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
Related subject area
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Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity
Geographically divergent trends in snow disappearance timing and fire ignitions across boreal North America
Dune belt restoration effectiveness assessed by UAV topographic surveys (northern Adriatic coast, Italy)
High-resolution data reveal a surge of biomass loss from temperate and Atlantic pine forests, contextualizing the 2022 fire season distinctiveness in France
Forest Types Show Divergent Biophysical Responses After Fire: Challenges to Ecological Modeling
Local environmental context drives heterogeneity of early succession dynamics in alpine glacier forefields
Hamadou Balde, Gabriel Hmimina, Yves Goulas, Gwendal Latouche, Abderrahmane Ounis, and Kamel Soudani
Biogeosciences, 21, 1259–1276, https://doi.org/10.5194/bg-21-1259-2024, https://doi.org/10.5194/bg-21-1259-2024, 2024
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We show that FyieldLIF was not correlated with SIFy at the diurnal timescale, and the diurnal patterns in SIF and PAR did not match under clear-sky conditions due to canopy structure. Φk was sensitive to canopy structure. RF models show that Φk can be predicted using reflectance in different bands. RF models also show that FyieldLIF was more sensitive to reflectance and radiation than SIF and SIFy, indicating that the combined effect of reflectance bands could hide the SIF physiological trait.
Peng Li, Rong Shang, Jing M. Chen, Mingzhu Xu, Xudong Lin, Guirui Yu, Nianpeng He, and Li Xu
Biogeosciences, 21, 625–639, https://doi.org/10.5194/bg-21-625-2024, https://doi.org/10.5194/bg-21-625-2024, 2024
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The amount of carbon that forests gain from the atmosphere, called net primary productivity (NPP), changes a lot with age. These forest NPP–age relationships could be modeled from field survey data, but we are not sure which model works best. Here we tested five different models using 3121 field survey samples in China, and the semi-empirical mathematical (SEM) function was determined as the optimal. The relationships built by SEM can improve China's forest carbon modeling and prediction.
Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst
Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
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We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
Thomas D. Hessilt, Brendan M. Rogers, Rebecca C. Scholten, Stefano Potter, Thomas A. J. Janssen, and Sander Veraverbeke
Biogeosciences, 21, 109–129, https://doi.org/10.5194/bg-21-109-2024, https://doi.org/10.5194/bg-21-109-2024, 2024
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In boreal North America, snow and frozen ground prevail in winter, while fires occur in summer. Over the last 20 years, the northwestern parts have experienced earlier snow disappearance and more ignitions. This is opposite to the southeastern parts. However, earlier ignitions following earlier snow disappearance timing led to larger fires across the region. Snow disappearance timing may be a good proxy for ignition timing and may also influence important atmospheric conditions related to fires.
Regine Anne Faelga, Luigi Cantelli, Sonia Silvestri, and Beatrice Maria Sole Giambastiani
Biogeosciences, 20, 4841–4855, https://doi.org/10.5194/bg-20-4841-2023, https://doi.org/10.5194/bg-20-4841-2023, 2023
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A dune restoration project on the northern Adriatic coast (Ravenna, Italy) was assessed using UAV monitoring. Structure-from-motion photogrammetry, elevation differencing, and statistical analysis were used to quantify dune development in terms of sand volume and vegetation cover change. Results show that the installed fence has been effective as there was significant sand accumulation, embryo dune development, and a decrease in blowout features due to increased vegetation colonization.
Lilian Vallet, Martin Schwartz, Philippe Ciais, Dave van Wees, Aurelien de Truchis, and Florent Mouillot
Biogeosciences, 20, 3803–3825, https://doi.org/10.5194/bg-20-3803-2023, https://doi.org/10.5194/bg-20-3803-2023, 2023
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This study analyzes the ecological impact of the 2022 summer fire season in France by using high-resolution satellite data. The total biomass loss was 2.553 Mt, equivalent to a 17 % increase of the average natural mortality of all French forests. While Mediterranean forests had a lower biomass loss, there was a drastic increase in burned area and biomass loss over the Atlantic pine forests and temperate forests. This result revisits the distinctiveness of the 2022 fire season.
