Quantifying Amazon's resilience through structural breaks associated with extreme climatic events

• Grant number: 18/06918-3
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: September 01, 2018
• End date: November 30, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Ricardo da Silva Torres
• Grantee: Nathália Menini Cardoso dos Santos
• Supervisor: Milena Holmgren
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: Wageningen University, Netherlands
• Associated to the scholarship: 16/26170-8 - Structural breaks detection in time series and its use in the definition of stability measures, BP.MS

Abstract

The detection and characterization of temporal changes are of paramount importance in the process of understanding how complex mechanisms work and evolve. Especially in the context of changes in vegetation distribution it is essential to understand the impact of and response to changes in conditions and disturbance regimes. In this context, understanding how tropical biomes respond to extreme climatic events, such as droughts, and identifying potential mechanic interactions driving these responses is essential. Thus, during this research internship, we propose to use satellite data to aggregate regions from remote sensing images into similar groups according to their vegetation distribution and climatic patterns. The clusters of time series and climatic patterns will be used in conjunction with abrupt and smooth structural breaks detected in vegetation time series (detected using the Breaks for Additive Season and Trend algorithm) in order to quantify and explain resistance and recovery in the Amazon basin regarding climatic extreme events.

Scientific publications

(4)

(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)

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. (14/50715-9, 16/26170-8, 18/06918-3, 13/50155-0, 14/12236-1, 15/02105-0, 17/12646-3, 13/50169-1)

NOGUEIRA, KEILLER; DOS SANTOS, JEFERSSON A.; MENINI, NATHALIA; SILVA, THIAGO S. F.; MORELLATO, LEONOR PATRICIA C.; TORRES, RICARDO DA S.. Spatio-Temporal Vegetation Pixel Classification by Using Convolutional Networks. IEEE Geoscience and Remote Sensing Letters, v. 16, n. 10, p. 1665-1669, OCT 2019. (16/26170-8, 13/50155-0, 09/54208-6, 13/50169-1, 18/06918-3)

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. (15/02105-0, 14/50715-9, 13/50169-1, 16/08085-3, 14/12236-1, 17/12646-3, 16/26170-8, 13/50155-0, 18/06918-3)

MENINI, NATHALIA; ALMEIDA, ALEXANDRE E.; LAMPARELLI, RUBENS; LE MAIRE, GUERRIC; OLIVEIRA, RAFAEL S.; VERBESSELT, JAN; HIROTA, MARINA; TORRES, RICARDO DA S.. Tucuma A toolbox for spatiotemporal remote sensing image analysis. IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, v. 7, n. 3, p. 13-pg., 2019-09-01. (13/50169-1, 13/50155-0, 14/12236-1, 15/02105-0, 17/12646-3, 18/06918-3, 14/50715-9, 16/26170-8, 16/08085-3)