[en] Unmanned Aircraft Systems (UAS), with the ability to fly close to the ground and under clouds, make it possible to collect data at unprecedented spatial and temporal resolutions. LiDAR systems are more commonly being used on UAS platforms as these sensors become smaller and more accessible. Within the field of precision farming, UAS LiDAR is often used for height calculations that takes advantage of its ability to penetrate through the canopy to the ground but the rate at which these signals pass through can provide important metrics on crop structure and (vegetation) density. These can be related to well known (or classically used) vegetation indices such as Leaf Area Index (LAI) which often plays a major contributor in monitoring plant health and predicting crop yield. This study exploits UAS LiDAR advantages and investigates its ability to estimate LAI for crops such as winter wheat. It was found that LiDAR LAI spatial patterns were consistent with other forms of data while providing estimates similar to ground measurements.
Bates, Jordan ; Université de Liège - ULiège > Département de géographie > Earth Observation and Ecosystem Modelling (EOSystM Lab)
Montzka, Carsten; Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Schmidt, Marius; Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany
Jonard, François ; Université de Liège - ULiège > Département de géographie ; Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, Germany ; Earth and Life Institute, Université catholique de Louvain, Belgium
Language :
English
Title :
UAS LiDAR Crop LAI estimations from Canopy Density
Publication date :
2021
Event name :
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Event place :
Brussels, Belgium
Event date :
12-07-2021 => 16-07-2021
Audience :
International
Main work title :
IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Publisher :
Institute of Electrical and Electronics Engineers Inc.
Dhami, H.; Yu, K.; Xu, T.; Zhu, Q.; Dhakal, K.; Friel, J.; Li, S.; Tokekar, P. Crop Height and Plot Estimation from Unmanned Aerial Vehicles Using 3D LiDAR. ArXiv191014031 Cs 2020.
Neuville, R.; Bates, J.S.; Jonard, F. Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning. Remote Sens. 2021, 13, 352.
Richardson, J.J.; Moskal, L.M.; Kim, S.-H. Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete-Return LIDAR. Agric. For. Meteorol. 2009, 149, 1152-1160.
Morsdorf, F.; Kötz, B.; Meier, E.; Itten, K.I.; Allgöwer, B. Estimation of LAI and Fractional Cover from Small Footprint Airborne Laser Scanning Data Based on Gap Fraction. Remote Sens. Environ. 2006, 104, 50-61.
Saskai, T.; Imanishi, J.; Ioki, K.; Song, Y.; Morimoto, Y. Estimation of leaf area index and gap fraction in two broad-leaved forests by using small-footprint airborne LiDAR. Springer Professional 2016
Becirevic, D.; Klingbeil, L.; Honecker, A.; Schumann, H.; Léon, J.; Kuhlmann, H. On the Derivation of Crop Heights from Multitemporal UAV Based Imagery. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2019, IV-2/W5, 95-102.
Brogi, C.; Huisman, J.A.; Herbst, M.; Weihermüller, L.; Klosterhalfen, A.; Montzka, C.; Reichenau, T.G.; Vereecken, H. Simulation of Spatial Variability in Crop Leaf Area Index and Yield Using Agroecosystem Modeling and Geophysics-Based Quantitative Soil Information. Vadose Zone J. 2020, 19, e20009.
Bates, J.S.; Montzka, C.; Schmidt, M.; Jonard, F. Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR. Remote Sensing 2021, 13, 710.
Zheng, G.; Moskal, L.M. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors 2009, 9, 2719-2745.