Surendra Shrestha, Christopher A. Williams, Brendan M. Rogers, John Rogan, and Dominik Kulakowski
EGUsphere, https://doi.org/10.5194/egusphere-2023-1002, https://doi.org/10.5194/egusphere-2023-1002, 2023
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Here, we generated chronosequences of Leaf area index (LAI) and surface albedo as a function of time since fire to demonstrate the differences in characteristic trajectories of post-fire biophysical changes across seven forest types and 21 level III ecoregions of the western United States (US) using satellite data from different sources. We also demonstrated how climate played the dominant role in the recovery of LAI and albedo after 10 and 20 years of wildfire events in the western US.
Arthur Bayle, Bradley Z. Carlson, Anaïs Zimmer, Sophie Vallée, Antoine Rabatel, Edoardo Cremonese, Gianluca Filippa, Cédric Dentant, Christophe Randin, Andrea Mainetti, Erwan Roussel, Simon Gascoin, Dov Corenblit, and Philippe Choler
Biogeosciences, 20, 1649–1669, https://doi.org/10.5194/bg-20-1649-2023, https://doi.org/10.5194/bg-20-1649-2023, 2023
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Glacier forefields have long provided ecologists with a model to study patterns of plant succession following glacier retreat. We used remote sensing approaches to study early succession dynamics as it allows to analyze the deglaciation, colonization, and vegetation growth within a single framework. We found that the heterogeneity of early succession dynamics is deterministic and can be explained well by local environmental context. This work has been done by an international consortium.
Cited articles
Adam, E., Mutanga, O., and Rugege, D.: Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review, Wetl. Ecol. Manage., 18, 281–296, https://doi.org/10.1007/s11273-009-9169-z, 2010. a
Andreatta, D., Gianelle, D., Scotton, M., and Dalponte, M.: Estimating grassland vegetation cover with remote sensing: A comparison between Landsat-8, Sentinel-2 and PlanetScope imagery, Ecol. Indic., 141, 109102, https://doi.org/10.1016/j.ecolind.2022.109102, 2022. a
Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T.: Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data, in: Proceedings of the 5th International Conference on Precision Agriculture and other resource management, 16–19 July 2000, Bloomington, MN USA, https://hdl.handle.net/10113/4190 (last access: 17 March 2024), 2000. a
Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M. L., and Bareth, G.: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley, Int. J. Appl. Earth Obs., 39, 79–87, https://doi.org/10.1016/j.jag.2015.02.012, 2015. a
Berg, M., Joyce, C., and Burnside, N.: Differential responses of abandoned wet grassland plant communities to reinstated cutting management, Hydrobiologia, 692, 83–97, https://doi.org/10.1007/s10750-011-0826-x, 2012. a, b
Berg, M. J.: Abandonment and Reinstated Management upon Coastal Wet Grasslands in Estonia, Phd thesis, University of Brighton, https://research.brighton.ac.uk/en/studentTheses/abandonment-and-reinstated-management-upon-coastal-wet-grasslands (last access: 17 March 2024), 2008. a
Bergamo, T. F., Ward, R. D., Joyce, C. B., Villoslada, M., and Sepp, K.: Experimental climate change impacts on Baltic coastal wetland plant communities, Sci. Rep., 12, 20362, https://doi.org/10.1038/s41598-022-24913-z, 2022. a
Bergamo, T. F., de Lima, R. S., Kull, T., Ward, R. D., Sepp, K., and Villoslada, M.: From UAV to PlanetScope: Upscaling fractional cover of an invasive species Rosa rugosa, J. Environ. Manage., 336, 117693, https://doi.org/10.1016/j.jenvman.2023.117693, 2023. a
