Articles | Volume 18, issue 2
https://doi.org/10.5194/essd-18-1243-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-1243-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
16 Feb 2026
Data description article |
| 16 Feb 2026
| 16 Feb 2026
ForestScan: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data
Cecilia Chavana-Bryant
CORRESPONDING AUTHOR
Department of Geography, University College London, London, WC1E 6BT, UK
NERC National Centre for Earth Observation, UCL Geography, London, WC1E 6BT, UK
Phil Wilkes
Department of Geography, University College London, London, WC1E 6BT, UK
NERC National Centre for Earth Observation, UCL Geography, London, WC1E 6BT, UK
Kew Wakehurst, Ardingly, West Sussex, RH17 6TN, UK
Wanxin Yang
Department of Geography, University College London, London, WC1E 6BT, UK
NERC National Centre for Earth Observation, UCL Geography, London, WC1E 6BT, UK
Andrew Burt
Sylvera Ltd., London, EC1Y 4TW, UK
Peter Vines
independent researcher: 8 Havelock Terrace, Plymouth, PL2 1AT, UK
Amy C. Bennett
School of Geography, University of Leeds, Leeds, LS2 9JT, UK
Georgia C. Pickavance
School of Geography, University of Leeds, Leeds, LS2 9JT, UK
Declan L. M. Cooper
Department of Geography, University College London, London, WC1E 6BT, UK
Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
Simon L. Lewis
Department of Geography, University College London, London, WC1E 6BT, UK
School of Geography, University of Leeds, Leeds, LS2 9JT, UK
Oliver L. Phillips
School of Geography, University of Leeds, Leeds, LS2 9JT, UK
Benjamin Brede
Section 1.4 Remote Sensing and Geoinformatics, GFZ Helmholtz Centre for Geosciences, Potsdam, 14473, Germany
Alvaro Lau
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, 6708 PB Wageningen, the Netherlands
Martin Herold
Section 1.4 Remote Sensing and Geoinformatics, GFZ Helmholtz Centre for Geosciences, Potsdam, 14473, Germany
Iain M. McNicol
School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3JN, UK
Edward T. A. Mitchard
School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3JN, UK
Space Intelligence Ltd. 93 George Street, Edinburgh, EH2 3ES, UK
David A. Coomes
Plant Science and Cambridge Conservation Initiative, University of Cambridge, Cambridge, CB2 3QZ, UK
Toby D. Jackson
Plant Science and Cambridge Conservation Initiative, University of Cambridge, Cambridge, CB2 3QZ, UK
Löic Makaga
Agence Nationale des Parcs Nationaux (ANPN), P.O. Box 20379, Libreville, Gabon
Heddy O. Milamizokou Napo
Agence Nationale des Parcs Nationaux (ANPN), P.O. Box 20379, Libreville, Gabon
Alfred Ngomanda
Institut de Recherche en Ecologie Tropicale, IRET/CENAREST, Libreville, P.O. Box 13354, Gabon
Stephan Ntie
Agence Nationale des Parcs Nationaux (ANPN), P.O. Box 20379, Libreville, Gabon
Vincent Medjibe
Agence Nationale des Parcs Nationaux (ANPN), P.O. Box 20379, Libreville, Gabon
Pacôme Dimbonda
Agence Nationale des Parcs Nationaux (ANPN), P.O. Box 20379, Libreville, Gabon
Luna Soenens
Q-ForestLab, Department of Environment, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
Virginie Daelemans
Gembloux Agro-Bio Tech Liège University, Passage des déportés 2, 5030 Gembloux, Belgium
Laetitia Proux
CIRAD, UMR EcoFoG (AgroParistech, CNRS, INRAE, Université des Antilles, Université de Guyane), Campus Agronomique, Kourou, 20040, French Guiana
Reuben Nilus
Forest Research Centre, Sabah Forestry Department, P.O. Box 1407, Sabah, 90715, Malaysia
Nicolas Labrière
Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), UMR 5300 CNRS-IRD-INP-UT3, Toulouse, 31062 CEDEX 9, France
Kathryn Jeffery
Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
ESA Centre for Earth Observation (ESA-ESRIN), Frascati, 00044, Italy
David F. R. P. Burslem
School of Biological Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
Dan Clewley
