Assessing the performance of remotely sensed functional traits and spectral metrics for estimating multidimensional plant diversity in wetlands using UAV data

Plant diversity underpins wetland ecosystem stability and functional sustainability, and its reliable assessment is vital for effective conservation and management. Remote sensing provides an efficient mean of plant diversity monitoring, however the potential of remotely sensed functional traits (RS-traits) and spectral metrics for plant diversity estimation in wetland ecosystems has not been fully investigated. In this study, we integrated UAV hyperspectral and LiDAR data to extract spectral band metrics, Rao’s quadratic entropy and principal components, texture features, and retrieve physiological and morphological RS-traits. We then used these features to predict multi-dimensional (species, functional, and phylogenetic) plant diversity using multiple stepwise regression (MSR), generalized additive models (GAMs), and random forest regression (RF). The results demonstrated that the retrieved RS-traits were generally consistent with field measurements (R2 = 0.36–0.78 for physiological, R2 = 0.47–0.87 for morphological traits). Among the different models, MSR performed best for species diversity, GAMs for functional diversity, and RF for phylogenetic diversity. The highest predictive performance was achieved for species diversity (R2adj = 0.62–0.73), followed by functional (R2adj = 0.41–0.83) and phylogenetic diversity (R2adj = 0.55–0.64). Models based on RS-traits consistently performed better than those based on spectral metrics, while combining spectral metrics and RS-traits did not lead to statistically significant improvements. While our results provide preliminary evidence towards a unified RS-trait framework for the multi-dimensional monitoring of wetland plant diversity, further work is needed to generalise these findings to other sites and wetland types.