Remote Spectral Detection of Canopy Functional Dimensions Varying Within and Across Forest Types.

Abstract

Global trait axes reveal overarching dimensions of plant functional variation. However, how these dimensions are spatially organized within and across forest types remains unclear. We combined drone-based full-range imaging spectroscopy with crown-level measurements of 16 physiological, morphological and biochemical traits across temperate, subtropical and tropical forests in China to enable spatially-explicit trait mapping. Through site-training scenario, leaf-to-canopy scaling and spectral-domain modelling tests, we find that reliable canopy trait retrieval depends not only on trait and spectral coverage, but also on preserving trait-spectral relationships across sites and scales. Spectral predictions recovered observed multivariate covariation, summarizing crown variation into a leaf-economics dimension and two additional biochemical dimensions related to hydro-thermal regulation and defence/metabolism. Mapping these dimensions revealed distinct community-level trait organization alongside substantial species- and crown-level variation within forests. These findings link remotely sensed trait retrieval to environmental filtering and plant functional differentiation, providing a scalable framework for monitoring forest functional diversity.

Code and data availability

The supplied blocks contain only front matter, abstract, introduction, and methods/results text describing UAS imaging spectroscopy, leaf trait measurements, and PLSR modelling. No public phenotype/trait dataset, imagery, code repository, model checkpoint, or supplement URL is mentioned, and no data or codeavailability

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