Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning
The decline of segetal flora species across Europe, driven by intensified agricultural practices, is impacting other taxa and ecosystem functions. Result-based payments to farmers offer an effective solution to conserve these species, but the high cost of biodiversity monitoring remains a challenge....
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24012378 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850065075768918016 |
|---|---|
| author | Caterina Barrasso Robert Krüger Anette Eltner Anna F. Cord |
| author_facet | Caterina Barrasso Robert Krüger Anette Eltner Anna F. Cord |
| author_sort | Caterina Barrasso |
| collection | DOAJ |
| description | The decline of segetal flora species across Europe, driven by intensified agricultural practices, is impacting other taxa and ecosystem functions. Result-based payments to farmers offer an effective solution to conserve these species, but the high cost of biodiversity monitoring remains a challenge. In this study, we conducted UAV flights with an RGB camera and used the deep learning model YOLO to detect these species in four winter barley fields under different management intensities in Germany. Field measurements of plant traits were used to evaluate their impact on species detectability. Additionally, we investigated the potential of spatial co-occurrence and canopy height heterogeneity to predict the presence of species difficult to detect by UAVs. We found that half of the species observed could be remotely detected, with a minimum ground sampling distance (GSD) of 1.22 mm required for accurate annotation. The same detection ratio was estimated for key indicator species not present in our study area based on trait information. Plant height was crucial for species detection, with accuracy ranging between 49–100 %. YOLO models effectively predicted species from images taken at 40 m, reducing the monitoring time to eight minutes per hectare. Co-occurrence with UAV-detectable species and canopy height heterogeneity proved promising for identifying areas where undetectable species are likely to occur, although further research is needed for landscape-level applications. Our study highlights the potential for large-scale, cost-effective monitoring of segetal flora species in agricultural landscapes, and provides valuable insights for developing robust ‘smart indicators’ for future biodiversity monitoring. |
| format | Article |
| id | doaj-art-cb4d80ebb3bb4cda8c63773304b8099b |
| institution | DOAJ |
| issn | 1470-160X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-cb4d80ebb3bb4cda8c63773304b8099b2025-08-20T02:49:06ZengElsevierEcological Indicators1470-160X2024-12-0116911278010.1016/j.ecolind.2024.112780Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learningCaterina Barrasso0Robert Krüger1Anette Eltner2Anna F. Cord3Chair of Computational Landscape Ecology, TUD Dresden University of Technology, Germany; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Germany; Corresponding author.Institute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, GermanyInstitute of Photogrammetry and Remote Sensing, TUD Dresden University of Technology, GermanyChair of Computational Landscape Ecology, TUD Dresden University of Technology, Germany; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Germany; Agro-Ecological Modeling Group, Institute of Crop Science and Resource Conservation, University of Bonn, GermanyThe decline of segetal flora species across Europe, driven by intensified agricultural practices, is impacting other taxa and ecosystem functions. Result-based payments to farmers offer an effective solution to conserve these species, but the high cost of biodiversity monitoring remains a challenge. In this study, we conducted UAV flights with an RGB camera and used the deep learning model YOLO to detect these species in four winter barley fields under different management intensities in Germany. Field measurements of plant traits were used to evaluate their impact on species detectability. Additionally, we investigated the potential of spatial co-occurrence and canopy height heterogeneity to predict the presence of species difficult to detect by UAVs. We found that half of the species observed could be remotely detected, with a minimum ground sampling distance (GSD) of 1.22 mm required for accurate annotation. The same detection ratio was estimated for key indicator species not present in our study area based on trait information. Plant height was crucial for species detection, with accuracy ranging between 49–100 %. YOLO models effectively predicted species from images taken at 40 m, reducing the monitoring time to eight minutes per hectare. Co-occurrence with UAV-detectable species and canopy height heterogeneity proved promising for identifying areas where undetectable species are likely to occur, although further research is needed for landscape-level applications. Our study highlights the potential for large-scale, cost-effective monitoring of segetal flora species in agricultural landscapes, and provides valuable insights for developing robust ‘smart indicators’ for future biodiversity monitoring.http://www.sciencedirect.com/science/article/pii/S1470160X24012378Segetal floraIndicator speciesResult-based paymentsAgricultural landscapesDeep learningUAV-based RGB imagery |
| spellingShingle | Caterina Barrasso Robert Krüger Anette Eltner Anna F. Cord Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning Ecological Indicators Segetal flora Indicator species Result-based payments Agricultural landscapes Deep learning UAV-based RGB imagery |
| title | Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning |
| title_full | Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning |
| title_fullStr | Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning |
| title_full_unstemmed | Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning |
| title_short | Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning |
| title_sort | mapping indicator species of segetal flora for result based payments in arable land using uav imagery and deep learning |
| topic | Segetal flora Indicator species Result-based payments Agricultural landscapes Deep learning UAV-based RGB imagery |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24012378 |
| work_keys_str_mv | AT caterinabarrasso mappingindicatorspeciesofsegetalfloraforresultbasedpaymentsinarablelandusinguavimageryanddeeplearning AT robertkruger mappingindicatorspeciesofsegetalfloraforresultbasedpaymentsinarablelandusinguavimageryanddeeplearning AT anetteeltner mappingindicatorspeciesofsegetalfloraforresultbasedpaymentsinarablelandusinguavimageryanddeeplearning AT annafcord mappingindicatorspeciesofsegetalfloraforresultbasedpaymentsinarablelandusinguavimageryanddeeplearning |