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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2022
Research article |
| 01 Jun 2022
| 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
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT),
82467 Garmisch-Partenkirchen, Germany
Bumsuk Seo
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT),
82467 Garmisch-Partenkirchen, Germany
Alexander Krämer
WWL Umweltplanung und Geoinformatik GbR, 79189 Bad Krozingen, Germany
Sarah Asam
German Remote Sensing Data Center, German Aerospace Center,
82234 Wessling, Germany
Clement Atzberger
Institute of Geomatics, University of Natural Resources and Life
Sciences (BOKU), 1190 Vienna, Austria
Ralf Kiese
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT),
82467 Garmisch-Partenkirchen, Germany
Related authors
Marco Spagnolli, Egor Prikaziuk, Matthias Wocher, Tian Hu, Zbigniew Kawalec, Żaneta Swacha, and Anne Schucknecht
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-12-2026, 91–96, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-91-2026, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-91-2026, 2026
Matthias Wocher, Anne Schucknecht, Tobias Hank, Zbigniew Kawalec, Rodolfo Ceriani, Ralf Kiese, and Anita Bayer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-12-2026, 135–140, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-135-2026, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-135-2026, 2026
Sophie Reinermann, Carolin Boos, Andrea Kaim, Anne Schucknecht, Sarah Asam, Ursula Gessner, Sylvia H. Annuth, Thomas M. Schmitt, Thomas Koellner, and Ralf Kiese
Biogeosciences, 22, 4969–4992, https://doi.org/10.5194/bg-22-4969-2025, https://doi.org/10.5194/bg-22-4969-2025, 2025
Short summary
Short summary
Grasslands shape the landscape in many parts of the world and serve as the main source of fodder for livestock. There is a lack of comprehensive data on grassland yield, although these data are highly valuable for authorities and research. By applying three approaches to estimate grassland yields, namely, a satellite data model, a biogeochemical model and a field measurements approach, we provide annual grassland yield maps for the Ammer region in 2019 and highlight the potentials and limitations of the approaches.
Carolin Boos, Sophie Reinermann, Raul Wood, Ralf Ludwig, Anne Schucknecht, David Kraus, and Ralf Kiese
EGUsphere, https://doi.org/10.5194/egusphere-2024-2864, https://doi.org/10.5194/egusphere-2024-2864, 2024
Preprint archived
Short summary
Short summary
We applied a biogeochemical model on grasslands in the pre-Alpine Ammer region in Germany and analyzed the influence of soil and climate on annual yields. In drought affected years, total yields were decreased by 4 %. Overall, yields decrease with rising elevation, but less so in drier and hotter years, whereas soil organic carbon has a positive impact on yields, especially in drier years. Our findings imply, that adapted management in the region allows to mitigate yield losses from drought.
Marco Spagnolli, Egor Prikaziuk, Matthias Wocher, Tian Hu, Zbigniew Kawalec, Żaneta Swacha, and Anne Schucknecht
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-12-2026, 91–96, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-91-2026, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-91-2026, 2026
Matthias Wocher, Anne Schucknecht, Tobias Hank, Zbigniew Kawalec, Rodolfo Ceriani, Ralf Kiese, and Anita Bayer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-12-2026, 135–140, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-135-2026, https://doi.org/10.5194/isprs-archives-XLVIII-M-12-2026-135-2026, 2026
Andrew Smerald, Hannes Imhof, Edwin Haas, David Kraus, Lioba Martin, Kathrin Fuchs, John Akubia, Ali Sakhaee, Cora Vos, Roland Fuß, Clemens Scheer, and Ralf Kiese
EGUsphere, https://doi.org/10.5194/egusphere-2026-4010, https://doi.org/10.5194/egusphere-2026-4010, 2026
Short summary
Short summary
Countries produce a yearly greenhouse gas inventory that is used to monitor and design climate policies. Agricultural N2O emissions are especially uncertain, due to measurement difficulties. Using a process-based model combined with high resolution management data we show that, in comparison with current methods, N2O emissions can be mapped with high spatial and temporal resolution.
Hassane Moutahir, Markus Sulzer, Ralf Kiese, Andreas Christen, Markus Weiler, Lea Dedden, Julian Brzozon, Pia Labenski, Prajwal Khanal, Ladislav Šigut, and Rüdiger Grote
Biogeosciences, 23, 1719–1738, https://doi.org/10.5194/bg-23-1719-2026, https://doi.org/10.5194/bg-23-1719-2026, 2026
Short summary
Short summary
Eddy covariance (EC) data are vital for studying carbon and water fluxes but often mask species-specific responses in mixed forests. At a Black Forest site with beech and Douglas fir, we combined EC data with ecosystem modeling to separate species contributions. Results show EC fluxes reflect species abundance within flux footprints, though responses vary seasonally. Accounting for these differences is key for gap-filling, accurate budgets, and understanding mixed forests’ climate resilience.
Samin Payrosangari, Kathrin Fuchs, Benjamin Wolf, Ralf Kiese, and Clemens Scheer
EGUsphere, https://doi.org/10.5194/egusphere-2025-5155, https://doi.org/10.5194/egusphere-2025-5155, 2025
Short summary
Short summary
In our manuscript, we implement a machine learning based model to improve the simulation of daily N2O emissions at regional scale across several pre-alpine grasslands in Central Europe. The study focuses on evaluating the model’s general applicability to other grasslands in the region. This work contributes to the development of generic N₂O emission models, thereby reducing the effort required to estimate emissions from sites lacking direct measurements.
