Advancing Forest Monitoring and Assessment Through Immersive Virtual Reality
Raphael Zürcher
Leica Geosystems/Hexagon, Switzerland
Jiayan Zhao
Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, Wageningen, The Netherlands
Alvaro Lau Sarmiento
Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, Wageningen, The Netherlands
Benjamin Brede
Helmholtz-Centre Potsdam - German Research Centre GFZ, Germany
Alexander Klippel
Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, Wageningen, The Netherlands
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Cecilia Chavana-Bryant, Phil Wilkes, Wanxin Yang, Andrew Burt, Peter Vines, Amy C. Bennett, Georgia C. Pickavance, Declan L. M. Cooper, Simon L. Lewis, Oliver L. Phillips, Benjamin Brede, Alvaro Lau, Martin Herold, Iain McNicol, Edward T. A. Mitchard, David A. Coomes, Toby Jackson, Loic Makaga, Heddy O. Milamizokou Napo, Alfred Ngomanda, Stephan Ntie, Vincent Medjibe, Pacome Dimbonda, Luna Soenens, Virginie Daelemans, Laetitia Proux, Reuben Nilus, Nicolas Labriere, Kathryn Jeffery, David F. R. P. Burslem, Daniel Clewley, David Moffat, Lan Qie, Harm Bartholomeus, Vincent Gregoire, Nicolas Barbier, Geraldine Derroire, Katharine Abernethy, Klaus Scipal, and Mat Disney
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-67, https://doi.org/10.5194/essd-2025-67, 2025
Preprint under review for ESSD
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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.
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.
Suzan Evers, Gamze Z. Dane, Pauline E. W. van den Berg, Alexander K. A. J. Klippel, Timon Verduijn, and Theo A. Arentze
AGILE GIScience Ser., 4, 24, https://doi.org/10.5194/agile-giss-4-24-2023, https://doi.org/10.5194/agile-giss-4-24-2023, 2023