UAV data and deep learning: efficient tools to map ant mounds and their ecological impact
Abstract High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in e...
Published in: | Remote Sensing in Ecology and Conservation |
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Online Access: | http://dx.doi.org/10.1002/rse2.400 https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.400 |
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crwiley:10.1002/rse2.400 2024-06-23T07:51:48+00:00 UAV data and deep learning: efficient tools to map ant mounds and their ecological impact Monsimet, Jérémy Sjögersten, Sofie Sanders, Nathan J. Jonsson, Micael Olofsson, Johan Siewert, Matthias Svenska Forskningsrådet Formas 2024 http://dx.doi.org/10.1002/rse2.400 https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.400 en eng Wiley http://creativecommons.org/licenses/by-nc-nd/4.0/ Remote Sensing in Ecology and Conservation ISSN 2056-3485 2056-3485 journal-article 2024 crwiley https://doi.org/10.1002/rse2.400 2024-06-13T04:23:04Z Abstract High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact. Article in Journal/Newspaper Betula nana Tundra Wiley Online Library Remote Sensing in Ecology and Conservation |
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English |
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Abstract High‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact. |
author2 |
Svenska Forskningsrådet Formas |
format |
Article in Journal/Newspaper |
author |
Monsimet, Jérémy Sjögersten, Sofie Sanders, Nathan J. Jonsson, Micael Olofsson, Johan Siewert, Matthias |
spellingShingle |
Monsimet, Jérémy Sjögersten, Sofie Sanders, Nathan J. Jonsson, Micael Olofsson, Johan Siewert, Matthias UAV data and deep learning: efficient tools to map ant mounds and their ecological impact |
author_facet |
Monsimet, Jérémy Sjögersten, Sofie Sanders, Nathan J. Jonsson, Micael Olofsson, Johan Siewert, Matthias |
author_sort |
Monsimet, Jérémy |
title |
UAV data and deep learning: efficient tools to map ant mounds and their ecological impact |
title_short |
UAV data and deep learning: efficient tools to map ant mounds and their ecological impact |
title_full |
UAV data and deep learning: efficient tools to map ant mounds and their ecological impact |
title_fullStr |
UAV data and deep learning: efficient tools to map ant mounds and their ecological impact |
title_full_unstemmed |
UAV data and deep learning: efficient tools to map ant mounds and their ecological impact |
title_sort |
uav data and deep learning: efficient tools to map ant mounds and their ecological impact |
publisher |
Wiley |
publishDate |
2024 |
url |
http://dx.doi.org/10.1002/rse2.400 https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.400 |
genre |
Betula nana Tundra |
genre_facet |
Betula nana Tundra |
op_source |
Remote Sensing in Ecology and Conservation ISSN 2056-3485 2056-3485 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
op_doi |
https://doi.org/10.1002/rse2.400 |
container_title |
Remote Sensing in Ecology and Conservation |
_version_ |
1802642925908131840 |