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
David Moffat
Plymouth Marine Laboratory, Plymouth, PL1 3DH, UK
Lan Qie
College of Health and Science, Department of Life Sciences, University of Lincoln, Lincoln, LN6 7TS, UK
Harm Bartholomeus
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, 6708 PB Wageningen, the Netherlands
Gregoire Vincent
AMAP, Univ. Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, 34398, France
Nicolas Barbier
AMAP, Univ. Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, 34398, France
Geraldine Derroire
CIRAD, UMR EcoFoG (AgroParistech, CNRS, INRAE, Université des Antilles, Université de Guyane), Campus Agronomique, Kourou, 20040, French Guiana
Katharine Abernethy
Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
Institut de Recherche en Ecologie Tropicale, IRET/CENAREST, Libreville, P.O. Box 13354, Gabon
Klaus Scipal
ESA Centre for Earth Observation (ESA-ESRIN), Frascati, 00044, Italy
Mathias Disney
Department of Geography, University College London, London, WC1E 6BT, UK
NERC National Centre for Earth Observation, UCL Geography, London, WC1E 6BT, UK
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-226, https://doi.org/10.5194/essd-2026-226, 2026
Preprint under review for ESSD
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This study introduces the first global, spatially explicit dataset of tree diameter structure, capturing key aspects of forest organization, including average tree size, large-tree dominance, and within-stand variability. Built from over one million ground-based field plots combined with more than 50 satellite and environmental layers using machine learning, it provides a consistent representation of forest structure and supports ecosystem research, climate modeling, and forest management.
Jozef Skákala, Shubha Sathyendranath, Yuri Artioli, Deep S. Banerjee, Heather Bouman, Robert J. W. Brewin, Momme Butenschön, Stefano Ciavatta, Stephanie Dutkiewicz, Yanna Fidai, David Ford, Grinson George, Karen Guihou, Bror Jönsson, Marija Bačeković Koloper, Žarko Kovač, Lekshmi Krishnakumary, Gemma Kulk, Charlotte Laufkötter, Gennadi Lessin, Jann Paul Mattern, Angélique Melet, Alexandre Mignot, David Moffat, Fanny Monteiro, Mayra Rodriguez Bennadji, Cécile S. Rousseaux, Ranjini Swaminathan, Osvaldo Ulloa, and Jerry Tjiputra
Ocean Sci., 22, 1457–1481, https://doi.org/10.5194/os-22-1457-2026, https://doi.org/10.5194/os-22-1457-2026, 2026
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Marine primary production (PP) is a key component of the Earth's climate system, but its current estimates and future projections are highly uncertain. We review the PP uncertainties and discuss their sources both across the ecosystem and satellite models. We propose to reduce the PP uncertainties by better addressing the PP model structures and parametrizations. We also argue that for many models it is desirable to consider spatial and temporal variability in the model parameter values.
Mazen Nakad, Mahmoud Mbarak, Jihad Karaki, Tehreem Qureshi, Matteo Detto, Benjamin I. Cook, Marcos Longo, Géraldine Derroire, Xiangtao Xu, Jeremy Lichstein, Zong-Liang Yang, Pierre Gentine, and Ensheng Weng
EGUsphere, https://doi.org/10.5194/egusphere-2026-145, https://doi.org/10.5194/egusphere-2026-145, 2026
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Because long-term drought and heat experiments are hard to run in tropical forests, we used a computer model to test how less rain and warmer, drier air affect forest growth and water loss at three sites. Forest productivity stayed stable until rainfall dropped below a threshold, but warmer air reduced carbon uptake even when soils were moist. Persistent heat and drought caused far larger declines than brief pulses, raising risks for future carbon storage.
