Inferring methane emissions from African livestock by fusing drone, tower, and satellite data
<p>Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observation...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-08-01
|
| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/22/4163/2025/bg-22-4163-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849222002325323776 |
|---|---|
| author | A. van Hove K. Aalstad V. Lind C. Arndt V. Odongo R. Ceriani R. Ceriani F. Fava J. Hulth N. Pirk |
| author_facet | A. van Hove K. Aalstad V. Lind C. Arndt V. Odongo R. Ceriani R. Ceriani F. Fava J. Hulth N. Pirk |
| author_sort | A. van Hove |
| collection | DOAJ |
| description | <p>Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an atmospheric dispersion model. We used our novel Bayesian inference approach to estimate emissions from various ruminant livestock species in Kenya, including diverse herds of cattle, goats, and sheep, as well as camels, for which methane emission estimates are particularly sparse. Our Bayesian estimates aligned with Tier 2 emission values of the Intergovernmental Panel on Climate Change. In addition, we observed the hypothesized increase in methane emissions after feeding. Our findings suggest that the Bayesian inference method is more robust under non-stationary wind conditions compared to a conventional mass balance approach using drone observations. Furthermore, the Bayesian inference method performed better in quantifying emissions from weaker sources, estimating methane emission rates as low as 100 <span class="inline-formula">g h<sup>−1</sup></span>. We found a <span class="inline-formula">±</span> 50 % uncertainty in emission rate estimates for these weaker sources, such as sheep and goat herds, which reduced to <span class="inline-formula">±</span> 12 % for stronger sources, like cattle herds emitting 1000–1500 <span class="inline-formula">g h<sup>−1</sup></span>. Finally, we showed that radiance anomalies identified in hyperspectral satellite data can inform the planning of flight paths for targeted drone missions in areas where source locations are unknown, as these anomalies may serve as indicators of potential methane sources. These promising results demonstrate the efficacy of the Bayesian inference method for source term estimation. Future applications of drone-based Bayesian inference could extend to estimating methane emissions in Africa and other regions from various sources with complex spatiotemporal emission patterns, such as wetlands, landfills, and wastewater disposal sites. The Bayesian observing system could thereby contribute to the improvement of emission inventories and verification of other emission estimation methods.</p> |
| format | Article |
| id | doaj-art-e961b9c5ce694e92afba89406491fe9b |
| institution | Kabale University |
| issn | 1726-4170 1726-4189 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Biogeosciences |
| spelling | doaj-art-e961b9c5ce694e92afba89406491fe9b2025-08-26T09:56:46ZengCopernicus PublicationsBiogeosciences1726-41701726-41892025-08-01224163418610.5194/bg-22-4163-2025Inferring methane emissions from African livestock by fusing drone, tower, and satellite dataA. van Hove0K. Aalstad1V. Lind2C. Arndt3V. Odongo4R. Ceriani5R. Ceriani6F. Fava7J. Hulth8N. Pirk9Department of Geosciences, University of Oslo (UiO), Oslo, NorwayDepartment of Geosciences, University of Oslo (UiO), Oslo, NorwayDivision of Food Production and Society, Department of Grassland and Livestock, Norwegian Institute of Bioeconomy Research (NIBIO), Tjøtta, NorwayMazingira Centre, International Livestock Research Institute (ILRI), Nairobi, KenyaMazingira Centre, International Livestock Research Institute (ILRI), Nairobi, KenyaDepartment of Agricultural and Environmental Sciences, University of Milan (UNIMI), Milan, ItalyDepartment of Environmental Science and Policy, University of Milan (UNIMI), Milan, ItalyDepartment of Environmental Science and Policy, University of Milan (UNIMI), Milan, ItalyDepartment of Geosciences, University of Oslo (UiO), Oslo, NorwayDepartment of Geosciences, University of Oslo (UiO), Oslo, Norway<p>Considerable uncertainties and unknowns remain in the regional mapping of methane sources, especially in the extensive agricultural areas of Africa. To address this issue, we developed an observing system that estimates methane emission rates by assimilating drone and flux tower observations into an atmospheric dispersion model. We used our novel Bayesian inference approach to estimate emissions from various ruminant livestock species in Kenya, including diverse herds of cattle, goats, and sheep, as well as camels, for which methane emission estimates are particularly sparse. Our Bayesian estimates aligned with Tier 2 emission values of the Intergovernmental Panel on Climate Change. In addition, we observed the hypothesized increase in methane emissions after feeding. Our findings suggest that the Bayesian inference method is more robust under non-stationary wind conditions compared to a conventional mass balance approach using drone observations. Furthermore, the Bayesian inference method performed better in quantifying emissions from weaker sources, estimating methane emission rates as low as 100 <span class="inline-formula">g h<sup>−1</sup></span>. We found a <span class="inline-formula">±</span> 50 % uncertainty in emission rate estimates for these weaker sources, such as sheep and goat herds, which reduced to <span class="inline-formula">±</span> 12 % for stronger sources, like cattle herds emitting 1000–1500 <span class="inline-formula">g h<sup>−1</sup></span>. Finally, we showed that radiance anomalies identified in hyperspectral satellite data can inform the planning of flight paths for targeted drone missions in areas where source locations are unknown, as these anomalies may serve as indicators of potential methane sources. These promising results demonstrate the efficacy of the Bayesian inference method for source term estimation. Future applications of drone-based Bayesian inference could extend to estimating methane emissions in Africa and other regions from various sources with complex spatiotemporal emission patterns, such as wetlands, landfills, and wastewater disposal sites. The Bayesian observing system could thereby contribute to the improvement of emission inventories and verification of other emission estimation methods.</p>https://bg.copernicus.org/articles/22/4163/2025/bg-22-4163-2025.pdf |
| spellingShingle | A. van Hove K. Aalstad V. Lind C. Arndt V. Odongo R. Ceriani R. Ceriani F. Fava J. Hulth N. Pirk Inferring methane emissions from African livestock by fusing drone, tower, and satellite data Biogeosciences |
| title | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_full | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_fullStr | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_full_unstemmed | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_short | Inferring methane emissions from African livestock by fusing drone, tower, and satellite data |
| title_sort | inferring methane emissions from african livestock by fusing drone tower and satellite data |
| url | https://bg.copernicus.org/articles/22/4163/2025/bg-22-4163-2025.pdf |
| work_keys_str_mv | AT avanhove inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT kaalstad inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT vlind inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT carndt inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT vodongo inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT rceriani inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT rceriani inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT ffava inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT jhulth inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata AT npirk inferringmethaneemissionsfromafricanlivestockbyfusingdronetowerandsatellitedata |