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Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach...

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Main Authors: Mehmood, Kaleem, Anees, Shoaib Ahmad, Muhammad, Sultan, Hussain, Khadim, Shahzad, Fahad, Liu, Qijing, Ansari, Mohammad Javed, Alharbi, Sulaiman Ali, Khan, Waseem Razzaq
Format: Article
Language:English
Published: Nature Research 2024
Online Access:http://psasir.upm.edu.my/id/eprint/113375/1/113375.pdf
http://psasir.upm.edu.my/id/eprint/113375/
https://www.nature.com/articles/s41598-024-62464-7
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spelling my.upm.eprints.1133752024-11-22T03:32:16Z http://psasir.upm.edu.my/id/eprint/113375/ Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables Mehmood, Kaleem Anees, Shoaib Ahmad Muhammad, Sultan Hussain, Khadim Shahzad, Fahad Liu, Qijing Ansari, Mohammad Javed Alharbi, Sulaiman Ali Khan, Waseem Razzaq This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes. Nature Research 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/113375/1/113375.pdf Mehmood, Kaleem and Anees, Shoaib Ahmad and Muhammad, Sultan and Hussain, Khadim and Shahzad, Fahad and Liu, Qijing and Ansari, Mohammad Javed and Alharbi, Sulaiman Ali and Khan, Waseem Razzaq (2024) Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14 (1). art. no. 11775. pp. 1-22. ISSN 2045-2322; eISSN: 2045-2322 https://www.nature.com/articles/s41598-024-62464-7 10.1038/s41598-024-62464-7
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.
format Article
author Mehmood, Kaleem
Anees, Shoaib Ahmad
Muhammad, Sultan
Hussain, Khadim
Shahzad, Fahad
Liu, Qijing
Ansari, Mohammad Javed
Alharbi, Sulaiman Ali
Khan, Waseem Razzaq
spellingShingle Mehmood, Kaleem
Anees, Shoaib Ahmad
Muhammad, Sultan
Hussain, Khadim
Shahzad, Fahad
Liu, Qijing
Ansari, Mohammad Javed
Alharbi, Sulaiman Ali
Khan, Waseem Razzaq
Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables
author_facet Mehmood, Kaleem
Anees, Shoaib Ahmad
Muhammad, Sultan
Hussain, Khadim
Shahzad, Fahad
Liu, Qijing
Ansari, Mohammad Javed
Alharbi, Sulaiman Ali
Khan, Waseem Razzaq
author_sort Mehmood, Kaleem
title Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables
title_short Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables
title_full Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables
title_fullStr Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables
title_full_unstemmed Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables
title_sort analyzing vegetation health dynamics across seasons and regions through ndvi and climatic variables
publisher Nature Research
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/113375/1/113375.pdf
http://psasir.upm.edu.my/id/eprint/113375/
https://www.nature.com/articles/s41598-024-62464-7
_version_ 1817844628854931456
score 13.223943

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