date: 2020-03-12T10:42:44Z
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pdf:docinfo:title: Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest
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subject: 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 Niņo-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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dc:title: Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest
modified: 2020-03-12T10:42:44Z
cp:subject: 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 Niņo-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.
pdf:docinfo:subject: 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 Niņo-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.
pdf:docinfo:creator: Teodoro Semeraro, Andrea Luvisi, Antonio O. Lillo, Roberta Aretano, Riccardo Buccolieri and Norbert Marwan
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meta:author: Teodoro Semeraro, Andrea Luvisi, Antonio O. Lillo, Roberta Aretano, Riccardo Buccolieri and Norbert Marwan
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Creation-Date: 2020-03-12T10:42:44Z
Author: Teodoro Semeraro, Andrea Luvisi, Antonio O. Lillo, Roberta Aretano, Riccardo Buccolieri and Norbert Marwan
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Keywords: remote sensing; EVI; NDWI; LST; ecological functions; recurrence analysis
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dc:creator: Teodoro Semeraro, Andrea Luvisi, Antonio O. Lillo, Roberta Aretano, Riccardo Buccolieri and Norbert Marwan
dcterms:created: 2020-03-12T10:42:44Z
Last-Modified: 2020-03-12T10:42:44Z
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title: Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest
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pdf:docinfo:keywords: remote sensing; EVI; NDWI; LST; ecological functions; recurrence analysis
pdf:docinfo:modified: 2020-03-12T10:42:44Z
meta:save-date: 2020-03-12T10:42:44Z
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creator: Teodoro Semeraro, Andrea Luvisi, Antonio O. Lillo, Roberta Aretano, Riccardo Buccolieri and Norbert Marwan
dc:subject: remote sensing; EVI; NDWI; LST; ecological functions; recurrence analysis
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meta:keyword: remote sensing; EVI; NDWI; LST; ecological functions; recurrence analysis
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pdf:docinfo:created: 2020-03-12T10:42:44Z