Data processing and acquisition geometry impact the estimation of plant trait-based functional richness from airborne imaging spectroscopy

Abstract

Functional diversity can be assessed remotely from optical sensors using vegetation index-based plant traits. Without effective corrections, employed reflectance values are affected by absorption and scattering processes in the atmosphere and on the ground, which modify radiance and irradiance values used for the reflectance retrieval. Additionally, the anisotropic nature of vegetation canopies induces observation and illumination angle-dependent reflectance variations. Often, however, the reflectance retrieval is not accurate enough to compensate for these effects in the atmosphere and on the surface, resulting in uncertain reflectance values. Furthermore, the effects in retrieved reflectance values propagate into derived products, like the vegetation indices used for calculating functional diversity, where they manifest as apparent differences between temporally close observations of the same area. A key to compensating for these effects lies in the capacity and consideration of several processing steps, such as atmospheric, topographic, and anisotropy correction.

To date, it is unknown how these effects and their correction influence the estimation of functional richness. Here, we estimate functional richness based on three differently retrieved reflectance datasets in the overlapping area of three consecutively acquired flight lines with short temporal differences but with three distinct acquisition geometries. We analyze how atmospheric, topographic, and anisotropy effects influence functional richness estimates and how functional richness varies due to different observation and illumination angles.

We show that reflectance data before correction for atmospheric, topographic, and anisotropy effects yield up to 15% larger median functional richness estimates compared to data after respective corrections. We discuss under which circumstances comprehensive data processing can reduce between-observation differences. Furthermore, we show that resulting functional richness estimates correlate with the number of shaded pixels (r² $\approx$ 0.7). Consequently, observations in the solar principal plane with more or fewer shadows can lead to larger or smaller functional richness estimates and to differences compared to observations perpendicular to the solar principal plane.

We conclude with recommendations concerning best-suited data processing and acquisition geometry for reliable and repeatable assessments of functional richness from optical remote sensing data and discuss applications to aerial and space-based observations of functional diversity.