Estimation of forage biomass and vegetation cover in grasslands using UAV imagery
Estimation of forage biomass and vegetation cover in grasslands using UAV imagery
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Full item details
- creativework.keywords - en
- Grasslands
- Prairies
- Biomasse
- Biomass
- Traitement d'images--Logiciels
- creativework.keywords - fr
- Image processing--Computer programs
- Plantes--Développement
- Plants--Development
- Télédétection
- Remote sensing
- dc.contributor.author
- Théau, Jérôme
- Lauzier-Hudon, Étienne
- Aubé, Lydiane
- Devillers, Nicolas
- dc.date.accepted
- 2021-01-07
- dc.date.accessioned
- 2024-10-11T19:46:27Z
- dc.date.available
- 2024-10-11T19:46:27Z
- dc.date.issued
- 2021-01-25
- dc.date.submitted
- 2020-07-15
- dc.description.abstract - en
- Grasslands are among the most widespread ecosystems on Earth and among the most degraded. Their characterization and monitoring are generally based on field measurements, which are incomplete spatially and temporally. The recent advent of unmanned aerial vehicles (UAV) provides data at unprecedented spatial and temporal resolutions. This study aims to test and compare three approaches based on multispectral imagery acquired by UAV to estimate forage biomass or vegetation cover in grasslands. The study site is composed of 30 pasture plots (25 × 50 m), 5 bare soil plots (25 x 50), and 6 control plots (5 × 5 m) on a 14-ha field maintained at various biomass levels by grazing rotations and clipping over a complete growing season. A total of 14 flights were performed. A first approach based on structure from motion was used to generate a volumetric-based biomass estimation model (R2 of 0.93 and 0.94 for fresh biomass [FM] and dry biomass [DM], respectively). This approach is not very sensitive to low vegetation levels but is accurate for FM estimation greater than 0.5 kg/m2 (0.1 kg DM/m2). The Green Normalized Difference Vegetation Index (GNDVI) was selected to develop two additional approaches. One is based on a regression biomass prediction model (R2 of 0.80 and 0.66 for FM and DM, respectively) and leads to an accurate estimation at levels of FM lower than 3 kg/m2 (0.6 kg DM/m2). The other approach is based on a classification of vegetation cover from clustering of GNDVI values in four classes. This approach is more qualitative than the other ones but more robust and generalizable. These three approaches are relatively simple to use and applicable in an operational context. They are also complementary and can be adapted to specific applications in grassland characterization.
- dc.identifier.citation
- Théau, J., Lauzier-Hudon, E., Aubé, L., & Devillers, N. (2021). Estimation of forage biomass and vegetation cover in grasslands using UAV imagery. PLoS ONE, 16(1), Article e0245784. https://doi.org/10.1371/journal.pone.0245784
- dc.identifier.doi
- https://doi.org/10.1371/journal.pone.0245784
- dc.identifier.issn
- 1932-6203
- dc.identifier.uri
- https://open-science.canada.ca/handle/123456789/3062
- dc.language.iso
- en
- dc.publisher
- Public Library of Science
- dc.rights - en
- Creative Commons Attribution 4.0 International (CC BY 4.0)
- dc.rights - fr
- Creative Commons Attribution 4.0 International (CC BY 4.0)
- dc.rights.openaccesslevel - en
- Gold
- dc.rights.openaccesslevel - fr
- Or
- dc.rights.uri - en
- https://creativecommons.org/licenses/by/4.0/
- dc.rights.uri - fr
- https://creativecommons.org/licenses/by/4.0/deed.fr
- dc.subject - en
- Nature and environment
- Science and technology
- dc.subject - fr
- Nature et environnement
- Sciences et technologie
- dc.subject.en - en
- Nature and environment
- Science and technology
- dc.subject.fr - fr
- Nature et environnement
- Sciences et technologie
- dc.title - en
- Estimation of forage biomass and vegetation cover in grasslands using UAV imagery
- dc.type - en
- Article
- dc.type - fr
- Article
- local.acceptedmanuscript.articlenum
- e0245784
- local.article.journalissue
- 1
- local.article.journaltitle
- PLoS ONE
- local.article.journalvolume
- 16
- local.pagination
- 1-18
- local.peerreview - en
- Yes
- local.peerreview - fr
- Oui
- local.requestdoi
- No
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