Universität zu Köln

Huett, Christoph, Bolten, Andreas, Hueging, Hubert and Bareth, Georg . UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial. PFG-J. Photogramm. Remote Sens. Geoinf. Sci.. CHAM: SPRINGER INT PUBL AG. ISSN 2512-2819

Full text not available from this repository.

Abstract

Efficient monitoring of crop traits such as biomass and nitrogen uptake is essential for an optimal application of nitrogen fertilisers. However, currently available remote sensing approaches suffer from technical shortcomings, such as poor area efficiency, long postprocessing requirements and the inability to capture ground and canopy from a single acquisition. To overcome such shortcomings, LiDAR scanners mounted on unmanned aerial vehicles (UAV LiDAR) represent a promising sensor technology. To test the potential of this technology for crop monitoring, we used a RIEGL Mini-VUX-1 LiDAR scanner mounted on a DJI Matrice 600 pro UAV to acquire a point cloud from a winter wheat field trial. To analyse the UAV-derived LiDAR point cloud, we adopted LiDAR metrics, widely used for monitoring forests based on LiDAR data acquisition approaches. Of the 57 investigated UAV LiDAR metrics, the 95th percentile of the height of normalised LiDAR points was strongly correlated with manually measured crop heights (R-2 = 0.88) and with crop heights derived by monitoring using a UAV system with optical imaging (R-2 = 0.92). In addition, we applied existing models that employ crop height to approximate dry biomass (DBM) and nitrogen uptake. Analysis of 18 destructively sampled areas further demonstrated the high potential of the UAV LiDAR metrics for estimating crop traits. We found that the bincentile 60 and the 90th percentile of the reflectance best revealed the relevant characteristics of the vertical structure of the winter wheat plants to be used as proxies for nitrogen uptake and DBM. We conclude that UAV LiDAR metrics provide relevant characteristics not only of the vertical structure of winter wheat plants, but also of crops in general and are, therefore, promising proxies for monitoring crop traits, with potential use in the context of Precision Agriculture.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Huett, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Bolten, Andreas UNSPECIFIED UNSPECIFIED UNSPECIFIED Hueging, Hubert UNSPECIFIED UNSPECIFIED UNSPECIFIED Bareth, Georg UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-665229
DOI:	10.1007/s41064-022-00228-6
Journal or Publication Title:	PFG-J. Photogramm. Remote Sens. Geoinf. Sci.
Publisher:	SPRINGER INT PUBL AG
Place of Publication:	CHAM
ISSN:	2512-2819
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language SURFACE MODELS CSMS; VEGETATION INDEXES; LASER; RICE; DIAGNOSIS Multiple languages Remote Sensing; Imaging Science & Photographic Technology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/66522