Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas.

Visible and near infrared spectroscopy is an effective method for monitoring the content of heavy metals in soil. However, due to the difference between polluted soil with phytoremediation and without phytoremediation, the common estimation model cannot meet accuracy requirements. To solve this problem, combined with an ecological restoration experiment for soil contamination using the plant Neyraudia reynaudiana, this study explored the feasibility of using a hyperspectral technology to estimate the heavy metal content (Cd, Cr, and Pb) of soil under phytoremediation. A total of 108 surface soil samples (from depths of 0–20 cm) were collected. Inversion models were established using partial least squares regression (PLSR) and the back propagation neural network optimized by a mind evolutionary algorithm (MEA-BPNN). The results revealed that: (1) modeling with derivative-transformed spectra can effectively enhance the correlation between soil spectral reflectance and heavy metal content. (2) Compared with the BP neural network model, the estimation accuracy (R²) was improved from 0.728, 0.737, and 0.675 to 0.873, 0.884, and 0.857 using the MEA-BP neural network model. The residual prediction deviation (RPD) values for the three heavy metals Cd, Cr, and Pb using the MEA-BPNN model were 2.114, 3.000, and 2.560, respectively. Among them, the estimated model of Cd was an excellent prediction. (3) Compared with PLSR, the model prediction results established by the MEA-BP neural network had higher estimation accuracy. In summary, the use of diffuse reflectance spectroscopy to predict heavy metal content provides a theoretical basis for further study of the large-scale monitoring of soil heavy-metal pollution and its remediation evaluation in the polluted area, which is of great significance.

The concept of high-quality development has become the current theme of China’s economic construction. High-quality development requires maintaining a healthy and cyclical approach to economic development, which is a challenge in the original development approach. Yet, a great deal of evidence suggests that there is a strong interrelationship between economic development and the ecological environment, and developing a method to quantify this interrelationship is important for studying the extent of high-quality development. Here, we propose a new indicator system using the coupling degree model and the coupling coordination degree model to assess the coupled coordination of economic development and the ecological environment in the Yellow River basin as a whole and in each province. We found that: (1) the economic development and ecological health of the Yellow River basin exhibit a slowly increasing trend; (2) the coupling degree of the economic development and ecological environment is high, indicating that the interaction between the economy and ecology is very strong; and (3) the increasing degree of coupling and coordination reflects the trend of continuous improvement and coordination in the relationship between the economy and ecological environment, and the level of high-quality development in the basin has continuously increased. The results of this study indicate that to continue to strengthen the high-quality development in the Yellow River basin, the contradiction between the economy and ecology should be alleviated, and coordinated development of both should be achieved.

Mining often generates large amounts of inefficiently used land. Clarifying the multifunctional characteristics of mined land and its spatial and temporal evolution is important to environmental protection and promoting the economic and social benefits of mined areas. This article analyzed the conditions of mined land in Jiawang, Jiangsu province, China. The InVEST model was used to assess landscape functions, including those related to water and soil conservation, productivity, habitats, carrying capacity, recreation, and carbon sequestration, to explore the multifunctional changes and trade-off–synergy relationships of the landscape from 2005 to 2020. The results show that (1) ecological restoration of the mined land significantly improved the regional landscape multifunctionality during the study period, with each function enhanced more obviously after restoration was completed in 2012, and (2) the trade-offs and synergistic relationships for landscape multifunctionality varied during the study period because the time series evolved; some trade-offs gradually transformed into synergistic relationships. This study establishes a set of effective systems useful in evaluating the multifunctionality of mined land, and initially evaluated the trade-off–synergistic relationships among eight landscape functions. This will provide ideas supporting the management and restoration of mined land and help in the formulation of spatial planning strategies for ecological restoration.

Under the growing restrictions of the Chinese eco-environmental policies, the impact of under-lake coal mining on wetlands is receiving increasing attention from both coal mining enterprises and local governments. This paper focuses on the impact of under-lake coal mining on the Nansi Lake wetland from 1991 to 2021. Field measurements, resident surveys, and remote sensing inversion were comprehensively employed to quantitatively assess the impact. The calculation of the assessment indicators refers to the elastic coefficient, the information for which comes from four major categories of ecosystem service values (ESVs) and eight sub-ESVs. According to the results of the remote sensing interpretation and inversion, by 2021 the range had enlarged by 32.3 km², and the water depth had increased by 1.9 m in the mining-disturbed area relative to 1991. The ESV fluctuations in the Nansi Lake wetland also exhibited a generally increasing trend over time. Our results show that the under-lake mining disturbs the ESVs, but the disturbance is not sufficient to result in significant consequences. Based on the data analysis, we suggest several well-directed, appropriate restoration strategies to achieve the desired objectives and target the response of the ESV changes. Such measures will help to relieve some of the anxiety and concern about the wetland changes caused by the under-lake mining.