Julien Lamour, Shawn P. Serbin, Alistair Rogers, Kelvin T. Acebron, Elizabeth Ainsworth, Loren P. Albert, Michael Alonzo, Jeremiah Anderson, Owen K. Atkin, Nicolas Barbier, Mallory L. Barnes, Carl J. Bernacchi, Ninon Besson, Angela C. Burnett, Joshua S. Caplan, Jérôme Chave, Alexander W. Cheesman, Ilona Clocher, Onoriode Coast, Sabrina Coste, Holly Croft, Boya Cui, Clément Dauvissat, Kenneth J. Davidson, Christopher Doughty, Kim S. Ely, John R. Evans, Jean-Baptiste Féret, Iolanda Filella, Claire Fortunel, Peng Fu, Robert T. Furbank, Maquelle Garcia, Bruno O. Gimenez, Kaiyu Guan, Zhengfei Guo, David Heckmann, Patrick Heuret, Marney Isaac, Shan Kothari, Etsushi Kumagai, Thu Ya Kyaw, Liangyun Liu, Lingli Liu, Shuwen Liu, Joan Llusià, Troy Magney, Isabelle Maréchaux, Adam R. Martin, Katherine Meacham-Hensold, Christopher M. Montes, Romà Ogaya, Joy Ojo, Regison Oliveira, Alain Paquette, Josep Peñuelas, Antonia Debora Placido, Juan M. Posada, Xiaojin Qian, Heidi J. Renninger, Milagros Rodriguez-Caton, Andrés Rojas-González, Urte Schlüter, Giacomo Sellan, Courtney M. Siegert, Viridiana Silva-Perez, Guangqin Song, Charles D. Southwick, Daisy C. Souza, Clément Stahl, Yanjun Su, Leeladarshini Sujeeun, To-Chia Ting, Vicente Vasquez, Amrutha Vijayakumar, Marcelo Vilas-Boas, Diane R. Wang, Sheng Wang, Han Wang, Jing Wang, Xin Wang, Andreas P. M. Weber, Christopher Y. S. Wong, Jin Wu, Fengqi Wu, Shengbiao Wu, Zhengbing Yan, Dedi Yang, and Yingyi Zhao
Earth Syst. Sci. Data, 18, 245–265, https://doi.org/10.5194/essd-18-245-2026, https://doi.org/10.5194/essd-18-245-2026, 2026
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Lammert Kooistra, Hasib Mustafa, Domantas Girzidas, Chenglong Zhang, Berry Onderstal, and Harm Bartholomeus
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W11-2025, 177–182, https://doi.org/10.5194/isprs-archives-XLVIII-2-W11-2025-177-2025, https://doi.org/10.5194/isprs-archives-XLVIII-2-W11-2025-177-2025, 2025
Sylvain Schmitt, Fabian J. Fischer, James G. C. Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy W. Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
Geosci. Model Dev., 18, 5205–5243, https://doi.org/10.5194/gmd-18-5205-2025, https://doi.org/10.5194/gmd-18-5205-2025, 2025
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Simone Matias Reis, Yadvinder Malhi, Ben Hur Marimon Junior, Beatriz Schwantes Marimon, Huanyuan Zhang-Zheng, Igor Araújo, Renata Freitag, Edmar Almeida de Oliveira, Karine da Silva Peixoto, Luciana Januário de Souza, Ediméia Laura Souza da Silva, Eduarda Bernardes Santos, Kamila Parreira da Silva, Maélly Dállet Alves Gonçalves, Cécile Girardin, Cecilia Dahlsjö, Oliver L. Phillips, and Imma Oliveras Menor
Biogeosciences, 22, 3949–3964, https://doi.org/10.5194/bg-22-3949-2025, https://doi.org/10.5194/bg-22-3949-2025, 2025
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Dale Partridge, Deep Banerjee, David Ford, Ke Wang, Jozef Skakala, Juliane Wihsgott, Prathyush Menon, Susan Kay, Daniel Clewley, Andrea Rochner, Emma Sullivan, and Matthew Palmer
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This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August–September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi.
