Mapping elevational patterns of functional diversity of canopy species in an alpine forest using drone multispectral and LiDAR data

Abstract

Understanding the patterns and drivers of plant functional diversity is crucial for assessing the functioning and resilience of terrestrial ecosystems to global climate change. Despite the well-documented variability in plant species richness with elevation, the elevational patterns and underlying mechanisms of functional diversity remain poorly understood. This study employed drone equipped with multispectral and light detection and ranging (LiDAR) sensors to investigate the elevational gradients (3208 m to 3984 m) of physiological and morphological functional diversity of canopy tree species in six 1-hectare plots in Baima Snow Mountain, Yunnan, China. Results demonstrated that drone-based multispectral and LiDAR data enabled high-resolution mapping of physiological and morphological functional diversity in forest canopies over large areas. Importantly, functional diversity declined with increasing elevation, with a more pronounced pattern observed for physiological traits compared to morphological traits. Variance partitioning analysis revealed that functional richness was primarily driven by climatic and edaphic factors, particularly temperature, while plant factors played a subordinate role, interacting with climate and soil. Our findings highlight the potential of drone for rapid, large-scale monitoring and mapping of canopy species functional diversity in forests. Moreover, the influences of climate and soil factors on functional diversity suggest that future global warming may enhance functional diversity in alpine forest ecosystems, with implications for ecosystem functioning and services.

Keywords

• Functional traits
• Functional diversity
• Altitudinal patterns
• Baima Snow Mountain
• Drone remote sensing