Analysing a Multi-Criteria Analysis to Prioritise Munition Piles in the German Baltic Sea for Remediation
Alexander Pilz
Institute for Geoinformatics, University of Münster, Münster, Germany
Torsten Frey
GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany
Edzer Pebesma
Institute for Geoinformatics, University of Münster, Münster, Germany
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Henning Teickner, Edzer Pebesma, and Klaus-Holger Knorr
Biogeosciences, 22, 417–433, https://doi.org/10.5194/bg-22-417-2025, https://doi.org/10.5194/bg-22-417-2025, 2025
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Decomposition rates for Sphagnum mosses, the main peat-forming plants in northern peatlands, are often derived from litterbag experiments. Here, we estimate initial leaching losses from available Sphagnum litterbag experiments and analyze how decomposition rates are biased when initial leaching losses are ignored. Our analyses indicate that initial leaching losses range between 3 to 18 mass-% and that this may result in overestimated mass losses when extrapolated to several decades.
Carles Milà, Marvin Ludwig, Edzer Pebesma, Cathryn Tonne, and Hanna Meyer
Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024, https://doi.org/10.5194/gmd-17-6007-2024, 2024
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Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
Henning Teickner, Edzer Pebesma, and Klaus-Holger Knorr
EGUsphere, https://doi.org/10.5194/egusphere-2024-1739, https://doi.org/10.5194/egusphere-2024-1739, 2024
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The Holocene Peatland Model (HPM) is a widely used peatland model to understand and predict long-term peatland dynamics. Here, we test whether the HPM can predict Sphagnum litterbag decomposition rates from oxic to anoxic conditions. Our results indicate that decomposition rates change more gradually from oxic to anoxic conditions and may be underestimated under anoxic conditions, possibly because the effect of water table fluctuations on decomposition rates is not considered.
M. Ludwig, J. Bahlmann, E. Pebesma, and H. Meyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 135–141, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-135-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-135-2022, 2022
A. Joshi, E. Pebesma, R. Henriques, and M. Appel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W3, 43–47, https://doi.org/10.5194/isprs-archives-XLII-5-W3-43-2019, https://doi.org/10.5194/isprs-archives-XLII-5-W3-43-2019, 2019
Sergio Ibarra-Espinosa, Rita Ynoue, Shane O'Sullivan, Edzer Pebesma, María de Fátima Andrade, and Mauricio Osses
Geosci. Model Dev., 11, 2209–2229, https://doi.org/10.5194/gmd-11-2209-2018, https://doi.org/10.5194/gmd-11-2209-2018, 2018
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An emissions inventory is a compilation of the mass of pollutants released by different sources. The quantification of vehicular emissions is difficult because these sources are in movement across streets. Also, emissions processes are multiple and complex. In this paper, we present an open-source software for calculating spatial vehicular emissions, including exhaust, evaporation and wear, named VEIN. The software is an R package available at
https://github.com/atmoschem/vein.