Blan, L. and Butler, R.: Comparing Effects of Aggregation Methods on Statistical and Spatial Properties of Simulated Spatial Data, Photogramm. Eng. Rem. S., 65, 73–84, 1999. a
Breiman, L.: Random Forests, Mach. Learning, 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Brodu, N.: Super-Resolving Multiresolution Images With Band-Independent Geometry of Multispectral Pixels, IEEE T. Geosci. Remote, 55, 4610–4617, https://doi.org/10.1109/TGRS.2017.2694881, 2017. a
Burnside, N., Joyce, C., Puurmann, E., and Scott, D.: Use of vegetation classification and plant indicators to assess grazing abandonment in Estonian coastal wetlands, J. Veg. Sci., 18, 645–654, https://doi.org/10.1111/j.1654-1103.2007.tb02578.x, 2007. a, b, c
Butler, L. and Sanderson, R. A.: National-scale predictions of plant assemblages via community distribution models: Leveraging published data to guide future surveys, J. Appl. Ecol., 59, 1559–1571, https://doi.org/10.1111/1365-2664.14166, 2022. a
Celis-Hernandez, O., Giron-Garcia, M. P., Ontiveros-Cuadras, J. F., Canales-Delgadillo, J. C., Pérez-Ceballos, R. Y., Ward, R. D., Acevedo-Gonzales, O., Armstrong-Altrin, J. S., and Merino-Ibarra, M.: Environmental risk of trace elements in mangrove ecosystems: An assessment of natural vs oil and urban inputs, Sci. Total Environ., 730, 138643, https://doi.org/10.1016/j.scitotenv.2020.138643, 2020. a
Celis-Hernandez, O., Cundy, A., Croudace, I., and Ward, R.: Environmental risk of trace metals and metalloids in estuarine sediments: An example from Southampton Water, U.K., Mar. Pollut. Bull., 178, https://doi.org/10.1016/j.marpolbul.2022.113580, 2022. a
Chen, A., Orlov-Levin, V., and Meron, M.: Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management, Agr. Water Manage., 216, 196–205, https://doi.org/10.1016/j.agwat.2019.02.017, 2019. a
Chen, P.-F., Nicolas, T., Wang, J.-H., Philippe, V., Huang, W.-J., and Li, B.-G.: New index for crop canopy fresh biomass estimation, Spectrosc. Spect. Anal., 30, 512–517, 2010. a
Colomina, I. and Molina, P.: Unmanned aerial systems for photogrammetry and remote sensing: A review, ISPRS J. Photogramm., 92, 79–97, https://doi.org/10.1016/j.isprsjprs.2014.02.013, 2014. a
Copernicus Hub: European Space Agency (ESA), Copernicus Open Access Hub, https://dataspace.copernicus.eu/, last access: 15 March 2024. a
Corbane, C., Lang, S., Pipkins, K., Alleaume, S., Deshayes, M., García Millán, V. E., Strasser, T., Vanden Borre, J., Toon, S., and Michael, F.: Remote sensing for mapping natural habitats and their conservation status – New opportunities and challenges, Int. J. Appl. Earth Obs., 37, 7–16, https://doi.org/10.1016/j.jag.2014.11.005, 2015. a
Cracknell, A. P.: UAVs: regulations and law enforcement, Int. J. Remote Sens., 38, 3054–3067, https://doi.org/10.1080/01431161.2017.1302115, 2017. a
Datt, B.: Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves, Remote Sens. Environ., 66, 111–121, https://doi.org/10.1016/S0034-4257(98)00046-7, 1998. a
de Lacerda, L. D., Ward, R. D., Godoy, M. D. P., de Andrade Meireles, A. J., Borges, R., and Ferreira, A. C.: 20-Years Cumulative Impact From Shrimp Farming on Mangroves of Northeast Brazil, Frontiers in Forests and Global Change, 4, 653096, https://doi.org/10.3389/ffgc.2021.653096, 2021. a
de Lacerda, L. D., Ward, R. D., Borges, R., and Ferreira, A. C.: Mangrove Trace Metal Biogeochemistry Response to Global Climate Change, Frontiers in Forests and Global Change, 5, 817992, https://doi.org/10.3389/ffgc.2022.817992, 2022. a
