 2022
Audience :
International
Main work title :
Proceedings of the 25th AGILE Conference on Geographic Information Science, 2022
Publisher :
AGILE
Peer reviewed :
Peer reviewed
Available on ORBi :
since 22 January 2024

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