The excessive exploitation of mineral resources will lead to environmental pollution, resource depletion, environmental disaster, and other problems. The contradiction between the environment and development, and the management of the ecological environment in mining areas are urgent p-problems to be solved. An ecological environment assessment is an important part of the ecological environment in a mining area. The accurate evaluation of the ecological environment is the premise behind environmental governance in a mining area. However, current ecological assessment indicators were not developed specifically for mine environment monitoring and, thus, cannot provide an effective and comprehensive assessment of the mineral environment. To this end, in order to improve the environmental monitoring performance in mining areas, a novel Mine-Specific Eco-Environment Index (MSEEI) was proposed, integrating factors from five main aspects associated with minerals, including temperature, vegetation, soil moisture, atmospheric environment, and mining scale. Meanwhile, a widely concerned mine—Luanchuan mine—was used as the case area to test the performance of our MSEEI. The results showed a significant correlation between RSEI and MSEEI (p < 0.01). The mean correlation achieved between RSEI and MSEEI was 0.91, which was much higher than the correlations between RSEI and enhanced vegetation index (EVI), soil moisture monitoring index (SMMI), normalized difference built-up and soil index (NDBSI), PM2.5 concentration (DI), and heat (LST). In addition, based on our long-term MSEEI results of Luanchuan mine from 1997 to 2021, the ecological status of Luanchuan mine showed a trend of first declining and then rising. Specifically, the MSEEI first declined from 0.85 to 0.77 between 1997 and 2012, and then rebounded to about 0.8 in recent years. The MSEEI exhibited a good applicability in the ecological assessment of mining areas. Our MSEEI can provide useful guidance for mine environment monitoring. MSEEI can directly reflect the ecological damage after mining, provide scientific guidance for the exploitation and utilization of mineral resources, and promote the protection and sustainable development of Earth’s resources and mine ecological environments.

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR_V), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR² = 0.85), followed by NIR_V (meanR² = 0.84), NDVI (meanR² = 0.76), and kNDVI (meanR² = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR² = 0.85). In capture during drought, NIR_V and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR² = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIR_V, and kNDVI will improve our understanding of GPP changes.

The Taohe River Basin is an essential ecological function area in the upper reaches of the Yellow River. Understanding the intricate trade-offs and synergies between ecosystem services (ESs) and exploring the impact of different factors are essential for achieving win–win outcomes in ecosystem management and socioeconomic development. The role of impact factors on the relationship between ESs, nevertheless, is more challenging to spatialize. This study used different models to estimate the net primary productivity (NPP), water yield (WY), and soil conservation (SC), and analyzed synergies and trade-offs between Ess. The spatial heterogeneity of the effects of natural and social factors on the relationships between Ess was explored using a geographic detector and a multi-scale geographically weighted regression (MGWR) model. The results show that: (1) NPP, WY, and SC all exhibit a rising trend, with multi-year averages of 488.99 gC/m², 157.29 mm, and 1441.51 t/hm², respectively; (2) NPP–WY and NPP–SC exhibit trade-offs in the majority of regions, while WY–SC are primarily synergistic in the upper and middle reaches, and they have the highest percentage of cropland, forest, and grassland; and (3) precipitation (PRE) has the greatest impact on the trade-off between NPP–WY and NPP–SC in the upper and middle reaches, and the gross domestic product (GDP), population density (POP), and distance from cropland (CROP) are the primary factors determining the synergy between NPP and WY in the lower reaches of the Loess Plateau cropping sector. PRE, digital elevation model (DEM), and CROP are the primary impact factors affecting the synergy of WY–SC. This study may serve as a reference for examining the evolutionary mechanism underlying the trade-offs and synergies between ESs and provide a scientific basis for future ecological environmental protection and regional land management in the Taohe River Basin.

