Urban green space has a critical impact on the urban ecological environment, residents’ health, and urban sustainability. Quantifying residential exposure to green space and proposing targeted enhancement strategies in urban areas is helpful to rationally plan urban green space construction, reduce the inequality in residential exposure to green space, and promote environmental equity. However, the long-time evolution analysis of residential exposure to green space at different scales and the influence of green space quality on residential exposure to green space are rarely reported. Here we produced a long-time series dataset of urban green space from 1990 to 2020 based on the 30 m Landsat data and used the Normalized Difference Vegetation Index (NDVI) as a representation of the green space quality to comprehensively analyze residential exposure to green space at the city and block scales within the 5th ring of Beijing, China. We found that the urban green space in Beijing is mainly distributed in urban areas between the 4th and 5th rings (i.e., 153.4 km² in 2020), and there is little green space within the 2nd ring area (i.e., 12.6 km² in 2020). There is clear spatial inequality in residential exposure to green space, and about 2.88 million (i.e., ~27%) residents have experienced different degrees of decline in residential exposure to green space from 2015 to 2020. However, the degree of inequality in residential exposure to green space has gradually weakened from a high level (Palma ratio = 2.84) in 1990 to a relatively low level (Palma ratio = 0.81) in 2020. In addition, the spatial-temporal analysis method of residential exposure to green space based on green space quality has certain advantages that can help explore the degraded and lost areas of green space.

How to recognize the land use change in urban villages during dynamic transformation in Haidian District, Beijing, has become a hot topic with the promotion of urban renewal. The GF-1 high-resolution remote sensing images of 2013, 2015, and 2020 were used in this study to reflect the land use change in urban villages before and after urban renewal by using a hierarchical machine learning recognition method based on scene-based and random forest classification. The overall scale of urban village blocks in Haidian was 10.46 km², showing the distribution pattern along the traffic arteries in 2013. In 2015, it dropped to 10.11 km². The scale of urban village blocks in 2020 decreased to 1.02 km², 9.75% of that in 2013. Three kinds of urban village renewal logic are revealed by further taking Chuanying Village as an example: “urban village–blue–green space”, “urban village–real estate”, and “urban village–municipal facilities”.

Mountain ecosystems host rich biodiversity and often act as last refuges for species under growing human pressure. However, the extent and recent trajectory of anthropogenic impacts on global mountain biodiversity remain poorly understood. Here, we evaluate spatio-temporal changes in human pressure across 6717 mountain ranges worldwide from 2000 to 2020 and assess future exposure to anthropogenic pressures under three Shared Socioeconomic Pathways (SSPs). We show that more than 60% of mountain ranges are exposed to elevated human pressure and that intensification of human pressure between 2000 and 2020 is associated with the loss of 71.69 Mha of natural mountain habitat. Protected areas provide incomplete buffering from human pressure, while substantial representation gaps remain for mountain vertebrates: 4234 species have less than 10% of their mountain-specific area of habitat represented within protected areas, including 1165 threatened species. We further identify mountain ranges where high biodiversity importance coincides with limited protection and elevated future exposure to land-use change, with priority rankings remaining broadly consistent across socioeconomic scenarios. Together, these results identify persistent conservation gaps and mountain systems where future conservation action could contribute to global biodiversity targets.