Multilabel classification of peatland plant species from high-resolution drone images
Biodiversity monitoring programs are essential for detecting changes in species distributions and correlating these changes with biotic and abiotic factors. This information is crucial for identifying early problems before they become too difficult to address and for implementing effective managemen...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003759 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233575000408064 |
|---|---|
| author | Charles Picard-Krashevski Mickaël Germain Etienne Laliberté |
| author_facet | Charles Picard-Krashevski Mickaël Germain Etienne Laliberté |
| author_sort | Charles Picard-Krashevski |
| collection | DOAJ |
| description | Biodiversity monitoring programs are essential for detecting changes in species distributions and correlating these changes with biotic and abiotic factors. This information is crucial for identifying early problems before they become too difficult to address and for implementing effective management strategies. Traditionally, biodiversity monitoring for small plant species has relied on the quadrat method, which requires botanists to identify species in the field. While this method has its advantages, it is limited by the availability of botanists, restricting the scale of monitoring programs. In this study, we explored the potential of using high-resolution photos and artificial intelligence to estimate small plant species cover in peatlands, thereby reducing the need for field-based species identification by botanists. Our approach involves dividing quadrat images into smaller tiles, applying a multi-label classification model to each tile, and calculating species cover based on the identified tiles. Data were collected from 32 sites across Quebec, and images were annotated for five common species: Chamaedaphne calyculata, Kalmia angustifolia, Andromeda polifolia, Rhododendron groenlandicum, and Larix laricina. Our model achieved a global F1 score of 71.68 %, with the highest-performing species (Larix laricina) reaching 87.17 %. Although some species showed lower performance, the estimated species cover by our model in a whole quadrat was comparable to traditional methods. Our results demonstrate that this method offers significant advantages for monitoring broad changes in vegetation. |
| format | Article |
| id | doaj-art-d37ded3ea7a340bcb8c3d4cc89190408 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-d37ded3ea7a340bcb8c3d4cc891904082025-08-20T05:05:59ZengElsevierEcological Informatics1574-95412025-12-019010336610.1016/j.ecoinf.2025.103366Multilabel classification of peatland plant species from high-resolution drone imagesCharles Picard-Krashevski0Mickaël Germain1Etienne Laliberté2Institut de recherche en biologie végétale, Département de sciences biologiques, Université de Montréal, Université de Montréal, 4101 Sherbrooke Est, Montréal, Québec H1X 2B2, Canada; Corresponding author.Centre d'applications et de recherches en télédétection (CARTEL), Département de géomatique appliquée, Université de Sherbrooke, 2500, boulevard de l'Université, Sherbrooke, QC J1K 2R1, CanadaInstitut de recherche en biologie végétale, Département de sciences biologiques, Université de Montréal, Université de Montréal, 4101 Sherbrooke Est, Montréal, Québec H1X 2B2, CanadaBiodiversity monitoring programs are essential for detecting changes in species distributions and correlating these changes with biotic and abiotic factors. This information is crucial for identifying early problems before they become too difficult to address and for implementing effective management strategies. Traditionally, biodiversity monitoring for small plant species has relied on the quadrat method, which requires botanists to identify species in the field. While this method has its advantages, it is limited by the availability of botanists, restricting the scale of monitoring programs. In this study, we explored the potential of using high-resolution photos and artificial intelligence to estimate small plant species cover in peatlands, thereby reducing the need for field-based species identification by botanists. Our approach involves dividing quadrat images into smaller tiles, applying a multi-label classification model to each tile, and calculating species cover based on the identified tiles. Data were collected from 32 sites across Quebec, and images were annotated for five common species: Chamaedaphne calyculata, Kalmia angustifolia, Andromeda polifolia, Rhododendron groenlandicum, and Larix laricina. Our model achieved a global F1 score of 71.68 %, with the highest-performing species (Larix laricina) reaching 87.17 %. Although some species showed lower performance, the estimated species cover by our model in a whole quadrat was comparable to traditional methods. Our results demonstrate that this method offers significant advantages for monitoring broad changes in vegetation.http://www.sciencedirect.com/science/article/pii/S1574954125003759Artificial intelligenceComputer visionConvolutional neural networkDeep learningMulti-label classificationRemote sensing |
| spellingShingle | Charles Picard-Krashevski Mickaël Germain Etienne Laliberté Multilabel classification of peatland plant species from high-resolution drone images Ecological Informatics Artificial intelligence Computer vision Convolutional neural network Deep learning Multi-label classification Remote sensing |
| title | Multilabel classification of peatland plant species from high-resolution drone images |
| title_full | Multilabel classification of peatland plant species from high-resolution drone images |
| title_fullStr | Multilabel classification of peatland plant species from high-resolution drone images |
| title_full_unstemmed | Multilabel classification of peatland plant species from high-resolution drone images |
| title_short | Multilabel classification of peatland plant species from high-resolution drone images |
| title_sort | multilabel classification of peatland plant species from high resolution drone images |
| topic | Artificial intelligence Computer vision Convolutional neural network Deep learning Multi-label classification Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003759 |
| work_keys_str_mv | AT charlespicardkrashevski multilabelclassificationofpeatlandplantspeciesfromhighresolutiondroneimages AT mickaelgermain multilabelclassificationofpeatlandplantspeciesfromhighresolutiondroneimages AT etiennelaliberte multilabelclassificationofpeatlandplantspeciesfromhighresolutiondroneimages |