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.
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
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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
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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.
Roxanne Daelman, Marijn Bauters, Matti Barthel, Emmanuel Bulonza, Lodewijk Lefevre, José Mbifo, Johan Six, Klaus Butterbach-Bahl, Benjamin Wolf, Ralf Kiese, and Pascal Boeckx
EGUsphere, https://doi.org/10.5194/egusphere-2024-2346, https://doi.org/10.5194/egusphere-2024-2346, 2024
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The increase in atmospheric concentrations of several greenhouse gasses (GHG) 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 for CO2 and N2O and a minor sink for CH4.
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
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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
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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
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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
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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
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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
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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
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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.
Dong-Gill Kim, Ben Bond-Lamberty, Youngryel Ryu, Bumsuk Seo, and Dario Papale
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-85, https://doi.org/10.5194/bg-2021-85, 2021
Manuscript not accepted for further review
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While greenhouse gas (GHG) research has adopted highly advanced technology some have adopted appropriate technology and approach (AT&A) such as low-cost instrument, open source software and participatory research and their results were well accepted by scientific communities. In terms of cost, feasibility and performance, integration of low-cost and low-technology, participatory and networking based research approaches can be AT&A for enhancing GHG research in developing countries.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Chris R. Flechard, Andreas Ibrom, Ute M. Skiba, Wim de Vries, Marcel van Oijen, David R. Cameron, Nancy B. Dise, Janne F. J. Korhonen, Nina Buchmann, Arnaud Legout, David Simpson, Maria J. Sanz, Marc Aubinet, Denis Loustau, Leonardo Montagnani, Johan Neirynck, Ivan A. Janssens, Mari Pihlatie, Ralf Kiese, Jan Siemens, André-Jean Francez, Jürgen Augustin, Andrej Varlagin, Janusz Olejnik, Radosław Juszczak, Mika Aurela, Daniel Berveiller, Bogdan H. Chojnicki, Ulrich Dämmgen, Nicolas Delpierre, Vesna Djuricic, Julia Drewer, Eric Dufrêne, Werner Eugster, Yannick Fauvel, David Fowler, Arnoud Frumau, André Granier, Patrick Gross, Yannick Hamon, Carole Helfter, Arjan Hensen, László Horváth, Barbara Kitzler, Bart Kruijt, Werner L. Kutsch, Raquel Lobo-do-Vale, Annalea Lohila, Bernard Longdoz, Michal V. Marek, Giorgio Matteucci, Marta Mitosinkova, Virginie Moreaux, Albrecht Neftel, Jean-Marc Ourcival, Kim Pilegaard, Gabriel Pita, Francisco Sanz, Jan K. Schjoerring, Maria-Teresa Sebastià, Y. Sim Tang, Hilde Uggerud, Marek Urbaniak, Netty van Dijk, Timo Vesala, Sonja Vidic, Caroline Vincke, Tamás Weidinger, Sophie Zechmeister-Boltenstern, Klaus Butterbach-Bahl, Eiko Nemitz, and Mark A. Sutton
Biogeosciences, 17, 1583–1620, https://doi.org/10.5194/bg-17-1583-2020, https://doi.org/10.5194/bg-17-1583-2020, 2020
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Experimental evidence from a network of 40 monitoring sites in Europe suggests that atmospheric nitrogen deposition to forests and other semi-natural vegetation impacts the carbon sequestration rates in ecosystems, as well as the net greenhouse gas balance including other greenhouse gases such as nitrous oxide and methane. Excess nitrogen deposition in polluted areas also leads to other environmental impacts such as nitrogen leaching to groundwater and other pollutant gaseous emissions.
Calum Brown, Bumsuk Seo, and Mark Rounsevell
Earth Syst. Dynam., 10, 809–845, https://doi.org/10.5194/esd-10-809-2019, https://doi.org/10.5194/esd-10-809-2019, 2019
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Concerns are growing that human activity will lead to social and environmental breakdown, but it is hard to anticipate when and where such breakdowns might occur. We developed a new model of land management decisions in Europe to explore possible future changes and found that decision-making that takes into account social and environmental conditions can produce unexpected outcomes that include societal breakdown in challenging conditions.
Erkan Ibraim, Benjamin Wolf, Eliza Harris, Rainer Gasche, Jing Wei, Longfei Yu, Ralf Kiese, Sarah Eggleston, Klaus Butterbach-Bahl, Matthias Zeeman, Béla Tuzson, Lukas Emmenegger, Johan Six, Stephan Henne, and Joachim Mohn
Biogeosciences, 16, 3247–3266, https://doi.org/10.5194/bg-16-3247-2019, https://doi.org/10.5194/bg-16-3247-2019, 2019
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Nitrous oxide (N2O) is an important greenhouse gas and the major stratospheric ozone-depleting substance; therefore, mitigation of anthropogenic N2O emissions is needed. To trace N2O-emitting source processes, in this study, we observed N2O isotopocules above an intensively managed grassland research site with a recently developed laser spectroscopy method. Our results indicate that the domain of denitrification or nitrifier denitrification was the major N2O source.