Fawad Khan, Samuel Franco Luesma, Frederik Hartmann, Michael Dannenmann, Rainer Gasche, Clemens Scheer, Andreas Gattinger, Wiebke Niether, Elizabeth Gachibu Wangari, Ricky Mwangada Mwanake, Ralf Kiese, and Benjamin Wolf
Biogeosciences, 22, 5081–5102, https://doi.org/10.5194/bg-22-5081-2025, https://doi.org/10.5194/bg-22-5081-2025, 2025
Short summary
Short summary
Crop rotations with legumes and use of organic and mineral fertilizers show potential to reduce agricultural N losses. This study examined N losses, including direct N2 flux, on two adjacent sites with different management histories: organic farming (OF) with legume cultivation and integrated farming (IF) using synthetic and organic N inputs. IF increased soil organic carbon and nitrogen content and 15N recovery and showed a balanced N budget (i.e. more efficient N cycling compared to OF).
Sophie Reinermann, Carolin Boos, Andrea Kaim, Anne Schucknecht, Sarah Asam, Ursula Gessner, Sylvia H. Annuth, Thomas M. Schmitt, Thomas Koellner, and Ralf Kiese
Biogeosciences, 22, 4969–4992, https://doi.org/10.5194/bg-22-4969-2025, https://doi.org/10.5194/bg-22-4969-2025, 2025
Short summary
Short summary
Grasslands shape the landscape in many parts of the world and serve as the main source of fodder for livestock. There is a lack of comprehensive data on grassland yield, although these data are highly valuable for authorities and research. By applying three approaches to estimate grassland yields, namely, a satellite data model, a biogeochemical model and a field measurements approach, we provide annual grassland yield maps for the Ammer region in 2019 and highlight the potentials and limitations of the approaches.
Roxanne Daelman, Marijn Bauters, Matti Barthel, Emmanuel Bulonza, Lodewijk Lefevre, José Mbifo, Johan Six, Klaus Butterbach-Bahl, Benjamin Wolf, Ralf Kiese, and Pascal Boeckx
Biogeosciences, 22, 1529–1542, https://doi.org/10.5194/bg-22-1529-2025, https://doi.org/10.5194/bg-22-1529-2025, 2025
Short summary
Short summary
The increase in atmospheric concentrations of several greenhouse gases (GHGs) since 1750 is attributed to human activity. However, natural ecosystems, such as tropical forests, also contribute to GHG budgets. The Congo Basin hosts the second largest tropical forest and is understudied. In this study, measurements of soil GHG exchange were carried out during 16 months in a tropical forest in the Congo Basin. Overall, the soil acted as a major source of CO2 and N2O and a minor sink of CH4.
Carolin Boos, Sophie Reinermann, Raul Wood, Ralf Ludwig, Anne Schucknecht, David Kraus, and Ralf Kiese
EGUsphere, https://doi.org/10.5194/egusphere-2024-2864, https://doi.org/10.5194/egusphere-2024-2864, 2024
Preprint archived
Short summary
Short summary
We applied a biogeochemical model on grasslands in the pre-Alpine Ammer region in Germany and analyzed the influence of soil and climate on annual yields. In drought affected years, total yields were decreased by 4 %. Overall, yields decrease with rising elevation, but less so in drier and hotter years, whereas soil organic carbon has a positive impact on yields, especially in drier years. Our findings imply, that adapted management in the region allows to mitigate yield losses from drought.
Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst
Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
Short summary
Short summary
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
Elizabeth Gachibu Wangari, Ricky Mwangada Mwanake, Tobias Houska, David Kraus, Gretchen Maria Gettel, Ralf Kiese, Lutz Breuer, and Klaus Butterbach-Bahl
Biogeosciences, 20, 5029–5067, https://doi.org/10.5194/bg-20-5029-2023, https://doi.org/10.5194/bg-20-5029-2023, 2023
Short summary
Short summary
Agricultural landscapes act as sinks or sources of the greenhouse gases (GHGs) CO2, CH4, or N2O. Various physicochemical and biological processes control the fluxes of these GHGs between ecosystems and the atmosphere. Therefore, fluxes depend on environmental conditions such as soil moisture, soil temperature, or soil parameters, which result in large spatial and temporal variations of GHG fluxes. Here, we describe an example of how this variation may be studied and analyzed.
Ricky Mwangada Mwanake, Gretchen Maria Gettel, Elizabeth Gachibu Wangari, Clarissa Glaser, Tobias Houska, Lutz Breuer, Klaus Butterbach-Bahl, and Ralf Kiese
Biogeosciences, 20, 3395–3422, https://doi.org/10.5194/bg-20-3395-2023, https://doi.org/10.5194/bg-20-3395-2023, 2023
Short summary
Short summary
Despite occupying <1 %; of the globe, streams are significant sources of greenhouse gas (GHG) emissions. In this study, we determined anthropogenic effects on GHG emissions from streams. We found that anthropogenic-influenced streams had up to 20 times more annual GHG emissions than natural ones and were also responsible for seasonal peaks. Anthropogenic influences also altered declining GHG flux trends with stream size, with potential impacts on stream-size-based spatial upscaling techniques.
Joseph Okello, Marijn Bauters, Hans Verbeeck, Samuel Bodé, John Kasenene, Astrid Françoys, Till Engelhardt, Klaus Butterbach-Bahl, Ralf Kiese, and Pascal Boeckx
Biogeosciences, 20, 719–735, https://doi.org/10.5194/bg-20-719-2023, https://doi.org/10.5194/bg-20-719-2023, 2023
Short summary
Short summary
The increase in global and regional temperatures has the potential to drive accelerated soil organic carbon losses in tropical forests. We simulated climate warming by translocating intact soil cores from higher to lower elevations. The results revealed increasing temperature sensitivity and decreasing losses of soil organic carbon with increasing elevation. Our results suggest that climate warming may trigger enhanced losses of soil organic carbon from tropical montane forests.
