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Replacing Manual Rising Plate Meter Measurements with Low-cost UAV-Derived Sward Height Data in Grasslands for Spatial Monitoring

  • Georg Bareth
  • Jürgen Schellberg
Original Article
  • 137 Downloads

Abstract

Non-destructive methods to derive spatial information on the development of forage mass are of key importance in managed grasslands. Established methods are rising plate meter (RPM) and rapid pasture meter, which both require in-field work, are rather time consuming, and do not provide spatially continuous data. Therefore, the overall objective of this study is to investigate low-cost unmanned aerial vehicle (UAV)-based RGB image acquisition for grassland monitoring. The idea of this paper is to transfer the successfully introduced approach of crop surface models (CSMs) for ultrahigh resolution analysis of plant height to managed grasslands. The study area is the Rengen Long-term Grassland Experiment, Germany, which is a two-cut experiment and was established in 1941. We conducted RPM and UAV-based data acquisition over six growth periods in 2014, 2015, and 2016. In 3 years, 26 RPM and 46 UAV campaigns were conducted under varying weather conditions (cloudy/sunny). The UAV-based RGB imagery was photogrammetrically processed with Structure from Motion and Multi-view Stereopsis techniques, producing multi-temporal CSMs for grassland sward height analysis. The regression analysis of UAV-derived sward height (CSM-SH) against RPM-measured sward height (RPM-SH) resulted in R2 of 0.91, 0.87, and 0.83 for 2014, 2015, and 2016, respectively. The pooling of the data for all 3 years resulted in an R2 of 0.86. These findings prove the successful transfer of the CSM approach for grassland monitoring and the potential of UAV-based monitoring to replace manual or in-field measurements with RPM or rapid pasture meter.

Keywords

Grassland Biomass Plant height UAV SfM Crop surface model 

Zusammenfassung

Kostengünstiges, UAV-basiertes Grünlandmonitoring als Ersatz für manuelle Feldmessungen mittels Rising Plate Meter. Nichtdestruktive Methoden für die räumliche Bestimmung von Biomasse sind in bewirtschafteten Grünlandsystemen von zentraler Bedeutung. Etablierte und validierte, nichtdestruktive Messmethoden hierfür sind z.B. das Rising Plate Meter (RPM) sowie das Rapid Pasture Meter. Beide Verfahren benötigen zeit- und arbeitsaufwendige Geländearbeiten und liefern keine kontinuierliche räumliche Abdeckung, sondern Punkt- bzw. Transektmessungen, die interpoliert werden. Deshalb ist die übergeordnete Zielsetzung dieser Studie das Monitoring von Grünland anhand von kostengünstigen Drohnen (Unmanned Aerial Vehicles—UAVs) zur Erfassung und Auswertung von RGB-Bildern. Hierfür soll die Methode von Crop Surface Models (CSMs), die erfolgreich für die Biomassebestimmung in Ackerfrüchten validiert wurde, auf Grünlandschnittsysteme übertragen werden. Der CSM-Ansatz basiert auf der Bestimmung von absoluten Pflanzenhöhen in ultrahoher räumlicher Auflösung. Der Untersuchungsstandort ist das Dauergrünlandexperiment in Rengen (RGE) bei Daun, das in der Eifel in Westdeutschland liegt. Das RGE ist ein Versuch mit zwei Aufwüchsen pro Jahr und wurde 1941 etabliert. In den Jahren 2014, 2015 und 2016 wurden für insgesamt sechs Aufwüchse 26 RPM- und 46 UAV-Kampagnen durchgeführt. Die Befliegungen wurden unter verschiedenen Wetterverhältnissen durchgeführt (bewölkt/sonnig). Die UAV-erfassten RGB-Bilddaten wurden photogrammetrisch mit Structure-from-Motion (SfM) und Multi-View Stereopsis (MVS) analysiert. Das Ergebnis hiervon sind multitemporale CSMs für das RGE, die für die Analyse von Pflanzenwachstumsraten genutzt werden können. Die Ergebnisse der Regressionsanalysen zwischen den RPM-Messungen und der UAV-basierten CSM-Daten sind sehr vielversprechend. Die Bestimmtheitsmaße betragen für 2014 0,91, für 2015 0,87, für 2016 0,83 und für alle drei Jahre zusammen 0,86. Diese Ergebnisse zeigen den erfolgreichen Transfer der CSM-Methode auf Grünlandsysteme sowie das große Potential von UAV-basierter Bilddatenerfassung für das Grünlandmonitoring. Letzteres könnte sogar etablierte Messmethoden wie RPM oder Rapid Pasture Meter in Zukunft ersetzen.

Notes

Acknowledgements

We thank Jens Hollberg for conducting and providing RPM data for 2014, Andreas Bolten, who carried out UAV campaigns during holiday breaks, and Juliane Bendig for proofreading.

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Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2018

Authors and Affiliations

  1. 1.GIS & RS Group, Institute of GeographyUniversity of CologneCologneGermany
  2. 2.Institute of Agricultural EcologyRheinische Friedrich-Wilhelms-Universität BonnBonnGermany

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