Friederike Gerschlauer, Gustavo Saiz, David Schellenberger Costa, Michael Kleyer, Michael Dannenmann, and Ralf Kiese
Biogeosciences, 16, 409–424, https://doi.org/10.5194/bg-16-409-2019, https://doi.org/10.5194/bg-16-409-2019, 2019
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Mount Kilimanjaro is an iconic environmental asset under serious threat due to increasing human pressures and climate change constraints. We studied variations in the stable isotopic composition of carbon and nitrogen in plant, litter, and soil material sampled along a strong land-use and altitudinal gradient. Our results show that, besides management, increasing temperatures in a changing climate may promote carbon and nitrogen losses, thus altering the stability of Kilimanjaro ecosystems.
Tobias Houska, David Kraus, Ralf Kiese, and Lutz Breuer
Biogeosciences, 14, 3487–3508, https://doi.org/10.5194/bg-14-3487-2017, https://doi.org/10.5194/bg-14-3487-2017, 2017
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CO2 and N2O are two prominent GHGs contributing to global warming. We combined measurement and modelling to quantify GHG emissions from adjacent arable, forest and grassland sites in Germany. Measured emissions reveal seasonal patterns and management effects like fertilizer application, tillage, harvest and grazing. Modelling helps to estimate the magnitude and uncertainty of not measurable C and N fluxes and indicates missing input source, e.g. nitrate uptake from groundwater.
Stephanie K. Jones, Carole Helfter, Margaret Anderson, Mhairi Coyle, Claire Campbell, Daniela Famulari, Chiara Di Marco, Netty van Dijk, Y. Sim Tang, Cairistiona F. E. Topp, Ralf Kiese, Reimo Kindler, Jan Siemens, Marion Schrumpf, Klaus Kaiser, Eiko Nemitz, Peter E. Levy, Robert M. Rees, Mark A. Sutton, and Ute M. Skiba
Biogeosciences, 14, 2069–2088, https://doi.org/10.5194/bg-14-2069-2017, https://doi.org/10.5194/bg-14-2069-2017, 2017
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We assessed the nitrogen (N), carbon (C) and greenhouse gas (GHG) budget from an intensively managed grassland in southern Scotland using flux budget calculations as well as changes in soil N and C pools over time. Estimates from flux budget calculations indicated that N and C were sequestered, whereas soil stock measurements indicated a smaller N storage and a loss of C from the ecosystem. The GHG sink strength of the net CO2 ecosystem exchange was strongly affected by CH4 and N2O emissions.
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Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
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This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Simon Scheiter, Sophie Wolf, and Teja Kattenborn
Biogeosciences, 21, 4909–4926, https://doi.org/10.5194/bg-21-4909-2024, https://doi.org/10.5194/bg-21-4909-2024, 2024
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Biomes are widely used to map vegetation patterns at large spatial scales and to assess impacts of climate change, yet there is no consensus on a generally valid biome classification scheme. We used crowd-sourced species distribution data and trait data to assess whether trait information is suitable for delimiting biomes. Although the trait data were heterogeneous and had large gaps with respect to the spatial distribution, we found that a global trait-based biome classification was possible.
Cheng-Hsien Lin, Colleen Zumpf, Chunhwa Jang, Thomas Voigt, Guanglong Tian, Olawale Oladeji, Albert Cox, Rehnuma Mehzabin, and DoKyoung Lee
Biogeosciences, 21, 4765–4784, https://doi.org/10.5194/bg-21-4765-2024, https://doi.org/10.5194/bg-21-4765-2024, 2024
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Riparian areas are subject to environmental issues (nutrient leaching) associated with low productivity. Perennial grasses can improve ecosystem services from riparian zones while producing forage/bioenergy feedstock biomass as potential income for farmers. The forage-type buffer can be an ideal short-term candidate due to its great efficiency of nutrient scavenging; the bioenergy-type buffer showed better sustainability than the forage buffer and a continuous yield supply potential.
Gabriela Sophia, Silvia Caldararu, Benjamin David Stocker, and Sönke Zaehle
Biogeosciences, 21, 4169–4193, https://doi.org/10.5194/bg-21-4169-2024, https://doi.org/10.5194/bg-21-4169-2024, 2024
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Through an extensive global dataset of leaf nutrient resorption and a multifactorial analysis, we show that the majority of spatial variation in nutrient resorption may be driven by leaf habit and type, with thicker, longer-lived leaves having lower resorption efficiencies. Climate, soil fertility and soil-related factors emerge as strong drivers with an additional effect on its role. These results are essential for comprehending plant nutrient status, plant productivity and nutrient cycling.
Madeleine-Zoé Corbeil-Robitaille, Éliane Duchesne, Daniel Fortier, Christophe Kinnard, and Joël Bêty
Biogeosciences, 21, 3401–3423, https://doi.org/10.5194/bg-21-3401-2024, https://doi.org/10.5194/bg-21-3401-2024, 2024
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In the Arctic tundra, climate change is transforming the landscape, and this may impact wildlife. We focus on three nesting bird species and the islets they select as refuges from their main predator, the Arctic fox. A geomorphological process, ice-wedge polygon degradation, was found to play a key role in creating these refuges. This process is likely to affect predator–prey dynamics in the Arctic tundra, highlighting the connections between nature's physical and ecological systems.
