Forests play an irreplaceable role in preserving soil and water, as well as realizing carbon neutrality. However, logging and urban expansion have caused widespread forest fragmentation globally, resulting in biodiversity loss and carbon emissions. Therefore, it is a prerequisite to develop a comprehensive index for evaluating the degree of forest fragmentation to propose effective policies for forest protection and restoration. In this study, a forest fragmentation comprehensive index (FFCI) was constructed through principal component analysis (PCA) based on land-use data from 2000 to 2020 in Fujian Province, composed of five commonly used landscape metrics: patch density (PD), largest patch index (LPI), mean patch area (MPA), aggregation index (AI), and division. Then, the semivariogram function and moving windows method were employed to explore the scale effect and spatiotemporal variations of FFCI. The spatial autocorrelation analysis was used to distinguish the spatial relationship of forest fragmentation, while the driving mechanisms were explored using the geographic detector (GD). The results show that the optimal scale to reflect forest fragmentation based on the semivariogram and moving window method was 3500 m. The proposed FFCI could explain more than 85% of the information for all landscape metrics, and the effectivity of FFCI was validated by urban–rural gradient and transect analysis. We also found that, despite having the highest forest coverage in China, Fujian Province has experienced severe forest fragmentation. High and medium fragmentation accounted for over 50% of all types of fragmentation, with decreasing trends in low and very low fragmentation and increasing trends in high fragmentation over time, indicating that the degree of forest fragmentation in the study area was aggravated over time. Moreover, the spatial distribution pattern of FFCI was mainly high–high clusters and low–low clusters, showing a decreasing trend year by year. The areas with high fragmentation were mainly distributed in the urban center of coastal cities, while the internal cities in western and central regions had a relatively low degree of fragmentation. Additionally, the spatial differentiation in the variation in FFCI was mainly influenced by elevation, slope, and nighttime light intensity. The superimposed impact of two factors on the variation in FFCI was greater than the impact of individual factors. These results provide an effective approach for assessing the degree of forest fragmentation and offer scientific support for mitigating forest fragmentation.

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.