Friedrich Boeing, Oldrich Rakovec, Rohini Kumar, Luis Samaniego, Martin Schrön, Anke Hildebrandt, Corinna Rebmann, Stephan Thober, Sebastian Müller, Steffen Zacharias, Heye Bogena, Katrin Schneider, Ralf Kiese, Sabine Attinger, and Andreas Marx
Hydrol. Earth Syst. Sci., 26, 5137–5161, https://doi.org/10.5194/hess-26-5137-2022, https://doi.org/10.5194/hess-26-5137-2022, 2022
Short summary
Short summary
In this paper, we deliver an evaluation of the second generation operational German drought monitor (https://www.ufz.de/duerremonitor) with a state-of-the-art compilation of observed soil moisture data from 40 locations and four different measurement methods in Germany. We show that the expressed stakeholder needs for higher resolution drought information at the one-kilometer scale can be met and that the agreement of simulated and observed soil moisture dynamics can be moderately improved.
Dong-Gill Kim, Ben Bond-Lamberty, Youngryel Ryu, Bumsuk Seo, and Dario Papale
Biogeosciences, 19, 1435–1450, https://doi.org/10.5194/bg-19-1435-2022, https://doi.org/10.5194/bg-19-1435-2022, 2022
Short summary
Short summary
As carbon (C) and greenhouse gas (GHG) research has adopted appropriate technology and approach (AT&A), low-cost instruments, open-source software, and participatory research and their results were well accepted by scientific communities. In terms of cost, feasibility, and performance, the integration of low-cost and low-technology, participatory and networking-based research approaches can be AT&A for enhancing C and GHG research in developing countries.
Matthias Mauder, Andreas Ibrom, Luise Wanner, Frederik De Roo, Peter Brugger, Ralf Kiese, and Kim Pilegaard
Atmos. Meas. Tech., 14, 7835–7850, https://doi.org/10.5194/amt-14-7835-2021, https://doi.org/10.5194/amt-14-7835-2021, 2021
Short summary
Short summary
Turbulent flux measurements suffer from a general systematic underestimation. One reason for this bias is non-local transport by large-scale circulations. A recently developed model for this additional transport of sensible and latent energy is evaluated for three different test sites. Different options on how to apply this correction are presented, and the results are evaluated against independent measurements.
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.
Akaike, H.: Information theory and an extension of the maximum likelihood
principle, in: Proceedings of the 2nd International Symposium on Information
Theory, Budapest, 267–281, 1973.
Arlot, S. and Celisse, A.: A survey of cross-validation procedures for model
selection, Statist. Surv., 4, 40–79, https://doi.org/10.1214/09-SS054,
2010.
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.
Atzberger, C.: Advances in remote sensing of agriculture: Context
description, existing operational monitoring systems and major information
needs, Remote Sens., 5, 949–981, https://doi.org/10.3390/rs5020949, 2013.
Atzberger, C., Richter, K., Vuolo, F., Darvishzadeh, R., and Schlerf, M.:
Why confining to vegetation indices?, Exploiting the potential of improved
spectral observations using radiative transfer models, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 8174, 263–278, 2011.
Atzberger, C., Darvishzadeh, R., Immitzer, M., Schlerf, M., Skidmore, A.,
and le Maire, G.: Comparative analysis of different retrieval methods for
mapping grassland leaf area index using airborne imaging spectroscopy, Int.
J. Appl. Earth Obs., 43, 19–31, https://doi.org/10.1016/j.jag.2015.01.009,
2015.
Baret, F. and Buis, S.: Estimating canopy characteristics from remote
sensing observations: review of methods and associated problems, in:
Advances in Land Remote Sensing: System, Modeling, Inversion and
Application, edited by: Liang, S., Springer Netherlands, Dordrecht,
173–201, 2008.
Baret, F. and Guyot, G.: Potentials and limits of vegetation indices for LAI
and APAR assessment, Remote Sens. Environ., 35, 161–173,
https://doi.org/10.1016/0034-4257(91)90009-U, 1991.
Bareth, G. and Schellberg, J.: Replacing manual rising plate meter
measurements with low-cost UAV-derived sward height data in grasslands for
spatial monitoring, PFG, 86, 157–168,
https://doi.org/10.1007/s41064-018-0055-2, 2018.
Berger, K., Verrelst, J., Féret, J.-B., Hank, T., Wocher, M., Mauser,
W., and Camps-Valls, G.: Retrieval of aboveground crop nitrogen content with
a hybrid machine learning method, Int. J. Appl. Earth Obs., 92, 102174,
https://doi.org/10.1016/j.jag.2020.102174, 2020.
Bernard, S., Heutte, L., and Adam, S.: Influence of hyperparameters on
random forest accuracy, in: Multiple Classifier Systems, vol. 5519, edited
by: Benediktsson, J. A., Kittler, J., and Roli, F., Springer Berlin
Heidelberg, Berlin, Heidelberg, 171–180,
https://doi.org/10.1007/978-3-642-02326-2_18, 2009.
Bischl, B., Wessing, S., Bauer, N., Friedrichs, K., and Weihs, C.: MOI-MBO:
Multiobjective infill for parallel model-based optimization, in: Learning
and Intelligent Optimization, Springer, Cham, 173–186,
https://doi.org/10.1007/978-3-319-09584-4_17, 2014.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Brooks, S. P. and Gelman, A.: General methods for monitoring convergence of
iterative simulations, J. Comput. Graph. Stat., 7, 434–455,
https://doi.org/10.2307/1390675, 1998.
Burai, P., Tomor, T., Bekő, L., and Deák, B.: Airborne hyperspectral
remote sensing for identification grassland vegetation, Int. Arch.
Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 427–431,
https://doi.org/10.5194/isprsarchives-XL-3-W3-427-2015, 2015.
