Vegetation-Cover Change Trends Across Different Lengths of Time Series Using NDVI: Contrasting Theil–Sen and Mann–Kendall with Piece-Wise Regression

Quantifying vegetation dynamics has become a critical scientific imperative in the context of global ecosystem restoration initiatives targeting degraded forests. Previous studies have explored vegetation-cover change trends at different spatial scales worldwide using the Theil–Sen (TS) estimator and Mann–Kendall (MK) test, yet few have accounted for the uncertainty in resulting trends across time-series datasets of varying lengths. Taking the coastal zone of Fujian Province in Southeast China as a case study, we investigated the uncertainty of vegetation-cover change trends using normalized difference vegetation index (NDVI) datasets of different lengths (e.g., 20-year, 15-year, and 10-year) via the TS estimator and MK test. Additionally, piece-wise regression was employed to detect turning points and shifts in vegetation trends between 2001 and 2020. The results indicate significant discrepancies in trend estimation across datasets of different lengths, with consistency ratios ranging from 46.1% to 64.7% among the 20-year, 15-year, and 10-year series. The MK test is more sensitive to time-series length than the TS estimator, with areas of significant change decreasing by over 50% when transitioning from a 20-year to a 10-year dataset. The spatial distribution of trend shifts exhibits a distinct “coastal–inland” polarization pattern, with 2010 as the turning point. Eight modes of vegetation trend shifts were identified based on pre- and post-turning point dynamics. Furthermore, piece-wise regression improved trend accuracy by approximately 15%. This research advances the mechanistic understanding of spatiotemporal vegetation dynamics and supports adaptive ecosystem management strategies.