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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-02-01
|
| Series: | Remote Sensing in Ecology and Conservation |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/rse2.400 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849394241903525888 |
|---|---|
| author | Jérémy Monsimet Sofie Sjögersten Nathan J. Sanders Micael Jonsson Johan Olofsson Matthias Siewert |
| author_facet | Jérémy Monsimet Sofie Sjögersten Nathan J. Sanders Micael Jonsson Johan Olofsson Matthias Siewert |
| author_sort | Jérémy Monsimet |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8600c7cb5ecf4da3901dbd54dff69235 |
| institution | Kabale University |
| issn | 2056-3485 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Remote Sensing in Ecology and Conservation |
| spelling | doaj-art-8600c7cb5ecf4da3901dbd54dff692352025-08-20T03:40:02ZengWileyRemote Sensing in Ecology and Conservation2056-34852025-02-0111151910.1002/rse2.400UAV data and deep learning: efficient tools to map ant mounds and their ecological impactJérémy Monsimet0Sofie Sjögersten1Nathan J. Sanders2Micael Jonsson3Johan Olofsson4Matthias Siewert5Department of Ecology and Environmental Science Umeå University Umeå SwedenSchool of Biosciences, University of Nottingham Loughborough UKDepartment of Ecology and Evolutionary Biology University of Michigan Ann Arbor USADepartment of Ecology and Environmental Science Umeå University Umeå SwedenDepartment of Ecology and Environmental Science Umeå University Umeå SwedenDepartment of Ecology and Environmental Science Umeå University Umeå SwedenAbstract 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.https://doi.org/10.1002/rse2.400Ant moundsFormica sp.object detectiontreelineUAV |
| spellingShingle | Jérémy Monsimet Sofie Sjögersten Nathan J. Sanders Micael Jonsson Johan Olofsson Matthias Siewert UAV data and deep learning: efficient tools to map ant mounds and their ecological impact Remote Sensing in Ecology and Conservation Ant mounds Formica sp. object detection treeline UAV |
| title | 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_short | 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 |
| topic | Ant mounds Formica sp. object detection treeline UAV |
| url | https://doi.org/10.1002/rse2.400 |
| work_keys_str_mv | AT jeremymonsimet uavdataanddeeplearningefficienttoolstomapantmoundsandtheirecologicalimpact AT sofiesjogersten uavdataanddeeplearningefficienttoolstomapantmoundsandtheirecologicalimpact AT nathanjsanders uavdataanddeeplearningefficienttoolstomapantmoundsandtheirecologicalimpact AT micaeljonsson uavdataanddeeplearningefficienttoolstomapantmoundsandtheirecologicalimpact AT johanolofsson uavdataanddeeplearningefficienttoolstomapantmoundsandtheirecologicalimpact AT matthiassiewert uavdataanddeeplearningefficienttoolstomapantmoundsandtheirecologicalimpact |