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.

Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments.

There is an urgent need for a thorough assessment of forest landscape fragmentation to inform forest protection and restoration, and reforestation policies. However, there is currently a lack of an effective comprehensive index for forest landscape fragmentation, and detailed knowledge of the forest landscape fragmentation dynamics remains insufficient. Here, taking Putian City of Fujian Province in Southeastern China as a case, we employed a forest fragmentation comprehensive index (FFCI) to capture key features of forest landscape fragmentation, such as patch size, number, and distribution. Then, bivariate spatial autocorrelation analysis was employed to identify the spatial associations between the static forest landscape fragmentation (FFCI) and the dynamic forest landscape fragmentation (ΔFFCI), and the spatial coupling modes among the three individual components of FFCI (mean patch area, MPA; aggregation index, AI; patch density, PD) were identified to explore the detail process of forest landscape fragmentation. Finally, the random forest model was applied to observe the impact factors of forest landscape fragmentation dynamics. The findings showed that forest landscapes with different degrees of fragmentation exhibited more noticeable changes at both ends (i.e., either high or lower-level fragmentation), with the intermediate level remaining consistent from 2000 to 2020. Around 18.3% of forest landscapes experienced a decrease in fragmentation, particularly in the northern part of the study area, while approximately 81.7% of forest landscapes exhibited an increasing trend in fragmentation. The bivariate spatial autocorrelation analysis indicated that the proportion of Low–High-type grids was the highest at 17.3%, followed by the High–High type at 7.0%. We also identified eight forest landscape fragmentation modes, which indicate the most significant forest landscape fragmentation pattern is a decrease in MPA and an increase in PD. Moreover, the anthropogenic factors (e.g., population density and night light intensity) were found to dominate the FFCI dynamics during 2000–2020. This study offers an efficient research paradigm for the dynamics of forest landscape fragmentation. The outcomes are conducive to an in-depth comprehension of the detailed dynamic information of forest landscape fragmentation, and supply a scientific foundation for enhancing the overall ecological service function of the forest.