Remote sensing can augment ground-based surveys by providing contiguous spatial coverage and an effective method of quantifying forest functional characteristics and FD.
The research was published on March 10th, 2023, in Remote Sensing of Environment.
Relying on trait values distributed in the multidimensional trait space, the researchers calculated functional diversity indices (functional richness, evenness, and divergence) across ecosystem and landscape scales, extending their prior scaling study’s individual tree-based functional diversity mapping strategy to a larger area.
Airborne LiDAR-derived morphological traits corresponded well with in-situ plot-level morphological data (R2 ≥ 0.67), according to the findings. Partial least squares regression outperformed the vegetation index technique for physiological characteristics.
Community-weighted means of morphological and physiological features had a significant impact on forest carbon stocks and primary production, and morphological FDs were also significant predictors of variation in these productivity proxies.
The current research emphasized the potential of employing remotely sensed functional traits to analyze the link between trait diversity and ecosystem functioning at different scales, providing a scientific foundation for forest resource conservation and management.
Recently, the study team made significant strides in estimating the variety of forest and grassland species (Front. Plant Sci., 2023 and Front. Ecol. Evol., 2023). Future research will aim to scale up plant diversity monitoring to regional or national sizes by combining ground surveys, UAV-borne, airborne, and spaceborne observations.
Journal Reference
Zheng, Z., et al. (2023). Remotely sensed functional diversity and its association with productivity in a subtropical forest. Remote Sensing of Environment. doi.org/10.1016/j.rse.2023.113530.