Nicolas Barbier

The paper finds that consistent crown phenology, not functional traits, is the key factor determining whether tropical tree species can be accurately mapped using imaging spectroscopy.

The paper describes a drone-based photogrammetric method to monitor tropical forests with high spatial and temporal consistency, using Time-SIFT and Arosics algorithms to improve alignment and vegetation analysis.

The paper introduces the Global Canopy Atlas, a harmonized dataset of 3D canopy structure from ALS data, and demonstrates its applications in validating satellite maps, analyzing canopy gap patterns, and quantifying forest turnover.

The paper analyzes global tree crown architecture to identify factors driving diversity in height, crown size, and shape across species.

The paper tests the WBE model of metabolic scaling in trees using laser-scanned data, finding support for [3/4] scaling across tree crowns and intraspecific variation.

This study evaluates species-level canopy mapping in a hyperdiverse tropical forest using multi-temporal crown segmentation and airborne hyperspectral data, finding 70% canopy area correctly mapped to species.