Capolupo, A., Kooistra, L., Berendonk, C., Boccia, L., and Suomalainen, J.:
Estimating plant traits of grasslands from UAV-acquired hyperspectral
images: a comparison of statistical approaches, SPRS International Journal of Geo-Information, 4, 2792–2820,
https://doi.org/10.3390/ijgi4042792, 2015.
Caruana, R. and Niculescu-Mizil, A.: An empirical comparison of supervised
learning algorithms, in: Proceedings of the 23rd International Conference on
Machine Learning, Pittsburgh, Pennsylvania, USA, Proceedings of the 23rd international conference on Machine learning, 161–168,
https://doi.org/10.1145/1143844.1143865, 2006.
Caturegli, L., Corniglia, M., Gaetani, M., Grossi, N., Magni, S., Migliazzi,
M., Angelini, L., Mazzoncini, M., Silvestri, N., Fontanelli, M., Raffaelli,
M., Peruzzi, A., and Volterrani, M.: Unmanned aerial vehicle to estimate
nitrogen status of turfgrasses, PloS One, 11, e0158268,
https://doi.org/10.1371/journal.pone.0158268, 2016.
Culbert, P. D., Pidgeon, A. M., St.-Louis, V., Bash, D., and Radeloff, V.
C.: The impact of phenological variation on texture measures of remotely
sensed imagery, IEEE J. Sel. Top. Appl., 2, 299–309,
https://doi.org/10.1109/JSTARS.2009.2021959, 2009.
Darvishzadeh, R., Atzberger, C., Skidmore, A., and Schlerf, M.: Mapping
grassland leaf area index with airborne hyperspectral imagery: A comparison
study of statistical approaches and inversion of radiative transfer models,
ISPRS J. Photogramm., 66, 894–906,
https://doi.org/10.1016/j.isprsjprs.2011.09.013, 2011.
Datt, B.: Visible/near infrared reflectance and chlorophyll concentration in Eucalyptus leaves, Int. J. Remote Sens., 20, 2741–2759, https://doi.org/10.1080/014311699211778, 1999.
Dinno, A.: dunn.test: Dunn's test of multiple comparisons using rank sums,
https://CRAN.R-project.org/package=dunn.test (last access: 10 September 2021), 2017.
Dunn, O. J.: Multiple comparisons using rank sums, Technometrics, 6,
241–252, https://doi.org/10.1080/00401706.1964.10490181, 1964.
DWD Climate Data Center: Multi-annual grids of precipitation height over
Germany 1981–2010, version v1.0,
https://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/precipitation/, last access: 9 July 2019a.
DWD Climate Data Center: Multi-annual means of grids of air temperature (2 m)
over Germany 1981–2010, version v1.0,
https://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/air_temperature_mean/,
last access: 9 July 2019b.
Egarter Vigl, L., Schirpke, U., Tasser, E., and Tappeiner, U.: Linking
long-term landscape dynamics to the multiple interactions among ecosystem
services in the European Alps, Landscape Ecol., 31, 1903–1918,
https://doi.org/10.1007/s10980-016-0389-3, 2016.
Egarter Vigl, L., Candiago, S., Marsoner, T., Pecher, C., Tasser, E.,
Jäger, H., Meisch, C., Rüdisser, J., Schirpke, U., Tappeiner, U.,
and Labadini, A.: Ecosystem services in the Alps: a short report, Eurac
Research, Institute for Alpine Environment, Bolzano/Bozen, 2018.
European Environmental Agency: Europe's ecological backbone: recognising the
true value of our mountains, European Environmental Agency, Copenhagen, ISBN 978-92-9213-108-1
2010.
Ewald, J., Von Heßberg, A., Diewald, W., Rösler, S., Klotz, J.,
Fütterer, S., Eibes, P., and Jentsch, A.: Erfassung der Farn- und
Blütenpflanzenarten auf der Wiederbeweidungsfläche am
Brunnenkopf-Südhang (Ammergebirge), Berichte der Bayerischen Botanischen
Gesellschaft, 88, 128–132, 2018.
Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D.: Do we
need hundreds of classifiers to solve real world classification problems?,
J. Mach. Learn. Res., 15, 3133–3181, 2014.
Fernández-Delgado, M., Sirsat, M., Cernadas, E., Alawadi, S., Barro, S.,
and Febrero-Bande, M.: An extensive experimental survey of regression
methods, Neural Networks, 111, 11–34,
https://doi.org/10.1016/j.neunet.2018.12.010, 2019.
Fontana, V., Radtke, A., Walde, J., Tasser, E., Wilhalm, T., Zerbe, S., and
Tappeiner, U.: What plant traits tell us: Consequences of land-use change of
a traditional agro-forest system on biodiversity and ecosystem service
provision, Agr. Ecosyst. Environ., 186, 44–53,
https://doi.org/10.1016/j.agee.2014.01.006, 2014.
Friedman, J. H.: Greedy function approximation: A gradient boosting machine,
Ann. Statist., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Friedman, J. H.: Stochastic gradient boosting. Computational statistics & data analysis, 38, 367–378, https://doi.org/10.1016/S0167-9473(01)00065-2, 2002.
Gallardo-Cruz, J. A., Meave, J. A., González, E. J., Lebrija-Trejos, E.
E., Romero-Romero, M. A., Pérez-García, E. A., Gallardo-Cruz, R.,
Hernández-Stefanoni, J. L., and Martorell, C.: Predicting Tropical Dry
Forest Successional Attributes from Space: Is the Key Hidden in Image
Texture?, PLOS ONE, 7, e30506, https://doi.org/10.1371/journal.pone.0030506,
2012.
Gelman, A. and Rubin, D. B.: A single series from the Gibbs sampler provides
a false sense of security, Bayesian Statistics, 4, 625–631, 1992.
Gibson, D. J.: Grasses and grassland ecology, Oxford University Press,
Oxford, 305 pp., 2009.
