Articles | Volume 21, issue 6
https://doi.org/10.5194/bg-21-1411-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-1411-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Mar 2024
Research article |
| 19 Mar 2024
| 19 Mar 2024
Synergistic use of Sentinel-2 and UAV-derived data for plant fractional cover distribution mapping of coastal meadows with digital elevation models
Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia
Miguel Villoslada
Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland
Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia
Raymond D. Ward
Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia
School of Geography, Queen Mary University of London, London E1 4NS, UK
Thaisa F. Bergamo
Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia
Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland
Chris B. Joyce
Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia
JBA Consulting, Haywards Heath, East Sussex, UK
Kalev Sepp
Institute of Agriculture and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia
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Cited
15 citations as recorded by crossref.
- UAS Remote Sensing for Coastal Wetland Vegetation Biomass Estimation: A Destructive vs. Non-Destructive Sampling Experiment G. Morgan et al. https://doi.org/10.3390/rs17142335
- A systematic review of unmanned aerial vehicles (UAVs) for coastal ecosystem monitoring R. Makumbura et al. https://doi.org/10.1016/j.srs.2026.100438
- How UAVs Bridge the Sea–Air–Space Gaps in Ocean Observing: Technological chains, collaborative frameworks, and system implementation G. Zhou et al. https://doi.org/10.1109/MGRS.2026.3685716
- Generation of a Multispectral Dataset Based on Satellite Images for Poverty Prediction at Municipal Level in Mexico A. Castillo González et al. https://doi.org/10.61467/2007.1558.2026.v17i3.1283
- Time series changes and influencing factors of fractional vegetation coverage under weed competition in wheat field ecosystems through remote sensing G. Yang et al. https://doi.org/10.1080/17538947.2025.2504134
- Integrating UAV and Landsat data: A two-scale approach to topsoil moisture mapping in coastal wetlands R. Prentice et al. https://doi.org/10.1016/j.ecoinf.2025.103197
- Unmixing marsh vegetation species across multiple sensors and spatial scales Z. Yang et al. https://doi.org/10.1080/15481603.2025.2481689
- A cross-scale synergistic framework integrating UAV LiDAR and sentinel-2 imagery for fine-grained vegetation mapping in coastal wetlands Y. Wang et al. https://doi.org/10.1080/17538947.2026.2714612
- Machine and Deep Learning for Wetland Mapping and Bird-Habitat Monitoring: A Systematic Review of Remote-Sensing Applications (2015–April 2025) M. Zerrouk et al. https://doi.org/10.3390/rs17213605
- Classification of salt marsh vegetation in the Yangtze Estuary using remote sensing and UAV technologies J. Cao et al. https://doi.org/10.1088/2515-7620/ae9d21
- Integrating multi-source remote sensing and UAV data for landscape-scale soil carbon mapping: uncertainty hotspots at ecotone boundaries in forest-steppe ecosystems D. Bardashov et al. https://doi.org/10.1080/01431161.2026.2676255
- Wetland mapping in the Liaohe River Estuary using multi-source remote sensing image feature selection J. He et al. https://doi.org/10.1080/01431161.2024.2391588
- Fine-Scale Identification of Lodged Spartina alterniflora Using UAV Multispectral Imagery and LiDAR Data Y. Li et al. https://doi.org/10.3390/app16178428
- Coastal Sustainability and Environmental Resilience in France: A Decadal Assessment of Littoral Dynamics Using Satellite Images P. Lemenkova https://doi.org/10.3390/coasts6030027
- A UAV-to-Satellite Scaling Framework for Monitoring Cotton Boll Opening Using Sentinel-2 Earth Observations A. Manimozhian et al. https://doi.org/10.3390/land15081361
15 citations as recorded by crossref.
- UAS Remote Sensing for Coastal Wetland Vegetation Biomass Estimation: A Destructive vs. Non-Destructive Sampling Experiment G. Morgan et al. https://doi.org/10.3390/rs17142335
- A systematic review of unmanned aerial vehicles (UAVs) for coastal ecosystem monitoring R. Makumbura et al. https://doi.org/10.1016/j.srs.2026.100438
- How UAVs Bridge the Sea–Air–Space Gaps in Ocean Observing: Technological chains, collaborative frameworks, and system implementation G. Zhou et al. https://doi.org/10.1109/MGRS.2026.3685716
- Generation of a Multispectral Dataset Based on Satellite Images for Poverty Prediction at Municipal Level in Mexico A. Castillo González et al. https://doi.org/10.61467/2007.1558.2026.v17i3.1283
- Time series changes and influencing factors of fractional vegetation coverage under weed competition in wheat field ecosystems through remote sensing G. Yang et al. https://doi.org/10.1080/17538947.2025.2504134
- Integrating UAV and Landsat data: A two-scale approach to topsoil moisture mapping in coastal wetlands R. Prentice et al. https://doi.org/10.1016/j.ecoinf.2025.103197
- Unmixing marsh vegetation species across multiple sensors and spatial scales Z. Yang et al. https://doi.org/10.1080/15481603.2025.2481689
- A cross-scale synergistic framework integrating UAV LiDAR and sentinel-2 imagery for fine-grained vegetation mapping in coastal wetlands Y. Wang et al. https://doi.org/10.1080/17538947.2026.2714612
- Machine and Deep Learning for Wetland Mapping and Bird-Habitat Monitoring: A Systematic Review of Remote-Sensing Applications (2015–April 2025) M. Zerrouk et al. https://doi.org/10.3390/rs17213605
- Classification of salt marsh vegetation in the Yangtze Estuary using remote sensing and UAV technologies J. Cao et al. https://doi.org/10.1088/2515-7620/ae9d21
- Integrating multi-source remote sensing and UAV data for landscape-scale soil carbon mapping: uncertainty hotspots at ecotone boundaries in forest-steppe ecosystems D. Bardashov et al. https://doi.org/10.1080/01431161.2026.2676255
- Wetland mapping in the Liaohe River Estuary using multi-source remote sensing image feature selection J. He et al. https://doi.org/10.1080/01431161.2024.2391588
- Fine-Scale Identification of Lodged Spartina alterniflora Using UAV Multispectral Imagery and LiDAR Data Y. Li et al. https://doi.org/10.3390/app16178428
- Coastal Sustainability and Environmental Resilience in France: A Decadal Assessment of Littoral Dynamics Using Satellite Images P. Lemenkova https://doi.org/10.3390/coasts6030027
- A UAV-to-Satellite Scaling Framework for Monitoring Cotton Boll Opening Using Sentinel-2 Earth Observations A. Manimozhian et al. https://doi.org/10.3390/land15081361
Saved (final revised paper)
Latest update: 20 Sep 2026
Short summary
Despite hosting a wide range of ecosystem services, coastal wetlands face threats from global changes. This study models the plant fractional cover of plant communities in Estonian coastal meadows with a synergistic use of drone, satellite imagery and digital elevation models. This approach highlights the significant contribution of digital elevation models to multispectral data, enabling the modelling of heterogeneous plant community distributions in such wetlands.
Despite hosting a wide range of ecosystem services, coastal wetlands face threats from global...
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