Long-term Spatio-temporal Vegetation Dynamics to Climate Change in Koppen Climatic Regions of India

The long-term spatiotemporal vegetation dynamics with climate variables is essential for effectively managing environmental assets. This study presented the long-term spatio-temporal trends of vegetation dynamics and its association with rainfall/temperature in Koppen climate regions from 2000 to 2022. Linear regression (LR) and the Mann-Kendall (MK) test used to analyse the inter-annual and seasonal long-term spatiotemporal trend of vegetation dynamics across the Koppen climate regions during 2000-2022. The parameters (slope, Sen's slope, Pearson correlation coefficients, Z value, p-value, Kendall Tau, etc.) from the LR and MK test with Sen's slope are quantifying the significance of vegetation dynamics trends, and their association strengths with climate variables. The statistically significant and strong upward trend of inter-annual NDVI dynamics are found across the Indian region (growth rate of 0.0034/yr) and Koppen climate regions (growth rate ranges for five climate regions is 0.0029/yr-0.0043/yr and for Mountain Climate Region is 0.0011/yr). The statistically significant and strong upward trend found in seasonal NDVI growth for the monsoon, post monsoon, and winter seasons. However, the statistically significant and weak upward trend found in the pre-monsoon season for the Indian region and most of the climate regions. A significant positive association of vegetation dynamics observed with the rainfall across all seasons and climate regions, except the Mountain Climate Region. However, an inverse association of vegetation dynamics observed with temperature across all climate regions, except in a few cases. These findings have significant impacts, influencing decisions in land management, conservation, and strategies for regional climate resilience.