De Simone, W., Allegrezza, M., Frattaroli, A. R., Montecchiari, S., Tesei, G., Zuccarello, V., and Di Musciano, M.: From Remote Sensing to Species Distribution Modelling: An Integrated Workflow to Monitor Spreading Species in Key Grassland Habitats, Remote Sens., 13, 1904, https://doi.org/10.3390/rs13101904, 2021. a
Díaz-Delgado, R., Cazacu, C., and Adamescu, M.: Rapid Assessment of Ecological Integrity for LTER Wetland Sites by Using UAV Multispectral Mapping, Drones, 3, 3, https://doi.org/10.3390/drones3010003, 2019. a, b, c
Emilien, A.-V., Thomas, C., and Houet, T.: UAV & Satellite synergies for optical remote sensing applications: a literature review, Science of Remote Sensing, 3, 100019, https://doi.org/10.1016/j.srs.2021.100019, 2021. a
ESA: SNAP.ESA Sentinel Application Platform v9, http://step.esa.int (last access: 15 March 2024), 2014. a
Evans, J. S., Murphy, M. A., Holden, Z. A., and Cushman, S. A.: Modeling Species Distribution and Change Using Random Forest, 139–159, Springer New York, New York, NY, ISBN 978-1-4419-7390-0, https://doi.org/10.1007/978-1-4419-7390-0_8, 2011. a
Fawcett, D., Panigada, C., Tagliabue, G., Boschetti, M., Celesti, M., Evdokimov, A., Biriukova, K., Colombo, R., Miglietta, F., Rascher, U., and Anderson, K.: Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions, Remote Sens., 12, 514, https://doi.org/10.3390/rs12030514, 2020. a
Fernández-Guisuraga, J. M., Sanz-Ablanedo, E., Suárez-Seoane, S., and Calvo, L.: Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges, Sensors, 18, 586, https://doi.org/10.3390/s18020586, 2018. a
Ferreira, B., Silva, R. G., and Iten, M.: Earth Observation Satellite Imagery Information Based Decision Support Using Machine Learning, Remote Sens., 14, 3776, https://doi.org/10.3390/rs14153776, 2022. a
Findell, K. L., Berg, A., Gentine, P., Krasting, J. P., Lintner, B. R., Malyshev, S., Santanello, J. A., and Shevliakova, E.: The impact of anthropogenic land use and land cover change on regional climate extremes, Nat. Commun., 8, 989, https://doi.org/10.1038/s41467-017-01038-w, 2017. a
Gillies, S.: Rasterio: geospatial raster I/O for Python programmers, GitHub, https://github.com/mapbox/rasterio (last access: 15 March 2024), 2013. a
Gitelson, A. and Merzlyak, M. N.: Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation, J. Plant Physiol., 143, 286–292, https://doi.org/10.1016/S0176-1617(11)81633-0, 1994. a, b
Gitelson, A. A., Kaufman, Y. J., and Merzlyak, M. N.: Use of a green channel in remote sensing of global vegetation from EOS-MODIS, Remote Sens. Environ., 58, 289–298, https://doi.org/10.1016/S0034-4257(96)00072-7, 1996. a
Gitelson, A. A., Viña, A., Arkebauer, T., Rundquist, D., Keydan, G., Leavitt, B., and Keydan, G.: Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophys. Res. Lett., 30, 1248, https://doi.org/10.1029/2002GL016450, 2003. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with NumPy, Nature, 585, 357–362, https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Heil, J., Jörges, C., and Stumpe, B.: Fine-Scale Mapping of Soil Organic Matter in Agricultural Soils Using UAVs and Machine Learning, Remote Sens., 14, 3349, https://doi.org/10.3390/rs14143349, 2022. a
Isenburg, M., Liu, Y., Shewchuk, J., Snoeyink, J., and Thirion, T.: Generating Raster DEM from Mass Points Via TIN Streaming, 186–198, ISBN 978-3-540-44526-5, https://doi.org/10.1007/11863939_13, 2006. a
Isgró, M. A., Basallote, M. D., Caballero, I., and Barbero, L.: Comparison of UAS and Sentinel-2 Multispectral Imagery for Water Quality Monitoring: A Case Study for Acid Mine Drainage Affected Areas (SW Spain), Remote Sens., 14, 4053, https://doi.org/10.3390/rs14164053, 2022. a
