The 400 mm annual precipitation fluctuation zone (75°55′–127°6′E and 26°55′–53°6′N) is located in central and western China, which is a transition area from traditional agricultural to animal husbandry. It is extremely sensitive to climatic changes. The corresponding changes of the ecosystem, represented by vegetation, under the dual influences of climate change and human activities are important issues in the study of the regional ecological environment. Based on the Savitzky–Golay (S–G) filtering method, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Differential Vegetation Index (NDVI) dataset (NDVI3g) was reconstructed in this paper. Sen’s slope estimation, Mann–Kendall (M–K), multiple regression residual analysis, and the Hurst index were used to quantify the impacts of climate change and human activities on vegetation; in addition, the future persistence characteristics of the vegetation changes trend were analyzed. Vegetation changes in the study area had an obvious spatio-temporal heterogeneity. On an annual scale, the vegetation increased considerably, with a growth rate of 0.50%/10a. The multi-year mean value of NDVI and growth rate of cultivated land were the highest, followed by the forest land and grassland. On a seasonal scale, the vegetation cover increased most significantly in autumn, followed by spring and summer. In the southeastern and central parts of the study area, the vegetation cover increased significantly (P < 0.05), while it decreased significantly in the northeastern and southwestern parts. In summer, the NDVI value of all vegetation types (cultivated land, forest land and grassland) reached the maximum. The change rate of NDVI value for cultivated land reached the highest in autumn (1.57%/10a), forest land reached the highest in spring (1.15%/10a), and grassland reached the highest in autumn (0.49%/10a). The NDVI of cultivated land increased in all seasons, while forest land (−0.31%/10a) and grassland (−0.009%/10a) decreased in winter. Partial correlation analysis between vegetation and precipitation, temperature found that the areas with positive correlation accounted for 66.29% and 55.05% of the total area, respectively. Under the influence of climate change alone, 62.79% of the study area showed an increasing tendency, among which 46.79% showed a significant upward trend (P < 0.05). The NDVI decreased in 37.21% of the regions and decreased significantly in 14.88% of the regions (P < 0.05). Under the influence of human activities alone, the vegetation in the study area showed an upward trend in 59.61%, with a significant increase in 41.35% (P < 0.05), a downward trend in 40.39%, and a significant downward trend in 7.95% (P < 0.05). Vegetation growth is highly unstable and prone to drastic changes, depending on the environmental conditions.

Temperatures over the past three decades have exhibited an asymmetric warming pattern between night and day throughout the Tibetan Plateau. However, the implications of such diurnally heterogeneous warming on vegetation growth is still poorly understood. In this paper, we evaluate how vegetation growth has responded to daytime and night-time warming at the regional, biome, and pixel scales based on normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2015. We found a persistent increase in the growing seasonal minimum temperature (T_min) and maximum temperature (T_max) over the Tibetan Plateau between 1982–2015, whereas the rate of increase of T_min was 1.7 times that of T_max. After removing the correlations between T_min, precipitation, and solar radiation, we found that the partial correlation between T_max and NDVI was positive in wetter and colder areas and negative in semi-arid and arid regions. In contrast, the partial correlation between T_min and NDVI was positive in high-cold steppe and meadow steppe and negative in montane steppe or wet forest. We also found diverse responses of vegetation type to daytime and night-time warming across the Tibetan Plateau. Our results provide a demonstration for studying regional responses of vegetation to climate extremes under global climate change.

The accessibility of hospital facilities is of great importance not only for maintaining social stability, but also for protecting the basic human right to health care. Traditional accessibility research often lacks consideration of the dynamic changes in transport costs and does not reflect the actual travel time of urban residents, which is critical to time-sensitive hospital services. To avoid these defects, this study considered the city of Kaifeng, China, as an empirical case, and directly acquired travel time data for two travel modes to the hospital in different time periods through web mapping API (Application Program Interface). Further, based on travel time calculations, we compared five baseline indicators. For the last indicator, we used the optimal weighted accessibility model to measure hospital accessibility for each residential area. The study discovered significant differences in the frequency and spatial distribution of hospital accessibility using public transit and self-driving modes of transportation. In addition, there is an imbalance between accessibility travel times in the study area and the number of arrivals at hospitals. In particular, different modes of transportation and different travel periods also have a certain impact on accessibility of medical treatment. The research results shed new light on the accessibility of urban public facilities and provide a scientific basis with which local governments can optimize the spatial structure of hospital resources.

Quantitative assessment can scientifically determine the health status of a regional ecosystem, identify regional eco-environmental problems, and assist in promoting regional sustainable development and environmental management. Taking China’s important ecological function region, the Yellow River affected area as an example, this study constructed an extended evaluation index system based on the pressure-state-response framework, and remote sensing and GIS techniques were used to dynamically evaluate the spatial and temporal characteristics of ecosystem health in the study area. Furthermore, influencing factors on ecosystem health in the study area were extensively analyzed using the GeoDetector model. The results show that the ecosystem health level in the study area shows significant spatial heterogeneity from 1995–2015, and showed a fluctuating change process. Areas with large fluctuations in health level were mainly distributed in extreme climate areas, ecologically fragile areas, on plains and in hilly areas. Spatial differences of ecosystem health were well explained by using the biological abundance index, relief degree of land surface, soil type, annual average precipitation, elevation, annual average temperature, and population density. Influencing factors have significant interactive effects on ecosystem health.

The Loess Plateau is located at the transition zone between agriculture and livestock farming; its spatial and temporal pattern of drought is the key for an appropriate adaptation to climate change. This study investigated monthly meteorological observation data of 79 meteorological stations from 1955 to 2014 to calculate the standardized precipitation evapotranspiration index at different time scales. The spatial and temporal characteristics and persistence of drought were analyzed. The results showed the following: (i) The drought trend is most apparent in spring (0.096/10a) and lower in summer (0.036/10a) and autumn (0.009/10a). (ii) A higher drought level indicates a lower frequency of droughts occurrence and vice versa. The frequency of light drought was highest (11.36%), while that of extreme drought was lowest (0.12%). (iii) The mean drought intensity was highest in summer, followed by spring, autumn, and winter. The drought intensity was mainly light, showing a pattern of severe drought in the northwest and light drought in the southeast. (iv) The Loess Plateau will continue a trend of drought in the future, but the season of the continuous intensity will differ. Droughts in spring and summer are highly persistent, autumn drought trends continue but may slow, and winter droughts become random events.