A novel approach for surveying flowers as a proxy for bee pollinators using drone images
The abundance and diversity of plants and insects are important indicators of biodiversity, overall ecosystem health and agricultural production. Bees in particular are interesting indicators as they provide a key ecosystem service in many agricultural crops. Worldwide, habitat loss and fragmentatio...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-05-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23002650 |
| _version_ | 1797852011841978368 |
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| author | Michele Torresani David Kleijn Jan Peter Reinier de Vries Harm Bartholomeus Ludovico Chieffallo Roberto Cazzolla Gatti Vítězslav Moudrý Daniele Da Re Enrico Tomelleri Duccio Rocchini |
| author_facet | Michele Torresani David Kleijn Jan Peter Reinier de Vries Harm Bartholomeus Ludovico Chieffallo Roberto Cazzolla Gatti Vítězslav Moudrý Daniele Da Re Enrico Tomelleri Duccio Rocchini |
| author_sort | Michele Torresani |
| collection | DOAJ |
| description | The abundance and diversity of plants and insects are important indicators of biodiversity, overall ecosystem health and agricultural production. Bees in particular are interesting indicators as they provide a key ecosystem service in many agricultural crops. Worldwide, habitat loss and fragmentation, agricultural intensification and climate change are important drivers of plant and bee decline. Monitoring of plants and bees is a crucial first step to safeguard their diversity and the services they provide but traditional in situ methods are time consuming and expensive. Remote sensing and Earth observation have the advantages that they can cover large areas and provides repeated, spatially continuous and standardized information. However, to date it has proven challenging to use these methods to assess small-scaled species-level biodiversity components with this approach. Here we surveyed bees and flowering plants using conventional field methods in 30 grasslands along a land-use intensity gradient in the Southeast of the Netherlands. We took RGB (true colored Red-Green–Blue) images using an Unmanned Aerial Vehicle (UAV) from the same fields and tested whether remote sensing can provide accurate assessments of flower cover and diversity and, by association, bee abundance and diversity. We explored the performance of different machine learning methods: Random Forest (RF), Neural Networks (NNET) and Support-Vector Machine (SVM). To evaluate the effect of the spatial resolution on the accuracy of the estimates, we tested all approaches using images at the original spatial resolution (∼ 0.5 cm) and re-sampled at 1 cm, 2 cm and 5 cm. We generally found significant relationships between UAV RGB derived estimates of flower cover and in situ estimates of flower cover and bee abundance and diversity. The highest resolution images generally resulted in the strongest relationships, with RF and NNET methods producing considerably better results than SVM methods (flower cover RF R2 = 0.8, NNET R2 = 0.79; bee abundance RF R2 = 0.65, NNET R2 = 0.54, bee species richness RF R2 = 0.62, NNET R2 = 0.52; bee species diversity RF R2 = 0.54, NNET R2 = 0.46). Our results suggest that methods based on the coupling of UAV imagery and machine learning methods can be developed into valuable tools for large-scale, standardized and cost-effective monitoring of flower cover and therefore of an important aspect of habitat quality for bees. |
| first_indexed | 2024-04-09T19:27:09Z |
| format | Article |
| id | doaj.art-ded0483920d7474ab13c720de11a258b |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-09T19:27:09Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-ded0483920d7474ab13c720de11a258b2023-04-05T08:06:09ZengElsevierEcological Indicators1470-160X2023-05-01149110123A novel approach for surveying flowers as a proxy for bee pollinators using drone imagesMichele Torresani0David Kleijn1Jan Peter Reinier de Vries2Harm Bartholomeus3Ludovico Chieffallo4Roberto Cazzolla Gatti5Vítězslav Moudrý6Daniele Da Re7Enrico Tomelleri8Duccio Rocchini9Free University of Bolzano/Bozen, Faculty of Agricultural, Environmental and Food Sciences, Piazza Universitá/ Universitätsplatz 1, 39100 Bolzano/Bozen, Italy; BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy; Corresponding author.Plant Ecology and Nature Conservation Group, Wageningen University, Droevendaalsesteeg 3a, Wageningen 6708PB, The NetherlandsPlant Ecology and Nature Conservation Group, Wageningen University, Droevendaalsesteeg 3a, Wageningen 6708PB, The NetherlandsLaboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, The NetherlandsBIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, ItalyBIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, ItalyCzech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Spatial Sciences, Kamýcka 129, Praha - Suchdol 16500, Czech RepublicGeorges Lemaı̂tre Center for Earth and Climate Research, Earth and Life Institute, UCLouvain, Louvain-la-Neuve, BelgiumFree University of Bolzano/Bozen, Faculty of Agricultural, Environmental and Food Sciences, Piazza Universitá/ Universitätsplatz 1, 39100 Bolzano/Bozen, ItalyBIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, 40126 Bologna, Italy; Czech University of Life Sciences Prague, Faculty of Environmental Sciences, Department of Spatial Sciences, Kamýcka 129, Praha - Suchdol 16500, Czech RepublicThe abundance and diversity of plants and insects are important indicators of biodiversity, overall ecosystem health and agricultural production. Bees in particular are interesting indicators as they provide a key ecosystem service in many agricultural crops. Worldwide, habitat loss and fragmentation, agricultural intensification and climate change are important drivers of plant and bee decline. Monitoring of plants and bees is a crucial first step to safeguard their diversity and the services they provide but traditional in situ methods are time consuming and expensive. Remote sensing and Earth observation have the advantages that they can cover large areas and provides repeated, spatially continuous and standardized information. However, to date it has proven challenging to use these methods to assess small-scaled species-level biodiversity components with this approach. Here we surveyed bees and flowering plants using conventional field methods in 30 grasslands along a land-use intensity gradient in the Southeast of the Netherlands. We took RGB (true colored Red-Green–Blue) images using an Unmanned Aerial Vehicle (UAV) from the same fields and tested whether remote sensing can provide accurate assessments of flower cover and diversity and, by association, bee abundance and diversity. We explored the performance of different machine learning methods: Random Forest (RF), Neural Networks (NNET) and Support-Vector Machine (SVM). To evaluate the effect of the spatial resolution on the accuracy of the estimates, we tested all approaches using images at the original spatial resolution (∼ 0.5 cm) and re-sampled at 1 cm, 2 cm and 5 cm. We generally found significant relationships between UAV RGB derived estimates of flower cover and in situ estimates of flower cover and bee abundance and diversity. The highest resolution images generally resulted in the strongest relationships, with RF and NNET methods producing considerably better results than SVM methods (flower cover RF R2 = 0.8, NNET R2 = 0.79; bee abundance RF R2 = 0.65, NNET R2 = 0.54, bee species richness RF R2 = 0.62, NNET R2 = 0.52; bee species diversity RF R2 = 0.54, NNET R2 = 0.46). Our results suggest that methods based on the coupling of UAV imagery and machine learning methods can be developed into valuable tools for large-scale, standardized and cost-effective monitoring of flower cover and therefore of an important aspect of habitat quality for bees.http://www.sciencedirect.com/science/article/pii/S1470160X23002650BeesBiodiversityFlowering plantsMachine learningMonitoringSpatial resolution |
| spellingShingle | Michele Torresani David Kleijn Jan Peter Reinier de Vries Harm Bartholomeus Ludovico Chieffallo Roberto Cazzolla Gatti Vítězslav Moudrý Daniele Da Re Enrico Tomelleri Duccio Rocchini A novel approach for surveying flowers as a proxy for bee pollinators using drone images Ecological Indicators Bees Biodiversity Flowering plants Machine learning Monitoring Spatial resolution |
| title | A novel approach for surveying flowers as a proxy for bee pollinators using drone images |
| title_full | A novel approach for surveying flowers as a proxy for bee pollinators using drone images |
| title_fullStr | A novel approach for surveying flowers as a proxy for bee pollinators using drone images |
| title_full_unstemmed | A novel approach for surveying flowers as a proxy for bee pollinators using drone images |
| title_short | A novel approach for surveying flowers as a proxy for bee pollinators using drone images |
| title_sort | novel approach for surveying flowers as a proxy for bee pollinators using drone images |
| topic | Bees Biodiversity Flowering plants Machine learning Monitoring Spatial resolution |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23002650 |
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