Jiang, J., Johansen, K., Tu, Y.-H., and McCabe, M.: Multi-sensor and multi-platform consistency and interoperability between UAV, Planet CubeSat, Sentinel-2, and Landsat reflectance data, GISci. Remote Sens., 59, 936–958, https://doi.org/10.1080/15481603.2022.2083791, 2022. a
Jordahl, K., den Bossche, J. V., Fleischmann, M., Wasserman, J., McBride, J., Gerard, J., Tratner, J., Perry, M., Badaracco, A. G., Farmer, C., Hjelle, G. A., Snow, A. D., Cochran, M., Gillies, S., Culbertson, L., Bartos, M., Eubank, N., maxalbert, Bilogur, A., Rey, S., Ren, C., Arribas-Bel, D., Wasser, L., Wolf, L. J., Journois, M., Wilson, J., Greenhall, A., Holdgraf, C., Filipe, and Leblanc, F.: geopandas/geopandas: v0.8.1, Zenodo, https://doi.org/10.5281/zenodo.3946761, 2020. a
Knight, J. F., Lunetta, R. S., Ediriwickrema, J., and Khorram, S.: Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach, GISci. Remote Sens., 43, 1–23, https://doi.org/10.2747/1548-1603.43.1.1, 2006. a
Kont, A., Jaagus, J., and Aunap, R.: Climate change scenarios and the effect of sea-level rise for Estonia, Global Planet. Change, 36, 1–15, https://doi.org/10.1016/S0921-8181(02)00149-2, 2003. a
Kuhn, M. and Johnson, K.: Applied Predictive Modeling, Springer, New York, ISBN 978-1-4614-6848-6, 2013. a
Laliberte, A. S., Goforth, M. A., Steele, C. M., and Rango, A.: Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments, Remote Sens., 3, 2529–2551, https://doi.org/10.3390/rs3112529, 2011. a
Li, C., Wang, H., Liao, X., Xiao, R., Liu, K., Bai, J., Li, B., and He, Q.: Heavy Metal Pollution in Coastal Wetlands: A Systematic Review of Studies Globally Over the Past Three Decades, J. Hazard. Mater., 424, 127312, https://doi.org/10.1016/j.jhazmat.2021.127312, 2021. a
Lima-Cueto, F. J., Blanco-Sepúlveda, R., Gómez-Moreno, M. L., and Galacho-Jiménez, F. B.: Using Vegetation Indices and a UAV Imaging Platform to Quantify the Density of Vegetation Ground Cover in Olive Groves (Olea Europaea L.) in Southern Spain, Remote Sens., 11, 2564, https://doi.org/10.3390/rs11212564, 2019. a
Maa-amet geoportaal: Eesti Maa-amet [Estonian Land Board], https://geoportaal.maaamet.ee/ (last access: 21 March 2022), 2018. a
Mafi-Gholami, D., Zenner, E., Jaafari, A., and Ward, R.: Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran, Sci. Total Environ., 656, 1326–1336, https://doi.org/10.1016/j.scitotenv.2018.11.462, 2019. a
Mahdianpari, M., Granger, J. E., Mohammadimanesh, F., Salehi, B., Brisco, B., Homayouni, S., Gill, E., Huberty, B., and Lang, M.: Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America, Remote Sens., 12, 1882, https://doi.org/10.3390/rs12111882, 2020. a
Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., and Gascon, F.: Sen2Cor for Sentinel-2, in: Image and Signal Processing for Remote Sensing XXIII, edited by: Bruzzone, L., Vol. 10427, International Society for Optics and Photonics, SPIE, https://doi.org/10.1117/12.2278218, 2017. a
Mao, P., Ding, J., Jiang, B., Qin, L., and Qiu, G. Y.: How can UAV bridge the gap between ground and satellite observations for quantifying the biomass of desert shrub community?, ISPRS J. Photogramm., 192, 361–376, https://doi.org/10.1016/j.isprsjprs.2022.08.021, 2022. a
Martinetto, P., Alberti, J., Becherucci, M., Cebrian, J., Iribarne, O., Marbà, N., Montemayor, D., Sparks, E., and Ward, R.: South West Atlantic blue carbon: a reassessment of global averages, Nat. Commun., 14, 8500, https://doi.org/10.1038/s41467-023-44196-w, 2023. a
Martínez Prentice, R., Villoslada Peciña, M., Ward, R. D., Bergamo, T. F., Joyce, C. B., and Sepp, K.: Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands, Remote Sens., 13, 3669, https://doi.org/10.3390/rs13183669, 2021. a, b, c, d, e, f, g, h, i, j