Greenwell, B., Boehmke, B., Cunningham, J., and GBM Developers: gbm:
generalized boosted regression models,
https://cran.r-project.org/web/packages/gbm/index.html (last access: 10 September 2021), 2020.
Grüner, E., Astor, T., and Wachendorf, M.: Biomass prediction of
heterogeneous temperate grasslands using an SfM approach based on UAV
imaging, Agronomy, 9, 54, https://doi.org/10.3390/agronomy9020054, 2019.
Grüner, E., Wachendorf, M., and Astor, T.: The potential of UAV-borne
spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures, PLOS ONE, 15, e0234703,
https://doi.org/10.1371/journal.pone.0234703, 2020.
Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H.,
Sakai, T., Nakano, K., Ohdan, H., and Takahashi, K.: Assessing correlation
of high-resolution NDVI with fertilizer application level and yield of rice
and wheat crops using small UAVs, Remote Sens., 11, 112, https://doi.org/10.3390/rs11020112,
2019.
Handique, B. K., Khan, A. Q., Goswami, C., Prashnani, M., Gupta, C., and
Raju, P. L. N.: Crop discrimination using multispectral sensor onboard
unmanned aerial vehicle, P. Natl. Acad. Sci. USA, 87, 713–719,
https://doi.org/10.1007/s40010-017-0443-9, 2017.
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., and Gräler,
B.: Random forest as a generic framework for predictive modeling of spatial
and spatio-temporal variables, Peer J., 6, e5518,
https://doi.org/10.7717/peerj.5518, 2018.
Jay, S., Gorretta, N., Morel, J., Maupas, F., Bendoula, R., Rabatel, G.,
Dutartre, D., Comar, A., and Baret, F.: Estimating leaf chlorophyll content
in sugar beet canopies using millimeter- to centimeter-scale reflectance
imagery, Remote Sens. Environ., 198, 173–186,
https://doi.org/10.1016/j.rse.2017.06.008, 2017.
Jay, S., Baret, F., Dutartre, D., Malatesta, G., Héno, S., Comar, A.,
Weiss, M., and Maupas, F.: Exploiting the centimeter resolution of UAV
multispectral imagery to improve remote-sensing estimates of canopy
structure and biochemistry in sugar beet crops, Remote Sens. Environ., 231,
110898, https://doi.org/10.1016/j.rse.2018.09.011, 2019.
Jin, X., Liu, S., Baret, F., Hemerlé, M., and Comar, A.: Estimates of
plant density of wheat crops at emergence from very low altitude UAV
imagery, Remote Sens. Environ., 198, 105–114,
https://doi.org/10.1016/j.rse.2017.06.007, 2017.
Kiese, R., Fersch, B., Baessler, C., Brosy, C., Butterbach-Bahl, K., Chwala,
C., Dannenmann, M., Fu, J., Gasche, R., Grote, R., Jahn, C., Klatt, J.,
Kunstmann, H., Mauder, M., Rödiger, T., Smiatek, G., Soltani, M.,
Steinbrecher, R., Völksch, I., Werhahn, J., Wolf, B., Zeeman, M., and
Schmid, H. P.: The TERENO pre-Alpine observatory: integrating
meteorological, hydrological, and biogeochemical measurements and modeling,
Vadose Zone J., 17, 180060, https://doi.org/10.2136/vzj2018.03.0060, 2018.
Kim, D.-G., Bond-Lamberty, B., Ryu, Y., Seo, B., and Papale, D.: Ideas and perspectives: Enhancing research and monitoring of carbon pools and land-to-atmosphere greenhouse gases exchange in developing countries, Biogeosciences, 19, 1435–1450, https://doi.org/10.5194/bg-19-1435-2022, 2022.
Koukal, T. and Atzberger, C.: Potential of multi-angular data derived from a
digital aerial frame camera for forest classification, IEEE J. Sel. Top.
Appl., 5, 30–43, https://doi.org/10.1109/JSTARS.2012.2184527, 2012.
Kruskal, W. H. and Wallis, W. A.: Use of ranks in one-criterion variance
analysis, J. Am. Stat. Assoc., 47, 583–621,
https://doi.org/10.1080/01621459.1952.10483441, 1952.
Kuhn, M. and Johnson, K.: Applied predictive modeling, Springer-Verlag, New
York, ISBN 978-1-4614-6848-6, 2013.
Lamarque, P., Tappeiner, U., Turner, C., Steinbacher, M., Bardgett, R. D.,
Szukics, U., Schermer, M., and Lavorel, S.: Stakeholder perceptions of
grassland ecosystem services in relation to knowledge on soil fertility and
biodiversity, Reg. Environ. Change, 11, 791–804,
https://doi.org/10.1007/s10113-011-0214-0, 2011.
Datasheet of Leica Viva GNSS GS10 receiver:
https://w3.leica-geosystems.com/downloads123/zz/gpsgis/viva%20gnss/brochures-datasheet/leica_viva_gnss_gs10_receiver_ds_en.pdf
last access: 7 June 2020.
Lumley, T.: leaps: regression subset selection (based on Fortran code by
Alan Miller), https://CRAN.R-project.org/package=leaps (last access: 10 September 2021), 2020.
Lussem, U., Bolten, A., Menne, J., Gnyp, M. L., Schellberg, J., and Bareth,
G.: Estimating biomass in temperate grassland with high resolution canopy
surface models from UAV-based RGB images and vegetation indices, J. Appl.
Remote Sens., 13, 1–26, 2019.
Lussem, U., Schellberg, J., and Bareth, G.: Monitoring forage mass with
low-cost UAV data: Case study at the Rengen grassland experiment, PFG, 88,
407–422, https://doi.org/10.1007/s41064-020-00117-w, 2020.
