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

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 bio-
diversity 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 allow 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 dif-
ferent 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: i) multivariate statistical analysis, ii) the
spectral species concept, iii) self-organizing feature maps, iv) multi-
dimensional distance matrices, and the v) 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.