Samuel M. Fischer, Xugao Wang, and Andreas Huth
Biogeosciences, 21, 3305–3319, https://doi.org/10.5194/bg-21-3305-2024, https://doi.org/10.5194/bg-21-3305-2024, 2024
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Understanding the drivers of forest productivity is key for accurately assessing forests’ role in the global carbon cycle. Yet, despite significant research effort, it is not fully understood how the productivity of a forest can be deduced from its stand structure. We suggest tackling this problem by identifying the share and structure of immature trees within forests and show that this approach could significantly improve estimates of forests’ net productivity and carbon uptake.
Arsène Druel, Julien Ruffault, Hendrik Davi, André Chanzy, Olivier Marloie, Miquel De Cáceres, Florent Mouillot, Christophe François, Kamel Soudani, and Nicolas K. Martin-StPaul
EGUsphere, https://doi.org/10.5194/egusphere-2024-1800, https://doi.org/10.5194/egusphere-2024-1800, 2024
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Accurate radiation data are essential for understanding ecosystem growth. Traditional large-scale data lack the precision needed for complex terrains, e.g. mountainous regions. This study introduces a new model to enhance radiation data resolution using elevation maps, which accounts for sub-daily direct and diffuse radiation effects caused by terrain features. Tested on Mont Ventoux, this method significantly improves radiation estimates, benefiting forest growth and climate risk models.
Mery Ingrid Guimarães de Alencar, Rafael D. Guariento, Bertrand Guenet, Luciana S. Carneiro, Eduardo L. Voigt, and Adriano Caliman
Biogeosciences, 21, 3165–3182, https://doi.org/10.5194/bg-21-3165-2024, https://doi.org/10.5194/bg-21-3165-2024, 2024
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Flowers are ephemeral organs for reproduction, and their litter is functionally different from leaf litter. Flowers can affect decomposition and interact with leaf litter, influencing decomposition non-additively. We show that mixing flower and leaf litter from the Tabebuia aurea tree creates reciprocal synergistic effects on decomposition in both terrestrial and aquatic environments. We highlight that flower litter input can generate biogeochemical hotspots in terrestrial ecosystems.
Pierre-Alexis Herrault, Albin Ullmann, and Damien Ertlen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1935, https://doi.org/10.5194/egusphere-2024-1935, 2024
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Mountain grasslands are impacted by Climate Change and need to adapt. Low mountain grasslands are poorly understood compared to High Mountains massif. Thanks to satellite archives, we found that grasslands occurring in the Vosges Mountains (France) exhibited stable productivity or tended to decrease in specific regions of the massif, a reverse signal observed in High Mountains massif. We also noted a high responsiveness in their growth strategy to soil moisture, snow regimes and topography.
Salim Soltani, Olga Ferlian, Nico Eisenhauer, Hannes Feilhauer, and Teja Kattenborn
Biogeosciences, 21, 2909–2935, https://doi.org/10.5194/bg-21-2909-2024, https://doi.org/10.5194/bg-21-2909-2024, 2024
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In this research, we developed a novel method using citizen science data as alternative training data for computer vision models to map plant species in unoccupied aerial vehicle (UAV) images. We use citizen science plant photographs to train models and apply them to UAV images. We tested our approach on UAV images of a test site with 10 different tree species, yielding accurate results. This research shows the potential of citizen science data to advance our ability to monitor plant species.
Xue Feng, Ruzhen Wang, Tianpeng Li, Jiangping Cai, Heyong Liu, Hui Li, and Yong Jiang
Biogeosciences, 21, 2641–2653, https://doi.org/10.5194/bg-21-2641-2024, https://doi.org/10.5194/bg-21-2641-2024, 2024
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Plant functional traits have been considered as reflecting adaptations to environmental variations, indirectly affecting ecosystem productivity. How soil acidification affects above- and belowground biomass by altering leaf and root traits remains poorly understood. We found divergent trait responses driven by soil environmental conditions in two dominant species, resulting in a decrease in aboveground biomass and an increase in belowground biomass.
Minhua Zhang, Xiaoqing Hu, and Fangliang He
Biogeosciences, 21, 2133–2142, https://doi.org/10.5194/bg-21-2133-2024, https://doi.org/10.5194/bg-21-2133-2024, 2024
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Plant sexual systems are important to understanding the evolution and maintenance of plant diversity. We quantified region effects on their proportions while incorporating local climate factors and evolutionary history. We found regional processes and climate effects both play important roles in shaping the geographic distribution of sexual systems, providing a baseline for predicting future changes in forest communities in the context of global change.
Christian H. Mohr, Michael Dietze, Violeta Tolorza, Erwin Gonzalez, Benjamin Sotomayor, Andres Iroume, Sten Gilfert, and Frieder Tautz
Biogeosciences, 21, 1583–1599, https://doi.org/10.5194/bg-21-1583-2024, https://doi.org/10.5194/bg-21-1583-2024, 2024
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Coastal temperate rainforests, among Earth’s carbon richest biomes, are systematically underrepresented in the global network of critical zone observatories (CZOs). Introducing here a first CZO in the heart of the Patagonian rainforest, Chile, we investigate carbon sink functioning, biota-driven landscape evolution, fluxes of matter and energy, and disturbance regimes. We invite the community to join us in cross-disciplinary collaboration to advance science in this particular environment.
