Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness
Analysing changes in vegetation seasonality of terrestrial ecosystems is important to understand ecological responses to global change. Based on over three decades of observations by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors, the Global Inventory Modelling and Mapping St...
Published in: | International Journal of Applied Earth Observation and Geoinformation |
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Online Access: | https://doi.org/10.1016/j.jag.2020.102238 https://doaj.org/article/82d7f4d7aef44446aed1beb9761a448e |
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ftdoajarticles:oai:doaj.org/article:82d7f4d7aef44446aed1beb9761a448e 2023-05-15T15:15:44+02:00 Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness Wentao Ye Albert I.J.M. van Dijk Alfredo Huete Marta Yebra 2021-02-01T00:00:00Z https://doi.org/10.1016/j.jag.2020.102238 https://doaj.org/article/82d7f4d7aef44446aed1beb9761a448e EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S0303243420308813 https://doaj.org/toc/1569-8432 1569-8432 doi:10.1016/j.jag.2020.102238 https://doaj.org/article/82d7f4d7aef44446aed1beb9761a448e International Journal of Applied Earth Observations and Geoinformation, Vol 94, Iss , Pp 102238- (2021) Vegetation seasonality NDVI Trend analysis Robustness NDVI3g MODIS Physical geography GB3-5030 Environmental sciences GE1-350 article 2021 ftdoajarticles https://doi.org/10.1016/j.jag.2020.102238 2022-12-30T21:25:31Z Analysing changes in vegetation seasonality of terrestrial ecosystems is important to understand ecological responses to global change. Based on over three decades of observations by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors, the Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset has been widely used for monitoring vegetation trends. However, it is not well known how robust long-term trends in vegetation seasonality derived from GIMMS NDVI are, given inevitable influences from sensor and processing artefacts. Here we analyse long-term seasonality trends in the GIMMS third generation (NDVI3g) record (1982–2013). Changes in vegetation seasonality are decomposed into changes in duration (related to growing season length) and timing (related to peak growing season). We compare seasonality trends from the previous version (NDVI3g v0) with those in the subsequently released version (NDVI3g v1) and, for their common period, with those derived from MODerate Resolution Imaging Spectroradiometer (MODIS) collection 6 NDVI. We find that NDVI3g v0 shows marked seasonality trends for 1982–2013 over more than one-third of the global vegetated area. Long-term trends based on v1 are generally consistent with v0, but v1 shows a strong trend towards earlier timing across the Arctic regions that is absent in v0. NDVI3g v0, v1, and MODIS all point towards an increased duration across the tundra of North Asia and later timing across North Africa. However, several discrepancies are also found between the NDVI datasets. For example, for the North-American tundra, MODIS shows earlier and v0 later timing, while MODIS shows an increased duration and v1 a reduced duration. For North Africa, v0 and v1 exhibit a reduced duration that is absent in MODIS. We conclude that both the primary observations and the subsequent processing can have a marked influence on inferred seasonality trends, and propose that the robustness of trends should be examined and ... Article in Journal/Newspaper Arctic Tundra Directory of Open Access Journals: DOAJ Articles Arctic International Journal of Applied Earth Observation and Geoinformation 94 102238 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Vegetation seasonality NDVI Trend analysis Robustness NDVI3g MODIS Physical geography GB3-5030 Environmental sciences GE1-350 |
spellingShingle |
Vegetation seasonality NDVI Trend analysis Robustness NDVI3g MODIS Physical geography GB3-5030 Environmental sciences GE1-350 Wentao Ye Albert I.J.M. van Dijk Alfredo Huete Marta Yebra Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness |
topic_facet |
Vegetation seasonality NDVI Trend analysis Robustness NDVI3g MODIS Physical geography GB3-5030 Environmental sciences GE1-350 |
description |
Analysing changes in vegetation seasonality of terrestrial ecosystems is important to understand ecological responses to global change. Based on over three decades of observations by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors, the Global Inventory Modelling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset has been widely used for monitoring vegetation trends. However, it is not well known how robust long-term trends in vegetation seasonality derived from GIMMS NDVI are, given inevitable influences from sensor and processing artefacts. Here we analyse long-term seasonality trends in the GIMMS third generation (NDVI3g) record (1982–2013). Changes in vegetation seasonality are decomposed into changes in duration (related to growing season length) and timing (related to peak growing season). We compare seasonality trends from the previous version (NDVI3g v0) with those in the subsequently released version (NDVI3g v1) and, for their common period, with those derived from MODerate Resolution Imaging Spectroradiometer (MODIS) collection 6 NDVI. We find that NDVI3g v0 shows marked seasonality trends for 1982–2013 over more than one-third of the global vegetated area. Long-term trends based on v1 are generally consistent with v0, but v1 shows a strong trend towards earlier timing across the Arctic regions that is absent in v0. NDVI3g v0, v1, and MODIS all point towards an increased duration across the tundra of North Asia and later timing across North Africa. However, several discrepancies are also found between the NDVI datasets. For example, for the North-American tundra, MODIS shows earlier and v0 later timing, while MODIS shows an increased duration and v1 a reduced duration. For North Africa, v0 and v1 exhibit a reduced duration that is absent in MODIS. We conclude that both the primary observations and the subsequent processing can have a marked influence on inferred seasonality trends, and propose that the robustness of trends should be examined and ... |
format |
Article in Journal/Newspaper |
author |
Wentao Ye Albert I.J.M. van Dijk Alfredo Huete Marta Yebra |
author_facet |
Wentao Ye Albert I.J.M. van Dijk Alfredo Huete Marta Yebra |
author_sort |
Wentao Ye |
title |
Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness |
title_short |
Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness |
title_full |
Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness |
title_fullStr |
Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness |
title_full_unstemmed |
Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness |
title_sort |
global trends in vegetation seasonality in the gimms ndvi3g and their robustness |
publisher |
Elsevier |
publishDate |
2021 |
url |
https://doi.org/10.1016/j.jag.2020.102238 https://doaj.org/article/82d7f4d7aef44446aed1beb9761a448e |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Tundra |
genre_facet |
Arctic Tundra |
op_source |
International Journal of Applied Earth Observations and Geoinformation, Vol 94, Iss , Pp 102238- (2021) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S0303243420308813 https://doaj.org/toc/1569-8432 1569-8432 doi:10.1016/j.jag.2020.102238 https://doaj.org/article/82d7f4d7aef44446aed1beb9761a448e |
op_doi |
https://doi.org/10.1016/j.jag.2020.102238 |
container_title |
International Journal of Applied Earth Observation and Geoinformation |
container_volume |
94 |
container_start_page |
102238 |
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1766346085063720960 |