Maurya, A., Nadeem, M., Singh, D., Singh, K., and Rajput, N.: Critical Analysis of Machine Learning Approaches for Vegetation Fractional Cover Estimation Using Drone and Sentinel-2 Data, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 343–346, https://doi.org/10.1109/IGARSS47720.2021.9554422, 2021. a, b
Maxwell, A. E., Warner, T. A., and Fang, F.: Implementation of machine-learning classification in remote sensing: an applied review, Int. J. Remote Sens., 39, 2784–2817, https://doi.org/10.1080/01431161.2018.1433343, 2018. a
Maxwell, T., Rovai, A., Adame, F., Adams, J., Álvarez Rogel, J., Austin, W., Beasy, K., Boscutti, F., Böttcher, M., Bouma, T., Bulmer, R., Burden, A., Burke, S., Camacho, S., Chaudhary, D., Chmura, G., Copertino, M., Cott, G., Craft, C., and Worthington, T.: Global dataset of soil organic carbon in tidal marshes, Sci. Data, 10, 797, https://doi.org/10.1038/s41597-023-02633-x, 2023. a
McKinney, W.: Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 445, 56–61, https://doi.org/10.25080/Majora-92bf1922-00a, 2010. a
Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., Solovchenko, A. E., and Pogosyan, S. I.: Application of Reflectance Spectroscopy for Analysis of Higher Plant Pigments, Russ. J. Plant Physl., 50, 704–710, https://doi.org/10.1023/A:1025608728405, 2003. a
Möller, I., Kudella, M., Rupprecht, F., Spencer, T., Paul, M., van Wesenbeeck, B. K., Wolters, G., Jensen, K., Bouma, T. J., Miranda-Lange, M., and Schimmels, S.: Wave attenuation over coastal salt marshes under storm surge conditions, Nat. Geosci., 7, 727–731, https://doi.org/10.1038/ngeo2251, 2014. a
Muukkonen, P. and Heiskanen, J.: Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories, Remote Sens. Environ., 107, 617–624, https://doi.org/10.1016/j.rse.2006.10.011, 2007. a
Okolie, C. J. and Smit, J. L.: A systematic review and meta-analysis of Digital elevation model (DEM) fusion: pre-processing, methods and applications, ISPRS J. Photogramm., 188, 1–29, https://doi.org/10.1016/j.isprsjprs.2022.03.016, 2022. a
Olden, J. D., Lawler, J. J., and Poff, N. L.: Machine learning methods without tears: a primer for ecologists, Q. Rev. Biol., 83, 171–193, 2008. a
Paal, J.: Rare and threatened plant communities of Estonia, Svensk Botanisk Tidskrift, 7, 1027–1049, https://doi.org/10.1023/A:1008857014648, 1998. a
Padró, J.-C., Muñoz, F.-J., Avila, L. A., Pesquer, L., and Pons, X.: Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry, Remote Sens., 10, 1687, https://doi.org/10.3390/rs10111687, 2018. a, b, c, d
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2018. a
Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., and Turner, W.: Satellite remote sensing for applied ecologists: opportunities and challenges, J. Appl. Ecol., 51, 839–848, https://doi.org/10.1111/1365-2664.12261, 2014. a
Pichler, M. and Hartig, F.: Machine learning and deep learning – A review for ecologists, Methods Ecol. Evol., 14, 994–1016, https://doi.org/10.1111/2041-210x.14061, 2023. a
Probst, P., Wright, M. N., and Boulesteix, A.-L.: Hyperparameters and tuning strategies for random forest, WIREs Data Min. Knowl., 9, e1301, https://doi.org/10.1002/widm.1301, 2019. a
Qi, J., Chehbouni, A., Huete, A., Kerr, Y., and Sorooshian, S.: A modified soil adjusted vegetation index, Remote Sens. Environ., 48, 119–126, https://doi.org/10.1016/0034-4257(94)90134-1, 1994. a
Rannap, R., Briggs, L., Lotman, K., Lepik, I., Rannap, V., and Põdra, P.: Coastal Meadow Management – Best Practice Guidelines, Vol. 4, Ministry of the Environment of the Republic of Estonia, ISBN 9985881265, 2004. a
