Measuring β‐diversity by remote sensing: A challenge for biodiversity monitoring

Abstract

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (β‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript, we propose novel techniques to measure β‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating β‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field.

Discovering floristic and geoecological gradients across Amazonia

“…With coarse-resolution imagery, these would lead to mixed pixels, but with medium resolution imagery, one can mask out the irrelevant pixels before extracting the reflectance values. Finally, medium resolution data allows generating entropy or variability metrics, which may be indicative of local taxonomic diversity (Rocchini et al, 2018), even though here we found the simple standard deviation metric to be uninformative.…”

Partitioning plant spectral diversity into alpha and beta components

“…This method and some of its variants (Rocchini et al ) do not measure spectral β ‐ diversity per se , but instead use spectra to estimate changes in community composition across the landscape. Rao’s quadratic entropy has been suggested as a measure of spectral β ‐ diversity, based on the dissimilarity among image pixels within a moving window (Rocchini et al ). However, a moving window approach expresses spectral β ‐ diversity for many small sub‐regions independently from one another and does not estimate the spectral β ‐ diversity of the region as a whole.…”

“…The difference is that we square the individual distances to the spectral centroid; doing so allows us to partition sums of squares into additive components (eqn 9). Fewer studies have considered spectral b-diversity (Rocchini et al 2018). One approach for studying b-diversity using spectra has been to combine ordination scores of species inventories with spectral data in multivariate models to predict the positions of pixels with unknown species composition in species-ordination-space (Schmidtlein et al 2007).…”

Scaling‐up biodiversity‐ecosystem functioning research

“…For that, we need multiscale measures of ecosystem processes (Soranno et al 2019) and biodiversity change (Barnes et al 2016;Chase et al 2019). For measuring biodiversity change at different scales, BEF research must harness current methodological developments (Bush et al 2017), like metagenomics, eDNA (Cristescu & Hebert 2018), remote sensing (Pau & Dee 2016;Rocchini et al 2018) and multi-site monitoring networks and experiments. Scale-explicit analyses will require multiscale statistical methods, such as generalised dissimilarity modelling (Ferrier et al 2007), that can be used to predict spatial patterns of turnover in diversity that are crucial to understanding how the BEF relationship will change across large spatial and temporal extents (Leibold et al 2017;Hu et al 2018;Mori et al 2018).…”