Mathew Williams, David T. Milodowski, T. Luke Smallman, Kyle G. Dexter, Gabi C. Hegerl, Iain M. McNicol, Michael O'Sullivan, Carla M. Roesch, Casey M. Ryan, Stephen Sitch, and Aude Valade
Biogeosciences, 22, 1597–1614, https://doi.org/10.5194/bg-22-1597-2025, https://doi.org/10.5194/bg-22-1597-2025, 2025
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Southern African woodlands are important in both regional and global carbon cycles. A new carbon analysis created by combining satellite data with ecosystem modelling shows that the region has a neutral C balance overall but with important spatial variations. Patterns of biomass and C balance across the region are the outcome of climate controls on production and vegetation–fire interactions, which determine the mortality of vegetation and spatial variations in vegetation function.
Nicolas Picard, Noël Fonton, Faustin Boyemba Bosela, Adeline Fayolle, Joël Loumeto, Gabriel Ngua Ayecaba, Bonaventure Sonké, Olga Diane Yongo Bombo, Hervé Martial Maïdou, and Alfred Ngomanda
Biogeosciences, 22, 1413–1426, https://doi.org/10.5194/bg-22-1413-2025, https://doi.org/10.5194/bg-22-1413-2025, 2025
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Allometric equations predict tree biomass and are crucial for estimating forest carbon storage, thus assessing the role of forests in climate change mitigation. Usually, these equations are selected based on tree-level predictive performance. However, we evaluated the model performance at plot and forest levels, finding it varies with plot size. This has significant implications for reducing uncertainty in biomass estimates at these levels.
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.
Raphael Zürcher, Jiayan Zhao, Alvaro Lau Sarmiento, Benjamin Brede, and Alexander Klippel
AGILE GIScience Ser., 4, 15, https://doi.org/10.5194/agile-giss-4-15-2023, https://doi.org/10.5194/agile-giss-4-15-2023, 2023
Selena Georgiou, Edward T. A. Mitchard, Bart Crezee, Paul I. Palmer, Greta C. Dargie, Sofie Sjögersten, Corneille E. N. Ewango, Ovide B. Emba, Joseph T. Kanyama, Pierre Bola, Jean-Bosco N. Ndjango, Nicholas T. Girkin, Yannick E. Bocko, Suspense A. Ifo, and Simon L. Lewis
EGUsphere, https://doi.org/10.5194/egusphere-2022-580, https://doi.org/10.5194/egusphere-2022-580, 2022
Preprint archived
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Two major vegetation types, hardwood trees and palms, overlay the Central Congo Basin peatland complex, each dominant in different locations. We investigated the influence of terrain and climatological variables on their distribution, using a regression model, and found elevation and seasonal rainfall and temperature contribute significantly. There are indications of an optimal range of net water input for palm swamp to dominate, above and below which hardwood swamp dominates.
Félicien Meunier, Sruthi M. Krishna Moorthy, Marc Peaucelle, Kim Calders, Louise Terryn, Wim Verbruggen, Chang Liu, Ninni Saarinen, Niall Origo, Joanne Nightingale, Mathias Disney, Yadvinder Malhi, and Hans Verbeeck
Geosci. Model Dev., 15, 4783–4803, https://doi.org/10.5194/gmd-15-4783-2022, https://doi.org/10.5194/gmd-15-4783-2022, 2022
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We integrated state-of-the-art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce such an ecosystem in silico. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
Rahayu Adzhar, Douglas I. Kelley, Ning Dong, Charles George, Mireia Torello Raventos, Elmar Veenendaal, Ted R. Feldpausch, Oliver L. Phillips, Simon L. Lewis, Bonaventure Sonké, Herman Taedoumg, Beatriz Schwantes Marimon, Tomas Domingues, Luzmila Arroyo, Gloria Djagbletey, Gustavo Saiz, and France Gerard
Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, https://doi.org/10.5194/bg-19-1377-2022, 2022
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The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
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The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Maurizio Santoro, Oliver Cartus, Nuno Carvalhais, Danaë M. A. Rozendaal, Valerio Avitabile, Arnan Araza, Sytze de Bruin, Martin Herold, Shaun Quegan, Pedro Rodríguez-Veiga, Heiko Balzter, João Carreiras, Dmitry Schepaschenko, Mikhail Korets, Masanobu Shimada, Takuya Itoh, Álvaro Moreno Martínez, Jura Cavlovic, Roberto Cazzolla Gatti, Polyanna da Conceição Bispo, Nasheta Dewnath, Nicolas Labrière, Jingjing Liang, Jeremy Lindsell, Edward T. A. Mitchard, Alexandra Morel, Ana Maria Pacheco Pascagaza, Casey M. Ryan, Ferry Slik, Gaia Vaglio Laurin, Hans Verbeeck, Arief Wijaya, and Simon Willcock
Earth Syst. Sci. Data, 13, 3927–3950, https://doi.org/10.5194/essd-13-3927-2021, https://doi.org/10.5194/essd-13-3927-2021, 2021
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Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we generated a global dataset of forest above-ground biomass, i.e. carbon stocks, from satellite data of 2010. This dataset provides a comprehensive and detailed portrait of the distribution of carbon in forests, although for dense forests in the tropics values are somewhat underestimated. This dataset will have a considerable impact on climate, carbon, and socio-economic modelling schemes.
