Articles | Volume 22, issue 16
https://doi.org/10.5194/bg-22-4163-2025
© Author(s) 2025. 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-22-4163-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Aug 2025
Research article |
| 26 Aug 2025
| 26 Aug 2025
Inferring methane emissions from African livestock by fusing drone, tower, and satellite data
Alouette van Hove
CORRESPONDING AUTHOR
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
Kristoffer Aalstad
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
Vibeke Lind
Division of Food Production and Society, Department of Grassland and Livestock, Norwegian Institute of Bioeconomy Research (NIBIO), Tjøtta, Norway
Claudia Arndt
Mazingira Centre, International Livestock Research Institute (ILRI), Nairobi, Kenya
Vincent Odongo
Mazingira Centre, International Livestock Research Institute (ILRI), Nairobi, Kenya
Rodolfo Ceriani
Department of Agricultural and Environmental Sciences, University of Milan (UNIMI), Milan, Italy
Department of Environmental Science and Policy, University of Milan (UNIMI), Milan, Italy
Francesco Fava
Department of Environmental Science and Policy, University of Milan (UNIMI), Milan, Italy
John Hulth
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
Norbert Pirk
Department of Geosciences, University of Oslo (UiO), Oslo, Norway
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Esteban Alonso-González, Ethan Gutmann, Kristoffer Aalstad, Abbas Fayad, Marine Bouchet, and Simon Gascoin
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Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.
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Short summary
Research on methane emissions from African livestock is limited. We used a probabilistic method fusing drone and flux tower observations with an atmospheric model to estimate emissions from various herds. This approach proved robust under non-stationary wind conditions and effective in estimating emissions as low as 100 g h-1. We also detected spectral anomalies in satellite data associated with the herds. Our method can be used for diverse point sources, thereby improving emission inventories.
Research on methane emissions from African livestock is limited. We used a probabilistic method...
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