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  Recurrence analysis of vegetation indices for highlighting the ecosystem response to drought events: An application to the Amazon Forest

Semeraro, T., Luvisi, A., Lillo, A. O., Aretano, R., Buccolieri, R., Marwan, N. (2020): Recurrence analysis of vegetation indices for highlighting the ecosystem response to drought events: An application to the Amazon Forest. - Remote Sensing, 12, 6, 907.
https://doi.org/10.3390/rs12060907

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Semeraro, T.1, Author
Luvisi, A.1, Author
Lillo, A. O.1, Author
Aretano, R.1, Author
Buccolieri, R.1, Author
Marwan, Norbert2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Forests are important in sequestering CO2 and therefore play a significant role in climate change. However, the CO2 cycle is conditioned by drought events that alter the rate of photosynthesis, which is the principal physiological action of plants in transforming CO2 into biological energy. This study applied recurrence quantification analysis (RQA) to describe the evolution of photosynthesis-related indices to highlight disturbance alterations produced by the Atlantic Multidecadal Oscillation (AMO, years 2005 and 2010) and the El Niño-Southern Oscillation (ENSO, year 2015) in the Amazon forest. The analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) images to build time series of the enhanced vegetation index (EVI), the normalized difference water index (NDWI), and the land surface temperature (LST) covering the period 2001–2018. The results did not show significant variations produced by AMO throughout the study area, while a disruption due to the global warming phase linked to the extreme ENSO event occurred, and the forest was able to recover. In addition, spatial differences in the response of the forest to the ENSO event were found. These findings show that the application of RQA to the time series of vegetation indices supports the evaluation of the forest ecosystem response to disruptive events. This approach provides information on the capacity of the forest to recover after a disruptive event and, therefore is useful to estimate the resilience of this particular ecosystem.

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 Dates: 2020
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3390/rs12060907
PIKDOMAIN: RD4 - Complexity Science
eDoc: 8988
MDB-ID: No data to archive
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Extremes
Research topic keyword: Ecosystems
Model / method: Nonlinear Data Analysis
Regional keyword: Europe
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
 Degree: -

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Title: Remote Sensing
Source Genre: Journal, SCI, Scopus, p3, OA
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Pages: - Volume / Issue: 12 (6) Sequence Number: 907 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals426
Publisher: MDPI