Rannap, R., Kaart, T., Pehlak, H., Kana, S., Soomets-Alver, E., and Lanno, K.: Coastal meadow management for threatened waders has a strong supporting impact on meadow plants and amphibians, J. Nat. Conserv., 35, 77–91, https://doi.org/10.1016/j.jnc.2016.12.004, 2016. a
Rhymer, C. M., Robinson, R. A., Smart, J., and Whittingham, M. J.: Can ecosystem services be integrated with conservation? A case study of breeding waders on grassland, Ibis, 152, 698–712, https://doi.org/10.1111/j.1474-919X.2010.01049.x, 2010. a
Riihimäki, H., Luoto, M., and Heiskanen, J.: Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data, Remote Sens. Environ., 224, 119–132, https://doi.org/10.1016/j.rse.2019.01.030, 2019. a
Rivis, R., Kont, A., Ratas, U., Palginomm, V., Antso, K., and Tõnisson, H.: Trends in the development of Estonian coastal land cover and landscapes caused by natural changes and human impact, J. Coast. Conserv., 20, 199–209, https://doi.org/10.1007/s11852-016-0430-3, 2016. a
Rodriguez-Galiano, V., Ghimire, B., Rogan, J., Chica-Olmo, M., and Rigol-Sanchez, J.: An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogramm., 67, 93–104, https://doi.org/10.1016/j.isprsjprs.2011.11.002, 2012. a
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W.: Monitoring vegetation systems in the great plains with ERTS, NASA Special Publications, 351, 309 pp., http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=PASCAL7730022596 (last access: 18 March 2024), 1973. a
Schuster, C., Förster, M., and Kleinschmit, B.: Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data, Int. J. Remote Sens., 33, 5583–5599, https://doi.org/10.1080/01431161.2012.666812, 2012. a
Shiferaw, H., Bewket, W., and Eckert, S.: Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem, Ecol. Evol., 9, 2562–2574, https://doi.org/10.1002/ece3.4919, 2019. a
Simon, S. M., Glaum, P., and Valdovinos, F. S.: Interpreting random forest analysis of ecological models to move from prediction to explanation, Sci. Rep., 13, 3881, https://doi.org/10.1038/s41598-023-30313-8, 2023. a
Sutton-Grier, A. E. and Sandifer, P. A.: Conservation of Wetlands and Other Coastal Ecosystems: a Commentary on their Value to Protect Biodiversity, Reduce Disaster Impacts, and Promote Human Health and Well-Being, Wetlands, 39, 1295–1302, https://doi.org/10.1007/s13157-018-1039-0, 2019. a
Tang, B., Frye, H. A., Gelfand, A. E., and Silander, J. A.: Zero-Inflated Beta Distribution Regression Modeling, J. Agr. Biol. Envir. St., 28, 117–137, https://doi.org/10.1007/s13253-022-00516-z, 2023. a
Tarasiewicz, T., Nalepa, J., Farrugia, R. A., Valentino, G., Chen, M., Briffa, J. A., and Kawulok, M.: Multitemporal and Multispectral Data Fusion for Super-Resolution of Sentinel-2 Images, IEEE T. Geosci. Remote Sens., 61, 1–19, https://doi.org/10.1109/TGRS.2023.3311622, 2023. a
Thessen, A. E.: Adoption of Machine Learning Techniques in Ecology and Earth Science, One Ecosystem, 1, e8621, https://doi.org/10.3897/oneeco.1.e8621, 2016. a, b
Torma, A., Császár, P., Bozsó, M., Balázs, D., Valkó, O., Kiss, O., and Gallé, R.: Species and functional diversity of arthropod assemblages (Araneae, Carabidae, Heteroptera and Orthoptera) in grazed and mown salt grasslands, Agr. Ecosyst. Environ., 273, 70–79, https://doi.org/10.1016/j.agee.2018.12.004, 2018. a
Turpie, K.: Explaining the Spectral Red-Edge Features of Inundated Marsh Vegetation, J. Coastal Res., 29, 1111–1117, https://doi.org/10.2112/JCOASTRES-D-12-00209.1, 2013. a, b
Valavi, R., Elith, J., Lahoz-Monfort, J., and Guillera-Arroita, G.: Modelling species presence‐only data with random forests, Ecography, 44, 1731–1742, https://doi.org/10.1111/ecog.05615, 2021. a
