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.

Remotely sensed images with low resolution can be effectively used for the large-area monitoring of vegetation restoration, but are unsuitable for accurate small-area monitoring. This limits researchers’ ability to study the composition of vegetation species and the biodiversity and ecosystem functions after ecological restoration. Therefore, this study uses LiDAR and hyperspectral data, develops a hierarchical classification method for classifying vegetation based on LiDAR technology, decision tree and a random forest classifier, and applies it to the eastern waste dump of the Heidaigou mining area in Inner Mongolia, China, which has been restored for around 15 years, to verify the effectiveness of the method. The results were as follows. (1) The intensity, height, and echo characteristics of LiDAR point cloud data and the spectral, vegetation indices, and texture features of hyperspectral image data effectively reflected the differences in vegetation species composition. (2) Vegetation indices had the highest contribution rate to the classification of vegetation species composition types, followed by height, while spectral data alone had a lower contribution rate. Therefore, it was necessary to screen the features of LiDAR and hyperspectral data before classifying vegetation. (3) The hierarchical classification method effectively distinguished the differences between trees (Populus spp., Pinus tabuliformis, Hippophae sp. (arbor), and Robinia pseudoacacia), shrubs (Amorpha fruticosa, Caragana microphylla + Hippophae sp. (shrub)), and grass species, with classification accuracy of 87.45% and a Kappa coefficient of 0.79, which was nearly 43% higher than an unsupervised classification and 10.7–22.7% higher than other supervised classification methods. In conclusion, the fusion of LiDAR and hyperspectral data can accurately and reliably estimate and classify vegetation structural parameters, and reveal the type, quantity, and diversity of vegetation, thus providing a sufficient basis for the assessment and improvement of vegetation after restoration.

Land consolidation of dryland-to-paddy conversion for improving tillage conditions and grain production capacity is widely implemented throughout the world. The conversion affects soil ecological stability, especially the most active soil microorganisms. However, the impacts of the dryland-to-paddy conversion has paid little attention in recent decades. In this study, a pot experiment was used to explore the responses of the microbial community and their interactions with soil properties after rice in the first season (five months). The results indicated that a significant decrease in the topsoil pH, organic matter content, nitrate nitrogen, and ammonical nitrogen, and an increase in soil electrical conductivity (EC) was observed (p < 0.05) after the dryland-to-paddy conversion. The richness and diversity of bacteria and fungi decreased in the short term. The composition of the soil microbial community and the soil microbial dominant bacteria had considerably changed after the conversion. Actinobacteria, Firmicutes, and Olpidiomycota were found to be highly sensitive to the dryland-to-paddy conversion. The soil microbial community structure had extremely significant positive correlations with soil pH, EC, organic matter, nitrate nitrogen, and ammonical nitrogen (p < 0.05). Microorganisms are the most important component of soil nutrient cycling. Converting a large area of dryland to paddy may lead to an imbalance in the soil carbonitride cycle and should be further examined in North China.

Wetlands provide many essential ecosystem services for human well-being. The ecological assessment of wetland ecosystem services is problematic and thus is an important focus in the field of ecological research. In this study, an ecological assessment system containing the ecosystem product value, ecosystem regulation service value, and ecosystem cultural service value was established to calculate the gross ecosystem product in the Nansi Lake Wetland, China. Based on remote sensing images, field studies, and literature reviews, the gross ecosystem product was estimated for the years 1985, 1992, 2005, 2011, and 2017. The results showed that the gross ecosystem product of the Nansi Lake Wetland increased from 40.91 × 10⁸ USD in 1985 to 46.28 × 10⁸ USD in 2017. The gross ecosystem product of the altered wetlands increased by about 8.5 times with a rising linear relationship, while natural wetlands presented a nonlinear relationship. Furthermore, except for the changes in climatic condition, anthropogenic interference factors such as coal mining activities, farming practices, and government policies have promoted significant services in the Nansi Lake Wetland over the past 30 years. This study could provide important insight into the ecological assessment of wetland ecosystems and thus inform policy for the protection and better use of wetland resources.