Laybros, A. | Search bioRxiv

bioRxiv2026

Phenological regularity, not functional traits, determines whether tropical tree species can be mapped from imaging spectroscopy

AO_SCPLOWBSTRACTC_SCPLOWO_LIAirborne imaging spectroscopy enables species-level classification in hyperdiverse tropical forests, but accuracy varies enormously among species. We asked which ecological and evolutionary attributes make a tropical tree species spectrally separable. C_LIO_LIUsing 3,256 field-verified crowns spanning 169 species in a hyperdiverse moist forest in French Guiana, we tested seven hypothesised determinants of classification accuracy at species, pairwise, and individual-crown scales using random forest, beta regression, elastic net, and binomial GLMM analyses. C_LIO_LIPhenological regularity - the strength and consistency of seasonal leaf-cycling - was the single strongest predictor of separability, emerging as the top-ranked variable across all analyses. The presence of congeneric species in the classification pool also reduced accuracy, while broader phylogenetic isolation contributed in multivariate models. At the crown level, crown area was the strongest predictor of correct classification, while liana infestation reduced odds of correct identification by 38%. Leaf chemical traits did not predict separability. C_LIO_LIIt is the consistency of a species ecological signal - its phenological rhythm, spatial sampling, and freedom from canopy contamination - rather than any single functional trait, that determines whether it can be reliably mapped from imaging spectroscopy. C_LI

ecology↗

bioRxiv2024

Harnessing temporal and spectral dimensionality to map and identify species of individual trees in diverse tropical forests

Species-level canopy maps underpin tropical forest biodiversity monitoring, conservation planning, and carbon accounting, yet high species richness and structural complexity make remote classification challenging. Here we evaluate how far a two-step mapping approach can be taken, and where it breaks down, in hyperdiverse moist forest at the Paracou Field Station, French Guiana. First, we delineate individual tree crowns from ten repeat uncrewed aerial vehicle (UAV) RGB surveys with Mask R-CNN, fusing predictions across dates by temporal consensus: mean segmentation F1 rose from 0.68 (single date) to 0.78 (ten dates), covering approximately 86% of test-region canopy area. Second, we classify each crown from a single airborne hyperspectral acquisition (416-2500 nm, 1 m) using machine learning classifiers trained and tested on 3,186 field-verified crowns spanning 169 species. Linear Discriminant Analysis performed best (weighted F1 = 0.75), outperforming more flexible models, but unevenly: across repeated cross-validation (20 x 5-fold), on average 50 species (95% CI: 41-63) attained F1 [≥] 0.7 in a given fold and only 15 did so reliably, with many rare species unclassifiable (macro-average F1 = 0.48). Combining both steps, we estimate approximately 70% of the landscapes canopy area was correctly mapped to species. Band-importance and ablation analyses identified the far-red edge (748-775 nm) as the most informative spectral region. These results advance on studies limited to 20 or fewer species and set out spectral-resolution and training-data requirements for airborne and forthcoming spaceborne imaging spectrometers, while showing that accuracy remains strongly conditioned by training-data availability and the single-site, single-acquisition design.

ecology↗