NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure
ABSTRACTSpatial-temporal dynamics monitoring of Arctic vegetation structure (i.e. distribution range of tundra and forest) is of great significance for evaluating global warming effect. Currently, time-series monitoring of Arctic vegetation structure relies primarily on the Normalized Difference Veg...
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ftdoajarticles:oai:doaj.org/article:a9ff0c4c2ba54481be1818f7bcc00d55 2024-09-15T18:08:06+00:00 NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure Zihong Liu Da He Qian Shi Xiao Cheng 2024-04-01T00:00:00Z https://doi.org/10.1080/10095020.2024.2336602 https://doaj.org/article/a9ff0c4c2ba54481be1818f7bcc00d55 EN eng Taylor & Francis Group https://www.tandfonline.com/doi/10.1080/10095020.2024.2336602 https://doaj.org/toc/1009-5020 https://doaj.org/toc/1993-5153 doi:10.1080/10095020.2024.2336602 1993-5153 1009-5020 https://doaj.org/article/a9ff0c4c2ba54481be1818f7bcc00d55 Geo-spatial Information Science, Pp 1-19 (2024) Arctic vegetation structure Normalized Difference Vegetation Index (NDVI) time series reconstruction Mathematical geography. Cartography GA1-1776 Geodesy QB275-343 article 2024 ftdoajarticles https://doi.org/10.1080/10095020.2024.2336602 2024-08-05T17:49:31Z ABSTRACTSpatial-temporal dynamics monitoring of Arctic vegetation structure (i.e. distribution range of tundra and forest) is of great significance for evaluating global warming effect. Currently, time-series monitoring of Arctic vegetation structure relies primarily on the Normalized Difference Vegetation Index (NDVI), which is derived from optical remote sensing images. However, because of factors such as the long revisit period of satellites and the impact of climate, optical observations are severely lacking in the Arctic region. This results in NDVI time-series data highly discontinuous and difficult to reflect actual variations in Arctic vegetation structure, and the traditional time-series reconstruction method would usually fail for severe missing conditions. Therefore, this study developed a Time Series Reconstruction method considering Periodic Trend (TSR-PT), which is specifically for alleviating the severe missing observation condition in the Arctic region. It can separate the phenological change and trend change of the incomplete time series NDVI, and borrow the information from the neighboring unchanged years for compensate of the missing observations in current years, based on the learned inter-annual and intra-annual correlation. We explore its usability in monitoring vegetation structure variation in Vorkuta region (transition zone of tundra and taiga in the Arctic Circle) based on MODIS data. It is found that the proposed TSR-PT is able to reconstruct NDVI with reasonable phenological feature even the missing rate reaches over 70%, which is usually falsely constructed by traditional filtering or fitting method, and suppress them by 0.038 in terms of RMSE; besides, we find that since 21-century, the Arctic trees have continued to increase and encroach the original tundra ecosystem, which caused a largely Arctic vegetation structural change, and we believe the proposed method would largely promote the Arctic vegetation research. Article in Journal/Newspaper Global warming taiga Tundra Vorkuta Directory of Open Access Journals: DOAJ Articles Geo-spatial Information Science 1 19 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic vegetation structure Normalized Difference Vegetation Index (NDVI) time series reconstruction Mathematical geography. Cartography GA1-1776 Geodesy QB275-343 |
spellingShingle |
Arctic vegetation structure Normalized Difference Vegetation Index (NDVI) time series reconstruction Mathematical geography. Cartography GA1-1776 Geodesy QB275-343 Zihong Liu Da He Qian Shi Xiao Cheng NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure |
topic_facet |
Arctic vegetation structure Normalized Difference Vegetation Index (NDVI) time series reconstruction Mathematical geography. Cartography GA1-1776 Geodesy QB275-343 |
description |
ABSTRACTSpatial-temporal dynamics monitoring of Arctic vegetation structure (i.e. distribution range of tundra and forest) is of great significance for evaluating global warming effect. Currently, time-series monitoring of Arctic vegetation structure relies primarily on the Normalized Difference Vegetation Index (NDVI), which is derived from optical remote sensing images. However, because of factors such as the long revisit period of satellites and the impact of climate, optical observations are severely lacking in the Arctic region. This results in NDVI time-series data highly discontinuous and difficult to reflect actual variations in Arctic vegetation structure, and the traditional time-series reconstruction method would usually fail for severe missing conditions. Therefore, this study developed a Time Series Reconstruction method considering Periodic Trend (TSR-PT), which is specifically for alleviating the severe missing observation condition in the Arctic region. It can separate the phenological change and trend change of the incomplete time series NDVI, and borrow the information from the neighboring unchanged years for compensate of the missing observations in current years, based on the learned inter-annual and intra-annual correlation. We explore its usability in monitoring vegetation structure variation in Vorkuta region (transition zone of tundra and taiga in the Arctic Circle) based on MODIS data. It is found that the proposed TSR-PT is able to reconstruct NDVI with reasonable phenological feature even the missing rate reaches over 70%, which is usually falsely constructed by traditional filtering or fitting method, and suppress them by 0.038 in terms of RMSE; besides, we find that since 21-century, the Arctic trees have continued to increase and encroach the original tundra ecosystem, which caused a largely Arctic vegetation structural change, and we believe the proposed method would largely promote the Arctic vegetation research. |
format |
Article in Journal/Newspaper |
author |
Zihong Liu Da He Qian Shi Xiao Cheng |
author_facet |
Zihong Liu Da He Qian Shi Xiao Cheng |
author_sort |
Zihong Liu |
title |
NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure |
title_short |
NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure |
title_full |
NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure |
title_fullStr |
NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure |
title_full_unstemmed |
NDVI time-series data reconstruction for spatial-temporal dynamic monitoring of Arctic vegetation structure |
title_sort |
ndvi time-series data reconstruction for spatial-temporal dynamic monitoring of arctic vegetation structure |
publisher |
Taylor & Francis Group |
publishDate |
2024 |
url |
https://doi.org/10.1080/10095020.2024.2336602 https://doaj.org/article/a9ff0c4c2ba54481be1818f7bcc00d55 |
genre |
Global warming taiga Tundra Vorkuta |
genre_facet |
Global warming taiga Tundra Vorkuta |
op_source |
Geo-spatial Information Science, Pp 1-19 (2024) |
op_relation |
https://www.tandfonline.com/doi/10.1080/10095020.2024.2336602 https://doaj.org/toc/1009-5020 https://doaj.org/toc/1993-5153 doi:10.1080/10095020.2024.2336602 1993-5153 1009-5020 https://doaj.org/article/a9ff0c4c2ba54481be1818f7bcc00d55 |
op_doi |
https://doi.org/10.1080/10095020.2024.2336602 |
container_title |
Geo-spatial Information Science |
container_start_page |
1 |
op_container_end_page |
19 |
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1810445451100422144 |