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Article

Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging

Grassland Science and Renewable Plant Resources, Faculty of Organic Agricultural Science, Universität Kassel, Steinstraße 19, 37213 Witzenhausen, Germany
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Received: 7 November 2018 / Revised: 15 January 2019 / Accepted: 23 January 2019 / Published: 26 January 2019
An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R2 = 0.59–0.81) as the ruler height measurements (R2 = 0.58–0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types. View Full-Text
Keywords: grassland; yield prediction; canopy height; remote sensing; unmanned aerial vehicle; RGB imaging; structure from motion grassland; yield prediction; canopy height; remote sensing; unmanned aerial vehicle; RGB imaging; structure from motion
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MDPI and ACS Style

Grüner, E.; Astor, T.; Wachendorf, M. Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging. Agronomy 2019, 9, 54. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy9020054

AMA Style

Grüner E, Astor T, Wachendorf M. Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging. Agronomy. 2019; 9(2):54. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy9020054

Chicago/Turabian Style

Grüner, Esther, Thomas Astor, and Michael Wachendorf. 2019. "Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging" Agronomy 9, no. 2: 54. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy9020054

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