Intrinsic dimensionality as a metric for temporal plant diversity evaluation: Case study from the SHIFT campaign

Current biodiversity metrics derived from remote sensing data are typically applied to small local areas, require significant training data, and are not easily extensible globally. Here we propose the mathematical concept of intrinsic dimensionality (ID)—quantifying the degrees of freedom in a process—as a method to quantify terrestrial plant diversity without a need for in situ training data. This technique has been applied to airborne imaging spectroscopy data from the Surface Biology and Geology HIgh Frequency Time series (SHIFT) airborne campaign, with weekly overflights from February to May 2022 over a region in California stretching from Figueroa Mountain in the Los Padres National Forest to Point Conception and adjacent coastal areas. Preliminary results show an encouraging and significant correlation between spatially calculated ID and in situ vegetation species richness data. In addition, the spatial ID remained largely unchanged over the course of three months during the spring green-up period when vegetation characteristics and spectral responses were changing rapidly, and temporal ID continues to increment. This robustness to seasonal change is desirable in any measure of species richness because it is insulated from changes in vegetation condition that are unrelated to biodiversity. Even though the spatial ID is consistent across acquisition dates, when considering the full time series (temporal ID), we find that there is a large amount of spectral information to be gained through high-frequency temporal sampling of spectral data over vegetated regions.