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

作者：Kaleem Mehmood, Shoaib Ahmad Anees, Sultan Muhammad, Khadim Hussain, Fahad Shahzad, Qijing Liu, Mohammad Javed Ansari, Sulaiman Ali Alharbi, Waseem Razzaq Khan · 发表于：Scientific Reports · 年份：2024 · DOI：10.1038/s41598-024-62464-7 · 被引用次数：114 · 研究领域：Remote Sensing in Agriculture、Plant Water Relations and Carbon Dynamics、Land Use and Ecosystem Services

Abstract 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/m 2 /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...