Toby D. Jackson, Sarab Sethi, Ebba Dellwik, Nikolas Angelou, Amanda Bunce, Tim van Emmerik, Marine Duperat, Jean-Claude Ruel, Axel Wellpott, Skip Van Bloem, Alexis Achim, Brian Kane, Dominick M. Ciruzzi, Steven P. Loheide II, Ken James, Daniel Burcham, John Moore, Dirk Schindler, Sven Kolbe, Kilian Wiegmann, Mark Rudnicki, Victor J. Lieffers, John Selker, Andrew V. Gougherty, Tim Newson, Andrew Koeser, Jason Miesbauer, Roger Samelson, Jim Wagner, Anthony R. Ambrose, Andreas Detter, Steffen Rust, David Coomes, and Barry Gardiner
Biogeosciences, 18, 4059–4072, https://doi.org/10.5194/bg-18-4059-2021, https://doi.org/10.5194/bg-18-4059-2021, 2021
Short summary
Short summary
We have all seen trees swaying in the wind, but did you know that this motion can teach us about ecology? We summarized tree motion data from many different studies and looked for similarities between trees. We found that the motion of trees in conifer forests is quite similar to each other, whereas open-grown trees and broadleaf forests show more variation. It has been suggested that additional damping or amplification of tree motion occurs at high wind speeds, but we found no evidence of this.
Cited articles
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Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coombes, D., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M.: ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-03: Kabili-Sepilok, Malaysian Borneo 1 ha plot SEP-12, March 2017, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/BB81C82352524DF99DDD411F6CA2EC81, 2025e.
Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coombes, D., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M.: ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-03: Kabili-Sepilok, Malaysian Borneo 1 ha plot SEP-11, March 2017, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/37B039605E9B4BB5A89371FD7F5B7BA1, 2025f.
Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coombes, D., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M.: ForestScan ProjecT: Terrestrial Laser Scanning (TLS) of FBRMS-02: Station d'Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon 1 ha plot OKO-03, June to July 2022, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/8ED3DDEC76B8470285BDB2EA643F54BC, 2025g.
Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coombes, D., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M.: ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-02: Station d'Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon 1 ha plot OKO-02, June to July 2022, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/FF4B43475C9641CCA1DAD2C8BE8DADAF, 2025h.
Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coombes, D., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M.: ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-02: Station d'Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon 1 ha plot OKO-01, June to July 2022, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/45AE3437F82F4E4FB75F9A5C26A194BA, 2025i.
Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coombes, D., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M.: ForestScan Project: Terrestrial Laser Scanning (TLS) of FBRMS-02: Station d'Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon 1 ha plot LPG-01, June to July 2022, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/8EA2C697EE53430A84825384BFDCF06A, 2025j.
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Short summary
The ForestScan project provides a comprehensive set of datasets of tropical forest 3D structural measurements using terrestrial, unpiloted aerial vehicle and aerial laser scanning, plus tree census data. Collected at three sites in French Guiana, Gabon, and Malaysia, these datasets are crucial for calibrating and validating earth observation-derived forest biomass estimates, therefore, expanding and enhancing their use, and aiding global conservation efforts.
The ForestScan project provides a comprehensive set of datasets of tropical forest 3D structural...
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