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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Bibliographic Details
Published in:Scientific reports 2024-05, Vol.14 (1), p.11775-11775, Article 11775
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
Subjects:
631/449
704/106
704/172
Climate
Climate adaptation
Climate Change
Climatic variability
Cross wavelet transform
Environmental management
Environmental Monitoring - methods
Humanities and Social Sciences
Landsat
Monsoons
multidisciplinary
NDVI
Pakistan
Plant Development
Plants
Precipitation
Remote sensing
Science
Science (multidisciplinary)
Seasonal variations
Seasons
Solar radiation
Statistical methods
Sustainable agriculture
Temperature
Temporal variations
Vegetation
Vegetation dynamics
Vegetation patterns
Wavelet transforms
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Summary: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 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-62464-7