The widespread construction of photovoltaic power stations within northwest China poses an environmental threat because of severe increased wind erosion and land degradation. Engineering, plant, and biocrust treatments were evaluated in this study for their effectiveness in the reduction of wind erosion. The placement of solar panels caused wind speed variation and resulted in distinct abrasion and deposition zones between the rows of the solar panels and the formation of deflation zones under the solar panels. Combined treatments (gravel and red clay mulch were applied within the abrasion and deposition zones, respectively) and moss‐crust were the optimal choices within the engineering and biocrust treatments, respectively. We found that for engineering treatments, the combined procedures led to treatments had sand transport rate reductions of 87%, while the straw checkerboard, gravel, and red clay treatments gave reductions of 51, 78, and 74%, respectively. Within the biocrust treatments, the moss‐crust decreased the sand transport rates and the sand erosion–deposit budget by 71 and 114%, respectively, while the cyanobacteria crust caused reductions of 65 and 109%, respectively, in comparison to the control. Both plant treatments decreased the sand transport rates and the sand erosion–deposit budgets, but were inferior to other optimal treatments with the best plant treatment dependent on the placement pattern used for plant establishment. All the treatments had effects on reducing wind erosion, and we strongly recommend the use of moss‐crust and combined treatments in the deflation zones and between the rows of the solar panels, respectively, to significantly reduce the severe wind erosion occurring at these photovoltaic power stations located in sandy areas.

Mining of mineral resources exposes various minerals to oxidizing environments, especially sulfide minerals, which are decomposed by water after oxidation and make the water in the mine area acidic. Acid mine drainage (AMD) from mining can pollute surrounding rivers and lakes, causing serious ecological problems. Compared with traditional field surveys, unmanned aerial vehicle (UAV) technology has advantages in terms of real-time imagery, security, and image accuracy. UAV technology can compensate for the shortcomings of traditional technology in mine environmental surveys and effectively improve the implementat ion efficiency of the work. UAV technology has gradually become one of the important ways of mine environmental monitoring. In this study, a UAV aerial photography system equipped with a Red, Green, Blue (RGB) camera collected very-high-resolution images of the stone coal mining area in Ziyang County, northwest China, and classified the very-high-resolution images by support vector machine (SVM), random forest (RF), and U-Net methods, and detected the distribution of five types of land cover, including AMD, roof, water, vegetation, and bare land. Finally, the accuracy of the recognition results was evaluated based on the land-cover map using the confusion matrix. The recognition accuracy of AMD using the U-Net method is significantly better than that of SVM and RF traditional machine-learning methods. The results showed that a UAV aerial photography system equipped with an RGB camera and the depth neural network algorithm could be combined for the competent detection of mine environmental problems.

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