RLQ and fourth-corner analysis of plant species traits and spectral indices derived from HyMap and CHRIS-PROBA imagery

Databases on plant traits as well as the availability of global coverage of high spatial and spectral resolution remote-sensing data are constantly growing. However, little effort has been made to analyse the relationship between plant traits and remote-sensing data while simultaneously taking species identity and abundance into consideration. We correlated quantitative and qualitative plant traits from a dwarf shrub savanna in Namibia, with spectral indices derived from two hyperspectral sensors, HyMap and the Compact High Resolution Imaging Spectrometer Project for On-Board Autonomy (CHRIS-PROBA), which differ in their spatial and spectral resolution. We used RLQ analysis and the fourth-corner statistic, which are two three-table ordination approaches that circumvent the so-called fourth-corner problem. A higher spatial resolution helped to identify trait–index correlations linked to vegetation structure, while a lower spatial resolution pointed at traits linked to vegetation cover. A higher spectral resolution did not improve the relationships between spectral indices and plant traits. However, continuous hyperspectral signatures allowed for the calculation of spectral indices that make use of the detailed spectra allowing for more sophisticated spectral indices. We propose RLQ and the fourth-corner statistic as suitable tools for the remote sensing and Earth observation community that allow the direct correlation of trait databases with remotely sensed information.