Abstract
AbstractBiodiversity 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.
Highlights
Biodiversity cannot be fully investigated without considering the spatial component of its variation
Remote sensing has widely been used for conservation practices including very different types of data such as night lights data (Mazor et al, 2013), Land Surface Temperature estimated from MODIS data (Metz, Rocchini, & Neteler, 2014), spectral indices (Gillespie, 2005)
The axes of the detrended correspondence analysis (DCA) ordination space are scaled in SD units, where a distance of 4 SD is related to a full species turnover
Summary
Biodiversity cannot be fully investigated without considering the spatial component of its variation. It is known that the dispersal of species over wide areas is driven by spatial constraints directly related to the distance among sites. K=1 where dik = distance between two locations i and k and a is a parameter regulating the dispersal from localized areas (low values of a) to widespread ones (high values of a; Meentemeyer, Anacker, Mark, & Rizzo, 2008). In this sense, distance acquires a significant role in ecology to estimate biodiversity change. Remote sensing has widely been used for conservation practices including very different types of data such as night lights data (Mazor et al, 2013), Land Surface Temperature estimated from MODIS data (Metz, Rocchini, & Neteler, 2014), spectral indices (Gillespie, 2005)
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