Vegetation phenology is a sensitive indicator of climate change, and can help understand the response of vegetation cycles to climate, which is important for understanding the impact of global climate change on terrestrial ecosystems. In this study, based on the enhanced vegetation index (EVI) time-series data, derived from the moderate-resolution imaging spectroradiometer (MODIS) data, the climate parameters were extracted using the Savitzky–Golay (S–G) filtering method to explore the spatial and temporal variation characteristics of the vegetation phenology in an agro-pasture ecotone in China, from 2000 to 2020. In addition, the response characteristics of the vegetation phenology to the climate elements (temperature and precipitation) were also analyzed. The results showed the following: (1) The start of the growing season (SOS) was widely advanced, and that was caused by climate change. The end of the growing season (EOS) was delayed, and the length of the growing season (LOS) was gradually extended with a large interannual fluctuation in the SOS and the LOS in the region; (2) the SOS showed significant negative correlations with the air temperature and precipitation. Precipitation was mainly positively correlated with the EOS, but there was no significant difference in the correlation between temperature and the EOS. In general, pre-season precipitation is the main driver of the vegetation phenology, while the influence of temperature on the phenology is less obvious; (3) in the semi-arid area and arid area, the phenology was mainly influenced by precipitation. The response of the vegetation phenology to the temperatures in different temperature zones was found to be regular, showing high spatial differences. In general, the higher the cumulus temperature, the lower the negative effect of the temperature on both the SOS and EOS. These results may provide new reference to study the non-systematic changes of the vegetation phenology in response to climate change.

With the rapid development of the construction industry, there have been an uncountable number of damages caused by safety accidents at construction sites. Traditional safety management methods are no longer able to meet the needs of production. This paper presents the concept of constructing safety management scenarios for construction engineering sites. Using a production base project as a research case, it analyzes the natural and human factors involved in constructing spatial–temporal scenarios at construction sites. By employing a spatial–temporal overlay method to analyze multiple safety assessment indicators, a spatial–temporal safety management scenario for the production base project is established. Subsequently, BIM and GIS technologies are applied to perform a spatial–temporal simulation of the construction site safety management scenario. This process delineates safety and hazard areas across different construction phases based on time and spatial dimensions, enabling a comprehensive safety assessment of the construction site of the production base project. The study offers a reference and guidance for improving the level of safety management at construction project sites.

Vegetation phenology is highly sensitive to climate change, and an examination of vegetation phenology across diverse climatic conditions is crucial for identifying key factors influencing vegetation dynamics. However, there is a significant lack of macroscopic research and quantitative assessments on the response of vegetation phenology to meteorological factors in large-scale zones. This study employed Whittaker filtering and dynamic thresholding to extract phenological parameters of vegetation in China. Trend analysis was used to investigate the spatiotemporal changes in vegetation phenology from 1982 to 2022, while partial correlation and ridge regression analysis were conducted to quantify the response of vegetation in different zones to meteorological factors. The findings of this study demonstrate that over the past four decades, the start of the growing season (SOS) of vegetation in China has progressively advanced annually, whereas the end of the growing season (EOS) has progressively delayed annually, leading to an annual increase in the length of the growing season (LOS). Notably, these changes exhibit significant spatial variations. The response of vegetation phenology to temperature and precipitation is relatively complex and is closely related to local climatic conditions, humidity, vegetation type, etc. Different zones and diverse vegetation types have very different sensitivities to the same meteorological factor, sometimes even demonstrating contrasting responses. Consequently, this study is expected to clarify the response relationship between different vegetation ecosystems and meteorological factors in large-scale areas.

Human settlements are areas closely related to human production and life, and their quality directly affects people’s physical health and quality of life. However, with air pollution continuing to worsen, people are becoming increasingly concerned about the exposure to air pollution in their residential regions. In addition, many studies ignore the long-term impact of environmental factors on the evolution of human settlement quality (HSQ). This study aims to assess the HSQ in the Mid-Spine Belt of Beautiful China (MSBBC) from 2000 to 2020 and to analyze the changes in its driving forces during different time periods. We divided the HSQ into five dimensions: terrain conditions, climate conditions, hydrological conditions, ground cover conditions, and air pollution exposure. The Entropy-TOPSIS and grey relational degree model were used to assess the HSQ in the MSBBC. To analyze the driving forces of HSQ, the optimal-parameters-based geographic detector model was utilized. The results show the following: (1) Within the study area, the degree of the population exposure risk to PM_2.5 and its change trend were significantly different on both sides of the Hu Line, with the east showing higher levels than the west. (2) The HSQ of the MSBBC decreased from east to west. Moreover, the HSQ in the metropolitan area of the urban agglomeration was characterized from low to high. The HSQ underwent three phases, consisting of an increase (2000–2010), a decrease (2010–2015), and an increase again (2015–2020). (3) Among the driving factors, the impact of PM_2.5 on the HSQ weakened year by year. The driving force of meteorological parameters on the HSQ was the strongest. Furthermore, the interactions of each factor could enhance the HSQ. The results of this study not only provide a strong reference for creating livable areas and promoting sustainability in the MSBBC but also contribute to addressing challenges such as pollution and climate change.