Maes, W. H. and Steppe, K.: Perspectives for remote sensing with unmanned
aerial vehicles in precision agriculture, Trends Plant Sci., 24, 152–164,
https://doi.org/10.1016/j.tplants.2018.11.007, 2019.
Malkomes, G., Schaff, C., and Garnett, R.: Bayesian optimization for
automated model selection, in: JMLR: Workshop and Conference Proceeding,
ICML 2016 AutoML Workshop, New York, 41–47, 2016.
Marchese Robinson, R. L., Palczewska, A., Palczewski, J., and Kidley, N.:
Comparison of the predictive performance and interpretability of random
forest and linear models on benchmark data sets, J. Chem. Inf. Model., 57,
1773–1792, https://doi.org/10.1021/acs.jcim.6b00753, 2017.
Martinez-Cantin, R., de Freitas, N., Doucet, A., and Castellanos, J.: Active
policy learning for robot planning and exploration under uncertainty, in:
Robotics: Science and Systems III, Robotics: Science and Systems 2007,
https://doi.org/10.15607/RSS.2007.III.041, 2007.
Maschler, J., Atzberger, C., and Immitzer, M.: Individual tree crown
segmentation and classification of 13 tree species using airborne
hyperspectral data, Remote Sens., 10, https://doi.org/10.3390/rs10081218,
2018.
Matsumura, K.: Unmanned Aerial Vehicle (UAV) for fertilizer management in
grassland of Hokkaido, Japan, in: Unmanned Aerial Vehicle: Applications in
Agriculture and Environment, edited by: Avtar, R. and Watanabe, T., Springer
International Publishing, Cham, 39–50,
https://doi.org/10.1007/978-3-030-27157-2_4, 2020.
Meier, U.: Growth stages of mono- and dicotyledonous plants: BBCH Monograph,
Open Agrar Repositorium, Quedlinburg,
https://doi.org/10.5073/20180906-074619, 2018
Meinshausen, N.: Quantile regression forests, J. Mach. Learn. Res., 7,
983–999, 2006.
Meinshausen, N.: quantregForest: quantile regression forests,
https://CRAN.R-project.org/package=quantregForest (10 September 2021), 2017.
Moncayo-Cevallos, L. N., Rivadeneira-García, J. L., Andrade-Suárez,
B. I., Leiva-González, C. A., González, I. S., Yépez-Campoverde,
J. A., MaiguashcaGuzmán, J. A., and Toulkeridis, T.: A NDVI analysis
contrasting different spectrum data methodologies applied in pasture crops
previous grazing – a case study from Ecuador, in: 2018 International
Conference on eDemocracy & eGovernment (ICEDEG), 2018 International
Conference on eDemocracy & eGovernment (ICEDEG), 126–135,
https://doi.org/10.1109/ICEDEG.2018.8372375, 2018.
Näsi, R., Viljanen, N., Kaivosoja, J., Alhonoja, K., Hakala, T.,
Markelin, L., and Honkavaara, E.: Estimating biomass and nitrogen amount of
barley and grass using UAV and aircraft based spectral and photogrammetric
3D features, Remote Sens., 10, 1082, https://doi.org/10.3390/rs10071082,
2018.
Ollinger, S. V.: Sources of variability in canopy reflectance and the
convergent properties of plants, New Phytol., 189, 375–394,
https://doi.org/10.1111/j.1469-8137.2010.03536.x, 2011.
Olsson, P.-O., Vivekar, A., Adler, K., Garcia Millan, V. E., Koc, A.,
Alamrani, M., and Eklundh, L.: Radiometric correction of multispectral UAS
images: Evaluating the accuracy of the Parrot Sequoia camera and sunshine
sensor, Remote Sens., 13, 577, https://doi.org/10.3390/rs13040577, 2021.
Orzechowski, P., La Cava, W., and Moore, J. H.: Where are we now?, A large benchmark study of recent symbolic regression methods, in: Proceedings of the Genetic and Evolutionary Computation Conference, 1183–1190, 2018.
Poley, L. G. and McDermid, G. J.: A systematic review of the factors
influencing the estimation of vegetation aboveground biomass using unmanned
aerial systems, Remote Sens., 12, 1052 https://doi.org/10.3390/rs12071052, 2020.
Poncet, A. M., Knappenberger, T., Brodbeck, C., Fogle, M., Shaw, J. N., and
Ortiz, B. V.: Multispectral UAS data accuracy for different radiometric
calibration methods, Remote Sens., 11, 1917, https://doi.org/10.3390/rs11161917,
2019.
Primi, R., Filibeck, G., Amici, A., Bückle, C., Cancellieri, L., Di
Filippo, A., Gentile, C., Guglielmino, A., Latini, R., Mancini, L. D.,
Mensing, S. A., Rossi, C. M., Rossini, F., Scoppola, A., Sulli, C., Venanzi,
R., Ronchi, B., and Piovesan, G.: From Landsat to leafhoppers: A
multidisciplinary approach for sustainable stocking assessment and
ecological monitoring in mountain grasslands, Agr. Ecosyst. Environ., 234,
118–133, https://doi.org/10.1016/j.agee.2016.04.028, 2016.
Probst, P., Wright, M. N., and Boulesteix, A.-L.: Hyperparameters and tuning
strategies for random forest, WIREs Data Mining and Knowledge Discovery, 9,
e1301, https://doi.org/10.1002/widm.1301, 2019.
Pullanagari, R. R., Dynes, R. A., King, W. M., Yule, I. J., Thulin, S.,
Knox, N. M., and Ramoelo, A.: Remote sensing of pasture quality, in:
Revitalising grasslands to sustain our communities, 22nd International
Grassland Congress, Sydney, 633–638, 2013.