Villoslada, M., Bergamo, T., Ward, R., Burnside, N., Joyce, C., Bunce, R., and Sepp, K.: Fine scale plant community assessment in coastal meadows using UAV based multispectral data, Ecol. Indic., 111, 105979, https://doi.org/10.1016/j.ecolind.2019.105979, 2020. a, b, c, d
Villoslada Peciña, M., Ward, R. D., Bunce, R. G. H., Sepp, K., Kuusemets, V., and Luuk, O.: Country-scale mapping of ecosystem services provided by semi-natural grasslands, Sci. Total Environ., 661, 212—225, https://doi.org/10.1016/j.scitotenv.2019.01.174, 2019. a
Villoslada Peciña, M., Bergamo, T., Ward, R., Joyce, C., and Sepp, K.: A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows, Ecol. Indic., 122, 107227, https://doi.org/10.1016/j.ecolind.2020.107227, 2021. a
Vincini, M., Frazzi, E., and D'Alessio, P.: A broad-band leaf chlorophyll vegetation index at the canopy scale, Precis. Agric., 9, 303–319, https://doi.org/10.1007/s11119-008-9075-z, 2008. a
Ward, R. D.: Landscape and ecological modelling: Development of a plant community prediction tool for Estonian coastal wetlands, Phd thesis, University of Brighton, https://research.brighton.ac.uk/en/studentTheses/landscape-and-ecological-modelling-development-of-a-plant-communi (last access: 17 March 2024), 2012. a
Ward, R. D.: Carbon sequestration and storage in Norwegian Arctic coastal wetlands: Impacts of climate change, Sci. Total Environ., 748, 141343, https://doi.org/10.1016/j.scitotenv.2020.141343, 2020. a
Ward, R. D., Burnside, N., Joyce, C., and Sepp, K.: The use of medium point density LiDAR elevation data to determine plant community types in Baltic coastal wetlands, Ecol. Indic., 33, 96–104, https://doi.org/10.1016/j.ecolind.2012.08.016, 2013. a, b, c, d
Ward, R. D., Burnside, N., Joyce, C., Sepp, K., and Teasdale, P.: Improved modelling of the impacts of sea level rise on coastal wetland plant communities, Hydrobiologia, 774, 203–216, https://doi.org/10.1007/s10750-015-2374-2, 2015. a
Ward, R. D., Burnside, N., Joyce, C., and Sepp, K.: Importance of Microtopography in Determining Plant Community Distribution in Baltic Coastal Wetlands, J. Coastal Res., 32, 1062–1070, https://doi.org/10.2112/JCOASTRES-D-15-00065.1, 2016a. a, b, c
Ward, R. D., Friess, D. A., Day, R. H., and MacKenzie, R. A.: Impacts of climate change on mangrove ecosystems: a region by region overview, Ecosystem Health and Sustainability, 2, e01211, https://doi.org/10.1002/ehs2.1211, 2016b. a
Wu, C., Niu, Z., Tang, Q., and Huang, W.: Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation, Agr. Forest Meteorol., 148, 1230–1241, https://doi.org/10.1016/j.agrformet.2008.03.005, 2008. a
Yang, Z., D'Alpaos, A., Marani, M., and Silvestri, S.: Assessing the Fractional Abundance of Highly Mixed Salt-Marsh Vegetation Using Random Forest Soft Classification, Remote Sens., 12, 3224, https://doi.org/10.3390/rs12193224, 2020. a, b
Zabala, S.: Comparison of Multi-Temporal and Multispectral Sentinel-2 and Unmanned Aerial Vehicle Imagery for Crop Type Mapping, Master's thesis, Lund University, Lund, Sweden, https://www.lu.se/lup/publication/8917610 (last access: 17 March 2024), 2017. a
Zhang, L., Huettmann, F., Zhang, X., Liu, S., Sun, P., Yu, Z., and Mi, C.: The use of classification and regression algorithms using the random forests method with presence-only data to model species’ distribution, MethodsX, 6, 2281–2292, https://doi.org/10.1016/j.mex.2019.09.035, 2019. a
Zhu, W., Rezaei, E. E., Nouri, H., Yang, T., Li, B., Gong, H., Lyu, Y., Peng, J., and Sun, Z.: Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data, Remote Sens., 13, 4716, https://doi.org/10.3390/rs13224716, 2021. a
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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