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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ftdoajarticles:oai:doaj.org/article:a3f04f8c8c8749c28c342845a930d9ec 2023-05-15T18:40:35+02:00 Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology Markus Reichstein Miguel D. Mahecha Christopher S.R. Neigh Jan Verbesselt Nuno Carvalhais Matthias Forkel 2013-05-01T00:00:00Z https://doi.org/10.3390/rs5052113 https://doaj.org/article/a3f04f8c8c8749c28c342845a930d9ec EN eng MDPI AG http://www.mdpi.com/2072-4292/5/5/2113 https://doaj.org/toc/2072-4292 doi:10.3390/rs5052113 2072-4292 https://doaj.org/article/a3f04f8c8c8749c28c342845a930d9ec Remote Sensing, Vol 5, Iss 5, Pp 2113-2144 (2013) greening browning breakpoints seasonal cycle season-trend model boreal forest tundra fire disturbances Alaska Science Q article 2013 ftdoajarticles https://doi.org/10.3390/rs5052113 2022-12-31T15:21:26Z 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. Article in Journal/Newspaper Tundra Alaska Directory of Open Access Journals: DOAJ Articles Browning ENVELOPE(164.050,164.050,-74.617,-74.617) Remote Sensing 5 5 2113 2144 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
greening browning breakpoints seasonal cycle season-trend model boreal forest tundra fire disturbances Alaska Science Q |
spellingShingle |
greening browning breakpoints seasonal cycle season-trend model boreal forest tundra fire disturbances Alaska Science Q Markus Reichstein Miguel D. Mahecha Christopher S.R. Neigh Jan Verbesselt Nuno Carvalhais Matthias Forkel 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 Science Q |
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 |
Article in Journal/Newspaper |
author |
Markus Reichstein Miguel D. Mahecha Christopher S.R. Neigh Jan Verbesselt Nuno Carvalhais Matthias Forkel |
author_facet |
Markus Reichstein Miguel D. Mahecha Christopher S.R. Neigh Jan Verbesselt Nuno Carvalhais Matthias Forkel |
author_sort |
Markus Reichstein |
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 |
MDPI AG |
publishDate |
2013 |
url |
https://doi.org/10.3390/rs5052113 https://doaj.org/article/a3f04f8c8c8749c28c342845a930d9ec |
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, Vol 5, Iss 5, Pp 2113-2144 (2013) |
op_relation |
http://www.mdpi.com/2072-4292/5/5/2113 https://doaj.org/toc/2072-4292 doi:10.3390/rs5052113 2072-4292 https://doaj.org/article/a3f04f8c8c8749c28c342845a930d9ec |
op_doi |
https://doi.org/10.3390/rs5052113 |
container_title |
Remote Sensing |
container_volume |
5 |
container_issue |
5 |
container_start_page |
2113 |
op_container_end_page |
2144 |
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