The Guanzhong–Tianshui economic zone is a strategic link in China’s Belt and Road network, faces the contradiction between ecological protection and economic development, and urgently needs to construct an ecological security pattern based on ecosystem services to permit sustainable development. In this study, we evaluated the ecological services of net primary productivity (NPP), water yield (WY), soil conservation (SC), habitat quality (HQ), and food production (FP). We explored the trade-offs and synergies between services using correlation analysis and geographically weighted regression and constructed an ecological security pattern through circuit theory. NPP, WY, SC, and FP increased during the study period, whereas HQ decreased. The NPP × WY, WY × SC, and WY × HQ shifted from synergies to trade-offs; NPP × SC, NPP × HQ, and SC × HQ were always synergies; NPP × FP, SC × FP, and FP × HQ were always trade-offs; and WY × FP shifted from trade-offs to synergies. We selected service bundles with significant synergies among NPP, SC, and HQ as ecological sources, which were mainly in the Qinling and Weibei mountains, comprising 47 ecological patches. We identified 58 ecological corridors, 330.83 km² of pinch points, and 401.30 km² of barriers, which form a mesh structure covering the study area, proposing a development pattern of six zones and one belt. Our results provide a framework for ecological protection and restoration, which may serve as a scientific foundation for upcoming regional land management initiatives.

To study the change in drought characteristics, it is helpful to explore the dynamics of drought, analyze the rules of drought, and prevent drought. The annual and seasonal standardized precipitation evapotranspiration index (SPEI) was calculated based on the meteorological data of the temperate steppe region of China from 1960 to 2020, and the spatio-temporal variation of drought characteristics was analyzed by combining the run-course theory to identify drought characteristics. The results show that: (1) During 1960–2020, the SPEI of the temperate steppe region fluctuated in the range of −1.5 to 1.5 and decreased significantly at a rate of −0.02·a⁻¹ (p < 0.01). In general, there is an increasing trend of drought in the temperate steppe region. (2) There is little difference in the duration of drought in the four seasons, and the very low duration of drought is about 2 months. The cumulative drought intensity was higher in the semi-arid and semi-humid zones of the temperate zone. (3) The very low value of drought frequency occurred in summer, and the very high value of drought frequency occurred in winter. From SPEI-3 to SPEI-12, that is, from seasonal scale to interannual scale, with the increase of time scale, the distribution of the extreme value of drought frequency moved to the southwest, semi-arid, and arid areas. The frequency of the four drought levels showed light drought > middle drought > severe drought > extreme drought in all time scales and all zones.

Due to global warming and other climate changes, it is increasingly important to study the response of regional environmental changes and dynamic changes in vegetation to climate change. Based on meteorological data from the last 60 years, this paper calculates the humidity index of western China under a wide range of long time series in different regions and explores the cross-correlation effect between series by offering a comparison with NDVI data, to analyze the cross-correlation between wet and dry changes and changes in vegetation in western China on a spatial scale. The results show that the spatial distribution of the interdecadal humidity index is different between different regions in western China. For example, the semi-arid and the semi-humid zones of the Weihe River region exhibit significant changes, while the Xinjiang and Qinghai–Tibet regions show a trend of constant wetness, on the whole, and the Sichuan and Yunnan–Guizhou regions are relatively humid and the distribution of wetness and dryness is relatively stable. The distribution of high and low values of the humidity index is very obvious and consistent with that of the distribution of desert bare land and precipitation in western China. In common with the distribution in the humidity index, the maximum correlation number between the NDVI and the humidity index in the whole western region is also significantly different in spatial distribution. There is a positive correlation between the NDVI and the humidity index in 99% of the study area. However, the delay in response time of the NDVI to changes in the humidity index in each region is inconsistent. For example, changes in the NDVI lag changes in the humidity index in the Menggan region by generally either 2 months or 5 months, while in the Sichuan region the delay in response time is generally 3 months. The variation and trend in dry and wet areas are closely related to the geographical location, climate zone, and topographic terrain, which may be the reason for the differences in the distribution of vegetation types and the response time to dry and wet changes. There is significant interaction between the humidity index and the vegetation type or precipitation distribution in western China. The positive correlation between the NDVI and the humidity index means that the positive effect is more sensitive, and the response of grassland is the most sensitive in the ecosystem.