Effects of Spatial Resolution, Mapping Window Size, and Spectral Species Clustering on Remote Sensing of Plant Beta Diversity Using biodivMapR and Hyperspectral Imagery

Abstract

AbstractUsing imaging spectroscopy (hyperspectral imaging), we assessed the effects of spatial resolution, size of mapping windows composed of pixels, and number of clustered spectral species on the capacity to map plant beta diversity using the biodivMapR package, in support of the planned NASA Surface Biology and Geology (SBG) satellite remote sensing mission. Specifically, we tested the ability of biodivMapR to distinguish natural communities among field‐verified locations. biodivMapR clusters pixels as spectral species used to calculate beta diversity among mapping windows composed of multiple pixels. We used NEON airborne 1 m resolution hyperspectral images collected at three sites representing native longleaf pine ecosystems in the southeastern U.S. and aggregated pixels to 1–90 m spatial resolution for comparative analyses. We also varied mapping window size using 30 m resolution images, commonly collected by satellite missions. The capacity to detect plant beta diversity decreased with coarser spatial resolution, which corresponded to fewer pixels per mapping window. Mapping window size in turn limited the spatial resolution of beta diversity maps composed of mapping windows. Assigning too few pixels per window, as well as assigning too many spectral species per image, results in overestimation of dissimilarity among locations representing the same community type and reduces the information content of beta diversity maps. These results demonstrate the advantage of maximizing spatial resolution of hyperspectral imaging instruments on the anticipated NASA SBG satellite mission and similar remote sensing projects, as well as the value of satellite‐borne hyperspectral imagers for mapping beta diversity worldwide.