Jorge F. Perez-Quezada, David Trejo, Javier Lopatin, David Aguilera, Bruce Osborne, Mauricio Galleguillos, Luca Zattera, Juan L. Celis-Diez, and Juan J. Armesto
Biogeosciences, 21, 1371–1389, https://doi.org/10.5194/bg-21-1371-2024, https://doi.org/10.5194/bg-21-1371-2024, 2024
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For 8 years we sampled a temperate rainforest and a peatland in Chile to estimate their efficiency to capture carbon per unit of water lost. The efficiency is more related to the water lost than to the carbon captured and is mainly driven by evaporation instead of transpiration. This is the first report from southern South America and highlights that ecosystems might behave differently in this area, likely explained by the high annual precipitation (~ 2100 mm) and light-limited conditions.
Fredrik Lagergren, Robert G. Björk, Camilla Andersson, Danijel Belušić, Mats P. Björkman, Erik Kjellström, Petter Lind, David Lindstedt, Tinja Olenius, Håkan Pleijel, Gunhild Rosqvist, and Paul A. Miller
Biogeosciences, 21, 1093–1116, https://doi.org/10.5194/bg-21-1093-2024, https://doi.org/10.5194/bg-21-1093-2024, 2024
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The Fennoscandian boreal and mountain regions harbour a wide range of ecosystems sensitive to climate change. A new, highly resolved high-emission climate scenario enabled modelling of the vegetation development in this region at high resolution for the 21st century. The results show dramatic south to north and low- to high-altitude shifts of vegetation zones, especially for the open tundra environments, which will have large implications for nature conservation, reindeer husbandry and forestry.
Florian Zellweger, Eric Sulmoni, Johanna T. Malle, Andri Baltensweiler, Tobias Jonas, Niklaus E. Zimmermann, Christian Ginzler, Dirk Nikolaus Karger, Pieter De Frenne, David Frey, and Clare Webster
Biogeosciences, 21, 605–623, https://doi.org/10.5194/bg-21-605-2024, https://doi.org/10.5194/bg-21-605-2024, 2024
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The microclimatic conditions experienced by organisms living close to the ground are not well represented in currently used climate datasets derived from weather stations. Therefore, we measured and mapped ground microclimate temperatures at 10 m spatial resolution across Switzerland using a novel radiation model. Our results reveal a high variability in microclimates across different habitats and will help to better understand climate and land use impacts on biodiversity and ecosystems.
Andrew Kulmatiski, Martin C. Holdrege, Cristina Chirvasă, and Karen H. Beard
Biogeosciences, 21, 131–143, https://doi.org/10.5194/bg-21-131-2024, https://doi.org/10.5194/bg-21-131-2024, 2024
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Warmer air and larger precipitation events are changing the way water moves through the soil and into plants. Here we show that detailed descriptions of root distributions can predict plant growth responses to changing precipitation patterns. Shrubs and forbs increased growth, while grasses showed no response to increased precipitation intensity, and these responses were predicted by plant rooting distributions.
Bonaventure Ntirugulirwa, Etienne Zibera, Nkuba Epaphrodite, Aloysie Manishimwe, Donat Nsabimana, Johan Uddling, and Göran Wallin
Biogeosciences, 20, 5125–5149, https://doi.org/10.5194/bg-20-5125-2023, https://doi.org/10.5194/bg-20-5125-2023, 2023
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Twenty tropical tree species native to Africa were planted along an elevation gradient (1100 m, 5.4 °C difference). We found that early-successional (ES) species, especially from lower elevations, grew faster at warmer sites, while several of the late-successional (LS) species, especially from higher elevations, did not respond or grew slower. Moreover, a warmer climate increased tree mortality in LS species, but not much in ES species.
Lilian Vallet, Charbel Abdallah, Thomas Lauvaux, Lilian Joly, Michel Ramonet, Philippe Ciais, Morgan Lopez, Irène Xueref-Remy, and Florent Mouillot
EGUsphere, https://doi.org/10.5194/egusphere-2023-2421, https://doi.org/10.5194/egusphere-2023-2421, 2023
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2022 fire season had a huge impact on European temperate forest, with several large fires exhibiting prolonged soil combustion reported. We analyzed CO and CO2 concentration recorded at nearby atmospheric towers, revealing intense smoldering combustion. We refined a fire emission model to incorporate this process. We estimated 7.95 MteqCO2 fire emission, twice the global estimate. Fires contributed to 1.97 % of the country's annual carbon footprint, reducing forest carbon sink by 30 % this year.
Philippe Choler
Biogeosciences, 20, 4259–4272, https://doi.org/10.5194/bg-20-4259-2023, https://doi.org/10.5194/bg-20-4259-2023, 2023
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The year 2022 was unique in that the summer heat wave and drought led to a widespread reduction in vegetation growth at high elevation in the European Alps. This impact was unprecedented in the southwestern, warm, and dry part of the Alps. Over the last 2 decades, water has become a co-dominant control of vegetation activity in areas that were, so far, primarily controlled by temperature, and the growth of mountain grasslands has become increasingly sensitive to moisture availability.
