Preprints
https://doi.org/10.5194/bg-2021-250
https://doi.org/10.5194/bg-2021-250

  20 Oct 2021

20 Oct 2021

Review status: this preprint is currently under review for the journal BG.

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 Schucknecht1, Bumsuk Seo1, Alexander Krämer2, Sarah Asam3, Clement Atzberger4, and Ralf Kiese1 Anne Schucknecht et al.
  • 1Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, 82467, Germany
  • 2WWL Umweltplanung und Geoinformatik GbR, Bad Krozingen, 79189, Germany
  • 3German Aerospace Center, German Remote Sensing Data Center, 82234 Wessling, Germany
  • 4Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Vienna, 1190, Austria

Abstract. Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UAS) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (Linear Model; Random Forests, RF; Gradient Boosting Machines, GBM) and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors, but was not available in our study. Therefore, we tested the added value of this structural information with in-situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in Southern Germany to obtain in-situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized and all model set-ups were run with a six-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor-predictor set combinations with average (avg) R2cv of 0.48, RMSEcv, avg of 53.0 g m2 and rRMSEcv, avg of 15.9 % for DM, and with R2cv, avg of 0.40, RMSEcv, avg of 0.48 wt.% and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms and predictor sets notably improved the model performance. The best model performance for the estimation of DM (R2cv = 0.67, RMSEcv = 41.9 g m2, rRMSEcv = 12.6 %) was achieved with a RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of a RF model with all predictors and SEQ sensor data (R2cv = 0.47, RMSEcv = 0.45 wt.%, rRMSEcv = 14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating ML algorithm improved the model performance substantially, which shows the importance of this step.

Anne Schucknecht et al.

Status: open (until 17 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Anne Schucknecht et al.

Data sets

In-situ reference data for aboveground vegetation traits of pre-Alpine grasslands in southern Germany Schucknecht, A., Krämer, A., Asam, S., Mejia Aguilar, A., Garcia Franco, N., Schuchardt, M. A., Jentsch, A., and Kiese, R. https://doi.org/10.1594/PANGAEA.920600

Anne Schucknecht et al.

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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 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 performance for biomass.
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