Evaluating the sensitivity of Sentinel-2 reflectance to vertical canopy profiles using a voxel-based point density proxy framework - Takumi Fujiwara; Dokrak Marod; Sathid Thinkamphaeng; Wongsatorn Phumphuang; Pisut Nakmuenwai; Torlarp Kamyo; Benjamin Brede; Hiroki Naito; Fumiki Hosoi (International Journal of Remote Sensing, 2026)

Assessment of EnMAP data quality through global product validation activities - Maximilian Brell; Philipp Reiners; Luis Guanter; Karl Segl; Sabine Chabrillat; Daniel Scheffler; Mariana A. Soppa; Niklas Bohn; Javier Gorrono; Alexander Kokhanovsky; Astrid Bracher; Martin Bachmann; et al. (Remote Sensing of Environment, 2026)

deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics - Clemens Mosig; Janusch Vajna-Jehle; Miguel D. Mahecha; Yan Cheng; Henrik Hartmann; David Montero; Samuli Junttila; Stéphanie Horion; Mirela Beloiu Schwenke; Michael J. Koontz; Khairul Nizam Abdul Maulud; Stephen Adu-Bredu; et al. (Remote Sensing of Environment, 2026)

Realistic virtual forests for understanding forest disturbances and recovery from space - Kim Calders; Martin Herold; Jennifer Adams; John Armston; Benjamin Brede; Wout Cherlet; Zane T. Cooper; Karun Dayal; Pieter De Frenne; Shaun R. Levick; Patrick Meir; Niall Origo; et al. (ISPRS Journal of Photogrammetry and Remote Sensing, 2025)

Detection of a vascular wilt disease in potato (‘Blackleg’) based on UAV hyperspectral imagery: Can structural features from LiDAR or SfM improve plant-wise classification accuracy? - Marston H.D. Franceschini; Benjamin Brede; Jan Kamp; Harm Bartholomeus; Lammert Kooistra (Computers and Electronics in Agriculture, 2024)

New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests - Louise Terryn; Kim Calders; Félicien Meunier; Marijn Bauters; Pascal Boeckx; Benjamin Brede; Andrew Burt; Jerome Chave; Antonio Carlos Lola da Costa; Barbara D'hont; Mathias Disney; Tommaso Jucker; et al. (Global Change Biology, 2024)
Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non‐destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory‐based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ‐400(i) TLS‐based D and H data, allowing accuracy assessment of local forest inventory‐based H:D allometries. Additionally, TLS‐based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory‐based H:D allometries underestimated H compared with TLS‐based allometries. For 30‐metre‐tall trees, these underestimations varied from −1.6 m (−5.3%) to −7.5 m (−25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as −31.7 m (−41.3%). We propose a TLS‐based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of −4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries.

TomoSense: A unique 3D dataset over temperate forest combining multi-frequency mono- and bi-static tomographic SAR with terrestrial, UAV and airborne lidar, and in-situ forest census - Stefano Tebaldini; Mauro Mariotti d'Alessandro; Lars M.H. Ulander; Patrik Bennet; Anders Gustavsson; Alex Coccia; Karlus Macedo; Mathias Disney; Phil Wilkes; Hans-Joachim Spors; Nico Schumacher; Jan Hanuš; et al. (Remote Sensing of Environment, 2023)

Distinguishing forest types in restored tropical landscapes with UAV-borne LIDAR - Janneke Scheeres; Johan de Jong; Benjamin Brede; Pedro H.S. Brancalion; Eben Noth Broadbent; Angelica Maria Almeyda Zambrano; Eric Bastos Gorgens; Carlos Alberto Silva; Ruben Valbuena; Paulo Molin; Scott Stark; Ricardo Ribeiro Rodrigues; et al. (Remote Sensing of Environment, 2023)

Multi-sensor airborne lidar requires intercalibration for consistent estimation of light attenuation and plant area density - Grégoire Vincent; Philippe Verley; Benjamin Brede; Guillaume Delaitre; Eliott Maurent; James Ball; Ilona Clocher; Nicolas Barbier (Remote Sensing of Environment, 2023)