Adriana Simonetti, Raquel Fernandes Araujo, Carlos Henrique Souza Celes, Flávia Ranara da Silva e Silva, Joaquim dos Santos, Niro Higuchi, Susan Trumbore, and Daniel Magnabosco Marra
Biogeosciences, 20, 3651–3666, https://doi.org/10.5194/bg-20-3651-2023, https://doi.org/10.5194/bg-20-3651-2023, 2023
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We combined 2 years of monthly drone-acquired RGB (red–green–blue) imagery with field surveys in a central Amazon forest. Our results indicate that small gaps associated with branch fall were the most frequent. Biomass losses were partially controlled by gap area, with branch fall and snapping contributing the least and greatest relative values, respectively. Our study highlights the potential of drone images for monitoring canopy dynamics in dense tropical forests.
Silvia Caldararu, Victor Rolo, Benjamin D. Stocker, Teresa E. Gimeno, and Richard Nair
Biogeosciences, 20, 3637–3649, https://doi.org/10.5194/bg-20-3637-2023, https://doi.org/10.5194/bg-20-3637-2023, 2023
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Ecosystem manipulative experiments are large experiments in real ecosystems. They include processes such as species interactions and weather that would be omitted in more controlled settings. They offer a high level of realism but are underused in combination with vegetation models used to predict the response of ecosystems to global change. We propose a workflow using models and ecosystem experiments together, taking advantage of the benefits of both tools for Earth system understanding.
Katharina Ramskogler, Bettina Knoflach, Bernhard Elsner, Brigitta Erschbamer, Florian Haas, Tobias Heckmann, Florentin Hofmeister, Livia Piermattei, Camillo Ressl, Svenja Trautmann, Michael H. Wimmer, Clemens Geitner, Johann Stötter, and Erich Tasser
Biogeosciences, 20, 2919–2939, https://doi.org/10.5194/bg-20-2919-2023, https://doi.org/10.5194/bg-20-2919-2023, 2023
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Primary succession in proglacial areas depends on complex driving forces. To concretise the complex effects and interaction processes, 39 known explanatory variables assigned to seven spheres were analysed via principal component analysis and generalised additive models. Key results show that in addition to time- and elevation-dependent factors, also disturbances alter vegetation development. The results are useful for debates on vegetation development in a warming climate.
Zijing Li, Zhiyong Li, Xuze Tong, Lei Dong, Ying Zheng, Jinghui Zhang, Bailing Miao, Lixin Wang, Liqing Zhao, Lu Wen, Guodong Han, Frank Yonghong Li, and Cunzhu Liang
Biogeosciences, 20, 2869–2882, https://doi.org/10.5194/bg-20-2869-2023, https://doi.org/10.5194/bg-20-2869-2023, 2023
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We used random forest models and structural equation models to assess the relative importance of the present climate and paleoclimate as determinants of diversity and aboveground biomass. Results showed that paleoclimate changes and modern climate jointly determined contemporary biodiversity patterns, while community biomass was mainly affected by modern climate. These findings suggest that contemporary biodiversity patterns may be affected by processes at divergent temporal scales.
William Rupert Moore Flynn, Harry Jon Foord Owen, Stuart William David Grieve, and Emily Rebecca Lines
Biogeosciences, 20, 2769–2784, https://doi.org/10.5194/bg-20-2769-2023, https://doi.org/10.5194/bg-20-2769-2023, 2023
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Quantifying vegetation indices is crucial for ecosystem monitoring and modelling. Terrestrial laser scanning (TLS) has potential to accurately measure vegetation indices, but multiple methods exist, with little consensus on best practice. We compare three methods and extract wood-to-plant ratio, a metric used to correct for wood in leaf indices. We show corrective metrics vary with tree structure and variation among methods, highlighting the value of TLS data and importance of rigorous testing.
Haiyang Shi, Geping Luo, Olaf Hellwich, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences, 20, 2727–2741, https://doi.org/10.5194/bg-20-2727-2023, https://doi.org/10.5194/bg-20-2727-2023, 2023
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In studies on the relationship between ecosystem functions and climate and plant traits, previously used data-driven methods such as multiple regression and random forest may be inadequate for representing causality due to limitations such as covariance between variables. Based on FLUXNET site data, we used a causal graphical model to revisit the control of climate and vegetation traits over ecosystem functions.
Josué Delgado-Balbuena, Henry W. Loescher, Carlos A. Aguirre-Gutiérrez, Teresa Alfaro-Reyna, Luis F. Pineda-Martínez, Rodrigo Vargas, and Tulio Arredondo
Biogeosciences, 20, 2369–2385, https://doi.org/10.5194/bg-20-2369-2023, https://doi.org/10.5194/bg-20-2369-2023, 2023
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In the semiarid grassland, an increase in soil moisture at shallow depths instantly enhances carbon release through respiration. In contrast, deeper soil water controls plant carbon uptake but with a delay of several days. Previous soil conditions, biological activity, and the size and timing of precipitation are factors that determine the amount of carbon released into the atmosphere. Thus, future changes in precipitation patterns could convert ecosystems from carbon sinks to carbon sources.
