Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets

Schucknecht, Anne; Seo, Bumsuk; Krämer, Alexander; Asam, Sarah; Atzberger, Clement; Kiese, Ralf

doi:https://doi.org/10.5194/bg-19-2699-2022

Articles | Volume 19, issue 10

https://doi.org/10.5194/bg-19-2699-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-19-2699-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 10

Research article

|

01 Jun 2022

Research article |

| 01 Jun 2022

Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets

Anne Schucknecht, Bumsuk Seo, Alexander Krämer, Sarah Asam, Clement Atzberger, and Ralf Kiese

Supplement

https://doi.org/10.5194/bg-19-2699-2022-supplement

Data sets

In-situ reference data for aboveground vegetation traits of pre-Alpine grasslands in southern Germany Anne Schucknecht, Alexander Krämer, Sarah Asam, Abraham Mejia Aguilar, Noelia Garcia Franco, Max A. Schuchardt, Anke Jentsch, and Ralf Kiese https://doi.org/10.1594/PANGAEA.920600

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Short summary

Actual maps of grassland traits could improve local farm management and support environmental assessments. We developed, assessed, and applied models to estimate dry biomass and plant nitrogen (N) concentration in pre-Alpine grasslands with drone-based multispectral data and canopy height information. Our results indicate that machine learning algorithms are able to estimate both parameters but reach a better level of performance for biomass.