Estimating Traits of Tillandsia landbeckii Using a Newly Developed VNIR/SWIR Multispectral UAV Imaging System in the Atacama Desert

Fog-dependent Tillandsia landbeckii in the hyper-arid Atacama Desert lacks the red-edge reflectance pattern that supports vegetation monitoring, motivating shortwave infrared (SWIR) approaches. We evaluated a newly developed UAV-borne multispectral SWIR camera system for estimating plant water status and additional plant functional traits (fresh and dry biomass, and N uptake) from four spectral bands (1100, 1200, 1510, and 1650 nm) across 20 destructively sampled plots. Of five traits tested, only canopy water content (CWC) retained statistically robust spectral associations after multiple-testing correction, with most significant predictors concentrated in the 1200–1510 nm wavelength region. A physically interpretable predictor, the mean spectral slope between 1200 and 1510 nm, yielded conditional cross-validated Rcv2=0.51 (RMSEcv≈170 g m−2), though fully selection-corrected estimates were substantially lower (Rcv2=0.10–0.20), reflecting feature-selection instability at the given sample size. The absence of robust biomass- and nitrogen-related signals is physically interpretable given the species’ atypical surface optics. While expanded sampling and independent validation remain necessary to establish transferable performance estimates, these results demonstrate that SWIR-based water-status retrieval is feasible for this spectrally challenging species, opening a pathway toward functional monitoring of fog-dependent desert ecosystems.