German Vargas Gutiérrez, Daniel Pérez-Aviles, Nanette Raczka, Damaris Pereira-Arias, Julián Tijerín-Triviño, L. David Pereira-Arias, David Medvigy, Bonnie G. Waring, Ember Morrisey, Edward Brzostek, and Jennifer S. Powers
Biogeosciences, 20, 2143–2160, https://doi.org/10.5194/bg-20-2143-2023, https://doi.org/10.5194/bg-20-2143-2023, 2023
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To study whether nutrient availability controls tropical dry forest responses to reductions in soil moisture, we established the first troughfall exclusion experiment in a tropical dry forest plantation system crossed with a fertilization scheme. We found that the effects of fertilization on net primary productivity are larger than the effects of a ~15 % reduction in soil moisture, although in many cases we observed an interaction between drought and nutrient additions, suggesting colimitation.
Alina Lucia Ludat and Simon Kübler
Biogeosciences, 20, 1991–2012, https://doi.org/10.5194/bg-20-1991-2023, https://doi.org/10.5194/bg-20-1991-2023, 2023
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Satellite-based analysis illustrates the impact of geological processes for the stability of the ecosystem in the Mara River basin (Kenya/Tanzania). Newly detected fault activity influences the course of river networks and modifies erosion–deposition patterns. Tectonic surface features and variations in rock chemistry lead to localized enhancement of clay and soil moisture values and seasonally stabilised vegetation growth patterns in this climatically vulnerable region.
Erica Jaakkola, Antje Gärtner, Anna Maria Jönsson, Karl Ljung, Per-Ola Olsson, and Thomas Holst
Biogeosciences, 20, 803–826, https://doi.org/10.5194/bg-20-803-2023, https://doi.org/10.5194/bg-20-803-2023, 2023
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Increased spruce bark beetle outbreaks were recently seen in Sweden. When Norway spruce trees are attacked, they increase their production of VOCs, attempting to kill the beetles. We provide new insights into how the Norway spruce act when infested and found the emitted volatiles to increase up to 700 times and saw a change in compound blend. We estimate that the 2020 bark beetle outbreak in Sweden could have increased the total monoterpene emissions from the forest by more than 10 %.
Georg Wohlfahrt, Albin Hammerle, Felix M. Spielmann, Florian Kitz, and Chuixiang Yi
Biogeosciences, 20, 589–596, https://doi.org/10.5194/bg-20-589-2023, https://doi.org/10.5194/bg-20-589-2023, 2023
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The trace gas carbonyl sulfide (COS), which is taken up by plant leaves in a process very similar to photosynthesis, is thought to be a promising proxy for the gross uptake of carbon dioxide by plants. Here we propose a new framework for estimating a key metric to that end, the so-called leaf relative uptake rate. The values we deduce by applying principles of plant optimality are considerably lower than published values and may help reduce the uncertainty of the global COS budget.
François Jonard, Andrew F. Feldman, Daniel J. Short Gianotti, and Dara Entekhabi
Biogeosciences, 19, 5575–5590, https://doi.org/10.5194/bg-19-5575-2022, https://doi.org/10.5194/bg-19-5575-2022, 2022
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We investigate the spatial and temporal patterns of light and water limitation in plant function at the ecosystem scale. Using satellite observations, we characterize the nonlinear relationships between sun-induced chlorophyll fluorescence (SIF) and water and light availability. This study highlights that soil moisture limitations on SIF are found primarily in drier environments, while light limitations are found in intermediately wet regions.
Nikolai Knapp, Sabine Attinger, and Andreas Huth
Biogeosciences, 19, 4929–4944, https://doi.org/10.5194/bg-19-4929-2022, https://doi.org/10.5194/bg-19-4929-2022, 2022
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The biomass of forests is determined by forest growth and mortality. These quantities can be estimated with different methods such as inventories, remote sensing and modeling. These methods are usually being applied at different spatial scales. The scales influence the obtained frequency distributions of biomass, growth and mortality. This study suggests how to transfer between scales, when using forest models of different complexity for a tropical forest.
Kai Chen, Kevin S. Burgess, Fangliang He, Xiang-Yun Yang, Lian-Ming Gao, and De-Zhu Li
Biogeosciences, 19, 4801–4810, https://doi.org/10.5194/bg-19-4801-2022, https://doi.org/10.5194/bg-19-4801-2022, 2022
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Why does plants' distributional range size vary enormously? This study provides evidence that seed mass, intraspecific seed mass variation, seed dispersal mode and phylogeny contribute to explaining species distribution variation on a geographic scale. Our study clearly shows the importance of including seed life-history traits in modeling and predicting the impact of climate change on species distribution of seed plants.
Ying Ying Chen, Huan Yang, Gen Sheng Bao, Xiao Pan Pang, and Zheng Gang Guo
Biogeosciences, 19, 4521–4532, https://doi.org/10.5194/bg-19-4521-2022, https://doi.org/10.5194/bg-19-4521-2022, 2022
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Investigating the effect of the presence of plateau pikas on ecosystem services of alpine meadows is helpful to understand the role of the presence of small mammalian herbivores in grasslands. The results of this study showed that the presence of plateau pikas led to higher biodiversity conservation, soil nitrogen and phosphorus maintenance, and carbon sequestration of alpine meadows, whereas it led to lower forage available to livestock and water conservation of alpine meadows.
