Spatiotemporal algorithms for measuring ecosystem heterogeneity from space

Aim: This thesis integrates new open-source algorithms for the monitor- ing of ecosystem heterogeneity through remote sensing. The project is or- ganized into three distinct parts, focusing on the measurement of spatial patterns, temporal trends, and spatiotemporal patterns. Methods: Due to its widespread use in ecological research, the algorithms presented in this thesis were developed using the R software. i) Chapter 1 introduces the functions included in rasterdiv package for the calculation of spatial heterogeneity. In Chapter 2 is described a new function (RaoAUC()) for the computation of spatial heterogeneity that summarizes the information of parametric Rao index in a single metric. ii) Chapter 3 introduces the helical graphs, a novel visualization method for temporal trends in biodiversity drivers, plotting the mean values of a variable calculated at various points in time against the corresponding rate of change of the selected variable. iii) Chapter 4 presents a new method to quantify and visualize spatiotemporal heterogeneity change of an area exploiting beta diversity measures. Results and Discussions: i) The metrics tested in Chapter 1 offer insights into various facets of spatial heterogeneity, integrating available information of Earth surface properties, including aspects of functional, taxonomic, phylogenetic and genetic diversity. The RaoAUC() function tested in Chapter 2, emerges as a valuable tool for identifying areas susceptible to environmental changes. ii) Chapter 3 proved that helical graphs efficiently highlight temporal trends of environmental variables and can be exploited in various applications. iii) The spatiotemporal maps developed in Chapter 4 are not only intuitive and easily interpretable but also provide a quantitative measure that seamlessly integrates into modeling frameworks. Conclusion: The algorithms presented in this thesis have proven their efficacy, interpretability, and versatility, contributing valuable insights into distinct aspects of ecosystem heterogeneity.