The value of a cultural ecosystem service depends on the perception of different cultural service categories. However, the data sources used in research on the perception of cultural service have limitations that mainly depend on social investigation, leading to slow progress in cultural service evaluation. With the advent of the era of network big data, social media provides a new data source for the study of cultural ecosystem service perception, so that the study of these services is expected to make new breakthroughs. Using search crawler software, this paper reviewed 7257 online comments related to 19 city parks in Xuzhou City, China. With the help of Rost Content mining semantic analysis software, the comment sentences were divided into keywords, and the Delphi expert method was used to classify these keywords. Thus, a cultural service perception database was established. Through statistical analysis, with the help of ArcGIS software, various cultural services were analyzed. The results showed that (1) the cultural services of urban parks could be divided into seven types (i.e., aesthetics, recreation, sports, inspiration, education, cultural heritage, and spiritual satisfaction) using social network comment data. (2) High-frequency keywords of online comment data can serve as the core basis during an analysis of the perception of cultural services by visitors of city parks. However, a large gap exists in the number of high frequency keywords in different parks. For example, Yunlong Lake Park has 2887 keywords, while Kuaizai Ting Park has only 33. (3) Differences exist in the perception of cultural service in urban parks, the park’s scale, and characteristics determine the visitor’s cultural service perception level. The aesthetic and recreation types were the most easily perceived, and 68% and 63% parks have the above two perceptual records, respectively. Therefore, the social media comment data has the ability to document perception of each park’s cultural service type and its differences, which can serve as the cultural ecosystem service perception as well as the valuation data source, to supplement the social investigation.

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.

Since the year 2000, China has implemented large-scale land consolidation, which was used to reduce land fragmentation, enhance grain yield capability, facilitate land tenure transfer, and promote agricultural operational scale. However, the impacts of land consolidation on agricultural technical efficiency of producers in practice is not yet clear. A field survey was executed at two points of time during July 2010 and July 2016. A total of 900 producers were chosen from 30 land consolidation projects at random in the Jiangsu Province. The agricultural technical efficiency caused by land consolidation was calculated by using a stochastic frontier analysis method. The results of a stochastic frontier production function reveal that land tenure transfer, land fragmentation, non-agricultural income, and crop diversity has undergone significant changes after land consolidation. The overall agricultural technical efficiency of producers had also increased considerably and the average technical efficiency was estimated at 0.924 after land consolidation. Land consolidation directly promotes land tenure transfer while indirectly encouraging non-agricultural employment, which could improve agricultural technical efficiency of producers. Non-agricultural income and crop diversity had a significant correlation with agricultural technical efficiency, but land fragmentation after land consolidation does not significantly improve technical efficiency. These conclusions are helpful in understanding the impacts of land consolidation, which enriches the academic literature in related fields and improves the policy of land consolidation in China and other developing countries.

Improvement of soil quality after land reclamation is a key concern in mining areas. However, the characteristics and internal mechanisms of variation of bacterial community structure over different reclamation periods are currently unclear. The recovery and evolution of soil microbial community structure are important indicators of the level of soil quality improvement of reclaimed soil. Therefore, this study investigated soil samples from coal gangue-filled land after reclamation periods of 1, 6, and 15 years. To accomplish this, 16S rRNA gene libraries were produced to determine the microbial community composition of the soils. In addition, various soil microbial community characteristics in the filled reclamation areas were compared with soil samples from areas unaffected by coal mining. The results showed the following: (1) The diversity and abundance of bacterial communities in reclaimed soils was slightly different from that of natural soils. However, the soil bacterial community structure was highly similar to natural soil after a 15-year reclamation period; therefore, the recovery of bacterial communities can be used as an indicator of the effects of rehabilitation. (2) Some soil physicochemical properties are significantly correlated with the main bacteria in the soil. (3) The dominant bacteria included members of the phyla Firmicutes and Proteobacteria, as well as members of the genera Bacillus, Enterococcus, and Lactococcus. Taken together, the results of this study indicated that the application of microbial remediation technology can be used to adjust the soil microbial community structure, improve soil quality, and shorten the soil recovery period.