Articles | Volume 19, issue 10
Biogeosciences, 19, 2699–2727, 2022
https://doi.org/10.5194/bg-19-2699-2022
Biogeosciences, 19, 2699–2727, 2022
https://doi.org/10.5194/bg-19-2699-2022
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 et al.

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Cited articles

Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P. J.: Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows, Remote Sens., 10, 1091, https://doi.org/10.3390/rs10071091, 2018. 
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Asam, S.: Potential of high resolution remote sensing data for Leaf Area Index derivation using statistical and physical models, PHD thesis, Julius-Maximilians-University Würzburg, Würzburg, 228 pp., 2014. 
Assmann, J. J., Kerby, J. T., Cunliffe, A. M., and Myers-Smith, I. H.: Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes, J. Unmanned Veh. Sys., 7, 54–75, https://doi.org/10.1139/juvs-2018-0018, 2018. 
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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.
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