The Qinghai–Tibet Plateau (QTP) is a major “river source” and “ecological source” in China, as well as South Asia and Southeast Asia, and is a typical plateau region. Studying the evolution characteristics and ecological effects of the production-living-ecological space (PLES) of the QTP is of great practical significance and theoretical value for strengthening its ecological construction and environmental protection. Based on 30 m × 30 m land use/cover data of the QTP at five time-points of 1980, 1990, 2000, 2010, and 2020, this paper investigates the PLES evolution characteristics, transfer characteristics, eco-environmental response, and influencing factors of the eco-environmental quality index (EEQI) in the region of China of the QTP from 1980 to 2020 by land use transfer matrix, eco-environmental response model, hot spot analysis, and geographically weighted regression (GWR). The results show that: (1) from 1980 to 2020, the ecological space of the QTP decreased, while the production and living space saw an increase. The PLES pattern of the QTP showed a clear shift from 2000 to 2010, while there was no significant change from 1980 to 2000 and from 2010 to 2020. (2) From 1980 to 2020, the EEQI of the QTP decreased from 0.5634 in 1980 to 0.5038 in 2010, and then increased to 0.5044 in 2020, showing a changing trend of first decreasing and then increasing; the degradation of grassland ecological space to other ecological space was the main cause leading to ecological environment deterioration. (3) From 1980 to 2000, the EEQI was high in the midwestern and southeastern parts of the QTP, presenting a double-center distribution. From 2010 to 2020, the EEQI decreased in the western part, while the high value area in the eastern part increased significantly, obviously low in the west and high in the east. The spatial variation characteristics of hot and cold spots and EEQI are generally similar. (4) Natural ecological and socioeconomic factors have significant differences on the spatial distribution of EEQI in the QTP, and natural ecological factors are the main driving factors, with topographic relief having the strongest effect on EEQI as a natural ecological factor, and population density having the strongest effect as a socioeconomic factor.

Banana is an important cash crop in tropical and subtropical areas; however, the development of banana farming has caused thorny ecological problems, such as water and soil loss. There are few studies on the runoff response to rainfall in banana land to date. In this study, several evaluation indexes, rainfall redistribution, throughfall erosivity, splash kinetic energy, soil splash loss and runoff, respectively, were used to clarify the mechanism of soil erosion in banana land. Results showed that the atmospheric rainfall was significantly redistributed by the banana canopy, with about 81.2% throughfall, 8.3% stemflow and the rest canopy interception.Although the throughfall erosivity evaluated by the model was slightly lower than that of open rainfall, the throughfall kinetic energy and the soil particle splash loss reached 1.5 times and 5 times higher than that of open rainfall, respectively.Consequently, throughfall has an obvious splash erosion effect on the surface soil. In addition, influenced by throughfall and stemflow, surface runoff during the rainy season (May-September) accounts for the annual 91.7%, July and August in particular having the highest incidence of soil erosion in banana land. The above results suggest that the convergence effect of the banana canopy on rainwater is the main inducement for the increase in throughfall volume and surface runoff volume. Therefore, it is necessary to implement soil and water conservation measures on banana fields during the rainy season, such as planting low vegetation under the banana canopy to mitigate splash effects.

Under the “ultra-conventional” control measures of the COVID-19 period, urban population distribution is different from usual time. Studying its evolution laws has a certain reference effect for the judgment of urban population aggregations, the division of precise control zoning, and the differentiated management of places during the COVID-19 control period. Based on the data of Baidu heat maps and points of interest (POIs), this paper uses three models of the population density index (PDI), the exploratory spatial data analysis (ESDA) and the geological detector (Geodetector) to analyze the characteristics and the influencing factors of the evolution of population distribution of Xi’an in three stages which are closed control stage, unsealed control stage and slack control stage. The results show that: in the three stages, 1) The value of the PDI and the range of the PDI change in Xi’an continue to increase. The single-day PDI curve shows “low-high-low” changing characteristics from morning to night in and within the third ring zone, and “high-low-high” in the suburb. And, by comparison, it is found that the social vitality of the first and second ring zone is more strongly impacted because of COVID-19 control measures. 2) The overall population density in Xi’an is gradually increasing, this is represented by gradual increase of the very high- and the high-density areas, and continuous decrease of the low- and the very low-density areas. And the centripetal distribution of population density, which is high inside the city and low outside the city, is becoming more and more obvious. 3) The spatial distribution of population density represents obvious high-value clusters or low-value clusters in the three stages, ESDA shows a circle structure of inner heat and outer cold, and this trend continues to be reinforced. 4) The intensity order of influencing factors of the 7 types of facilities on population distribution is: residential communities > catering facilities > living service facilities > healthy facilities > commercial facilities > office places > green spaces and squares, and the influencing factor intensities show a continuous increasing or a continuous decreasing process in the three stages.