Factors Affecting Long-Term Trends in Global NDVI

TLDR

Vegetation trend evolution and its drivers are crucial for ecological conservation, yet their spatial‑temporal distribution patterns and correlations remain unclear. We quantified vegetation activity using NDVI with Theil–Sen trend analysis, Mann–Kendall tests, Pearson correlation, and a Boosted Regression Trees model. Across 1982–2015, 34 % of global land greened—mainly in the Sahel, Europe, India, and South China—while 10 % browned in Canada, South America, central Africa, and Central Asia; Boosted Regression Trees identified rainfall as the dominant driver (63.1 %), followed by temperature (15 %), land‑cover change (8.6 %), population (6.5 %), elevation (6.4 %), and nightlight (0.4 %).

Abstract

Diagnosing the evolution trends of vegetation and its drivers is necessary for ecological conservation and restoration. However, it remains unclear what the underlying distribution pattern of these trends and its correlation with some drivers at large spatial-temporal scales. Here we use the normalized difference vegetation index (NDVI) to quantify the activity of vegetation by Theil–Sen median trend analysis and the Mann–Kendall test, Pearson correlation analysis and Boosted regression trees (BRT) model. Results show that about 34% of the global continent area has experienced greening in the grid annual NDVI from 1982 to 2015. The major greening areas were observed in the Sahel, European, India and south China. Only 10% of the global continent land areas were browning, and these were observed in Canada, South America, central Africa and Central Asia. BRT model shows that rainfall is the most important factor affecting vegetation evolution (63.1%), followed by temperature (15%), land cover change (8.6%), population (6.5%), elevation (6.4%) and nightlight (0.4%). It’s about 21% of the world’s continent were affected by rainfall, mainly in arid regions such as central Asia and Australia. The main temperature-affected areas accounted for 36%, located near the equator or in high latitudes.