The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q > 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

Vegetation phenology plays a critical role in the dynamic response of terrestrial ecosystems to climate change. However, the relationship between the phenology of winter wheat and hydrothermal factors is inadequate, especially in typical agricultural areas. In this study, the possible effects of preseason climate changes on the green-up date (GUD) of winter wheat over the North China Plain (NCP) was investigated, using the MODIS EVI 8-day time-series data from 2000 to 2015, as well as the concurrent monthly mean temperature (T_m), mean maximum (T_max) and minimum temperature (T_min) and total precipitation (TP) data. Firstly, we quantitatively identified the time lag effects of winter wheat GUD responses to different climatic factors; then, the major driving factors for winter wheat GUD were further explored by applying multiple linear regression models. The results showed that the time lag effects of winter wheat GUD response to climatic factors were site- and climatic parameters-dependent. Negative temperature effects with about a 3-month time lag dominated in most of the NCP, whereas positive temperature effects with a zero-month lag were most common in some of the southern parts. In comparison, total precipitation had a negative zero-month lag effect in the northern region, but two lagged months occurred in the south. Regarding the time lag effects, the explanation power of climatic factors improved relatively by up to 77%, and the explanation area increased by 41.20%. Additionally, change in winter wheat GUD was primarily determined by temperature rather than by TP, with a marked spatial heterogeneity of the T_max and T_min effect. Our results confirmed different time lag effects from different climatic factors on phenological processes in spring, and further suggested that both T_max and T_min should be considered to improve the performance of spring phenology models.

The Yellow River Basin is rich in coal resources, but the ecological environment is fragile, and the ecological degradation of vegetation is exacerbated by the disruption caused by high-intensity mining activities. Analyzing the dynamic evolution of vegetation in the Henan section of the Yellow River Basin and its mining areas over the long term run reveals the regional ecological environment and offers a scientific foundation for the region’s sustainable development. In this study, we obtained a long time series of Landsat imageries from 1987 to 2023 on the Google Earth Engine (GEE) platform and utilized geographically weighted regression models, Sen (Theil–Sen median) trend analysis, M-K (Mann–Kendall) test, coefficient of variation (CV), and the Hurst index to investigate the evolution of vegetation cover based on the kNDVI (the normalized difference vegetation index). This index is used to explore the spatial and temporal characteristics of vegetation cover and its future development trend. Our results showed that (1) The kNDVI value in the Henan section of the Yellow River Basin exhibited a trend of fluctuating upward at a rate of 0.0509/10a from 1987 to 2023. The kNDVI trend in the mining areas of the region aligned closely with the overall trend of the Henan section; however, the annual kNDVI in each mining area consistently remained lower than that of the Henan section and displayed a degree of fluctuation, predominantly characterized by medium–high variability, with areas of moderate and high fluctuations accounting for 73.5% of the total. (2) The kNDVI in the study area showed a significant improvement in vegetation cover and its future development trends. We detected a significant improvement in the kNDVI index in the area; yet, significant improvement in this index in the future might cause vegetation degradation in 87% of the study area, which may be closely related to multiple factors such as the intensity of mining at the mine site, anthropogenic disturbances, and climate change. (3) The vegetation status of the Henan section of the Yellow River Basin shows a significant positive correlation with distance from mining areas, accounting for 90.9% of the total, indicating that mining has a strong impact on vegetation cover. This study provides a scientific basis for vegetation restoration, green development of mineral resources, and sustainable development in the Henan section of the Yellow River Basin.

Understanding potential drought characteristics under climate change is essential for reducing vulnerability and establishing adaptation strategies, especially in North China, a major grain production area. In this study, the key growth period of winter wheat was taken as the entry point. We comprehensively utilized data from meteorological stations and circulation factors and employed methods such as the modified Mann–Kendall test, run theory, wavelet analysis, and cluster analysis. We quantitatively assessed the drought conditions during the winter wheat growth stages using the Standardized Precipitation Evapotranspiration Index at a 1-month timescale (SPEI-1). We analyzed the spatiotemporal evolution characteristics of drought and explored the inherent correlation between drought and atmospheric circulation factors. Results indicate that the SPEI-1 index increased significantly during the entire growth period, the overwintering-jointing stage, and the heading-maturity stage at rates of 0.0058, 0.0044, and 0.0140 per year, respectively, showing a wetting trend. Higher drought frequency values were observed in northern Shanxi, northern Hebei, western Henan, and southern Shandong during the entire growth period, with the drought frequency of the overwintering-jointing stage approximately twice that of the emergence-tillering and heading-maturity stages. Furthermore, drought frequency values in southern Hebei and southern Henan decreased from high to low frequency during the heading-maturity stage compared to the overwintering-jointing period. The SPEI-1 is predominantly influenced by the Arctic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO), varying by growth stages.

Vegetation indices are important representatives of plant growth. Climate change and human activities seriously affect vegetation. This study focuses on the Huojitu mining area in the Shendong region, utilizing the kNDVI index calculated via the Google Earth Engine (GEE) cloud platform. The Mann–Kendall mutation test and linear regression analysis were employed to examine the spatiotemporal changes in vegetation growth over a 25-year period from 1999 to 2023. Through correlation analysis, geographic detector models, and land use map fusion, combined with climate, topography, soil, mining, and land use data, this study investigates the influencing factors of vegetation growth evolution. The key findings are as follows: (1) kNDVI is more suitable for analyzing vegetation growth in this study compared to NDVI. (2) Over the past 25 years, vegetation growth has exhibited an overall fluctuating upward trend, with an annual growth rate of 0.0041/a. The annual average kNDVI value in the mining area is 0.121. Specifically, kNDVI initially increased gradually, then rapidly increased, and subsequently declined rapidly. (3) Vegetation growth in the study area has significantly improved, with areas of improved vegetation accounting for 89.08% of the total mining area, while degraded areas account for 11.02%. (4) Precipitation and air temperature are the primary natural factors influencing vegetation growth fluctuations in the mining area, with precipitation being the dominant factor (r = 0.81, p < 0.01). The spatial heterogeneity of vegetation growth is influenced by land use, topography, soil nutrients, and mining activities, with land use having the greatest impact (q = 0.43). Major land use changes contribute 46.45% to vegetation improvement and 13.43% to vegetation degradation. The findings of this study provide a scientific basis for ecological planning and the development of the Huojitu mining area.