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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Estimating determinism rates to detect patterns in geospatial datasets

Full text
Author(s):
Rios, Ricardo Araujo [1] ; Parrott, Lael [2] ; Lange, Holger [3] ; de Mello, Rodrigo Fernandes [1]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[2] Univ British Columbia, Kelowna, BC V1V 1V7 - Canada
[3] Norwegian Forest & Landscape Inst, N-1431 As - Norway
Total Affiliations: 3
Document type: Journal article
Source: REMOTE SENSING OF ENVIRONMENT; v. 156, p. 11-20, JAN 2015.
Web of Science Citations: 5
Abstract

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. (C) 2014 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 11/02655-9 - Analysis of influences of centralized and distributed process scheduling decisions
Grantee:Rodrigo Fernandes de Mello
Support Opportunities: Regular Research Grants
FAPESP's process: 09/18293-9 - A Hybrid Approach to Identify and Model Deterministic and Stochastic Components present in Time Series
Grantee:Ricardo Araújo Rios
Support Opportunities: Scholarships in Brazil - Doctorate