Sutter, Michael; Aebischer, Philippe; Reidy, Beat (17 May 2021). Estimating grassland biomass using multispectral UAV imagery, DTM and a random forest algorithm In: Sensing – New Insights into Grassland Science and Practice : Proceedings of the 21st Symposium of the European Grassland Federation. Grassland Science in Europe: Vol. 26 (pp. 71-73). European Grassland Federation (EGF)
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A prerequisite for efficient pasture management is the regular estimation of the dry matter yield (DMY) by means of a rising plate meter (RPM). With the latest generation of unmanned aerial vehicles (UAV) equipped with a real-time kinematic (RTK) positioning system and a multispectral camera, it should be possible to measure sward heights and to estimate dry matter yields. To investigate this possibility, we developed an algorithm enabling a digital terrain model to be calculated from the digital surface model of grassland. DMY is estimated using a random forest estimator. Initial estimates at a previously unseen site achieved a root-mean-square error (RMSE) of 332 kg DM ha-1. The results demonstrate that UAVs enable DMY predictions with an accuracy level close to RPM measurements. The underlying algorithm will be further developed and adapted to a wider variety of pasture types and meadows.
Item Type: |
Conference or Workshop Item (Paper) |
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Division/Institute: |
School of Agricultural, Forest and Food Sciences HAFL School of Agricultural, Forest and Food Sciences HAFL > Agriculture School of Agricultural, Forest and Food Sciences HAFL > Agriculture > Grasslands and Ruminant Production Systems |
Name: |
Sutter, Michael 0000-0003-0314-5697; Aebischer, Philippe and Reidy, Beat 0000-0002-8619-0209 |
Subjects: |
Q Science > QC Physics S Agriculture > S Agriculture (General) S Agriculture > SB Plant culture |
ISBN: |
978-3-00-068789-1 |
Series: |
Grassland Science in Europe |
Publisher: |
European Grassland Federation (EGF) |
Language: |
English |
Submitter: |
Michael Sutter |
Date Deposited: |
28 Jun 2021 11:37 |
Last Modified: |
30 Oct 2023 14:05 |
Related URLs: |
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Additional Information: |
Die Erlaubnis, diese PDF-Datei im ARBOR-Repository zu veröffentlichen, wurde eingeholt |
Uncontrolled Keywords: |
grassland, machine learning, random forest, NDVI, remote sensing, dry matter yield |
ARBOR DOI: |
10.24451/arbor.15032 |
URI: |
https://arbor.bfh.ch/id/eprint/15032 |
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