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Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach

Nonlinear contributions from climate change and anthropogenic activity to the Normalized Difference Vegetation Index (NDVI) are analyzed to better understand the mechanisms underlying the nonlinear response of vegetation growth. In this study, it was hypothesized that NDVI dynamics on a nonlinear tr...

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Autores principales: Shen, Chenhua, Wu, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245264/
https://www.ncbi.nlm.nih.gov/pubmed/37292263
http://dx.doi.org/10.1016/j.heliyon.2023.e16694
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author Shen, Chenhua
Wu, Rui
author_facet Shen, Chenhua
Wu, Rui
author_sort Shen, Chenhua
collection PubMed
description Nonlinear contributions from climate change and anthropogenic activity to the Normalized Difference Vegetation Index (NDVI) are analyzed to better understand the mechanisms underlying the nonlinear response of vegetation growth. In this study, it was hypothesized that NDVI dynamics on a nonlinear trajectory could track fluctuations of climate change and anthropogenic activity. Contributions from climate change and anthropogenic activity to NDVI were quantified using a locally weighted regression approach based on monthly timescale datasets. The findings showed that: 1) Vegetation cover fluctuated and increased in 81% of regions in China from 2000 to 2019. 2) The average predicted nonlinear contribution (APNC) of anthropogenic activity to NDVI was positive in China. The temperature APNC was positive in most of China but negative in Yunnan, where high temperatures and asynchronous temporal changes in temperature and NDVI were observed. The precipitation APNC was positive in the north of the Yangtze River, where precipitation is insufficient; but negative in South China, where precipitation is plentiful. Anthropogenic activity had the highest magnitude among the three nonlinear contributions, followed by temperature and precipitation. 3) The regions with contribution rates of anthropogenic activity greater than 80% were mainly distributed in the central Loess Plateau, North China Plain, and South China, while the areas with contribution rates of climate change greater than 80% were mainly concentrated in the northeastern QTP, Yunnan, and Northeast China. 4) The high temperature, drought, and asynchronous temporal changes in temperature, precipitation, and NDVI caused the negative average of changing trends in the predicted nonlinear contribution (PNC) of climate change to NDVI. Deforestation, land cover change, and grazing/fencing led to the negative average of changing trends in PNC from anthropogenic activity. These findings deepen our understanding of the mechanisms underlying the nonlinear responses of vegetation growth to climate change and anthropogenic activity.
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spelling pubmed-102452642023-06-08 Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach Shen, Chenhua Wu, Rui Heliyon Research Article Nonlinear contributions from climate change and anthropogenic activity to the Normalized Difference Vegetation Index (NDVI) are analyzed to better understand the mechanisms underlying the nonlinear response of vegetation growth. In this study, it was hypothesized that NDVI dynamics on a nonlinear trajectory could track fluctuations of climate change and anthropogenic activity. Contributions from climate change and anthropogenic activity to NDVI were quantified using a locally weighted regression approach based on monthly timescale datasets. The findings showed that: 1) Vegetation cover fluctuated and increased in 81% of regions in China from 2000 to 2019. 2) The average predicted nonlinear contribution (APNC) of anthropogenic activity to NDVI was positive in China. The temperature APNC was positive in most of China but negative in Yunnan, where high temperatures and asynchronous temporal changes in temperature and NDVI were observed. The precipitation APNC was positive in the north of the Yangtze River, where precipitation is insufficient; but negative in South China, where precipitation is plentiful. Anthropogenic activity had the highest magnitude among the three nonlinear contributions, followed by temperature and precipitation. 3) The regions with contribution rates of anthropogenic activity greater than 80% were mainly distributed in the central Loess Plateau, North China Plain, and South China, while the areas with contribution rates of climate change greater than 80% were mainly concentrated in the northeastern QTP, Yunnan, and Northeast China. 4) The high temperature, drought, and asynchronous temporal changes in temperature, precipitation, and NDVI caused the negative average of changing trends in the predicted nonlinear contribution (PNC) of climate change to NDVI. Deforestation, land cover change, and grazing/fencing led to the negative average of changing trends in PNC from anthropogenic activity. These findings deepen our understanding of the mechanisms underlying the nonlinear responses of vegetation growth to climate change and anthropogenic activity. Elsevier 2023-05-25 /pmc/articles/PMC10245264/ /pubmed/37292263 http://dx.doi.org/10.1016/j.heliyon.2023.e16694 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Shen, Chenhua
Wu, Rui
Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach
title Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach
title_full Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach
title_fullStr Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach
title_full_unstemmed Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach
title_short Analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across China using a locally weighted regression approach
title_sort analyzing nonlinear contributions from climate change and anthropogenic activity to the normalized difference vegetation index across china using a locally weighted regression approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245264/
https://www.ncbi.nlm.nih.gov/pubmed/37292263
http://dx.doi.org/10.1016/j.heliyon.2023.e16694
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