The Spectral Species Concept in Living Color

Abstract

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth.The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing. Plain Language SummaryBiodiversity monitoring based on field data is almost inconceivable at the scale of the entire Earth. Over the past decades, remote sensing has opened possibilities for Earth observation from air and space, allowing us to monitor ecological change, primarily expressed by changes in ROCCHINI ET AL.

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

“…In this study we accepted a priori that imagery with spatial resolution of 1 m or greater would result in mixed pixels in most of the community types studied (Rocchini et al., 2021). Our hope was that spectral species would represent optical types (Ustin & Gamon, 2010) corresponding to local, within‐community characteristics (Rocchini et al., 2022). Thus, similarity in such characteristics among mapping windows within the same community can be used to identify locations belonging to the same general community type, despite considerable within‐community heterogeneity.…”

“…In contrast, the goal of simplifying landscape complexity to a limited number of communities to identify patterns of beta diversity might be hindered by clustering pixels into too many spectral species, as demonstrated by our poorer results using 50 spectral species compared to 20 spectral species. It can be imagined that where two pixels are clustered into different spectral species when in fact they represent areas with species in common, such categorical distinction overestimates their dissimilarity (Rocchini et al., 2022).…”

“…Correlations between spectral diversity and plant species diversity, though often greatly underestimating the latter, have been detected at spatial resolutions ranging up to 30 m (Gamon et al., 2020; Gastauer et al., 2022; Gholizadeh et al., 2019; Kishore et al., 2023; Liccari et al., 2022; Rocchini, 2007; Zhang et al., 2023). However, within the range of spatial resolutions suggested for the SBG mission (30–45 m), such estimates of plant taxonomic diversity presumably will not be useful in certain environments (Rocchini et al., 2022). Examples include herb‐dominated plant communities such as those included in this study, which can have >20 species per m 2 (Glitzenstein et al., 2003).…”

“…biodivMapR maps beta diversity according to dissimilarities among mapping windows with regard to spectral species into which pixels are clustered. At coarser spatial resolutions, pixels may be clustered according to their “optical types” (Ustin & Gamon, 2010) representing local community characteristics, including species composition and community structure, instead of individual plant species (Féret & Asner, 2014; Rocchini et al., 2022). Thus, the degree of similarity in biodiversity between two areas of interest might be indicated by the similarity in such spectral species generated from mixed pixels (Chraibi et al., 2022; Liccari et al., 2022; Rocchini et al., 2021; Sagang et al., 2022).…”

“…Predictable community associations should also suggest the presence of plant species that are not detectable by remote sensing because of their low relative cover, position beneath other plants, or phenological stage at the time of image capture. In order to provide useful generalizations about species associations, the community concept must tolerate a range of optical types within a given community that are still associated with that community (Rocchini et al., 2022; Ustin & Gamon, 2010). Thus, how beta diversity is mapped using the spectral species concept will likely depend on the spatial resolution of available imagery relative to the spatial scale of variation in plant community characteristics on the landscape (Gamon et al., 2020).…”