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Identifying temporal changes in tropical South American vegetation: a breakpoint detection approach

Grant number: 15/02105-0
Support type:Scholarships in Brazil - Master
Effective date (Start): April 01, 2015
Effective date (End): May 31, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Cooperation agreement: Microsoft Research
Principal researcher:Ricardo da Silva Torres
Grantee:Alexandre Esteves Almeida
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Company:Universidade Estadual de Campinas (UNICAMP). Instituto de Computação (IC)
Associated research grant:13/50169-1 - Towards an understanding of tipping points within tropical South American biomes, AP.PITE
Associated scholarship(s):16/08085-3 - A tool based on time series-extracted artifacts for definition of ecology measures, BE.EP.MS


This master research will be dedicated to addressing the first part of question I (see post-doc work summary). It will focus on detecting changes and/or breakpoints along vegetation time series using mining algorithms such as the BFAST software. This sort of time series based method has been developed to detect three different types of breaks: 1) seasonal changes, e.g., changes in vegetation phenology driven by temperature or rainfall, without necessarily affecting the underlying trend component; 2) gradual changes, which may persist or reverse over time, i.e., may define a trend break (e.g., land degradation or long terms drought effects); 3) depending on the magnitude as well as the change in direction, this trend break may define abrupt changes (e.g., floods, deforestation events or fires). (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ALBARRACIN, JUAN F. H.; OLIVEIRA, RAFAEL S.; HIROTA, MARINA; DOS SANTOS, JEFERSSON A.; TORRES, RICARDO DA S. A Soft Computing Approach for Selecting and Combining Spectral Bands. REMOTE SENSING, v. 12, n. 14 JUL 2020. Web of Science Citations: 0.
MENINI, NATHALIA; ALMEIDA, ALEXANDRE E.; LAMPARELLI, RUBENS; LE MAIRE, GUERRIC; DOS SANTOS, JEFERSSON A.; PEDRINI, HELIO; HIROTA, MARINA; TORRES, RICARDO DA S. A Soft Computing Framework for Image Classification Based on Recurrence Plots. IEEE Geoscience and Remote Sensing Letters, v. 16, n. 2, p. 320-324, FEB 2019. Web of Science Citations: 1.
ALMEIDA, ALEXANDRE E.; TORRES, RICARDO DA S. Remote Sensing Image Classification Using Genetic-Programming-Based Time Series Similarity Functions. IEEE Geoscience and Remote Sensing Letters, v. 14, n. 9, p. 1499-1503, SEP 2017. Web of Science Citations: 3.
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
. Componentes e pontos de quebra em séries temporais na análise de imagens de sensoriamento remoto. 2017. 107 f. Master's Dissertation - Universidade Estadual de Campinas (UNICAMP). Instituto de Computação.

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