Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology
Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolutio...
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ftmdpi:oai:mdpi.com:/2072-4292/5/5/2113/ 2023-08-20T04:10:14+02:00 Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology Matthias Forkel Nuno Carvalhais Jan Verbesselt Miguel Mahecha Christopher Neigh Markus Reichstein agris 2013-05-03 application/pdf https://doi.org/10.3390/rs5052113 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs5052113 https://creativecommons.org/licenses/by/3.0/ Remote Sensing; Volume 5; Issue 5; Pages: 2113-2144 greening browning breakpoints seasonal cycle season-trend model boreal forest tundra fire disturbances Alaska Text 2013 ftmdpi https://doi.org/10.3390/rs5052113 2023-07-31T20:32:27Z Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy. Text Tundra Alaska MDPI Open Access Publishing Browning ENVELOPE(164.050,164.050,-74.617,-74.617) Remote Sensing 5 5 2113 2144 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
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greening browning breakpoints seasonal cycle season-trend model boreal forest tundra fire disturbances Alaska |
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greening browning breakpoints seasonal cycle season-trend model boreal forest tundra fire disturbances Alaska Matthias Forkel Nuno Carvalhais Jan Verbesselt Miguel Mahecha Christopher Neigh Markus Reichstein Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology |
topic_facet |
greening browning breakpoints seasonal cycle season-trend model boreal forest tundra fire disturbances Alaska |
description |
Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy. |
format |
Text |
author |
Matthias Forkel Nuno Carvalhais Jan Verbesselt Miguel Mahecha Christopher Neigh Markus Reichstein |
author_facet |
Matthias Forkel Nuno Carvalhais Jan Verbesselt Miguel Mahecha Christopher Neigh Markus Reichstein |
author_sort |
Matthias Forkel |
title |
Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology |
title_short |
Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology |
title_full |
Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology |
title_fullStr |
Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology |
title_full_unstemmed |
Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology |
title_sort |
trend change detection in ndvi time series: effects of inter-annual variability and methodology |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2013 |
url |
https://doi.org/10.3390/rs5052113 |
op_coverage |
agris |
long_lat |
ENVELOPE(164.050,164.050,-74.617,-74.617) |
geographic |
Browning |
geographic_facet |
Browning |
genre |
Tundra Alaska |
genre_facet |
Tundra Alaska |
op_source |
Remote Sensing; Volume 5; Issue 5; Pages: 2113-2144 |
op_relation |
https://dx.doi.org/10.3390/rs5052113 |
op_rights |
https://creativecommons.org/licenses/by/3.0/ |
op_doi |
https://doi.org/10.3390/rs5052113 |
container_title |
Remote Sensing |
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5 |
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5 |
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2113 |
op_container_end_page |
2144 |
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