Remotely sensed spectral diversity is a promising method for investigating biodiversity. However, studies designed to assess the effectiveness of tracking changes in diversity using historical satellite imagery are lacking. This study employs open-access multispectral Landsat imagery and the BiodivMapR package to estimate the multi-temporal alpha diversity in drylands affected by mining. Multi-temporal parameters of alpha diversity were identified, such as vegetation indices, buffer zone size, and the number of clusters. Variations in alpha diversity were compared for various plant communities over time. The results showed that this method could effectively assess the alpha diversity of vegetation (R2, 0.68). The optimal parameters used to maximize the accuracy of alpha diversity were NDVI threshold, 0.01; size of buffer zones, 120 m × 120 m; number of clusters, 100. The root mean square error of the alpha diversity of herbs was lowest (0.26), while those of shrub and tree communities were higher (0.34–0.41). During the period 1990–2020, the study area showed an overall trend of increasing diversity, with surface mining causing a significant decrease in diversity when compared with underground mining. This illustrates that the quick development of remote sensing and image processing techniques offers new opportunities for monitoring diversity in both single and multiple time phases. Researchers should consider the plant community types involved and select locally suitable parameters. In the future, the generation of long-time series and finer resolution maps of diversity should be studied further in the aspects of spatial, functional, taxonomic, and phylogenetic diversity.

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The mining process in deep mines occurs at elevated temperatures and thus is significantly jeopardized by the thermal damage. In this study, the main factors causing high-temperatures under particular mining geological and prevailing conditions of coal mine production, namely for the Longgu Coal Mine (LCM) in Shandong Province of China, were specified and analyzed in detail. This included exothermic heat from the surrounding rock of an underground roadway, inflow of high-temperature water, seasonal temperature rise, mechanical and electrical equipment operation, and airflow compression in the mine. The integrated artificial cooling mode was implemented on the basis of the original normal ventilation and cooling facilities of the LCM, which involved cooling by mobile refrigeration units, water source heat pump refrigeration units, and a ground centralized ice-cooling radiation system, as well as the underground centralized cooling system provided by Wärme-Austausch-Technik (WAT) GmbH. Eventually, a comprehensive set of measures for the overall control and reduction of high-temperature-induced damage was realized, which ensured more effective cooling of the LCM. Thus, the average temperature of the main operation sites was reduced by 8 K, while that of the underground working faces was maintained at 299.15 K. These measures also resulted in excellent technical and economic benefits: the total three-year increase in revenue and savings reached 76.3 million USD, hence relevant findings of the study are expected to provide technical guidance on the treatment of high-temperature-induced damage in deep mines.

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.

In the crystal structure of the title compound, C22H14O6··2C2H6OS, 4,6-dibenzoylisophthalic acid is connected to two dimethyl sulfoxide molecules by strong O—H...O=S hydrogen bonds. Weak intermolecular C—H...O hydrogen bonds connect molecules into dimers. Intramolecular three-centred C—H...O hydrogen bonding is favoured by a syn conformation of both carboxyl groups. The two dimethyl sulfoxide molecules were found to be disordered over two equally populated sites.

In the crystal structure of the title compound, C16H19O6P, inter­molecular O—H⋯O inter­actions link the mol­ecules into chains parallel to the b axis. These chains are linked by C—H⋯π and π–π inter­actions [centroid–centroid distance = 3.7307 (29) Å] into a three-dimensional network. The dihedral angle between the benzene rings is 73.5 (1)°. The C and O atoms of all four methoxy groups lie very close to the mean planes of their attached rings; the C atoms are 0.055 (2)–0.1038 (1) Å out of the mean plane of the attached rings.

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