Clement Jean Frédéric Delcourt and Sander Veraverbeke
Biogeosciences, 19, 4499–4520, https://doi.org/10.5194/bg-19-4499-2022, https://doi.org/10.5194/bg-19-4499-2022, 2022
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This study provides new equations that can be used to estimate aboveground tree biomass in larch-dominated forests of northeast Siberia. Applying these equations to 53 forest stands in the Republic of Sakha (Russia) resulted in significantly larger biomass stocks than when using existing equations. The data presented in this work can help refine biomass estimates in Siberian boreal forests. This is essential to assess changes in boreal vegetation and carbon dynamics.
Iris Johanna Aalto, Eduardo Eiji Maeda, Janne Heiskanen, Eljas Kullervo Aalto, and Petri Kauko Emil Pellikka
Biogeosciences, 19, 4227–4247, https://doi.org/10.5194/bg-19-4227-2022, https://doi.org/10.5194/bg-19-4227-2022, 2022
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Tree canopies are strong moderators of understory climatic conditions. In tropical areas, trees cool down the microclimates. Using remote sensing and field measurements we show how even intermediate canopy cover and agroforestry trees contributed to buffering the hottest temperatures in Kenya. The cooling effect was the greatest during hot days and in lowland areas, where the ambient temperatures were high. Adopting agroforestry practices in the area could assist in mitigating climate change.
Jing Wang and Xuefa Wen
Biogeosciences, 19, 4197–4208, https://doi.org/10.5194/bg-19-4197-2022, https://doi.org/10.5194/bg-19-4197-2022, 2022
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Excess radiation and low temperatures exacerbate drought impacts on canopy conductance (Gs) among transects. The primary determinant of drought stress on Gs was soil moisture on the Loess Plateau (LP) and the Mongolian Plateau (MP), whereas it was the vapor pressure deficit on the Tibetan Plateau (TP). Radiation exhibited a negative effect on Gs via drought stress within transects, while temperature had negative effects on stomatal conductance on the TP but no effect on the LP and MP.
Sylvain Monteux, Janine Mariën, and Eveline J. Krab
Biogeosciences, 19, 4089–4105, https://doi.org/10.5194/bg-19-4089-2022, https://doi.org/10.5194/bg-19-4089-2022, 2022
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Quantifying the feedback from the decomposition of thawing permafrost soils is crucial to establish adequate climate warming mitigation scenarios. Past efforts have focused on abiotic and to some extent microbial drivers of decomposition but not biotic drivers such as soil fauna. We added soil fauna (Collembola Folsomia candida) to permafrost, which introduced bacterial taxa without affecting bacterial communities as a whole but increased CO2 production (+12 %), presumably due to priming.
Mirjam Pfeiffer, Munir P. Hoffmann, Simon Scheiter, William Nelson, Johannes Isselstein, Kingsley Ayisi, Jude J. Odhiambo, and Reimund Rötter
Biogeosciences, 19, 3935–3958, https://doi.org/10.5194/bg-19-3935-2022, https://doi.org/10.5194/bg-19-3935-2022, 2022
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Smallholder farmers face challenges due to poor land management and climate change. We linked the APSIM crop model and the aDGVM2 vegetation model to investigate integrated management options that enhance ecosystem functions and services. Sustainable intensification moderately increased yields. Crop residue grazing reduced feed gaps but not for dry-to-wet season transitions. Measures to improve soil water and nutrient status are recommended. Landscape-level ecosystem management is essential.
Marina Corrêa Scalon, Imma Oliveras Menor, Renata Freitag, Karine S. Peixoto, Sami W. Rifai, Beatriz Schwantes Marimon, Ben Hur Marimon Junior, and Yadvinder Malhi
Biogeosciences, 19, 3649–3661, https://doi.org/10.5194/bg-19-3649-2022, https://doi.org/10.5194/bg-19-3649-2022, 2022
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We investigated dynamic nutrient flow and demand in a typical savanna and a transition forest to understand how similar soils and the same climate dominated by savanna vegetation can also support forest-like formations. Savanna relied on nutrient resorption from wood, and nutrient demand was equally partitioned between leaves, wood and fine roots. Transition forest relied on resorption from the canopy biomass and nutrient demand was predominantly driven by leaves.
Emma Bousquet, Arnaud Mialon, Nemesio Rodriguez-Fernandez, Stéphane Mermoz, and Yann Kerr
Biogeosciences, 19, 3317–3336, https://doi.org/10.5194/bg-19-3317-2022, https://doi.org/10.5194/bg-19-3317-2022, 2022
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Pre- and post-fire values of four climate variables and four vegetation variables were analysed at the global scale, in order to observe (i) the general fire likelihood factors and (ii) the vegetation recovery trends over various biomes. The main result of this study is that L-band vegetation optical depth (L-VOD) is the most impacted vegetation variable and takes the longest to recover over dense forests. L-VOD could then be useful for post-fire vegetation recovery studies.