Pullanagari, R. R., Kereszturi, G., and Yule, I. J.: Mapping of macro and
micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral
imagery, ISPRS J. Photogramm., 117, 1–01,
https://doi.org/10.1016/j.isprsjprs.2016.03.010, 2016.
R Core Team: R: A language and environment for statistical computing, R
Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/, last access: 10 September 2021.
Richter, K., Hank, T. B., Mauser, W., and Atzberger, C.: Derivation of
biophysical variables from Earth observation data: validation and
statistical measures, J. Appl. Remote Sens., 6, 1–23,
https://doi.org/10.1117/1.JRS.6.063557, 2012.
Richter, R.: Correction of satellite imagery over mountainous terrain, Appl.
Opt., 37, 4004–4015, https://doi.org/10.1364/AO.37.004004, 1998.
Rossi, C., Kneubühler, M., Schütz, M., Schaepman, M. E., Haller, R.
M., and Risch, A. C.: From local to regional: Functional diversity in
differently managed alpine grasslands, Remote Sens. Environ., 236, 111415,
https://doi.org/10.1016/j.rse.2019.111415, 2020.
Rossini, M., Cogliati, S., Meroni, M., Migliavacca, M., Galvagno, M.,
Busetto, L., Cremonese, E., Julitta, T., Siniscalco, C., Morra di Cella, U.,
and Colombo, R.: Remote sensing-based estimation of gross primary production
in a subalpine grassland, Biogeosciences, 9, 2565–2584, 2012.
Rubel, F., Brugger, K., Haslinger, K., and Auer, I.: The climate of the
European Alps: Shift of very high resolution Köppen-Geiger climate zones
1800–2100, Meteorol. Z., 26, 115–125,
https://doi.org/10.1127/metz/2016/0816, 2017.
Salamí, E., Barrado, C., and Pastor, E.: UAV flight experiments applied
to the remote sensing of vegetated areas, Remote Sens., 6, 11051–11081,
https://doi.org/10.3390/rs61111051, 2014.
Schirpke, U., Kohler, M., Leitinger, G., Fontana, V., Tasser, E., and
Tappeiner, U.: Future impacts of changing land-use and climate on ecosystem
services of mountain grassland and their resilience, Ecosyst. Serv., 26,
79–94, https://doi.org/10.1016/j.ecoser.2017.06.008, 2017.
Schlingmann, M., Tobler, U., Berauer, B., Garcia-Franco, N., Wilfahrt, P.,
Wiesmeier, M., Jentsch, A., Wolf, B., Kiese, R., and Dannenmann, M.:
Intensive slurry management and climate change promote nitrogen mining from
organic matter-rich montane grassland soils, Plant Soil, 456, 81–98,
https://doi.org/10.1007/s11104-020-04697-9, 2020.
Schuchardt, M. A. and Jentsch, A.: Plant species richness and cover in
pre-Alpine grasslands of southern Germany, PANGAEA,
https://doi.pangaea.de/10.1594/PANGAEA.920599, 2020.
Schucknecht, A., Krämer, A., Asam, S., Mejia Aguilar, A., Garcia Franco,
N., Schuchardt, M. A., Jentsch, A., and Kiese, R.: Vegetation traits of
pre-Alpine grasslands in southern Germany, Sci. Data, 7, 316,
https://doi.org/10.1038/s41597-020-00651-7, 2020a.
Schucknecht, A., Krämer, A., Asam, S., Mejia Aguilar, A., Garcia Franco,
N., Schuchardt, M. A., Jentsch, A., and Kiese, R.: In-situ reference data
for aboveground vegetation traits of pre-Alpine grasslands in southern
Germany, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.920600, 2020b.
Seo, B., Bogner, C., Koellner, T., and Reineking, B.: Mapping fractional
land use and land cover in a monsoon region: The effects of data processing
options, IEEE J. Sel. Top. Appl., 9, 3941–3956, 2016.
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N.:
Taking the human out of the loop: A review of Bayesian optimization, Proc.
IEEE, 104, 148–175, https://doi.org/10.1109/JPROC.2015.2494218, 2016.
Song, Y. and Wang, J.: Winter wheat canopy height extraction from UAV-based
point cloud data with a moving cuboid filter, Remote Sens., 11,
https://doi.org/10.3390/rs11101239, 2019.
Spiegelberger, T., Matthies, D., Müller-Schärer, H., and Schaffner,
U.: Scale-dependent effects of land use on plant species richness of
mountain grassland in the European Alps, Ecography, 29, 541–548, 2006.
Strobl, C., Malley, J., and Tutz, G.: An introduction to recursive
partitioning: Rationale, application, and characteristics of classification
and regression trees, bagging, and random forests, Psychol. Methods, 14,
323–348, https://doi.org/10.1037/a0016973, 2009.
Stroppiana, D., Villa, P., Sona, G., Ronchetti, G., Candiani, G., Pepe, M.,
Busetto, L., Migliazzi, M., and Boschetti, M.: Early season weed mapping in
rice crops using multi-spectral UAV data, Int. J. Remote Sens., 39,
5432–5452, https://doi.org/10.1080/01431161.2018.1441569, 2018.
Swersky, K., Snoek, J., and Adams, R. P.: Multi-task Bayesian optimization,
in: Advances in Neural Information Processing Systems, 2004–2012, 2013.
Tsouros, D. C., Bibi, S. and Sarigiannidis, P. G.: A review on UAV-based
applications for precision agriculture, Information, 10, 349,
https://doi.org/10.3390/info10110349, 2019.
Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A. J.: Machine learning
algorithm validation with a limited sample size, PLOS ONE, 14, e0224365,
https://doi.org/10.1371/journal.pone.0224365, 2019.
