Estimating determinism rates to detect patterns in geospatial datasets

The analysis of temporal geospatial data has provided important insights into global vegetation dynamics, particularly the interaction among different variables such as precipitation and vegetation indices. Nevertheless, this analysis is not a straightforward task due to the complex relationships among different systems driving the dynamics of the observed variables. Aiming at automatically extracting information from temporal geospatial data, we propose a new approach to detect stochastic and deterministic patterns embedded into time series and illustrate its effectiveness through an analysis of global geospatial precipitation and vegetation data captured over a 14 year period. By knowing such patterns, we can find similarities in the behavior of different systems even if these systems are characterized by different dynamics. In addition, we developed a novel determinism measure to evaluate the relative contribution of stochastic and deterministic patterns in a time series. Analyses showed that this measure permitted the detection of regions on the global map where the radiation absorbed by the vegetation and the incidence of rain occur with similar patterns of stochasticity. The methods developed in this study are generally applicable to any spatiotemporal data set and may be of particular interest for the analysis of the vast amount of remotely sensed geospatial data currently being collected routinely as part of national and international monitoring programs.