Chen Yang, Yue Shi, Wenjuan Sun, Jiangling Zhu, Chengjun Ji, Yuhao Feng, Suhui Ma, Zhaodi Guo, and Jingyun Fang
Biogeosciences, 19, 2989–2999, https://doi.org/10.5194/bg-19-2989-2022, https://doi.org/10.5194/bg-19-2989-2022, 2022
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Quantifying China's forest biomass C pool is important in understanding C cycling in forests. However, most of studies on forest biomass C pool were limited to the period of 2004–2008. Here, we used a biomass expansion factor method to estimate C pool from 1977 to 2018. The results suggest that afforestation practices, forest growth, and environmental changes were the main drivers of increased C sink. Thus, this study provided an essential basis for achieving China's C neutrality target.
Ramona J. Heim, Andrey Yurtaev, Anna Bucharova, Wieland Heim, Valeriya Kutskir, Klaus-Holger Knorr, Christian Lampei, Alexandr Pechkin, Dora Schilling, Farid Sulkarnaev, and Norbert Hölzel
Biogeosciences, 19, 2729–2740, https://doi.org/10.5194/bg-19-2729-2022, https://doi.org/10.5194/bg-19-2729-2022, 2022
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Fires will probably increase in Arctic regions due to climate change. Yet, the long-term effects of tundra fires on carbon (C) and nitrogen (N) stocks and cycling are still unclear. We investigated the long-term fire effects on C and N stocks and cycling in soil and aboveground living biomass.
We found that tundra fires did not affect total C and N stocks because a major part of the stocks was located belowground in soils which were largely unaltered by fire.
Aileen B. Baird, Edward J. Bannister, A. Robert MacKenzie, and Francis D. Pope
Biogeosciences, 19, 2653–2669, https://doi.org/10.5194/bg-19-2653-2022, https://doi.org/10.5194/bg-19-2653-2022, 2022
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Forest environments contain a wide variety of airborne biological particles (bioaerosols) important for plant and animal health and biosphere–atmosphere interactions. Using low-cost sensors and a free-air carbon dioxide enrichment (FACE) experiment, we monitor the impact of enhanced CO2 on airborne particles. No effect of the enhanced CO2 treatment on total particle concentrations was observed, but a potential suppression of high concentration bioaerosol events was detected under enhanced CO2.
Melanie S. Verlinden, Hamada AbdElgawad, Arne Ven, Lore T. Verryckt, Sebastian Wieneke, Ivan A. Janssens, and Sara Vicca
Biogeosciences, 19, 2353–2364, https://doi.org/10.5194/bg-19-2353-2022, https://doi.org/10.5194/bg-19-2353-2022, 2022
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Zea mays grows in mesocosms with different soil nutrition levels. At low phosphorus (P) availability, leaf physiological activity initially decreased strongly. P stress decreased over the season. Arbuscular mycorrhizal fungi (AMF) symbiosis increased over the season. AMF symbiosis is most likely responsible for gradual reduction in P stress.
Guoyu Lan, Bangqian Chen, Chuan Yang, Rui Sun, Zhixiang Wu, and Xicai Zhang
Biogeosciences, 19, 1995–2005, https://doi.org/10.5194/bg-19-1995-2022, https://doi.org/10.5194/bg-19-1995-2022, 2022
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Little is known about the impact of rubber plantations on diversity of the Great Mekong Subregion. In this study, we uncovered latitudinal gradients of plant diversity of rubber plantations. Exotic species with high dominance result in loss of plant diversity of rubber plantations. Not all exotic species would reduce plant diversity of rubber plantations. Much more effort should be made to balance agricultural production with conservation goals in this region.
Ulrike Hiltner, Andreas Huth, and Rico Fischer
Biogeosciences, 19, 1891–1911, https://doi.org/10.5194/bg-19-1891-2022, https://doi.org/10.5194/bg-19-1891-2022, 2022
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Quantifying biomass loss rates due to stem mortality is important for estimating the role of tropical forests in the global carbon cycle. We analyse the consequences of long-term elevated stem mortality for tropical forest dynamics and biomass loss. Based on simulations, we developed a statistical model to estimate biomass loss rates of forests in different successional states from forest attributes. Assuming a doubling of tree mortality, biomass loss increased from 3.2 % yr-1 to 4.5 % yr-1.
Jon Cranko Page, Martin G. De Kauwe, Gab Abramowitz, Jamie Cleverly, Nina Hinko-Najera, Mark J. Hovenden, Yao Liu, Andy J. Pitman, and Kiona Ogle
Biogeosciences, 19, 1913–1932, https://doi.org/10.5194/bg-19-1913-2022, https://doi.org/10.5194/bg-19-1913-2022, 2022
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Although vegetation responds to climate at a wide range of timescales, models of the land carbon sink often ignore responses that do not occur instantly. In this study, we explore the timescales at which Australian ecosystems respond to climate. We identified that carbon and water fluxes can be modelled more accurately if we include environmental drivers from up to a year in the past. The importance of antecedent conditions is related to ecosystem aridity but is also influenced by other factors.
Qing Sun, Valentin H. Klaus, Raphaël Wittwer, Yujie Liu, Marcel G. A. van der Heijden, Anna K. Gilgen, and Nina Buchmann
Biogeosciences, 19, 1853–1869, https://doi.org/10.5194/bg-19-1853-2022, https://doi.org/10.5194/bg-19-1853-2022, 2022
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Drought is one of the biggest challenges for future food production globally. During a simulated drought, pea and barley mainly relied on water from shallow soil depths, independent of different cropping systems.
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...
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