Väre, H., Lampinen, R., Humphries, C., and Williams, P.: Taxonomic
diversity of vascular plants in the European alpine areas, in: Alpine
Biodiversity in Europe, edited by: Nagy, L., Grabherr, G., Körner, C.,
and Thompson, D. B. A., Springer Berlin Heidelberg, Berlin, Heidelberg,
133–148, https://doi.org/10.1007/978-3-642-18967-8_5, 2003.
Varma, S. and Simon, R.: Bias in error estimation when using
cross-validation for model selection, BMC Bioinf., 7, 91,
https://doi.org/10.1186/1471-2105-7-91, 2006.
Veen, P., Jefferson, R., de, Smidt, J., and van der Straaten, J.: Grasslands
in Europe, KNNV Publishing, Leiden, The Netherlands, ISBN 978-90-04-27810-3, 2009.
Verger, A., Vigneau, N., Chéron, C., Gilliot, J.-M., Comar, A., and
Baret, F.: Green area index from an unmanned aerial system over wheat and
rapeseed crops, Remote Sens. Environ., 152, 654–664,
https://doi.org/10.1016/j.rse.2014.06.006, 2014.
Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O.,
and Kaivosoja, J.: A novel machine learning method for estimating biomass of
grass swards using a photogrammetric canopy height model, images and
vegetation indices captured by a drone, Agriculture, 8, 70,
https://doi.org/10.3390/agriculture8050070, 2018.
Wachendorf, M., Fricke, T., and Möckel, T.: Remote sensing as a tool to
assess botanical composition, structure, quantity and quality of temperate
grasslands, Grass Forage Sci., 73, 1–14, https://doi.org/10.1111/gfs.12312,
2017.
Walter, J., Grant, K., Beierkuhnlein, C., Kreyling, J., Weber, M., and
Jentsch, A.: Increased rainfall variability reduces biomass and forage
quality of temperate grassland largely independent of mowing frequency, Agr.
Ecosyst. Environ., 148, 1–10, https://doi.org/10.1016/j.agee.2011.11.015,
2012.
Wang, D., Xin, X., Shao, Q., Brolly, M., Zhu, Z., and Chen, J.: Modeling
aboveground biomass in Hulunber grassland ecosystem by using unmanned aerial
vehicle discrete lidar, Sensors, 17, 180, https://doi.org/10.3390/s17010180,
2017.
White, R. P., Murray, S., and Rohweder, M.: Pilot analysis of globale
ecosystems – Grassland ecosystems, World Resources Institute, ISBN 1-56973-461-5, 2000.
Wiesmeier, M., Hübner, R., Barthold, F., Spörlein, P., Geuß, U.,
Hangen, E., Reischl, A., Schilling, B., von Lützow, M., and
Kögel-Knabner, I.: Amount, distribution and driving factors of soil
organic carbon and nitrogen in cropland and grassland soils of southeast
Germany (Bavaria), Agr. Ecosyst. Environ., 176, 39–52,
https://doi.org/10.1016/j.agee.2013.05.012, 2013.
Wijesingha, J., Moeckel, T., Hensgen, F., and Wachendorf, M.: Evaluation of
3D point cloud-based models for the prediction of grassland biomass,
Int. J. Appl. Earth Obs. Geoinf., 78,
352–359, https://doi.org/10.1016/j.jag.2018.10.006, 2019.
Wijesingha, J., Astor, T., Schulze-Brüninghoff, D., Wengert, M., and
Wachendorf, M.: Predicting forage quality of grasslands using UAV-borne
imaging spectroscopy, Remote Sens., 12, https://doi.org/10.3390/rs12010126,
2020.
Wilcoxon, F.: Individual comparisons by ranking methods, Biometrics Bull.,
1, 80–83, https://doi.org/10.2307/3001968, 1945.
Yao, X., Wang, N., Liu, Y., Cheng, T., Tian, Y., Chen, Q., and Zhu, Y.:
Estimation of wheat LAI at middle to high levels using unmanned aerial
vehicle narrowband multispectral imagery, Remote Sens., 9, 1304,
https://doi.org/10.3390/rs9121304, 2017.
Zacharias, S., Bogena, H., Samaniego, L., Mauder, M., Fuß, R., Pütz,
T., Frenzel, M., Schwank, M., Baessler, C., Butterbach-Bahl, K., Bens, O.,
Borg, E., Brauer, A., Dietrich, P., Hajnsek, I., Helle, G., Kiese, R.,
Kunstmann, H., Klotz, S., Munch, J. C., Papen, H., Priesack, E., Schmid, H.
P., Steinbrecher, R., Rosenbaum, U., Teutsch, G., and Vereecken, H.: A
network of terrestrial environmental observatories in Germany, Vadose Zone
J., 10, 955–973, https://doi.org/10.2136/vzj2010.0139, 2011.
Zhang, H., Sun, Y., Chang, L., Qin, Y., Chen, J., Qin, Y., Du, J., Yi, S.,
and Wang, Y.: Estimation of grassland canopy height and aboveground biomass
at the quadrat scale using unmanned aerial vehicle, Remote Sens., 10, 851,
https://doi.org/10.3390/rs10060851, 2018.
Ziliani, G. M., Parkes, D. S., Hoteit, I., and McCabe, F. M.: Intra-season
crop height variability at commercial farm scales using a fixed-wing UAV,
Remote Sens., 10, 2007, https://doi.org/10.3390/rs10122007, 2018.
Zistl-Schlingmann, M., Kwatcho Kengdo, S., Kiese, R., and Dannenmann, M.:
Management intensity controls nitrogen-use-efficiency and flows in
grasslands – A 15∘ N tracing experiment, Agronomy, 10, 606,
https://doi.org/10.3390/agronomy10040606, 2020.
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.
Actual maps of grassland traits could improve local farm management and support environmental...
Altmetrics
Final-revised paper
Preprint