It is confirmed that China has been greening over the last two decades. Such greening and its driving factors are therefore significant for understanding the relationship between vegetation and environments. However, studies on vegetation changes and attribution analyses at the national scale are limited in China after 2000. In this study, fractional vegetation cover (FVC) data from Global Land Surface Satellite (GLASS) was used to detect vegetation change trends from 2001 to 2018, and the effects of CO₂, temperature, shortwave radiation, precipitation, and land cover change (LCC) on FVC changes were quantified using generalized linear models (GLM). The results showed that (1) FVC in China increased by 14% from 2001 to 2018 with a greening rate of approximately 0.0019/year (p < 0.01), which showed an apparent greening trend. (2) On the whole, CO₂, climate-related factors, and LCC accounted for 88% of FVC changes in China, and the drivers explained 82%, 89%, 90%, and 89% of the FVC changes in the Qinghai–Tibet region, northwest region, northern region, and southern region, respectively. CO₂ was the major driving factor for FVC changes, accounting for 31% of FVC changes in China, indicating that CO₂ was an essential factor in vegetation growth research. (3) The statistical results of pixels with land cover changes showed that LCC explained 12% of FVC changes, LCC has played a relatively important role and this phenomenon may be related to the ecological restoration projects. This study enriches the study of vegetation changes and its driving factors, and quantitatively describes the response relationship between vegetation and its driving factors. The results have an important significance for adjusting terrestrial ecosystem services.

Worldwide urbanization leads to ecological changes around urban areas. However, few studies have quantitatively investigated the impacts of urbanization on vegetation coverage so far. As an important indicator measuring regional environment change, fractional vegetation cover (FVC) is widely used to analyze changes in vegetation in urban areas. In this study, on the basis of a partial derivative model, we quantified the effect of temperature, precipitation, radiation, and urbanization represented as nighttime light on vegetation coverage changes in the Beijing–Tianjin–Hebei (BTH) region during its period of rapid resident population growth from 2001 to 2011. The results showed that (1) the FVC of the BTH region varied from 0.20 to 0.26, with significant spatial heterogeneity. The FVC increased in small cities such as Cangzhou and in the Taihang Mountains, while it decreased in megacities with populations greater than 1 million, such as Beijing and Zhangjiakou Bashang. (2) The BTH region experienced rapid urbanization, with the area of artificial surface increasing by 18.42%. From the urban core area to the fringe area, the urbanization intensity decreased, but the urbanization rate increased. (3) Urbanization and precipitation had the greatest effect on FVC changes. Urbanization dominated the FVC changes in the expanded area, while precipitation had the greatest impacts on the FVC changes in the core area. For future studies on the major influencing factors of FVC changes, quantitative analysis of the contribution of urbanization to FVC changes in urban regions is crucial and will provide scientific perspectives for sustainable urban planning.

Dynamic land cover (LC) information is an essential part of environmental and ecological research. Therefore, acquiring dynamic LC data with high spatial resolution has attracted a great deal of attention in the remote sensing community. Nevertheless, the high-temporal resolution satellite data tend to have a coarse spatial resolution, and satellite data with high temporal resolution are often relatively low. Obtaining LC with high spatiotemporal resolution is extremely challenging. The super-resolution method can help researchers achieve this goal, and the recently developed neural-network-based deep learning algorithms have great potential for use as an alternative solution. This study proposes a focal loss temporal convolutional long short-term memory (FL-T-ConvLSTM) model for super-resolution LC classification research. It first trains the deep FL-T-ConvLSTM network to establish a transformation between low-resolution quantitative remote sensing parameters and high-resolution quantitative remote sensing parameters and then engages in nonlinear mapping with a high-resolution LC map. A long-term series 1-km super-resolution LC classification model based on deep learning was established and applied to the Beijing-Tianjin-Hebei region. Based on this method, a long-term series of 1-km LC maps from 1982 to 2019 can be obtained. The test accuracy and field validation accuracy of the model reached 90.1% and 86.8% when using reliable test samples and field test samples, respectively. This study provides a method for obtaining high-resolution LC classification products from low-resolution quantitative remote-sensing products.